the highly limited resources and peculiar requirements of THz
nanocommunication.

While THz band communication is moving towards directive antennas and thus
will likely require efficient beaming methods, the nanocommunication scenario
discourages its use unless simple and effective methods are conceived. In this
direction, recent works have discussed the role of graphene antennas. Graphene
not only allows to miniaturize antennas, but also confers them with ultrafast
reconfigurability achievable by simply changing the electrostatic voltage
applied to the antenna. This has led to proposals where both the beam
direction and frequency of resonance can be controlled with very simple
approaches [19]. Leveraging these features, Hosseininejad _et al._ [177]
propose a programmable PHY interface to graphene antennas to expose such beam-
switching and frequency tunability to upper layers. As an example, such a
controller could easily implement the bit-level beam-switching [178] to
implement beam multiplexing methods compatible with TS-OOK and the
interference mitigation techniques discussed above.

At the receiver side, simple means of detection are crucial to ensure the
viability of nanocommunication. Cid-Fuentes _et al._ implement a low-
complexity Continuous-Time Moving Average (CTMA) with a single low-pass filter
and a peak detector [179]. The evaluations contained therein demonstrate the
potential for Tbps detection with relaxed synchronization requirements. In
[181], an iterative process employing an array of time-delayed CTMA detectors
is proposed to achieve joint detection and synchronization for TS-OOK
communication. A similar architecture is proposed in [180] that provides an
estimation of time-of-arrival for time-hopping PPM modulation. Finally, the
work by Iqbal _et al._ is worth highlighting as it proposes a simple
modulation mode detection and classification for intelligent nanonetworks
where transmitters may switch between modulations type and order [182]. Note
that the approaches discussed above have not been included in Tables V and
VII, as they are not physical layer protocols, but proposals for hardware to
support such protocols. $\blacksquare$

Table V provides a summary of the above-discussed protocols for the physical
layer in THz nanonetworks. It can be observed how the physical layer is well-
researched from a theoretical and simulated perspective. However, the main
hurdle for their realization is the actual circuit implementation of the
analog front-end of the transceiver. THz signal generation with compact and
efficient means remains as a huge open challenge, especially in the case of
the hundred-femtosecond-long pulses assumed in most of the works in the field
[160].

The literature mentions three main signal source technologies for THz
nanocommunications. One of them is photoconductive technology. For instance,
pulse-based photoconductive sources might provide the required signal with
high power and, as such, have been proposed for the experimental testbeds of
graphene antennas [193]. However, these sources depend on a bulky laser to
excite the photo-carriers that turn into the THz signal and, therefore, are
impractical for nanocommunications. A second alternative are electronic
sources [48] that generate THz signals from the upconversion of lower
frequency ones. However, these sources generally cannot provide sufficient
power with a compact form factor, although recent advances in nanoplasma
switches may disprove that tenet [194].

A very promising technology in this field is, again, graphene plasmonics.
Graphene supports the propagation of tunable plasmons in the THz band, leading
to unprecedented miniaturization and reconfigurability opportunities when
operating in this frequency band. These properties have been studied when
using graphene transistors as very compact THz signal sources [23] exploiting
the Dyakonov-Shur instability or also as direct modulators, translating
changes in electrostatic biasing voltage into modulated plasmons [195].
Graphene antennas, as discussed above, can not only be miniaturized down to a
few micrometers and still resonate in the THz band, but also deliver joint
frequency-beam reconfigurability with unprecedented simplicity [19, 177].

From the perspective of the receiver, the use of non-coherent detectors and
CTMA approaches relax the synchronization requirements. However,
synchronization keeps being the main challenge in impulse radio in general,
and in THz nanocommunication in particular. To reach the promised Tbps
barrier, sampling needs to occur at potentially very high speeds anyway, which
goes against the simplicity and efficiency demands of most envisaged
applications.

##  VII Channel Modeling

Channel characterization and modeling captures the changes that the
electromagnetic waves suffer as they propagate through a medium until reaching
the receiver. In general, comprehensive models incorporate all possible
sources of losses (e.g., spreading, blocking, dielectric losses), dispersion
(e.g., due to multipath), and noise (e.g., thermal noise and interferences).
By accounting for all these effects, channel modeling provides the physical
layer with the necessary considerations for the design of appropriate
modulations and coding schemes that fulfill the application requirements.

Channel modeling is critical for the development of THz band nanocommunication
due to the important differences of THz propagation with respect to microwave
or optical propagation. The most striking peculiarity of THz channels is the
appearance of molecular absorption effects, which create peaks of attenuation
whose depth and frequency depend on the transmission distance and molecular
composition of the medium, respectively. These effects limit the practicable
bandwidth and, as we have seen in the previous section, may lead to the use of
multi-carrier modulations for high-throughput applications. Another
peculiarity of THz propagation is that materials that were effectively
transparent and flat at the microwave or even mmWave regime start becoming
lossy and producing rough scattering upon reflection. More details on these
impairments are given in Section VII-A.

Obviously, the propagation channel is highly dependent on the actual
application scenario. In the following subsections, we analyze the existing
channel modeling efforts in two of the promising directions for THz
nanocommunication. In Section VII-B, we review the works that characterize the
channel within the body-centric applications. In Section VII-C, we discuss the
attempts to model the THz channel within computing packages for on-chip
communications. A summary of the papers discussed in the section is given in
Table VI.

###  VII-A THz Propagation Models

In the THz band, phenomena that are generally neglected become significant as
the wavelength reaches dimensions commensurate to the molecules found in the
medium or the tiny irregularities of the surfaces upon which the waves may
reflect. For instance, the pioneering work by Piesiewicz _et al._ discusses
how the resulting molecular absorption [196] could impair communication in
these frequencies. Later, scattering produced by certain particles suspended
on the environment was factored in [197]. Thus, propagation models in the THz
band need to account for these effects. In [197], the path loss for Line-of-
Sight (LoS) propagation is given by:

$A_{LoS}(f)=A_{spr}(f)A_{mol}(f)A_{sca}(f),$ (1)

where, on top of the typical spreading loss $A_{spr}(f)$, we have the
molecular absoprtion $A_{mol}(f)$ and the particle scattering loss
$A_{sca}(f)$. If waves reflect on rough surfaces, additional scattering would
need to be accounted for [198]. Moreover, in [199], the noise temperature
$T_{noise}$ is modeled as:

$T_{noise}=T_{sys}+T_{mol}+T_{other},$ (2)

where, on top of the typical system electronic noise $T_{sys}$, noise caused
by molecular absorption $T_{mol}$ is considered. $T_{other}$ refers to any
additional noise source.

Molecular absorption is the process by which part of the wave energy excites
molecules found along its path. This phenomenon occurs often in the atmosphere
as many of the molecules comprised therein resonate in the THz regime. From
the communications channel perspective, absorption manifests as (i) a
frequency-selective attenuation and (ii) added noise from the molecules
residual radiation. Jornet _et al._ [199] were among the first to incorporate
a model of the molecular absorption and noise into a complete THz channel
model. On the one hand, the attenuation $A_{mol}(f)$ caused by this molecular
absorption is modeled as [199]:

$A_{mol}(f,d)=e^{k_{A}(f)d},$ (3)

where $k_{A}(f)$ is the medium absorption coefficient that depends on the
molecular composition of the medium, $f$ is frequency, and $d$ is the
transmission distance. The result, illustrated in Figure 11, is a frequency-
selective attenuation that scales with the distance and can effectively limit
the channel bandwidth. On the other hand, the molecular noise is modeled as
[199]:

$T_{mol}(f,d)=T_{0}(1-e^{-k_{A}(f)d}),$ (4)

where $T_{0}$ is the reference temperature. Based on these models, Jornet _et
al._ [199] studied the impact of absorption in terms of channel capacity for
different medium compositions and distances. Later, Javed _et al._ [200]
modeled the molecular absorption as a log-normally distributed attenuation
(similar to how shadowing is generally accounted for) within a conventional
log-distance path loss model. In [201], the authors focused on the most
prominent transparency windows, i.e., bands where molecular attenuation is
low, and performed a thorough capacity and throughput analysis both with and
without energy constraints. Finally, Llatser _et al._ analyzed the effect of
molecular absorption in the time domain and targeting short distances [202].
There, it was confirmed that molecular absorption and its dispersive effects
are generally negligible up to a few centimeters, as exemplified in Figure 11.

Figure 11: Molecular absorption for 1 cm (blue) and 10 cm (red) in a standard
atmosphere as defined by the International Organization for Standardization
(ISO) in the standard ISO 2533:1975. The blue and red backgrounds indicate the
available bandwidth for distances of 1 cm and 10 cm, respectively, where
available bandwidth is defined as the frequency band where the attenuation
caused by molecular absorption is below 10 dB.

Besides molecular absorption, diffuse scattering caused by particles or rough
surfaces commensurate to the THz wavelength are also potential impairments in
the THz band. These effects are also frequency-selective and, therefore, have
an impact upon the response of the channel. Kokkoniemi _et al._ provide a
comprehensive channel model in both the time and frequency domains which
accounts for the aforementioned effects, proving that they might not be
negligible at certain distances [197]. In the case of particle scattering, the
attenuation $A_{sca}(f)$ is given by

$A_{sca}(f,d)=e^{k_{S}(f)d},$ (5)

where the frequency dependence is modeled through the particle scattering
coefficient $k_{S}(f)=\sum_{j}{N^{j}_{s}\sigma^{j}_{s}}$ [197]. This
coefficient requires knowledge on the density of particles $N$ and of the
scattering cross section $\sigma$ of each type of particle $j$. In the case of
rough surface scattering, several measurement campaigns have been carried out
to analyze the response of materials such as wood, plaster, concrete, plastic,
glass, or metal [203, 204, 205] and incorporate it into the channel model.

The relatively short wavelength of THz waves, besides leading to the
impairments discussed above, also suggests the use of ray tracing techniques
to develop comprehensive channel models even in nanocommunication scenarios.
The work in [206] also argues that ray tracing could be particularly
appropriate due to the high-gain antennas expected in THz applications.
Moreover, it provides a comprehensive ray-based modeling methodology and
exemplifies its use in indoor channel characterization. The methodology has
been later extended to the particular case of THz wireless communications with
graphene reflectarray antennas [207]. The same authors also discuss hybrid
methodologies combining ray tracing and full-wave simulations to account for
all effects accurately while being computationally affordable [208].
Furthermore, the authors of [209] propose to expose the design parameters of
graphene antennas in order to facilitate the design space exploration of
graphene-enabled wireless channels. Finally, and since most works in the field
assume the co-existence of multiplexed links either spatially (via beaming) or
temporally (via pulse-based modulations), Petrov _et al._ also employ ray-
based methods to evaluate interference and therefore derive SINR and spectral
efficiency metrics [210]. With the requirement of multiple transmissions, the
authors also estimate the optimal distance between receiving nodes to maximize
the area capacity and conclude that interference becomes more critical than
molecular absorption in these multi-user scenarios.

###  VII-B Intra-body Channels

Wireless communication within the human body presents many exciting
applications in the body-centric communication domain in as much as it poses a
significant challenge from the perspective of the propagation of THz signals.
From the modeling perspective, Equation (1) still holds. However, the terms
related to spreading loss, molecular absorption loss, and scattering loss will
vary. For instance, the human body is composed of multiple tissues such as
skin, fat, or blood, each with their own response to THz radiation. Wireless
propagation is impaired not only by the dielectric loss of each tissue, but
also the transition between tissues, thereby affecting $A_{spr}$. Moreover,
the composition very much depends on the position of transmitter and receiver
within the body and of the patient itself, affecting $A_{mol}$. Finally, the
presence of cells and other particles impact on the scattering term $A_{sca}$.

Among the first works on this regard, the authors in [211] studied the
attenuation of fat in the THz band. The characteristics of the fat layer were
extracted from characterization works in the optics domain, yielding an
attenuation factor of around 30 dB/mm. Later, the authors extended the work
and published a complete model in [212] containing blood, skin, and fat. The
molecular absorption of those tissues is used to determine the system noise
and, then, the channel capacity. Further, Elayan _et al._ also considered a
multi-layered model in the frequency and time domains and studied the response
when on-body and intra-body devices communicate [213]. They concluded that up
to 30% of the incident power from outside the body may be reflected back and
that the result is symmetrical. Finally, Zhang _et al._ provide a detailed
model of artificial skin and perform a capacity analysis in the THz band
[214].

TABLE VI: Summary of channel modelling works Reference |  Scope |  Domain |  Method |  Analyzed Features |  Evaluation Metrics
---|---|---|---|---|---
[199] |  General |  Frequency |  Analytical |  Molecular absorption, noise |  Path loss, capacity
[202] |  General |  Frequency, time |  Analytical |  Molecular absorption |  Practicable bandwidth, pulse width
[197] |  General |  Frequency, time |  Analytical |  Molecular absorption, particle scattering, rough surface scattering |  Path loss, delay spread, coherence bandwidth
[204, 215] |  General |  Frequency |  Experimental |  Rough surface scattering, diffraction |  Path loss (scattering power, diffraction angle)
[203] |  Indoor |  Frequency |  Experimental |  Rough surface scattering |  Scattering power, path loss
[206, 207] |  Indoor |  Frequency, time |  Analytical |  LoS and NLoS propagation, graphene reflectarray impact |  Path loss, delay spread, coherence bandwidth, capacity
[210] |  Indoor |  Frequency |  Numerical |  Blocking, Interference |  SIR, SINR
[200] |  Indoor, intra-body |  Frequency |  Analytical |  Molecular absorption, noise (fat, blood, bone) |  Path loss, capacity
[211, 212] |  Intra-body |  Frequency |  Numerical |  Absorption in blood, skin, fat |  Path loss, noise temperature, capacity
[213] |  Intra-body |  Frequency |  Analytical, numerical |  Discontinuities on multi-layer medium |  Reflected power, reflectance
[216] |  Intra-body |  Frequency |  Analytical |  Intra-body noise sources |  Noise spectral density
[217] |  Intra-body |  Frequency, time |  Numerical |  Time variation of medium composition |  SNR, BER
[218] |  Chip-to-chip |  Frequency, time |  Experimental |  Reverberation |  Path loss, coherence bandwidth
[219] |  On-chip |  Frequency |  Numerical |  Molecular absorption, dielectric losses |  Coupling (S21)
[220] |  On-chip |  Frequency |  Numerical |  Losses |  Coupling (S21)
[221, 222] |  On-chip |  Frequency |  Analytical, numerical |  Losses, interference |  Path loss, capacity
[223, 224] |  On-chip |  Frequency |  Experimental |  Antenna orientation, position |  Insertion loss (S11), Coupling (S21)
[164] |  Off-chip (SDM) |  Frequency, time |  Numerical |  Propagation path, SDM geometry |  Path loss, delay spread

The impact of the multiple phenomena occurring in intra-body channels on the
noise temperature has been also studied in depth. Existing models evolved from
the initial model from [212] where it was assumed that noise caused by
molecular absorption dominates and other potential sources can be neglected,
thereby obtaining an expression similar to Equation (2). A more comprehensive
discussion was included in [225], where the authors modeled signal-independent
body radiation noise using Planck’s law, as well as the signal-dependent
molecular absorption noise. A comparison between the two validated the
assumption of dominance of molecular absorption noise. Later, Elayan _et al._
revisited the model to include, besides the two sources mentioned above, other
components such as thermal noise at the transceiver circuitry or Doppler-
shift-induced noise [216]. Finally, in [226], noise models are combined with
interference models to derive SINR metrics towards accurately determining the
capacity and throughput achievable in intra-body networks. It is therefore
suggested that nanomachine density can be a factor as important as the
composition of the intra-body channel in assessing the viability of the
communication.

We finally describe papers that extend existing models to account for changes
in the transmission medium. The work of Zarepour _et al._ is worth mentioning
as it considers time-varying channels in the THz band [217]. The key takeaway
is that nanocommunication channels are not static: the temperature, pressure,
or molecular composition of the medium may vary over time. They provide as
example the composition of the blood when breathing in, which is clearly
different than when breathing out. A similar example is analyzed in [227],
where a nanonetwork for lung monitoring is explored. In that case, respiration
clearly changes the volume and composition of the lung, and the authors adapt
their design to that circumstance. Even further, all the works described in
this section can be used to determine the attenuation caused by vegetation in
plant monitoring nanosensor networks, which can also vary over time due to the
effects of photosynthesis [85].

###  VII-C On-chip Communication Channels

A channel model that takes into consideration the peculiarities of the chip-
scale scenario is fundamental to evaluate the available bandwidth and to
properly allocate power. The enclosed nature of the chip package suggests that
propagation losses may be small, but also that multipath effects may be
present. Additionally, the multiple metalization layers and the Through-
Silicon Vias (TSV) present in today’s chips may further challenge propagation
[228]. Fortunately, the scenario is unique in that all these elements are
fixed and known beforehand. Thus, the channel model will be virtually time-
invariant and quasi-deterministic. Moreover, molecular absorption and diffuse
scattering are not problems in this controlled environment [219, 229].
Therefore, only the spreading loss term of Equation (1) stands. The model,
however, has to take into consideration dielectric losses caused by the chip
package materials and by transitions between the different layers found within
the chip structure. In terms of noise, the most prominent source is thermal
noise and, thus, conventional models can be employed. It is worth noting that
the processor circuitry does not introduce noise as communication and
computation sub-systems operate at disjoint frequencies.

Thus far, few works have explored the chip-scale wireless channel down to the
nanoscale and in the THz band. The theory is well laid out [228] and a wide
variety of works exist in larger environments. THz propagation has been
investigated in small and enclosed environments such as across a computer
motherboard [230], or within the metallic encasement of a laptop computer
[218]. The experimental results, up to 300 GHz, have confirmed that such
systems act as reverberation chambers due to the metallic enclosure, leading
to rather low path loss but very high delay spreads.

Down to the chip level, most characterization efforts have thus far been
limited to mmWave frequencies [229]. Analytical models [231], simulation-based
studies [232], and actual measurement campaigns [223, 224] have been conducted
under different assumptions. In most cases, path loss is modeled by an
empirical log-scale approximation resulting in a derived the path loss
exponent, rather than using a bottom up approach with Equation (1). More
recently, a few authors have tried to study propagation in sub-THz
frequencies, i.e., up to around 140 GHz in [233] and 200 GHz in [220]. At the
time of writing, however, the only channel model at THz frequencies is that of
Chen _et al._ [221, 222], which employs ray tracing within a stratified model
of the chip structure to extract path loss and dispersion metrics.

Regardless, the main issue of the works mentioned above is that free-space
propagation in an unpackaged chip is assumed for simplicity. Such a model
generally falls short of capturing the enclosed nature of realistic on-chip
communication environments. Hence, recent efforts are starting to model
realistic flip-chip packages with lossy bulk silicon in the mmWave and sub-THz
spectrum [233]. The main conclusion is that the losses introduced by the
silicon substrate prevent the package to act as a reverberation chamber.
However, the price to pay is an unwanted path loss in excess of several tens
of dBs. When scaling to the THz band, the results will likely worsen due to
the smaller effective aperture of the antennas. For potential solutions we
refer the reader to the next subsection. $\blacksquare$

Channel modeling in THz nanocommunication has been well-researched in theory
and simulations. Actual measurements are more complex to achieve due to the
lack of mature measuring equipment and the difficulty of accessing the
nanoscale with enough accuracy. First experimental results have been achieved
that confirm molecular absorption and scattering effects and, in the case of
on-chip wireless communications, results up to sub-THz frequencies start
becoming available. However, there is still a large room for improving the
current characterizations experimentally.

In the intra-body networks, researchers have identified dielectric losses and
molecular absorption as huge sources of attenuation and noise. Moreover, these
can vary over time even assuming fixed transmitter-receiver positions. It is
yet unclear, hence, how nanomachines will overcome these issues and how
protocols will adapt to these very adverse and changing conditions while still
being bio-compatible.

In the on-chip scenario, a channel model in the THz band with a realistic chip
package is still largely missing. Moreover, the problem of relatively high
attenuation due to the losses within the silicon remains unsolved. In this
respect, Timoneda _et al._ propose to exploit the controlled nature of the
chip scenario to actually _design_ the wireless channel without affecting the
reliability of the digital circuits [234]. To that end, the thickness of
silicon and the thermal interface material are introduced as design parameters
and optimized to minimize path loss while maintaining an acceptable delay
spread.

Due to the similarities between the on-chip scenario and the intra-SDM network
scenario, some of the knowledge of the former can be reused in the latter.
First explorations in this regard [164] indeed show that, internally, the
mmWave wireless propagation paths within the SDMs suffer similar effects than
in on-chip channels: low loss, wave-guiding effects, and relatively high delay
spread. The challenge here is to extend those models to the THz band and
confirm the viability of wireless communication within the metamaterials.

##  VIII Simulation and Experimentation Tools

There are several tools for simulating the behaviour of nanonetworks operating
the THz frequencies. The pioneering simulator is NanoSim [235, 149], an event-
based ns-3 module for modeling nanonetworks based on electromagnetic
communications in the THz band. In NanoSim, a nanonetwork can be comprised of
nanonodes, nanorouters, and nanointerfaces. Nanonodes are small and simple
devices with very limited energy, computational, and storage capabilities.
Nanorouters have resources larger than the nanonodes and they are envisioned
to processing data coming from nanonodes, as well as controlling their
behavior through control messages. Nanointerfaces act as gateways between the
nano- and macro-scale world. All of them can be either static or different
mobility models can be employed according to the application requirements
(i.e., constant acceleration, constant velocity, random walk, random
direction, and random way-points). Moreover, their basic functionalities can
be customized to the demands of a given evaluation scenario. The nanonetwork
in NanoSim consists of the network, link, and physical layers. On the network
layer, random and selective flooding routing strategies are supported. Link
layer currently supports Transparent-MAC (i.e., simple forwarding from network
layer to the physical interface) and Smart-MAC protocols. Moreover, NanoSim
provides a TS-OOK-based physical layer with several adjustable parameters such
as pulse duration, pulse transmission interval, and transmission duration.
Finally, the radio channel can be modeled based on a cut-off transmission
distances between nanonodes. In addition, the selectivity of the THz channel
in both frequency and time domains can optionally be introduced based on the
spectrum-aware channel modeling from [236].

Vouivre [237] ([238, 239] in its preliminary version) is a C++ THz nano-
wireless simulation library developed as both an extension for Dynamic
Physical Rendering Simulator (DPRSim) and as a standalone discrete event
simulator. DPRSim [240] has been developed in the scope of the Claytronics
project for supporting simulations with a large number (up to millions) of
Claytronics micro-robots (also known as catoms). Original catoms do not have
wireless transmission capabilities, as they are envisioned to communicate only
through physical contact. Vouivre introduces wireless communication capability
to the catoms. In particular, it can be used for simulating the THz radio
channel and its concurrent accesses by catoms. The THz radio channel is
modeled by a continuous distance-dependent attenuation contribution increased
by a certain noise value caused by the concurrent transmissions, with the
noise value taken from [199, 189]. In addition, transmission delay in
combination with total attenuation have been utilized for determining packet
reception probability. Moreover, Vouivre implements a TS-OOK-based physical
layer, while the upper layers have not been implemented, apart from the
standard CSMA/CA scheme combined with the Friss propagation model in 2.4 GHz
frequency for “allowing ulterior studies of hybrid systems”.

BitSimulator [241] is another simulator specifically targeting THz band
nanonetworks. BitSimulator is implemented in C++ and utilizes a discrete event
model. At the physical layer, BitSimulator implements the TS-OOK scheme with
100 fs long pulses and per-frame configurable parameter $\beta$. The link
layer is not implemented, as it is assumed that multiple frames can be
temporally multiplexed and the nodes have the capability of tracking the
transmissions intended for them. Network layer implementation supports no
routing, flooding-based, and Stateless Linear-path Routing (SLR) [138]. Two
discovery modes are available: i) static, where neighbors are stored for each
node and calculated at the beginning of the simulation; ii) dynamic, where
neighbors are not stored, but computed at periodic time instances. Hence, the
support for simple mobility exists in BitSimulator. In terms of channel
modeling, a simple communicationRange parameter is used for specifying
achievable transmission range. In addition, collisions between frames are
determined based on propagation delay and TS-OOK-specific bit values of the
packets concurrently received at each nanonode [165]. If the number of
collisions is above a set threshold, the packet is discarded.

TeraSim [242] is a newer alternative to the NanoSim simulator and also
implemented on top of ns-3 . The simulator supports simulations of both major
types of application scenarios, i.e., THz nano- and macro-scale communication.
In TeraSim, the THz radio channel for a nanoscale scenario is modeled by
applying frequency and distance dependent spreading and absorption loss,
accounting for waveforms with realistic bandwidth. The simulator consists of a
common channel module, separate physical and link layers for each scenario,
and two assisting modules, namely, THz antenna module and energy harvesting
module, originally designed for the macro- and nanoscale scenario,
respectively. TeraSim allows the user to select several channel attributes
such as bandwidth, number of samples of the frequency-selective channel, and
detection threshold (i.e., noise floor). Collisions between two packets occur
if two pulses of different TS-OOK receptions overlap in time (packet dropping
based on SINR) or if the pulse in reception overlaps with the pulse of an
ongoing transmission (packet in reception is dropped). On the physical layer,
TeraSim implements the TS-OOK modulation and channel coding scheme with user
adjustable pulse and symbol durations. On the link layer in the nanoscenario,
TeraSim implements the ALOHA protocol in which the data is sent if there is
enough energy for the transmission. The receiver receives the data if there is
enough energy for reception and replies with an acknowledgment (ACK) packet.
The packet is dropped upon exceeding the maximum number of retransmissions. In
addition, TeraSim implements the CSMA handshake protocol, in which the usual
Ready-to-Send (RTS) / Clear-to-Send (CTS) packet exchange occurs. A CTS packet
is transmitted if the receiver has enough energy to complete the reception.
Same as before, the packet is dropped upon exceeding the maximum number of
retransmissions. Upper layer protocols are not specifically tuned to THz
nanocommunication, but utilize available ns-3 modules (e.g., User Datagram
Protocol (UDP) client/server, IPv4 addressing). Similarly, node mobility
support is based on the available ns-3 modules. An interesting feature of
TeraSim lies in the fact that it implements energy harvesting capability of
the nanonodes. Hence, the current energy levels of the nodes play a role in
the network performance simulations, arguably making TeraSim the most suitable
simulation tool for a variety of low-power THz nanoscale applications.
$\blacksquare$

As outlined above, there are several tools currently available for THz
nanocommunication and nanonetworking simulations. Being one of the most
recently proposed simulators, TeraSim seemingly provides the most extensive
capabilities, including the energy-harvesting module for nanonodes.
BitSimulator, another recently proposed tool, trades-off the complexity for
scalability, arguing that for many of the envisioned nanocommunication
applications scalability will be the primary requirement. However, at this
point the scalability vs. realism trade-off is merely a speculation, as it is
not clear how scalable the outlined simulators are. In addition, it would be
interesting to investigate the difference in simulation results of the
different simulators in order to evaluate their usability for different
scenarios, as well as the reliability of the simulation results. In other
words, large discrepancies in the results derived using different simulators
could put into question the reliability of findings obtained using these
simulators.

In many of the envisioned applications, the only feasible powering option for
the nanonodes will be through energy harvesting. This fact is only reflected
in the TeraSim simulator. Even there, the energy storage capacity of the
nanonodes is unlimited and the energy harvester is implemented as a simple
process in which energy is harvested at a constant rate. However, the majority
of nanoscale harvesters (e.g., exploiting piezo-electric effect of Zinc Oxide
(ZnO) nanowires [10] or ultrasound-based power transfer [243]) charge the node
in a non-linear way, with their harvesting rates being highly dependent on
their current energy levels and maximum storage capacity [244]. To address
these limitations, the insights from [245] could be utilized. [245] provides
N3Sim, an ns-3-based simulator for molecular nanocommunication. It provides
several harvesting options (with harvesting being a synonym for collecting
molecules from nanonodes’ local neighborhood), some of them realistically
assuming that the nanonode’s energy storage capacity is limited. In addition,
the charging operation due to harvesting is in N3Sim a non-linear process.
Moreover, in the currently available simulators the energy consumption of a
nanonode is attributed to transmission and reception only. However, idling
energy should be accounted for if the aim is accurate energy consumption
modeling, as well as energy consumed due to for example information processing
or data storage. Due to nanonode’s highly constrained energy resources,
accurate energy modeling should be of paramount importance, as will be
discussed in the next section in more details.

Furthermore, novel mobility models are needed for accurate simulations of
nanonetworks for several application scenarios. For example, as body-centric
applications obviously assume in-body communication, hence fine-grained models
of human mobility are needed, as well as models for blood stream and various
other in-body mobility effects (e.g., heart-beats). A very good initial step
in this direction is BloodVoyagerS [246], a model of a human body’s
cardiovascular system, developed with the idea of simulating nanonodes’
mobility. Similar tools are needed for other aspects of a human body. In
addition, the integration of such mobility models with the current simulators
will be needed for maximizing the benefits and realism of the THz
nanocommunication and nanonetworking simulations.

Finally, in terms of experimentation facilities or experimental datasets, the
areas of THz nanocommunication and nanonetworking are still uncharted.
Publicly available experimental results of experimentation infrastructure
would presumably give a strong boost to the research in these domains, which
has already been recognized in the community. One good example going in this
direction is VISORSURF, a European Union (EU)-funded research project whose
aim is to develop a full stack of hardware and software tools for THz-based
control of metamaterials [75, 64]. More initiatives targeting development of
hardware tools, integration into full prototypes, or generation of public
datasets are needed for other nanocommunication scenarios.

##  IX Additional Challenges, Open Issues, and Future Research Directions

The optimization objectives for the above-discussed protocols are indicated in
Table VII. These objectives represent the design aims of the protocols and
should not be mixed with the performance metrics used in the evaluation of the
protocols and listed in Tables III, IV, and V. The aim of Table VII is to help
the reader in the selection of suitable protocols for a given application with
specific requirements. In addition, the aim is to indicate the “missing
pieces” in the existing protocols, i.e., the potential improvement directions.
For example and as already mentioned, in terms of link-later protocols Akkari
_et al._ [144] is the only one explicitly aiming at latency optimization.
Hence, if an application requires certain bounds on latency, Akkari  _et al._
[144] would naturally be the first choice for the link layer. Moreover, given
that there is only one proposal targeting latency optimization, new link layer
protocols could be developed for its further optimization.

TABLE VII: Optimization objectives for existing protocols in different layers of the protocol stack Protocol |  | Network
---
scalability
| Node
---
density
Latency |  Throughput |  | Bidirectional
---
traffic
Reliability |  | Energy
---
consumption
Mobility |  Security
Network layer protocols
Xia  _et al._ [118] |  |  |  |  ✓ |  ✓ |  |  ✓ |  |
Rong  _et al._ [119] |  |  |  |  |  ✓ |  ✓ |  |  |
Yu  _et al._ [120] |  (✓) |  ✓ |  ✓ |  ✓ |  ✓ |  |  (✓) |  |
PESAWNSN [121] |  ✓ |  |  |  |  |  |  ✓ |  |
Cánovas-Carrasco _et al._ [123] |  |  |  |  (✓) |  |  |  ✓ |  ✓ |
Cánovas-Carrasco _et al._ [124] |  |  |  |  ✓ |  |  |  ✓ |  ✓ |
Liaskos  _et al._ [125] |  ✓ |  |  ✓ |  |  |  ✓ |  ✓ |  |
Tsioliaridou  _et al._ [126] |  ✓ |  |  ✓ |  |  |  ✓ |  ✓ |  |
Afsana  _et al._ [127] |  |  |  |  ✓ |  ✓ |  ✓ |  ✓ |  |
Stelzner  _et al._ [128] |  ✓ |  |  |  |  |  ✓ |  |  |
Buther  _et al._ [129] |  ✓ |  |  |  |  |  |  ✓ |  ✓ |
E3A [130] |  |  |  ✓ |  ✓ |  |  ✓ |  ✓ |  |
Pierobon  _et al._ [122] |  |  |  ✓ |  ✓ |  |  ✓ |  ✓ |  |
Tsioliaridou  _et al._ [137] |  ✓ |  |  |  |  ✓ |  ✓ |  ✓ |  |
Tsioliaridou  _et al._ [138] |  ✓ |  |  |  |  ✓ |  ✓ |  ✓ |  |
Link layer protocols
Akkari  _et al._ [144] |  |  |  ✓ |  ✓ |  |  |  |  |
Alsheikh  _et al._ [145] |  |  |  |  |  ✓ |  ✓ |  |  |
PHLAME [114] |  ✓ |  ✓ |  |  |  ✓ |  ✓ |  ✓ |  |
DRIH-MAC [146] |  ✓ |  |  |  |  ✓ |  ✓ |  ✓ |  |
TCN [147] |  |  |  |  |  |  ✓ |  ✓ |  |
Xia  _et al._ [148] |  |  |  |  ✓ |  |  ✓ |  |  |
Smart-MAC [149] |  |  |  |  |  |  ✓ |  |  |
APIS [150] |  |  |  |  ✓ |  |  ✓ |  ✓ |  |
Wang  _et al._ [115] |  ✓ |  ✓ |  |  ✓ |  |  |  |  |
EEWNSN-MAC [151] |  ✓ |  ✓ |  |  |  |  ✓ |  |  (✓) |
CSMA-MAC [153] |  |  |  |  ✓ |  |  |  |  |
Mansoor  _et al._ [154] |  |  |  |  ✓ |  ✓ |  ✓ |  ✓ |  |
BRS-MAC [108] |  ✓ |  |  |  |  ✓ |  ✓ |  |  |
Dynamic MAC [155] |  |  |  |  ✓ |  ✓ |  ✓ |  ✓ |  |
Physical layer protocols
TS-OOK [165] |  ✓ |  ✓ |  |  |  |  |  ✓ |  |
PHLAME [114] |  ✓ |  ✓ |  |  |  ✓ |  ✓ |  ✓ |  |
Shrestha  _et al._ [166] |  ✓ |  ✓ |  |  |  ✓ |  ✓ |  ✓ |  |
SRH-TSOOK [167] |  ✓ |  ✓ |  |  ✓ |  ✓ |  ✓ |  |  |
ASRH-TSOOK [168] |  ✓ |  ✓ |  |  ✓ |  ✓ |  ✓ |  |  |
Vavouris  _et al._ [169] |  ✓ |  |  |  |  |  |  ✓ |  |
Zarepour  _et al._ [170] |  |  |  |  |  |  ✓ |  |  ✓ |
Multi-band OOK [162] |  |  |  ✓ |  ✓ |  |  ✓ |  ✓ |  |
DAMC [184] |  |  |  |  ✓ |  |  ✓ |  |  ✓ |
Han  _et al._ [171] |  |  |  |  ✓ |  |  ✓ |  |  ✓ |
Jornet  _et al._ [172] |  ✓ |  ✓ |  |  |  |  ✓ |  ✓ |  |
MEC [173] |  |  |  |  |  |  ✓ |  ✓ |  |
Chi  _et al._ [174] |  |  |  ✓ |  |  |  ✓ |  ✓ |  |
SBN [175] |  |  |  ✓ |  |  |  |  ✓ |  |
DS-OOK [176] |  |  ✓ |  |  ✓ |  |  |  |  |

In addition, there are several overarching challenges not directly related to
the ones outlined in previous sections, which we discuss in the reminder of
this section.

###  IX-A Common Design Frameworks

There is a need for common design frameworks in order to simplify the
development of the THz nanonetworks for supporting different types of the
envisioned applications. We believe this issue should be approached from the
perspectives of both the applications and the nanodevices. In other words, in
order to support a certain application in one of the specified application
domains, one should be aware of the requirements that this application poses
on the underlying nanonetwork. Simultaneously, one should consider the
capabilities of the nanodevices expected to be used in that particular
context, as it is hardly imaginable that the same types of devices will be
used for enabling e.g., the on-chip, in-body, or software-defined
metamaterial-based applications.

In Section III, we aimed at deriving the application requirements on the level
of different application domains. Nevertheless, we believe such derivations
will be needed on the level of particular applications. The current literature
on this issue is sparse, with only a few works mostly targeting applications
in the domain of in-body communication [92, 94, 95, 96]. Further research is
needed and should, in contrast to the existing works, aim at qualitatively
specifying exhaustive sets of application requirements for all applications in
all of the promising application domains.

From the perspective of the nanodevices, we argue the full specifications of
their features are needed, with primarily concerns being their size, available
energy, processing power, and memory. In this direction, it is worth
emphasizing [247], where by following the guidelines originally outlined in
[5] the authors discuss a conceptual design of a nanodevice, consisting of a
nanoprocessor, nanomemory, nanoantenna, and nanogenerator. Seemingly, the
nanodevice will not be suitable for all the applications, for example because
it assumes a nanogenerator based on energy harvesting or wireless power
transfer. As such, it can be hardly characterized as feasible e.g., on-chip
communication in which the energy is abundant.

We believe the derivation of the mentioned perspectives will not only
substantially simplify the development of the supporting nanonetworks along
well-specified design requirements and constraints, but also highly contribute
to the intuition and reasoning on the feasibility of different approaches for
a particular set of applications.

###  IX-B Transport Layer Protocols

In [26, 27], the authors state that, as Gbps and Tbps links become a reality,
the network throughput will increase dramatically. It will, therefore, be
necessary to develop new transport layer solutions for mitigating network
congestion problems. The authors in [26, 27] also state that the overhead of
existing transport layer protocols requires minimization in order to reduce
the performance constraints. This has been recognized by only a few early
works on the topic [248, 249, 250].

In the scope of the VISORSURF project, Tsioliaridou _et al._ [248] consider a
software-defined metamaterial named the HyperSurface. The HyperSurface
encompasses a hardware layer that can change its internal structure by tuning
the state of its active elements, as well as a nanonetwork in which each
nanonode controls a single active element. For carrying sensed information to
the external world for processing, as well as configuration commands from the
external world to the HyperSurface, the authors propose HyperSurface Control
Protocol (HyperCP). The Hyper-CP uses Lyapunov drift analysis for avoiding
congested or out-of-power nanonetwork areas [249]. Specifically, the protocol
aims at optimizing the network throughput by accounting for outdated
nanonodes’ status information, as well as battery and latency constraints.

Alam _et al._ [250] state that the existing studies on nanocommunication and
nanonetworking in body area nanonetworks focus on lower layers of the protocol
stack, resulting in the upper layers (e.g., transport layer) remaining
unexplored. They further argue that electromagnetic waves will potentially be
harmful to sensitive body areas. Moreover, they claim that molecular
communication is slower and error-prone compared to electromagnetic one.
Motivated by the above arguments, they propose an energy-efficient transport
layer protocol for hybrid body area nanonetworks (i.e., both electromagnetic
and molecular communication supported). The protocol is based on a congestion
control mechanism with very limited overhead, in which the sender upon
receiving a “halt” signal suspends the packet transmission for a predefined
timeout period.

These initial contributions are constrained to narrow application domains,
specifically to software-defined metamaterials and body-centric communication.
Transport layer protocols for other application domains with differing
requirements are currently lacking. Even the two outlined protocols are
lacking relevant performance details, pertaining primarily to their
scalability and protocol overhead. For example, for Hyper-CP it is unclear if
the distribution of battery states and latency constraints among nanonodes is
at all feasible, given that it unavoidably causes signalling-related energy
dissipation at the nanonodes. Similarly, for the protocol proposed in [250],
halt signals can potentially be infeasible for low-energy nanonodes. In such a
case, the transmission of data packets could unnecessarily continue until the
depletion of the transmitter’s energy. In summary, transport layer protocols
for nanonetworks are currently to a large extent unexplored. Even more, it is
unclear if such protocols will at all be needed for many scenarios, as they
will inevitably increase the protocol overhead, which for energy-constrained
nanonodes could be infeasible.

###  IX-C Reduced / Integrated Protocol Stack

Along the above conclusions, in case of energy-constrained nanonodes, the
protocol stack will have to be substantially condensed and integrated for
maintaining feasible nanocommunication and nanonetworking. This it
predominantly due to the fact that more traditional networks trade-off a high
overhead of the utilized protocols with the support for heterogeneous
application requirements. In nanocommunication and nanonetworking, this
paradigm will potentially be shifted, which is currently largely unexplored
with the only two examples being the ones outlined below. Certainly, further
insights are needed in terms of the design choices for nanonetworks for
enabling different applications, pertaining to either reducing the complexity
of the protocol stack by making it condensed and application specific, or
providing a full stack at the cost of increased energy consumption, latency,
and complexity.

The first example where the authors argue that the paradigm shift is needed is
[251] (extended in [252]), in which a framework is proposed for enabling
energy-harvesting nanonodes to communicate their locations (i.e., addresses)
and sensed events using only one wireless pulse. These wireless pulses feature
two degrees of freedom pertaining to any two of the following parameters:
amplitude, pulse width, and transmitted energy (equaling amplitude multiplied
by pulse width). The framework requires each nanonode to use a particular
pulse width for their identification, while the event types are identified by
the amplitude/energy emitted by each nanonode.

Similarly, the authors in [253] argue that even exotic (i.e., hard to
integrate) power supplies relying on energy harvesting can only scavenge
energy for 1 packet transmission per approximately 10 sec [244]. This makes
the development of even basic protocols – such as addressing and routing –
highly challenging. To mitigate this effect, the authors propose BitSurfing, a
network adapter that does not generate physical data packets when transmitting
information, but assigns meaning to symbols created by an external source. It
does that by reading incoming symbols and waiting for desired messages to
appear in the stream. Once they appear, short low-energy pulse is then emitted
to notify neighboring nodes. The authors demonstrate BitSurfing’s perpetual
operation, as well as the ability to operate without link and transport layer
protocols.

Another example of a shallow protocol stack is given in the on-chip
communication context, where packets need to be served to guarantee forward
progress in the computation. This means that cores are _self-throttling_ ,
hence their injection speed will be reduced if the network becomes congested
[100]. As a result, the responsibilities of the transport layer are reduced
and generally implemented at the architecture level. For instance, cache
coherence protocols (which generate most of the traffic in multiprocessors)
typically implement end-to-end acknowledgment and timeouts to confirm the
reads and writes on shared data. In fact, on-chip communication take advantage
of the monolithic nature of the multiprocessor system to reduce the depth of
the protocol stack.

###  IX-D Energy Lifecycle Modeling

From the above discussions, it is clear that the energy consumption is one of
the most stringent constraints that will potentially impede on the feasibility
of nanocommunication and nanonetworking in the THz frequencies. In order to
develop feasible nanocommunication and nanonetworking protocols, there is a
need for accurate energy lifecycle modeling, especially for the nanonodes
whose only powering option is through energy harvesting. As the practical
implementations of the nanonodes are currently lacking, the development of
accurate analytical energy lifecycle models can be seen as a fundamental step
towards the design of feasible nanonetwork architectures and protocols [84].
This has to an extent been recognized in the research community.

In the pioneering works on the topic [84, 244], an energy model for self-
powered nanonodes is developed with the aim of capturing the correlation
between the nanonodes’ energy harvesting and the energy consumption processes.
The energy harvesting process is realized by means of a piezoelectric
nanogenerator, while the nanonode’s energy consumption is quantified by
assigning certain amounts of energy for the transmission and reception of “1”
bits, under the assumption of the TS-OOK communication scheme being employed.
A mathematical framework is then developed for deriving packet delivery
probability, end-to-end delay, and achievable network throughput. A similar
approach for energy lifecycle modeling has been taken in [254, 255], in which
the authors additionally evaluate the contributions of packet sizes,
repetitions, and code weights on the nanonode’s energy consumption.

The authors in [243] extended the work from [84, 244] by highlighting the
effects of the employed network topology, as well as of different energy
harvesting approaches and rates (i.e., blood currents and ultrasound-based
power transfer). Their results show that a micrometer-sized piezoelectric
system in lossy environments becomes inoperative for transmission distances
over 1.5 mm. Similarly, the authors in [256, 72] reason that, as any other
practical transceiver, a nanoscale transceiver will consume certain amounts of
energy during its idling periods, in addition to the consumption due to
transmission and reception. Once the idling energy is accounted for in the
overall energy consumption modeling, the authors show that for feasible
communication this energy consumption has to be nine orders of magnitude
smaller than the energy consumed for reception during the same period. This is
certainly a challenging requirement, as in current nanoscale systems the
idling energy is in the best case scenario up to three orders of magnitude
smaller than the corresponding energy in reception.

The above mentioned contributions are only the initial steps in a promising
direction. As discussed in [256, 72], certain energy will inevitably be
consumed when a nanonode is idling. By the same token, the nanodevice’s energy
will be distributed to different functions such as sensing, processing, etc.,
and not all of it can be used for transmission or reception. That being said,
seemingly many current works on the design of nanocommunication and
nanonetworking protocols could be infeasible under more realistic energy
consumption models, as they indeed make an assumption that the overall energy
of a nanodevice can be utilized for transmission and reception of information.
Example-wise, as the nanonodes are expected to experience intermittent on-off
behavior, they will often have to wake-up after harvesting sufficient energy
[157]. This wake-up process per-se will consume a certain amount of energy,
which the current energy models and consequently the protocols utilizing such
models do not account for.

As mentioned, in many applications the nanonodes will be powered solely
through harvesting environmental energy. Hence, it is first worth pointing out
that there is a variety of potentially feasible candidates for energy
harvesting, for example blood currents [243] and ultrasound-based power
transfer [257] in in-body communication applications, and Radio-Frequency
(RF)-based power transfer in the domain of SDMs [258]. Similar to energy
consumption modelling, there is a need for an accurate modelling of the
charging of an energy-harvesting nanonode. Here, it is worth pointing out the
effort in [257], where the authors outline an approach for ultrasound-based
powering of piezoelectric ZnO nanowires-based nanonodes placed on peripheral
nerves in the human body. In their characterization of the harvested energy
(i.e., intensity of ultrasound), the authors consider a variety of relevant
parameters such as the stimulus depth, size of the energy-harvesting array,
and duration of ultrasound pulses. Such a comprehensive characterization makes
it easy for the proposed approach to be utilized in future works, among others
in the domain of THz nanonetworks. Such efforts are needed for other types of
energy harvesters and harvesting sources. In more general terms, accurate
energy harvesting and consumption models would be highly beneficial.

###  IX-E End-to-End Architectures

To truly support many of the envisioned applications, seamless integration of
the nanonetworks with existing networking infrastructures will be needed [86].
Addressing this issue is not straightforward, as existing networks
predominantly utilize carrier-based electromagnetic communication, while
nanonetworks will seemingly have to rely on energy-constrained pulse-based
communication. Thus, special gateway nodes between the macro- and nano-worlds
will be required, which has been only sporadically addressed in the literature
to date.

The authors in [259] argue that data acquisition from nanonetworks faces two
challenges. First, the mismatch between the demands of the nanonetworks and
the available bandwidth of the backhaul link with the macro-world reduces the
bandwidth efficiency of the backhaul, as well as the energy efficiency of THz
band nanonetworks. To address this issue, the authors propose a polling
mechanism for the backhaul tier which is composed of nanosinks that aggregate
and transport data from nanonodes to the gateway. The mechanism is based on
on-demand polling and accounts for the dynamic backhaul bandwidth and THz
channel conditions. The mechanism is developed with the goal of supporting
applications in the domain of wireless robotic materials.

Several works (i.e., [32, 34, 260, 261]) propose hierarchical network
architectures for enabling body-centric communication and sensing only-based
applications. Specifically, the aim in these works is to enable uplink
communication between a nanonetwork deployed inside of a human body and
external monitoring devices through nanointerfaces (i.e., gateways). Moreover,
in [29, 30] the authors aim at enabling interconnection of sensing nanodevices
with existing communication networks, eventually forming the IoNT.

However, the current results mostly aim at enabling uplink communication from
the nanonetworks toward the macro-world. In order to unlock the full potential
of the envisioned applications, downlink (predominantly control) communication
will also be required. This will enable applications ranging from software-
controlled metamaterials to control of and actuation using wireless robotic
materials. Future investigations should also aim at minimizing the latency of
such communication, especially for control-related applications.

###  IX-F Security, Integrity, and Privacy

One question that did not receive substantial research attention is the
security of THz band nanocommunication. The authors in both [262] and [263]
argue that the security-related goals in THz band nanocommunication should be
confidentiality (protection against malicious or unauthenticated users),
integrity (protection against modification), and availability (protection
against disruption by a malicious user). There are several challenges
pertaining to achieving those goals, as in more details discussed in [262].
First, it will be necessary to develop methods for the establishment of shared
encryption keys, as well as for revoking them when needed. Second, the
overhead of secure communication protocols, cryptographic algorithms, and
access control and authentication methods will have to be minimized and
potentially reconsidered for ultra-low power nanonetworks. Finally, as the
prevention of all malicious attacks can hardly be guaranteed, it will be
important to at least develop means for their detection, as well as strategies
for reacting to them. However, the above-listed challenges are all but
explored in the context of THz band nanocommunication and nanonetworking.

###  IX-G Localization and Tracking

Many of the envisioned applications supported by THz nanonetworks require
localization or even tracking of the nanonodes. This is a highly challenging
requirement, given that such localization and tracking capabilities will, due
to the nature of the nanonodes, have to operate in a highly energy-constrained
way, as well as provide very high accuracy (due to the small sizes of the
nanonodes). Moreover, due to the low range of THz band nanocommunication, the
localization capability will potentially have to be based on multi-hopping,
which has a drawback in terms of propagation of localization errors with the
number of hops [264].

There are only a handful of attempts in localizing THz-operating nanonodes. In
[265], the authors propose two ranging- and hop-counting-based localization
algorithms. The algorithms are envisioned to be used for estimating the
locations of all nanonodes deployed within a certain area. The first technique
uses flooding-based forwarding to all nanonodes, where the locations of the
two considered nanonodes are estimated by counting the number of hops between
them. To reduce the overhead and energy dissipation, the second algorithm
works under the assumption that all nanonodes are grouped into clusters.
Cluster heads communicate together and count the number of hops in order to
localize different nodes within a cluster. Similarly, Zhou _et al._ [266]
propose a pulse-based distance accumulation (PBDA) localization algorithm. The
PDBA algorithm adopts TS-OOK pulses for estimating the distance between
clustered nanonodes. The algorithms proposed in [265, 266] can potentially
operate within the nanonodes’ energy constraints. However, their accuracy is
intrinsically going to be relatively low due to the propagation of
localization errors as the number of hops increases.

One potential direction in enhancing localization accuracy, while at the same
time maintaining its low energy consumption profile, is to base the
localization capabilities on backscattered signals. The feasibility and
promising accuracy of such an approach has been demonstrated in [267]. The
approach in [267] utilizes a backscattered signal from a nanonode (i.e., DR-
Lens tag) for extracting the round-trip time-of-flight (RToF) between the tag
and the localization anchor. The RToF readings from multiple anchors are then
used for estimating the corresponding distances to the nanonode, with the
nanonode’s location consequently being determined using linear least square
algorithm. The yet unresolved challenges of developing such systems include
angle and frequency-dependent response of the nanonodes, non-free space (e.g.,
in-body) propagation, and almost certainly a variety of hardware
imperfections.

###  IX-H Standardization

As stated in [27], the THz band is not yet regulated and it is up to the
scientific community to jointly define the future of the paradigm. The IEEE
P1906.1 standard [268] is, to the best of our knowledge, the only attempt
going in this direction for THz-based nanocommunication. The standard provides
a conceptual framework for future developments of nanoscale communication
networks. Cánovas-Carrasco _et al._ [269] provided a review of the latest IEEE
P1906.1 recommendations, in which they outlined the main features and
identified several shortcomings of the standard, to which, they argue, further
research efforts should be devoted. First, the characteristics and reference
powering solutions of potential nanodevices have not been discussed in the
standard. Second, the recommended values and ranges for respectively
transmission power and SNR for reception have not been specified. Third, the
standard does not specify the Open Systems Interconnection (OSI) layers 2 and
3 techniques, which hampers protocol interoperability. In order words,
standardization efforts aiming at media access control, addressing schemes,
flow control, error detection, and routing procedures are needed. Finally, the
standard currently lacks recommendations about the interconnection between
nanonetworks and existing communication networks. Substantial research efforts
are certainly still needed for addressing the above-stated limitations of the
standard.

##  X Conclusion

In this survey, we have outlined the most promising application domains that
could be enabled by the nanonetworks operating in the THz frequencies.
Moreover, we have derived the requirements that such applications pose on the
supporting nanonetworks, which could be utilized as a rule-of-thumb guidelines
in the development of the supporting nanocommunication and nanonetworking
protocols. We have then outlined the current State-of-the-Art (SotA)
nanocommunication and nanonetworking protocols, as well as the applicable
channel models and experimentation tools. In addition, we have discussed their
strengths and weaknesses, as well as summarized a set of potential directions
for future research. Future efforts could target the development of THz
nanonetwork prototypes and experimental testing infrastructures, development
of protocols for primarily higher layers of the protocol stack, mitigation of
mobility effects, enabling additional features such as security or
localization of nanodevices, to name some.

We believe that this survey demonstrates that THz band nanocommunication is a
vivid and promising research domain, as Prof. Feynman suggested it will be
more than 60 years ago. We encourage the community to focus on resolving the
indicated major challenges, so that the outlined set of exciting applications
could become a reality in the near future.

## Acknowledgments

The author Filip Lemic was supported by the EU Marie Skłodowska-Curie Actions
Individual Fellowships (MSCA-IF) project Scalable Localization-enabled In-body
Terahertz Nanonetwork (SCaLeITN, grant nr. 893760). The author Pieter
Stroobant was supported by a PhD grant of Ghent University’s Special Research
Fund (BOF). This work was partly funded by the Ghent University’s BOF/GOA
project “Autonomic Networked Multimedia Systems”. This work was also supported
by the Spanish Ministry of Economy and Competitiveness under contract
TEC2017-90034-C2-1-R (ALLIANCE project) that receives funding from FEDER, and
by the European Commission under grants No. 736876 and No. 863337.

## References

  * [1] Richard P Feynman  “There’s plenty of room at the bottom”  In _California Institute of Technology, Engineering and Science magazine_ , 1960
  * [2] Qammer H Abbasi et al.  “Nano-communication for biomedical applications: A review on the state-of-the-art from physical layers to novel networking concepts”  In _IEEE Access_ 4 IEEE, 2016, pp. 3920–3935
  * [3] S Hiyama  “Molecular communication”  In _Journal-Institute of Electronics Information and Communication Engineers_ 89.2 INST OF ELECTRONICS INFO & COMM ENGINEERS JAPAN, 2006, pp. 162
  * [4] Yifan Chen  “A touch-communication framework for drug delivery based on a transient microbot system”  In _IEEE Transactions on Nanobioscience_ 14.4 IEEE, 2015, pp. 397–408
  * [5] Ian F Akyildiz and Josep Miquel Jornet  “Electromagnetic wireless nanosensor networks”  In _Nano Communication Networks_ 1.1 Elsevier, 2010, pp. 3–19
  * [6] Ian F Akyildiz, Josep Miquel Jornet and Massimiliano Pierobon  “Propagation models for nanocommunication networks”  In _Antennas and Propagation (EuCAP)_ , 2010, pp. 1–5  IEEE
  * [7] Josep Miquel Jornet and Ian F Akyildiz  “Graphene-based nano-antennas for electromagnetic nanocommunications in the terahertz band”  In _Proceedings of the Fourth European Conference on Antennas and Propagation_ , 2010, pp. 1–5  IEEE
  * [8] Negar Rikhtegar and Manijeh Keshtgary  “A brief survey on molecular and electromagnetic communications in nano-networks”  In _International Journal of Computer Applications_ 79.3 Citeseer, 2013
  * [9] Josep Miquel Jornet and Ian F Akyildiz  “Fundamentals of electromagnetic nanonetworks in the terahertz band”  In _Foundations and Trends® in Networking_ 7.2-3 Now Publishers, Inc., 2013, pp. 77–233
  * [10] Zhong Lin Wang  “Towards self-powered nanosystems: from nanogenerators to nanopiezotronics”  In _Advanced Functional Materials_ 18.22 Wiley Online Library, 2008, pp. 3553–3567
  * [11] George W Hanson  “Fundamental transmitting properties of carbon nanotube antennas”  In _IEEE Transactions on antennas and propagation_ 53.11 IEEE, 2005, pp. 3426–3435
  * [12] Ignacio Llatser et al.  “Graphene-based nano-patch antenna for terahertz radiation”  In _Photonics and Nanostructures-Fundamentals and Applications_ 10.4 Elsevier, 2012, pp. 353–358
  * [13] M Rosenau Da Costa, OV Kibis and ME Portnoi  “Carbon nanotubes as a basis for terahertz emitters and detectors”  In _Microelectronics Journal_ 40.4-5 Elsevier, 2009, pp. 776–778
  * [14] Ashkan Vakil and Nader Engheta  “Transformation optics using graphene”  In _Science_ 332.6035 American Association for the Advancement of Science, 2011, pp. 1291–1294
  * [15] Jason Horng et al.  “Drude conductivity of Dirac fermions in graphene”  In _Physical Review B_ 83.16 APS, 2011, pp. 165113
  * [16] Sergey A Mikhailov  “Theory of the giant plasmon-enhanced second-harmonic generation in graphene and semiconductor two-dimensional electron systems”  In _Physical Review B_ 84.4 APS, 2011, pp. 045432
  * [17] Tao Zhou and Zhiqun Cheng  “Miniaturized tunable terahertz antenna based on graphene”  In _Microwave and Optical Technology Letters_ 56.8 Wiley Online Library, 2014, pp. 1792–1794
  * [18] Julien Perruisseau-Carrier, Michele Tamagnone, Juan Sebastian Gomez-Diaz and Eduardo Carrasco  “Graphene antennas: Can integration and reconfigurability compensate for the loss?”  In _2013 European Microwave Conference_ , 2013, pp. 369–372  Ieee
  * [19] D. Correas-Serrano and J. S. Gomez-Diaz  “Graphene-based Antennas for Terahertz Systems: A Review”  In _FERMAT_ , 2017  arXiv:1704.00371
  * [20] Yanqing Wu, Damon B Farmer, Fengnian Xia and Phaedon Avouris  “Graphene electronics: Materials, devices, and circuits”  In _Proceedings of the IEEE_ 101.7 IEEE, 2013, pp. 1620–1637
  * [21] Shu-Jen Han et al.  “Graphene radio frequency receiver integrated circuit”  In _Nature communications_ 5.1 Nature Publishing Group, 2014, pp. 1–6
  * [22] Daniel Neumaier, Stephan Pindl and Max C Lemme  “Integrating graphene into semiconductor fabrication lines”  In _Nature Materials_ 18.6 Nature Publishing Group, 2019, pp. 525
  * [23] Josep Miquel Jornet and Ian F. Akyildiz  “Graphene-based plasmonic nano-transceiver for terahertz band communication”  In _Proceedings of the EuCAP ’14_ , 2014, pp. 492–6
  * [24] Staffs Keele  “Guidelines for performing systematic literature reviews in software engineering”, 2007
  * [25] Juan Cruz-Benito  “Systematic literature review & mapping”, 2016
  * [26] Ian F Akyildiz, Josep Miquel Jornet and Chong Han  “TeraNets: Utra-broadband communication networks in the terahertz band”  In _IEEE Wireless Communications_ 21.4 IEEE, 2014, pp. 130–135
  * [27] Ian F Akyildiz, Josep Miquel Jornet and Chong Han  “Terahertz band: Next frontier for wireless communications”  In _Physical Communication_ 12 Elsevier, 2014, pp. 16–32
  * [28] Vitaly Petrov, Alexander Pyattaev, Dmitri Moltchanov and Yevgeni Koucheryavy  “Terahertz band communications: Applications, research challenges, and standardization activities”  In _2016 8th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)_ , 2016, pp. 183–190  IEEE
  * [29] Ian F Akyildiz and Josep Miquel Jornet  “The internet of nano-things”  In _IEEE Wireless Communications_ 17.6 IEEE, 2010, pp. 58–63
  * [30] Josep Miquel Jornet  “The internet of multimedia nano-things”  In _Nano Communication Networks_ 3.4 Elsevier, 2012, pp. 242–251
  * [31] Sasitharan Balasubramaniam and Jussi Kangasharju  “Realizing the internet of nano things: challenges, solutions, and applications”  In _Computer_ 46.2 IEEE, 2013, pp. 62–68
  * [32] Ian F Akyildiz, Max Pierobon, Sasi Balasubramaniam and Y Koucheryavy  “The internet of bio-nano things”  In _IEEE Communications Magazine_ 53.3 IEEE, 2015, pp. 32–40
  * [33] Mahdi H Miraz, Maaruf Ali, Peter S Excell and Rich Picking  “A review on Internet of Things (IoT), Internet of everything (IoE) and Internet of nano things (IoNT)”  In _2015 Internet Technologies and Applications (ITA)_ , 2015, pp. 219–224  IEEE
  * [34] Falko Dressler and Stefan Fischer  “Connecting in-body nano communication with body area networks: Challenges and opportunities of the internet of nano things”  In _Nano Communication Networks_ 6.2 Elsevier, 2015, pp. 29–38
  * [35] Rawan Alsheikh, Nadine Akkari and Etimad Fadel  “MAC protocols for Wireless Nano-sensor Networks: Performance analysis and design guidelines”  In _Digital Information Processing and Communications (ICDIPC)_ , 2016, pp. 129–134  IEEE
  * [36] Ali Rizwan et al.  “A review on the role of nano-communication in future healthcare systems: A big data analytics perspective”  In _IEEE Access_ 6 IEEE, 2018, pp. 41903–41920
  * [37] Saim Ghafoor, Noureddine Boujnah, Mubashir Husain Rehmani and Alan Davy  “MAC protocols for terahertz communication: A comprehensive survey”  In _IEEE Communications Surveys & Tutorials_ IEEE, 2020
  * [38] Kazi Mohammed Saidul Huq et al.  “THz communications for mobile heterogeneous networks”  In _IEEE Communications Magazine_ 56.6 IEEE, 2018, pp. 94–95
  * [39] Shahid Mumtaz et al.  “Terahertz communication for vehicular networks”  In _IEEE Transactions on Vehicular Technology_ 66.7 IEEE, 2017, pp. 5617–5625
  * [40] Zhi Chen et al.  “A survey on terahertz communications”  In _China Communications_ 16.2 IEEE, 2019, pp. 1–35
  * [41] Kazi Mohammed Saidul Huq et al.  “Terahertz-enabled wireless system for beyond-5G ultra-fast networks: A brief survey”  In _IEEE Network_ 33.4 IEEE, 2019, pp. 89–95
  * [42] Xinying Ma et al.  “Intelligent reflecting surface enhanced indoor terahertz communication systems”  In _Nano Communication Networks_ 24 Elsevier, 2020, pp. 100284
  * [43] Murat Kuscu et al.  “Transmitter and receiver architectures for molecular communications: A survey on physical design with modulation, coding, and detection techniques”  In _Proceedings of the IEEE_ 107.7 IEEE, 2019, pp. 1302–1341
  * [44] Ozgur B Akan et al.  “Fundamentals of molecular information and communication science”  In _Proceedings of the IEEE_ 105.2 IEEE, 2016, pp. 306–318
  * [45] Jiaxing Wang, Bonan Yin and Mugen Peng  “Diffusion based molecular communication: principle, key technologies, and challenges”  In _China Communications_ 14.2 IEEE, 2017, pp. 1–18
  * [46] Luca Felicetti, Mauro Femminella, Gianluca Reali and Pietro Liò  “Applications of molecular communications to medicine: A survey”  In _Nano Communication Networks_ 7 Elsevier, 2016, pp. 27–45
  * [47] Youssef Chahibi  “Molecular communication for drug delivery systems: A survey”  In _Nano Communication Networks_ 11 Elsevier, 2017, pp. 90–102
  * [48] Roger A Lewis  “A review of terahertz sources”  In _Journal of Physics D: Applied Physics_ 47.37 IOP Publishing, 2014, pp. 374001
  * [49] Efthymios Lallas  “Key Roles of Plasmonics in Wireless THz Nanocommunications—A Survey”  In _Applied Sciences_ 9.24 Multidisciplinary Digital Publishing Institute, 2019, pp. 5488
  * [50] Hadeel Elayan, Raed M Shubair and Asimina Kiourti  “On graphene-based THz plasmonic nano-antennas”  In _2016 16th Mediterranean Microwave Symposium (MMS)_ , 2016, pp. 1–3  IEEE
  * [51] SM Hossein Naghavi et al.  “Filling the Gap With Sand: When CMOS reaches THz”  In _IEEE Solid-State Circuits Magazine_ 11.3 IEEE, 2019, pp. 33–42
  * [52] Kaushik Sengupta, Tadao Nagatsuma and Daniel M Mittleman  “Terahertz integrated electronic and hybrid electronic–photonic systems”  In _Nature Electronics_ 1.12 Nature Publishing Group, 2018, pp. 622–635
  * [53] Ian F Akyildiz  “Nanonetworks: A new frontier in communications”  In _ACM Mobile computing and networking_ , 2012, pp. 1–2  ACM
  * [54] Sumeet Walia et al.  “Flexible metasurfaces and metamaterials: a review of materials and fabrication processes at micro-and nano-scales”  In _Applied Physics Reviews_ 2.1 AIP Publishing, 2015, pp. 011303
  * [55] Wenshan Cai and Uday K Chettiar  “Optical cloaking with metamaterials”  In _Nature photonics_ 1.4 Nature Publishing Group, 2007, pp. 224
  * [56] Xiaole Wang, Zhenyu Huang, Feng Han and Zixin Feng  “Reduction of aircraft engine noise by covering surface acoustic metamaterials on sidewalls”  In _Proceedings of the 24th International Congress on Sound and Vibration (ICSV), London, UK_ , 2017, pp. 23–27
  * [57] Alexander Poddubny, Ivan Iorsh, Pavel Belov and Yuri Kivshar  “Hyperbolic metamaterials”  In _Nature photonics_ 7.12 Nature Publishing Group, 2013, pp. 948
  * [58] David R Smith and John B Pendry  “Metamaterials and negative refractive index”  In _Science_ 305.5685 American Association for the Advancement of Science, 2004, pp. 788–792
  * [59] Tianrui Zhai, Jinwei Shi, Shujing Chen and Dahe Liu  “Electromagnetic shielding and energy concentration using zero-index metamaterials”  In _Applied physics express_ 4.7 IOP Publishing, 2011, pp. 074301
  * [60] Christos Liaskos et al.  “Design and development of software defined metamaterials for nanonetworks”  In _IEEE Circuits and Systems Magazine_ 15.4 IEEE, 2015, pp. 12–25
  * [61] Sergi Abadal, Tie-Jun Cui, Tony Low and Julius Georgiou  “Programmable Metamaterials for Software-Defined Electromagnetic Control: Circuits, Systems, and Architectures”  In _IEEE Journal on Emerging and Selected Topics in Circuits and Systems_ 10.1 IEEE, 2020, pp. 6–19
  * [62] Christos Liaskos et al.  “A New Wireless Communication Paradigm through Software-Controlled Metasurfaces”  In _IEEE Communications Magazine_ 56.9, 2018, pp. 162–169  DOI: 10.1109/MCOM.2018.1700659
  * [63] Shimin Gong et al.  “Towards Smart Radio Environment for Wireless Communications via Intelligent Reflecting Surfaces: A Comprehensive Survey”  In _arXiv preprint arXiv:1912.07794_ , 2019
  * [64] Christos Liaskos, Shuai Nie and Ageliki Tsioliaridou  “A novel communication paradigm for high capacity and security via programmable indoor wireless environments in next generation wireless systems”  In _Ad Hoc Networks_ 87 Elsevier, 2019, pp. 1–16
  * [65] Qingqing Wu and Rui Zhang  “Intelligent reflecting surface enhanced wireless network via joint active and passive beamforming”  In _IEEE Transactions on Wireless Communications_ 18.11 IEEE, 2019, pp. 5394–5409
  * [66] Chongwen Huang et al.  “Reconfigurable intelligent surfaces for energy efficiency in wireless communication”  In _IEEE Transactions on Wireless Communications_ 18.8 IEEE, 2019, pp. 4157–4170
  * [67] Özgecan Özdogan, Emil Björnson and Erik G Larsson  “Intelligent reflecting surfaces: Physics, propagation, and pathloss modeling”  In _IEEE Wireless Communications Letters_ IEEE, 2019
  * [68] Wankai Tang et al.  “Programmable metasurface-based RF chain-free 8PSK wireless transmitter”  In _Electronics Letters_ 55.7 IET, 2019, pp. 417–420
  * [69] Lianlin Li et al.  “Electromagnetic reprogrammable coding-metasurface holograms”  In _Nature Communications_ 8.1 Springer US, 2017, pp. 1–7  DOI: 10.1038/s41467-017-00164-9
  * [70] Sergi Abadal et al.  “Computing and communications for the software-defined metamaterial paradigm: A context analysis”  In _IEEE access_ 5 IEEE, 2017, pp. 6225–6235
  * [71] Emil Björnson and Luca Sanguinetti  “Power Scaling Laws and Near-Field Behaviors of Massive MIMO and Intelligent Reflecting Surfaces”  In _arXiv preprint arXiv:2002.04960_ , 2020
  * [72] Filip Lemic, Chong Han and Jeroen Famaey  “Idling Energy Modeling and Reduction in Energy Harvesting Terahertz Nanonetworks for Controlling Software-Defined Metamaterials”  In _IEEE Journal on Emerging and Selected Topics in Circuits and Systems_ 10.1 IEEE, 2020, pp. 88–99
  * [73] T. Saeed et al.  “Workload Characterization of Programmable Metasurfaces”  In _Proceeding of the NANOCOM ’19_ , 2019
  * [74] Harsh Tataria, Fredrik Tufvesson and Ove Edfors  “Real-Time Implementation Aspects of Large Intelligent Surfaces”  In _arXiv preprint arXiv:2003.01672_ , 2020
  * [75] Fu Liu et al.  “Intelligent metasurfaces with continuously tunable local surface impedance for multiple reconfigurable functions”  In _Physical Review Applied_ 11.4 APS, 2019, pp. 044024
  * [76] Hamidreza Taghvaee, Albert Cabellos-Aparicio, Julius Georgiou and Sergi Abadal  “Error Analysis of Programmable Metasurfaces for Beam Steering”  In _IEEE Journal on Emerging and Selected Topics in Circuits and Systems_ 10.1 IEEE, 2020, pp. 62–74
  * [77] Michael Andrew McEvoy and Nikolaus Correll  “Materials that couple sensing, actuation, computation, and communication”  In _Science_ 347.6228 American Association for the Advancement of Science, 2015, pp. 1261689
  * [78] Michael McEvoy and Nikolaus Correll  “Shape change through programmable stiffness”  In _Experimental Robotics_ , 2016, pp. 893–907  Springer
  * [79] Nikolaus Correll, Prabal Dutta, Richard Han and Kristofer Pister  “Wireless Robotic Materials”  In _ACM Conference on Embedded Network Sensor Systems_ , 2017, pp. 1–6  ACM
  * [80] Homa Hosseinmardi et al.  “Distributed spatiotemporal gesture recognition in sensor arrays”  In _ACM Transactions on Autonomous and Adaptive Systems (TAAS)_ 10.3 ACM, 2015, pp. 17
  * [81] Dana Hughes and Nikolaus Correll  “Texture recognition and localization in amorphous robotic skin”  In _Bioinspiration & biomimetics_ 10.5 IOP Publishing, 2015, pp. 055002
  * [82] Gerhard P Fettweis  “The tactile internet: Applications and challenges”  In _IEEE Vehicular Technology Magazine_ 9.1 IEEE, 2014, pp. 64–70
  * [83] Michiel Panne and Eugene Fiume  “Sensor-actuator networks”  In _Proceedings of the 20th annual conference on Computer graphics and interactive techniques_ , 1993, pp. 335–342  ACM
  * [84] Josep Miquel Jornet  “A joint energy harvesting and consumption model for self-powered nano-devices in nanonetworks”  In _IEEE International Conference on Communications (ICC)_ , 2012, pp. 6151–6156  IEEE
  * [85] Armita Afsharinejad, Alan Davy, Brendan Jennings and Conor Brennan  “Performance analysis of plant monitoring nanosensor networks at THz frequencies”  In _IEEE Internet of Things Journal_ 3.1 IEEE, 2016, pp. 59–69
  * [86] Vitaly Petrov  “Feasibility study of the THz band for communications between wearable electronics”  In _Open Innovations Association (FRUCT), 2015 17th Conference of_ , 2015, pp. 157–162  IEEE
  * [87] Jennifer Simonjan, Josep Miquel Jornet, Ian F Akyildiz and Bernhard Rinner  “Nano-cameras: A key enabling technology for the internet of multimedia nano-things”  In _ACM Nanoscale Computing and Communication_ , 2018, pp. 28  ACM
  * [88] Wojciech Knap et al.  “THz imaging and wireless communication using nanotransistor based detectors: From basic physics to first real world applications”  In _International Conference on Transparent Optical Networks (ICTON)_ , 2017, pp. 1–5  IEEE
  * [89] Metin Sitti, Hakan Ceylan, Wenqi Hu and Joshua Giltinan  “Biomedical applications of untethered mobile milli/microrobots”  In _Proceedings of the IEEE_ 103.2 IEEE, 2015, pp. 205–224
  * [90] Nazim Agoulmine et al.  “Enabling communication and cooperation in bio-nanosensor networks: Toward innovative healthcare solutions”  In _IEEE Wireless Communications_ 19.5 IEEE, 2012
  * [91] Giuseppe Piro et al.  “Terahertz communications in human tissues at the nanoscale for healthcare applications”  In _IEEE Transactions on Nanotechnology_ 14.3 IEEE, 2015, pp. 404–406
  * [92] Najah Abu Ali and Mervat Abu-Elkheir  “Internet of nano-things healthcare applications: Requirements, opportunities, and challenges”  In _Wireless and Mobile Computing, Networking and Communications (WiMob)_ , 2015, pp. 9–14  IEEE
  * [93] M Anthony Lewis and George A Bekey  “The Behavioral Self-organization Of Nanorobots Using Local Rules”  In _IEEE/RSJ Intelligent Robots and Systems (IROS)_ , 1992, pp. 1333–1338
  * [94] Rafael Asorey-Cacheda, Sebastian Canovas-Carrasco, Antonio-Javier Garcia-Sanchez and Joan Garcia-Haro  “An Analytical Approach to Flow-Guided Nanocommunication Networks”  In _Sensors_ 20.5 Multidisciplinary Digital Publishing Institute, 2020, pp. 1332
  * [95] Rafael Asorey-Cacheda, Antonio-Javier Garcia-Sanchez and Joan Garcia-Haro  “Throughput Optimization in Flow-Guided Nanocommunication Networks”  In _IEEE Access_ 8 IEEE, 2020, pp. 142875–142891
  * [96] Rafael Asorey-Cacheda, Antonio-Javier Garcia-Sanchez and Joan Garcia-Haro  “On the Feasibility of Flow-Guided Nanocommunication Networks for some Medical Applications”  In _1st ACM International Workshop on Nanoscale Computing, Communication, and Applications (NanoCoCoA’20)_ , 2020, pp. 1–7  ACM
  * [97] Brent Bohnenstiehl et al.  “KiloCore: A Fine-Grained 1,000-Processor Array for Task-Parallel Applications”  In _IEEE Micro_ 37.2, 2017, pp. 63–69
  * [98] S. Abadal et al.  “Medium Access Control in Wireless Network-on-Chip: A Context Analysis”  In _IEEE Communications Magazine_ 56.6, 2018, pp. 172–178
  * [99] Radu Marculescu et al.  “Outstanding research problems in NoC design: system, microarchitecture, and circuit perspectives”  In _IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems_ 28.1, 2009, pp. 3–21
  * [100] “On-chip networks from a networking perspective: congestion and scalability in many-core interconnects”  In _Proceedings of the SIGCOMM_ , 2012, pp. 407–18
  * [101] Davide Bertozzi, Giorgos Dimitrakopoulos, José Flich and Sören Sonntag  “The fast evolving landscape of on-chip communication”  In _Design Automation for Embedded Systems_ 19.1, 2015, pp. 59–76  DOI: 10.1007/s10617-014-9137-6
  * [102] John Kim, Kiyoung Choi and Gabriel Loh  “Exploiting new interconnect technologies in on-chip communication”  In _IEEE Journal on Emerging and Selected Topics in Circuits and Systems_ 2.2, 2012, pp. 124–136  DOI: 10.1109/JETCAS.2012.2201031
  * [103] Amlan Ganguly et al.  “Scalable hybrid wireless network-on-chip architectures for multicore systems”  In _IEEE Transactions on Computers_ 60.10 IEEE, 2011, pp. 1485–1502
  * [104] Chifeng Wang, Wen-Hsiang Hu and Nader Bagherzadeh  “A wireless network-on-chip design for multicore platforms”  In _2011 19th International Euromicro conference on parallel, distributed and network-based processing_ , 2011, pp. 409–416  IEEE
  * [105] Sergi Abadal et al.  “Graphene-enabled wireless communication for massive multicore architectures”  In _IEEE Communications Magazine_ 51.11 IEEE, 2013, pp. 137–143
  * [106] Sagar Saxena and Deekshith Shenoy Manur  “A folded wireless network-on-chip using graphene based THz-band antennas”  In _ACM Nanoscale Computing and Communication_ , 2017, pp. 29  ACM
  * [107] Sagar Saxena, Deekshith Shenoy Manur, Naseef Mansoor and Amlan Ganguly  “Scalable and energy efficient wireless inter chip interconnection fabrics using THz-band antennas”  In _Journal of Parallel and Distributed Computing_ Elsevier, 2020
  * [108] A. Mestres et al.  “A MAC protocol for Reliable Broadcast Communications in Wireless Network-on-Chip”  In _Proceedings of the NoCArc ’16_ , 2016, pp. 21–26
  * [109] Hyoukjun Kwon  “Rethinking NoCs for Spatial Neural Network Accelerators”  In _Proceedings of the NoCS ’17_ , 2017, pp. Art. 19
  * [110] Jacob Postman and Patrick Chiang  “A survey addressing on-chip interconnect: energy and reliability considerations”  In _ISRN Electronics_ 2012 Hindawi Publishing Corporation, 2012
  * [111] B. Lebiednik, S. Abadal, H. Kwon and T. Krishna  “Architecting a Secure Wireless Network-on-Chip”  In _Proceedings of the NOCS ’18_ , 2018
  * [112] O Yalgashev, Mohamed Bakhouya, Abderrahim Chariete and Jaafar Gaber  “Performance evaluation of nano-on-chip interconnect for SoCs”  In _High Performance Computing & Simulation (HPCS)_, 2014, pp. 512–515  IEEE
  * [113] Sergi Abadal et al.  “Broadcast-enabled massive multicore architectures: A wireless RF approach”  In _IEEE micro_ 35.5 IEEE, 2015, pp. 52–61
  * [114] Josep Miquel Jornet, Joan Capdevila Pujol and Josep Solé Pareta  “Phlame: A physical layer aware mac protocol for electromagnetic nanonetworks in the terahertz band”  In _Nano Communication Networks_ 3.1 Elsevier, 2012, pp. 74–81
  * [115] Pu Wang et al.  “Energy and spectrum-aware MAC protocol for perpetual wireless nanosensor networks in the Terahertz Band”  In _Ad Hoc Networks_ 11.8 Elsevier, 2013, pp. 2541–2555
  * [116] Hadeel Elayan et al.  “Terahertz band: The last piece of RF spectrum puzzle for communication systems”  In _IEEE Open Journal of the Communications Society_ 1 IEEE, 2019, pp. 1–32
  * [117] Jamal N Al-Karaki and Ahmed E Kamal  “Routing techniques in wireless sensor networks: a survey”  In _IEEE wireless communications_ 11.6 IEEE, 2004, pp. 6–28
  * [118] Qing Xia and Josep Miquel Jornet  “Cross-layer analysis of optimal relaying strategies for terahertz-band communication networks”  In _Wireless and Mobile Computing, Networking and Communications (WiMob),_ , 2017, pp. 1–8  IEEE
  * [119] Zhichao Rong, Mark Stephen Leeson and Matthew David Higgins  “Relay-assisted nanoscale communication in the THz band”  In _Micro & Nano Letters_ 12.6 IET, 2017, pp. 373–376
  * [120] Hang Yu, Bryan Ng and Winston KG Seah  “Forwarding schemes for EM-based wireless nanosensor networks in the terahertz band”  In _Nanoscale Computing and Communication_ , 2015, pp. 17  ACM
  * [121] Hong-Hsu Yen  “Energy aware and signal quality aware data aggregation touting in wireless nanosensor networks”  In _Consumer Communications & Networking Conference (CCNC), 2017 14th IEEE Annual_, 2017, pp. 879–884  IEEE
  * [122] Massimiliano Pierobon et al.  “A routing framework for energy harvesting wireless nanosensor networks in the Terahertz Band”  In _Wireless networks_ 20.5 Springer, 2014, pp. 1169–1183
  * [123] Sebastian Canovas-Carrasco, Antonio-Javier Garcia-Sanchez and Joan Garcia-Haro  “A nanoscale communication network scheme and energy model for a human hand scenario”  In _Nano communication networks_ 15 Elsevier, 2018, pp. 17–27
  * [124] S. Canovas-Carrasco, R. M. Sandoval, A. Garcia-Sanchez and J. Garcia-Haro  “Optimal Transmission Policy Derivation for IoNT Flow-Guided Nano-Sensor Networks”  In _IEEE Internet of Things Journal_ 6.2, 2019, pp. 2288–2298  DOI: 10.1109/JIOT.2019.2906015
  * [125] Christos Liaskos and Angeliki Tsioliaridou  “A promise of realizable, ultra-scalable communications at nano-scale: A multi-modal nano-machine architecture”  In _IEEE Transactions on Computers_ 64.5 IEEE, 2015, pp. 1282–1295
  * [126] Ageliki Tsioliaridou, Christos Liaskos, Sotiris Ioannidis and Andreas Pitsillides  “Lightweight, self-tuning data dissemination for dense nanonetworks”  In _Nano Communication Networks_ 8 Elsevier, 2016, pp. 2–15
  * [127] Fariha Afsana, SA Mamun, MS Kaiser and MR Ahmed  “Outage capacity analysis of cluster-based forwarding scheme for Body Area Network using nano-electromagnetic communication”  In _Electrical Information and Communication Technology (EICT)_ , 2015, pp. 383–388  IEEE
  * [128] Marc Stelzner, Fabian Busse and Sebastian Ebers  “In-body nanonetwork routing based on MANET and THz”  In _ACM Nanoscale Computing and Communication_ , 2018, pp. 9  ACM
  * [129] Florian Büther, Immo Traupe and Sebastian Ebers  “Hop count routing: A routing algorithm for resource constrained, identity-free medical nanonetworks”  In _ACM Nanoscale Computing and Communication_ , 2018, pp. 17  ACM
  * [130] Fadi Al-Turjman  “A Cognitive Routing Protocol for Bio-Inspired Networking in the Internet of Nano-Things (IoNT)”  In _Mobile Networks and Applications_ Springer, 2017, pp. 1–15
  * [131] Martin Radetzki, Chaochao Feng, Xueqian Zhao and Axel Jantsch  “Methods for fault tolerance in networks-on-chip”  In _ACM Computing Surveys (CSUR)_ 46.1 ACM, 2013, pp. 8
  * [132] Mohammad Fattah et al.  “A Low-Overhead, Fully-Distributed, Guaranteed-Delivery Routing Algorithm for Faulty Network-on-Chips”  In _Proceedings of the 9th International Symposium on Networks-on-Chip_ , NOCS ’15  Vancouver, BC, Canada: ACM, 2015, pp. 18:1–18:8
  * [133] Y. Fukushima, M. Fukushi and S. Horiguchi  “Fault-Tolerant Routing Algorithm for Network on Chip without Virtual Channels”  In _2009 24th IEEE International Symposium on Defect and Fault Tolerance in VLSI Systems_ , 2009, pp. 313–321
  * [134] S. Jovanovic, C. Tanougast, S. Weber and C. Bobda  “A new deadlock-free fault-tolerant routing algorithm for NoC interconnections”  In _Field Programmable Logic and Applications_ , 2009, pp. 326–331
  * [135] Masoumeh Ebrahimi, Masoud Daneshtalab, Juha Plosila and Farhad Mehdipour  “MD: minimal path-based fault-tolerant routing in on-chip networks”  In _Design Automation Conference (ASP-DAC), 2013 18th Asia and South Pacific_ , 2013, pp. 35–40  IEEE
  * [136] Taqwa Saeed et al.  “Fault Adaptive Routing in Metasurface Controller Networks”  In _2018 11th International Workshop on Network on Chip Architectures (NoCArc)_ , 2018, pp. 1–6  IEEE
  * [137] Ageliki Tsioliaridou, Christos Liaskos, Sotiris Ioannidis and Andreas Pitsillides  “CORONA: A Coordinate and Routing system for Nanonetworks”  In _Nanoscale Computing and Communication_ , 2015, pp. 18  ACM
  * [138] Ageliki Tsioliaridou, Christos Liaskos, Eugen Dedu and Sotiris Ioannidis  “Packet routing in 3D nanonetworks: A lightweight, linear-path scheme”  In _Nano communication networks_ 12 Elsevier, 2017, pp. 63–71
  * [139] S. Wang and T. Jin  “Wireless network-on-chip: a survey”  In _The Journal of Engineering_ 2014.3, 2014, pp. 98–104
  * [140] Marc Stelzner, Falko Dressler and Stefan Fischer  “Function Centric Nano-Networking: Addressing nano machines in a medical application scenario”  In _Nano communication networks_ 14 Elsevier, 2017, pp. 29–39
  * [141] Sheng Xu et al.  “Self-powered nanowire devices”  In _Nature nanotechnology_ 5.5 Nature Publishing Group, 2010, pp. 366
  * [142] Shahram Mohrehkesh and Michele C Weigle  “Optimizing energy consumption in terahertz band nanonetworks”  In _IEEE Journal on Selected Areas in Communications_ 32.12 IEEE, 2014, pp. 2432–2441
  * [143] Adnan Ismail Al-Sulaifanie, Subir Biswas and Bayez Khorsheed Al-Sulaifanie  “AH-MAC: adaptive hierarchical MAC protocol for low-rate wireless sensor network applications”  In _Journal of Sensors_ 2017 Hindawi, 2017
  * [144] Nadine Akkari et al.  “Distributed timely throughput optimal scheduling for the Internet of Nano-Things”  In _IEEE Internet of Things Journal_ 3.6 IEEE, 2016, pp. 1202–1212
  * [145] Rawan Alsheikh, Nadine Akkari and Etimad Fadel  “Grid Based Energy-Aware MAC Protocol for Wireless Nanosensor Network”  In _New Technologies, Mobility and Security (NTMS)_ , 2016, pp. 1–5  IEEE
  * [146] Shahram Mohrehkesh, Michele C Weigle and Sajal K Das  “DRIH-MAC: A distributed receiver-initiated harvesting-aware MAC for nanonetworks”  In _IEEE Transactions on Molecular, Biological and Multi-Scale Communications_ 1.1 IEEE, 2015, pp. 97–110
  * [147] Salvatore D’Oro, Laura Galluccio, Giacomo Morabito and Sergio Palazzo  “A timing channel-based MAC protocol for energy-efficient nanonetworks”  In _Nano Communication Networks_ 6.2 Elsevier, 2015, pp. 39–50
  * [148] Qing Xia, Zahed Hossain, Michael Medley and Josep Miquel Jornet  “A link-layer synchronization and medium access control protocol for terahertz-band communication networks”  In _2015 IEEE Global Communications Conference (GLOBECOM)_ , 2015, pp. 1–7  IEEE
  * [149] Giuseppe Piro, Luigi Alfredo Grieco, Gennaro Boggia and Pietro Camarda  “Nano-Sim: Simulating electromagnetic-based nanonetworks in the network simulator 3”  In _ICST Conference on Simulation Tools and Techniques_ , 2013, pp. 203–210  ICST (Institute for Computer Sciences, Social-Informatics and …
  * [150] Hang Yu, Bryan Ng and Winston KG Seah  “Pulse arrival scheduling for nanonetworks under limited IoT access bandwidth”  In _2017 IEEE 42nd Conference on Local Computer Networks (LCN)_ , 2017, pp. 18–26  IEEE
  * [151] Negar Rikhtegar, Manijeh Keshtgari and Zahra Ronaghi  “EEWNSN: Energy Efficient Wireless Nano Sensor Network MAC Protocol for Communications in the Terahertz Band”  In _Wireless Personal Communications_ 97.1 Springer, 2017, pp. 521–537
  * [152] V Srikanth and Sindhu Chaluvadi  “Energy efficient, scalable and reliable MAC protocol for electromagnetic communication among nano devices”  In _International Journal of Distributed and Parallel Systems_ 3.1 Academy & Industry Research Collaboration Center (AIRCC), 2012, pp. 249
  * [153] Suk Jin Lee, Hongsik Choi and Sungun Kim  “Slotted CSMA/CA Based Energy Efficinet MAC Protocol Design in Nanonetworks”  In _International Journal of Wireless & Mobile Networks (IJWMN)_ 10, 2018
  * [154] Naseef Mansoor, Shahriar Shamim and Amlan Ganguly  “A Demand-Aware Predictive Dynamic Bandwidth Allocation Mechanism for Wireless Network-on-Chip”  In _Proceedings of the SLIP ’16_ , 2016
  * [155] Naseef Mansoor and Amlan Ganguly  “Reconfigurable Wireless Network-on-Chip with a Dynamic Medium Access Mechanism”  In _Proceedings of the NoCS ’15_ , 2015, pp. Article 13
  * [156] Shahram Mohrehkesh and Michele C Weigle  “RIH-MAC: Receiver-initiated harvesting-aware MAC for nanonetworks”  In _ACM Nanoscale Computing and Communication_ , 2014, pp. 6  ACM
  * [157] Filip Lemic, Raja Usman Akbar, Johann Marquez-Barja and Jeroen Famaey  “Assessing the Reliability of Energy Harvesting Terahertz Nanonetworks for Controlling Software-Defined Metamaterials”  In _ACM Nanoscale Computing and Communication (NanoCom 2019)_ , 2019  ACM
  * [158] Xue Wu and Kaushik Sengupta  “Dynamic Waveform Shaping With Picosecond Time Widths”  In _IEEE Journal of Solid-State Circuits_ 52.2, 2017, pp. 389–405
  * [159] Sangyeop Lee et al.  “An 80Gb/s 300GHz-Band Single-Chip CMOS Transceiver”  In _Proceedings of the ISSCC ’19_ IEEE, 2019, pp. 170–172
  * [160] Himanshu Aggrawal, Peiyu Chen and M Mahdi Assefzadeh  “Gone in a Picosecond: Techniques for the Generation and Detection of Picosecond Pulses and Their Applications”  In _IEEE Microwave Magazine_ 17.12, 2016, pp. 24–38
  * [161] Ruonan Han  “Filling the Gap: Silicon Terahertz Integrated Circuits Offer Our Best Bet”  In _IEEE Microwave Magazine_ 20.4 IEEE, 2019, pp. 80–93
  * [162] Xinmin Yu et al.  “Architecture and Design of Multi-Channel Millimeter-Wave Wireless Network-on-Chip”  In _IEEE Design & Test_ 31.6, 2014, pp. 19–28
  * [163] Sanming Hu et al.  “A SiGe BiCMOS transmitter/receiver chipset with on-chip SIW antennas for terahertz applications”  In _IEEE Journal of Solid-State Circuits_ 47.11 IEEE, 2012, pp. 2654–2664
  * [164] A. C. Tasolamprou et al.  “Exploration of Intercell Wireless Millimeter-Wave Communication in the Landscape of Intelligent Metasurfaces”  In _IEEE Access_ PP.99, 2019, pp. 1–1
  * [165] Josep Miquel Jornet and Ian F Akyildiz  “Femtosecond-long pulse-based modulation for terahertz band communication in nanonetworks”  In _IEEE Communications_ 62.5 IEEE, 2014, pp. 1742–1754
  * [166] Anish Prasad Shrestha, Sang-Jo Yoo, Hyoung Jin Choi and Kyung Sup Kwak  “Enhanced rate division multiple access for electromagnetic nanonetworks”  In _IEEE Sensors Journal_ 16.19, 2016, pp. 7287–7296
  * [167] Hakim Mabed  “Enhanced spread in time ON-OFF keying technique for dense Terahertz nanonetworks”  In _Computers and Communications (ISCC), 2017 IEEE Symposium on_ , 2017, pp. 710–716  IEEE
  * [168] Hakim Mabed and Julien Bourgeois  “A flexible medium access control protocol for dense terahertz nanonetworks”  In _ACM Nanoscale Computing and Communication_ , 2018, pp. 10  ACM
  * [169] Apostolos K Vavouris, Foteini D Dervisi, Vasilis K Papanikolaou and George K Karagiannidis  “An energy efficient modulation scheme for body-centric nano-communications in the THz band”  In _Modern Circuits and Systems Technologies (MOCAST)_ , 2018, pp. 1–4  IEEE
  * [170] Eisa Zarepour, Mahbub Hassan, Chun Tung Chou and Adesoji A Adesina  “Frequency hopping strategies for improving terahertz sensor network performance over composition varying channels”  In _World of Wireless, Mobile and Multimedia Networks (WoWMoM), 2014 IEEE 15th International Symposium on a_ , 2014, pp. 1–9  IEEE
  * [171] Chong Han, A Ozan Bicen and Ian F Akyildiz  “Multi-wideband waveform design for distance-adaptive wireless communications in the terahertz band”  In _IEEE Transactions on Signal Processing_ 64.4 IEEE, 2015, pp. 910–922
  * [172] Josep Miquel Jornet  “Low-weight error-prevention codes for electromagnetic nanonetworks in the terahertz band”  In _Nano Communication Networks_ 5.1-2 Elsevier, 2014, pp. 35–44
  * [173] Murat Kocaoglu and Ozgur B Akan  “Minimum energy channel codes for nanoscale wireless communications”  In _IEEE Transactions on Wireless Communications_ 12.4 IEEE, 2013, pp. 1492–1500
  * [174] Kaikai Chi, Yi-hua Zhu, Xiaohong Jiang and Victor CM Leung  “Energy-efficient prefix-free codes for wireless nano-sensor networks using OOK modulation”  In _IEEE Transactions on Wireless Communications_ 13.5 IEEE, 2014, pp. 2670–2682
  * [175] Muhammad Agus Zainuddin, Eugen Dedu and Julien Bourgeois  “SBN: Simple block nanocode for nanocommunications”  In _ACM Nanoscale Computing and Communication_ , 2016, pp. 4  ACM
  * [176] Pankaj Singh, Byung Wook Kim and Sung-Yoon Jung  “DS-OOK for Terahertz Band Nanonetworks”  In _National Academy Science Letters_ Springer, 2020, pp. 1–4
  * [177] S. E. Hosseininejad et al.  “MAC-Oriented Programmable Terahertz PHY via Graphene-based Yagi-Uda Antennas”  In _Proceedings of the WCNC ’18_ , 2018
  * [178] Jian Lin and Mary Ann Weitnauer  “Pulse-level beam-switching for terahertz networks”  In _Wireless Networks_ 7 Springer US, 2018, pp. 1–16
  * [179] Raul Gomez Cid-fuentes, Josep Miquel Jornet, Ian F. Akyildiz and Eduard Alarcón  “A Receiver Architecture for Pulse-based Electromagnetic Nanonetworks in the Terahertz Band”  In _Proceedings of the ICC ’12_ , 2012, pp. 4937–4942
  * [180] Pankaj Singh, Byung Kim and Sung-Yoon Jung  “Non-Coherent Symbol Detection with ToA Estimation for Nanocommunication Networks”, 2019, pp. 1–5
  * [181] Ajeya Gupta, Michael Medley and Josep Miquel Jornet  “Joint synchronization and symbol detection design for pulse-based communications in the THz band”  In _2015 IEEE Global Communications Conference (GLOBECOM)_ , 2015, pp. 1–7  IEEE
  * [182] Muhammad Ozair Iqbal, Muhammad Mahboob Ur Rahman and Muhammad A Imran  “Modulation Mode Detection and Classification for In Vivo Nano-Scale Communication Systems Operating in Terahertz Band”  In _IEEE transactions on nanobioscience_ 18.1 IEEE, 2018, pp. 10–17
  * [183] Soumyasanta Laha et al.  “A New Frontier in Ultralow Power Wireless Links: Network-on-Chip and Chip-to-Chip Interconnects”  In _IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems_ 34.2, 2015, pp. 186–198
  * [184] Chong Han and Ian F Akyildiz  “Distance-aware multi-carrier (DAMC) modulation in terahertz band communication”  In _2014 IEEE International Conference on Communications (ICC)_ , 2014, pp. 5461–5467  IEEE
  * [185] Eisa Zarepour, Mahbub Hassan, Chun Tung Chou and Siavash Bayat  “Performance analysis of carrier-less modulation schemes for wireless nanosensor networks”  In _IEEE International Conference on Nanotechnology (IEEE-NANO)_ , 2015, pp. 45–50  IEEE
  * [186] K Witrisal et al.  “Noncoherent ultra-wideband systems”  In _IEEE Signal Processing Magazine_ 26.4, 2009, pp. 48–66
  * [187] Z. Hossain and C. N. Mollica  “Stochastic Interference Modeling and Experimental Validation for Pulse-Based Terahertz Communication”  In _IEEE Transactions on Wireless Communications_ 18.8, 2019, pp. 4103–4115
  * [188] Pankaj andothers Singh  “TH-PPM with non-coherent detection for multiple access in electromagnetic wireless nanocommunications”  In _Nano Communication Networks_ 17 Elsevier, 2018, pp. 1–13
  * [189] Josep Miquel Jornet and Ian F Akyildiz  “Low-weight channel coding for interference mitigation in electromagnetic nanonetworks in the terahertz band”  In _IEEE International Conference on Communications (ICC)_ , 2011, pp. 1–6  IEEE
  * [190] Kaikai Chi, Yi-hua Zhu, Xiaohong Jiang and Xianzhong Tian  “Optimal coding for transmission energy minimization in wireless nanosensor networks”  In _Nano Communication Networks_ 4.3 Elsevier, 2013, pp. 120–130
  * [191] Muhammad Agus Zainuddin, Eugen Dedu and Julien Bourgeois  “Low-Weight Code Comparison for Electromagnetic Wireless Nanocommunication”  In _IEEE Internet of Things Journal_ 3.1 IEEE, 2016, pp. 38–48
  * [192] X. Yao, D. Ma and C. Han  “ECP: A Probing-Based Error Control Strategy for THz-Based Nanonetworks With Energy Harvesting”  In _IEEE Access_ 7, 2019, pp. 25616–25626
  * [193] Albert Cabellos-Aparicio et al.  “Use of THz Photoconductive Sources to Characterize Tunable Graphene RF Plasmonic Antennas”  In _IEEE Transactions on Nanotechnology_ 14.2, 2015, pp. 390–396  eprint: 1401.6878
  * [194] Mohammad Samizadeh Nikoo et al.  “Nanoplasma-enabled picosecond switches for ultrafast electronics”  In _Nature_ 579.7800 Springer US, 2020, pp. 534–539  DOI: 10.1038/s41586-020-2118-y
  * [195] Berardi Sensale-Rodriguez et al.  “Broadband graphene terahertz modulators enabled by intraband transitions”  In _Nature communications_ 3 Nature Publishing Group, 2012, pp. 780
  * [196] R. Piesiewicz et al.  “Short-Range Ultra-Broadband Terahertz Communications: Concepts and Perspectives”  In _IEEE Antennas and Propagation Magazine_ 49.6, 2007, pp. 24–39
  * [197] Joonas Kokkoniemi, Janne Lehtomaki, Kenta Umebayashi and Markku Juntti  “Frequency and Time Domain Channel Models for Nanonetworks in Terahertz Band”  In _IEEE Transactions on Antennas and Propagation_ 63.2, 2015, pp. 678–691
  * [198] R. Piesiewicz et al.  “Properties of Building and Plastic Materials in the THz Range”  In _International Journal of Infrared and Millimeter Waves_ 28.5, 2007, pp. 363–371
  * [199] Josep Miquel Jornet and Ian F Akyildiz  “Channel modeling and capacity analysis for electromagnetic wireless nanonetworks in the terahertz band”  In _IEEE Transactions on Wireless Communications_ 10.10 IEEE, 2011, pp. 3211–3221
  * [200] IT Javed and Ijaz Haider Naqvi  “Frequency band selection and channel modeling for WNSN applications using simplenano”  In _IEEE International Conference on Communications (ICC)_ , 2013, pp. 5732–5736  Ieee
  * [201] Pavel Boronin, Vitaly Petrov and Dmitri Moltchanov  “Capacity and throughput analysis of nanoscale machine communication through transparency windows in the terahertz band”  In _Nano Communication Networks_ 5.3 Elsevier, 2014, pp. 72–82
  * [202] Ignacio Llatser, Albert Mestres, Sergi Abadal and Eduard Alarcón  “Time and frequency domain analysis of molecular absorption in short-range terahertz communications”  In _IEEE Antennas and Wireless Propagation Letters_ 14 IEEE, 2015, pp. 350–353
  * [203] Christian Jansen, Sebastian Priebe and C Moller  “Diffuse scattering from rough surfaces in THz communication channels”  In _IEEE Terahertz Science and Technology_ 1.2, 2011, pp. 462–472
  * [204] Joonas Kokkoniemi et al.  “Wideband Terahertz Band Reflection and Diffuse Scattering Measurements for Beyond 5G Indoor Wireless Networks”  In _Proceedings of the EW ’16_ , 2016
  * [205] A. Fricke, T. Kurner, M. Achir and P. Le Bars  “A model for the reflection of terahertz signals from printed circuit board surfaces”  In _Proceedings of the EuCAP ’17_ , 2017, pp. 711–715
  * [206] Chong Han, A. Ozan Bicen and Ian F. Akyildiz  “Multi-Ray Channel Modeling and Wideband Characterization for Wireless Communications in the Terahertz Band”  In _IEEE Transactions on Wireless Communications_ 14.5, 2015, pp. 2402–2412
  * [207] Chong Han and Ian F. Akyildiz  “Three-Dimensional End-to-End Modeling and Analysis for Graphene-Enabled Terahertz Band Communications”  In _IEEE Transactions on Vehicular Technology_ 66.7, 2017, pp. 5626–5634
  * [208] Chong Han and Yi Chen  “Propagation Modeling for Wireless Communications in the Terahertz Band”  In _IEEE Communications Magazine_ 56.6 IEEE, 2018, pp. 96–101
  * [209] Sergi Abadal et al.  “A Vertical Methodology for the Design Space Exploration of Graphene-enabled Wireless Communications”  In _Proceedings of the NANOCOM ’15_ , 2015
  * [210] Vitaly Petrov  “Interference and SINR in millimeter wave and terahertz communication systems with blocking and directional antennas”  In _IEEE Transactions on Wireless Communications_ 16.3 IEEE, 2017, pp. 1791–1808
  * [211] Ke Yang et al.  “Numerical analysis of the communication channel path loss at the THz band inside the fat tissue”  In _Microwave Workshop Series on RF and Wireless Technologies for Biomedical and Healthcare Applications (IMWS-BIO), 2013 IEEE MTT-S International_ , 2013, pp. 1–3  IEEE
  * [212] Ke Yang, Alice Pellegrini and Max O Munoz  “Numerical analysis and characterization of THz propagation channel for body-centric nano-communications”  In _IEEE Transactions on Terahertz Science and technology_ 5.3 IEEE, 2015, pp. 419–426
  * [213] Hadeel Elayan and Raed M Shubair  “Multi-layer intrabody terahertz wave propagation model for nanobiosensing applications”  In _Nano Communication Networks_ 14 Elsevier, 2017, pp. 9–15
  * [214] Rui Zhang, Qammer H Abbasi and Akram Alomainy  “Power Distribution and Performance Analysis of Terahertz Communication in Artificial Skin”  In _Proceedings of the Sixth Annual ACM International Conference on Nanoscale Computing and Communication_ , 2019, pp. 1–5
  * [215] Joonas Kokkoniemi, Perttu Rintanen, Janne Lehtomäki and Markku Juntti  “Diffraction effects in terahertz band - Measurements and analysis”  In _Proceedings of the GLOBECOM ’16_ , 2016
  * [216] Hadeel Elayan, Cesare Stefanini, Raed M Shubair and Josep M Jornet  “Stochastic noise model for intra-body terahertz nanoscale communication”  In _ACM Nanoscale Computing and Communication_ , 2018, pp. 8  ACM
  * [217] Eisa Zarepour et al.  “Reliability analysis of time-varying wireless nanoscale sensor networks”  In _IEEE International Conference on Nanotechnology (IEEE-NANO)_ , 2015, pp. 63–68  IEEE
  * [218] Jinbang Fu, Prateek Juyal and Alenka Zajić  “Modeling of 300 GHz Chip-to-Chip Wireless Channels in Metal Enclosures”  In _IEEE Transactions on Wireless Communications_ IEEE, 2020
  * [219] Michael Opoku Agyeman, Quoc-Tuan Vien and A. Ahmadnia  “A Resilient 2-D Waveguide Communication Fabric for Hybrid Wired-Wireless NoC Design”  In _IEEE Transactions on Parallel and Distributed Systems_ 28.2, 2016, pp. 359–373
  * [220] Ihsan El Masri et al.  “EM Analysis of a Propagation Channel in the Sub-THz Band for Many-Core Architectures”  In _2019 49th European Microwave Conference (EuMC)_ , 2019, pp. 972–975  IEEE
  * [221] Yi Chen, Xiuzhang Cai, Chong Han and Terahertz Link  “Wave Propagation Modeling for mmWave and Terahertz Wireless Networks-on-Chip Communications”  In _Proceedings of the ICC ’19_ IEEE, 2019
  * [222] Yi Chen and Chong Han  “Channel Modeling and Characterization for Wireless Networks-on-Chip Communications in the Millimeter Wave and Terahertz Bands”  In _IEEE Transactions on Molecular, Biological and Multi-Scale Communications_ IEEE, 2019
  * [223] Yue Ping Zhang, Zhi Ming Chen and Mei Sun  “Propagation Mechanisms of Radio Waves Over Intra-Chip Channels With Integrated Antennas: Frequency-Domain Measurements and Time-Domain Analysis”  In _IEEE Transactions on Antennas and Propagation_ 55.10, 2007, pp. 2900–2906
  * [224] Rounak Singh Narde and Jayanti Venkataraman  “Intra-and Inter-Chip Transmission of Millimeter-Wave Interconnects in NoC-based Multi-Chip Systems”  In _IEEE Access_ PP IEEE, 2019, pp. 1–1
  * [225] Rui Zhang et al.  “Modelling of the terahertz communication channel for in-vivo nano-networks in the presence of noise”  In _Microwave Symposium (MMS), 2016 16th Mediterranean_ , 2016, pp. 1–4  IEEE
  * [226] Rui Zhang et al.  “Analytical characterisation of the terahertz in-vivo nano-network in the presence of interference based on TS-OOK communication scheme”  In _IEEE Access_ 5 IEEE, 2017, pp. 10172–10181
  * [227] Eisa Zarepour et al.  “Design and analysis of a wireless nanosensor network for monitoring human lung cells”  In _EAI International Conference on Body Area Networks_ , 2015, pp. 139–145  ICST (Institute for Computer Sciences, Social-Informatics and …
  * [228] DW Matolak, S Kaya and A Kodi  “Channel modeling for wireless networks-on-chips”  In _IEEE Communications Magazine_ 51.6, 2013, pp. 180–186
  * [229] Sergi Abadal, Chong Han and Josep Miquel Jornet  “Wave Propagation and Channel Modeling in Chip-Scale Wireless Communications: A Survey From Millimeter-Wave to Terahertz and Optics”  In _IEEE Access_ 8 IEEE, 2019, pp. 278–293
  * [230] Seunghwan Kim and Alenka Zajic  “Characterization of 300 GHz Wireless Channel on a Computer Motherboard”  In _IEEE Transactions on Antennas and Propagation_ 64.12, 2016, pp. 5411–5423
  * [231] Liping Yan and George W. Hanson  “Wave propagation mechanisms for intra-chip communications”  In _IEEE Transactions on Antennas and Propagation_ 57.9, 2009, pp. 2715–2724
  * [232] William Rayess, David W. Matolak, Savas Kaya and Avinash Karanth Kodi  “Antennas and Channel Characteristics for Wireless Networks on Chips”  In _Wireless Personal Communications_ 95.4 Springer US, 2017, pp. 5039–5056
  * [233] X. Timoneda et al.  “Channel Characterization for Chip-scale Wireless Communications within Computing Packages”  In _Proceedings of the NOCS ’18_ , 2018
  * [234] Xavier Timoneda et al.  “Engineer the Channel and Adapt to it: Enabling Wireless Intra-Chip Communication”  In _IEEE Transactions on Communications_ IEEE, 2020
  * [235] Giuseppe Piro, Luigi Alfredo Grieco, Gennaro Boggia and Pietro Camarda  “Simulating wireless nano sensor networks in the ns-3 platform”  In _Advanced Information Networking and Applications Workshops (WAINA)_ , 2013, pp. 67–74  IEEE
  * [236] Nicola Baldo and Marco Miozzo  “Spectrum-aware Channel and PHY layer modeling for ns3”  In _Fourth International ICST Conference on Performance Evaluation Methodologies and Tools_ , 2009, pp. 2  ICST (Institute for Computer Sciences, Social-Informatics and …
  * [237] Nicolas Boillot, Dominique Dhoutaut and Julien Bourgeois  “Scalable simulation of wireless electro-magnetic nanonetworks”  In _Embedded and Ubiquitous Computing (EUC)_ , 2015, pp. 83–89  IEEE
  * [238] Nicolas Boillot, Dominique Dhoutaut and Julien Bourgeois  “Using nano-wireless communications in micro-robots applications”  In _ACM Nanoscale Computing and Communication_ , 2014, pp. 10  ACM
  * [239] Nicolas Boillot, Dominique Dhoutaut and Julien Bourgeois  “Large scale mems robots cooperative map building based on realistic simulation of nano-wireless communications”  In _Nano Communication Networks_ 6.2 Elsevier, 2015, pp. 51–73
  * [240] Benjamin D Rister, Jason Campbell, Padmanabhan Pillai and Todd C Mowry  “Integrated debugging of large modular robot ensembles”  In _IEEE Robotics and Automation_ , 2007, pp. 2227–2234  IEEE
  * [241] Dominique Dhoutaut, Thierry Arrabal and Eugen Dedu  “Bit simulator, an electromagnetic nanonetworks simulator”  In _ACM Nanoscale Computing and Communication_ , 2018, pp. 30  ACM
  * [242] Zahed Hossain, Qing Xia and Josep Miquel Jornet  “TeraSim: An ns-3 extension to simulate Terahertz-band communication networks”  In _Nano Communication Networks_ 17 Elsevier, 2018, pp. 36–44
  * [243] Sebastian Canovas-Carrasco, Antonio-Javier Garcia-Sanchez and Joan Garcia-Haro  “On the Nature of Energy-Feasible Wireless Nanosensor Networks”  In _Sensors_ 18.5 Multidisciplinary Digital Publishing Institute, 2018, pp. 1356
  * [244] Josep Miquel Jornet and Ian F Akyildiz  “Joint energy harvesting and communication analysis for perpetual wireless nanosensor networks in the terahertz band”  In _IEEE Transactions on Nanotechnology_ 11.3, 2012, pp. 570
  * [245] Ignacio Llatser et al.  “N3Sim: Simulation framework for diffusion-based molecular communication nanonetworks”  In _Simulation Modelling Practice and Theory_ 42 Elsevier, 2014, pp. 210–222
  * [246] Regine Geyer, Marc Stelzner, Florian Büther and Sebastian Ebers  “BloodVoyagerS: Simulation of the work environment of medical nanobots”  In _ACM Nanoscale Computing and Communication_ , 2018, pp. 5  ACM
  * [247] Sebastian Canovas-Carrasco, Antonio-Javier Garcia-Sanchez, Felipe Garcia-Sanchez and Joan Garcia-Haro  “Conceptual design of a nano-networking device”  In _Sensors_ 16.12 Multidisciplinary Digital Publishing Institute, 2016, pp. 2104
  * [248] Ageliki Tsioliaridou, Christos Liaskos, Andreas Pitsillides and Sotiris Ioannidis  “A novel protocol for network-controlled metasurfaces”  In _ACM Nanoscale Computing and Communication_ , 2017, pp. 3  ACM
  * [249] Leonidas Georgiadis, Michael J Neely and Leandros Tassiulas  “Resource allocation and cross-layer control in wireless networks”  In _Foundations and Trends® in Networking_ 1.1 Now Publishers, Inc., 2006, pp. 1–144
  * [250] Mayisha Alam, Novia Nurain, Suraiya Tairin and ABM Alim Al Islam  “Energy-efficient transport layer protocol for hybrid communication in body area nanonetworks”  In _Humanitarian Technology Conference (R10-HTC), 2017 IEEE Region 10_ , 2017, pp. 674–677  IEEE
  * [251] Najmul Hassan, Chun Tung Chou and Mahbub Hassan  “Event and node identification from a single-pulse transmission in self-powered nanosensor networks”  In _ACM Nanoscale Computing and Communication_ , 2017, pp. 2  ACM
  * [252] Najm Hassan, Chun Tung Chou and Mahbub Hassan  “eNEUTRAL IoNT: Energy-neutral event monitoring for Internet of nano things”  In _IEEE Internet of Things Journal_ 6.2 IEEE, 2019, pp. 2379–2389
  * [253] Ageliki Tsioliaridou, Christos Liaskos and Sotiris Ioannidis  “BitSurfing: Wireless Communications with Outsourced Symbol Generation”  In _Computer Aided Modeling and Design of Communication Links and Networks (CAMAD)_ , 2018, pp. 1–7  IEEE
  * [254] Shahram Mohrehkesh and Michele C Weigle  “Optimizing communication energy consumption in perpetual wireless nanosensor networks”  In _Global Communications Conference (GLOBECOM), 2013 IEEE_ , 2013, pp. 545–550  IEEE
  * [255] Shahram Mohrehkesh  “Energy harvesting in electromagnetic nanonetworks”  In _Computer_ 50.2 IEEE, 2017, pp. 59–67
  * [256] Filip Lemic, Johann Marquez-Barja and Jeroen Famaey  “Modeling and Reducing Idling Energy Consumption in Energy Harvesting Terahertz Nanonetworks”  In _IEEE Global Communications Conference_ , 2019  IEEE
  * [257] Michael Donohoe, Brendan Jennings, Josep Miquel Jornet and Sasitharan Balasubramaniam  “Nanodevice arrays for peripheral nerve fascicle activation using ultrasound energy-harvesting”  In _IEEE Transactions on Nanotechnology_ 16.6 IEEE, 2017, pp. 919–930
  * [258] Zhichao Rong, Mark S Leeson, Matthew D Higgins and Yi Lu  “Simultaneous wireless information and power transfer for AF relaying nanonetworks in the Terahertz Band”  In _Nano communication networks_ 14 Elsevier, 2017, pp. 1–8
  * [259] Hang Yu, Bryan Ng and Winston KG Seah  “On-Demand Probabilistic Polling for Nanonetworks Under Dynamic IoT Backhaul Network Conditions”  In _IEEE Internet of Things Journal_ 4.6 IEEE, 2017, pp. 2217–2227
  * [260] Giuseppe Piro, Gennaro Boggia and Luigi Alfredo Grieco  “On the design of an energy-harvesting protocol stack for Body Area Nano-NETworks”  In _Nano Communication Networks_ 6.2 Elsevier, 2015, pp. 74–84
  * [261] Jacek Jarmakiewicz, Krzysztof Parobczak and Krzysztof Maślanka  “On the Internet of Nano Things in healthcare network”  In _Military Communications and Information Systems (ICMCIS)_ , 2016, pp. 1–6  IEEE
  * [262] Falko Dressler and Frank Kargl  “Towards security in nano-communication: Challenges and opportunities”  In _Nano communication networks_ 3.3 Elsevier, 2012, pp. 151–160
  * [263] Hany F Atlam, Robert J Walters and Gary B Wills  “Internet of Nano Things: Security Issues and Applications”  In _Cloud and Big Data Computing_ , 2018, pp. 71–77  ACM
  * [264] Holger Karl and Andreas Willig  “Protocols and architectures for wireless sensor networks”  John Wiley & Sons, 2007
  * [265] Hoa Tran-Dang, Nicolas Krommenacker and Patrick Charpentier  “Localization algorithms based on hop counting for wireless nano-sensor networks”  In _Indoor Positioning and Indoor Navigation (IPIN)_ , 2014, pp. 300–306  IEEE
  * [266] Lina Zhou, Guangjie Han and Li Liu  “Pulse-Based Distance Accumulation Localization Algorithm for Wireless Nanosensor Networks”  In _IEEE Access_ 5 IEEE, 2017, pp. 14380–14390
  * [267] Mohammed El-Absi, Ali Alhaj Abbas and Ashraf Abuelhaija  “High-accuracy indoor localization based on chipless RFID systems at THz band”  In _IEEE Access_ 6 IEEE, 2018, pp. 54355–54368
  * [268] “IEEE Recommended Practice for Nanoscale and Molecular Communication Framework”  In _IEEE Std 1906.1-2015_ , 2016, pp. 1–64
  * [269] Sebastian Canovas-Carrasco, Antonio-Javier Garcia-Sanchez and Joan Garcia-Haro  “The IEEE 1906.1 Standard: Nanocommunications as a new source of data”  In _ITU Kaleidoscope: Challenges for a Data-Driven Society (ITU K), 2017_ , 2017, pp. 1–7  IEEE

  *[SPP]: Surface Plasmon Polariton
  *[SotA]: State-of-the-Art
  *[WiP]: Work-in-Progress
  *[WoS]: Web of Science
  *[WNSN]: Wireless Nano-Sensor Network
  *[IoMNT]: Internet of Multimedia Nano-Things
  *[IoT]: Internet of Things
  *[IoE]: Internet of Everything
  *[IoNT]: Internet of Nano-Things
  *[MAC]: Media Access Control
  *[WNSNs]: Wireless Nano-Sensor Network
  *[SDM]: Software-Defined Metamaterial
  *[SDMs]: Software-Defined Metamaterial
  *[NoCs]: Network-on-Chip
  *[WNoCs]: Wireless Network-on-Chip
  *[DoS]: Denial of Service
  *[BER]: Bit-Error-Ratio
  *[SINR]: Signal to Interference Plus Noise Ratio
  *[LLC]: Logical Link Control
  *[CSMA]: Carrier-Sense Multiple Access
  *[TS-OOK]: Time Spread ON-OFF keying
  *[CTR]: Critical Transmission Ratio
  *[PHLAME]: PHysical Layer Aware MAC protocol for Electromagnetic nanonetworks in the Terahertz Band
  *[DRIH-MAC]: Distributed Receiver-Initiated Harvesting-Aware MAC
  *[RTR]: Ready-to-Receive
  *[TCN]: Timing Channel for Nanonetworks
  *[TDMA]: Time Division Multiple Access
  *[NACK]: Negative ACKnowledgment
  *[PRR]: Packet Reception Rate
  *[LoS]: Line-of-Sight
  *[SLR]: Stateless Linear-path Routing
  *[RTS]: Ready-to-Send
  *[CTS]: Clear-to-Send
  *[UDP]: User Datagram Protocol
  *[ZnO]: Zinc Oxide
  *[EU]: European Union
  *[RF]: Radio-Frequency
  *[OSI]: Open Systems Interconnection