Li et al. [47] |  (1) Due to the multiple layers in deep learning, accurate information can be extracted from the large amount of raw sensor data generated from IoT devices, (2) Edge computing helps in reducing the traffic of the network, (3) Both deep learning and edge computing are utilized |  (1) Since deep learning is used, learning time improvement mechanisms are required, (2) Storage utilization and implementation details are not highlighted
Pandey et al. [154] |  Using a person’s heart beat rate, it is tried to predict whether or not a person is in stress |  (1) Details of improving the learning mechanism of the proposed model are not presented, (2) Lack of responsiveness analysis

###  4.1 Security

A supervised learning algorithm, Random Forest, is applied in [146] to
correctly identify types of IoT devices from the white list. To train the
multi-class classifier, the data was manually labelled and collected from the
traffic data. The results show that $99\%$ of the devices were correctly
identified from white list. The proposed mechanism also demonstrates good
transportability and it is highly resilient in respect to external attacks. In
[148], a systematic review of the attack vectors, security requirements, and
the security solutions currently implemented for the constrained IoT networks
is presented. Here, ML and DL are used in the IoT devices to increase the
security and privacy of the system. There are various limitations of using ML
and DL, such as the model does not fit in all problems, the convergence takes
long time, a minute change in the input creates havoc in the system, and the
models are also vulnerable to security breaches. Due to the increase in the
poisoning attack as described in [149], the overall performance of the system
decreases and presents various security challenges. Using the tamper-free
framework and contextual information, the poisonous data is detected and
filtered to train the supervised learning model. Two variations for fully
untrusted and partially trusted data sets are compared based on the
performance. It is found that, by using the proposed method, the network
performance is increased and the security is ensured.

Table 21: Contributions of Existing Works Integrating IoT and AI, and Concentrating on Security Item |  Contributions
---|---
[146] |  Identifies types of IoT devices from the white list using multi-class classifier.
[148] |  A systematic review of the attack vectors, security requirements, and security solutions for the constrained IoT networks is presented.
[149] |  Using tamper-free framework and contextual information, poisonous data is detected and filtered to train supervising learning model.
[150] |  By using artificial neural networks, anomalies in the data are intelligently detected.
[151] |  Performs DoS attack detection by using ML.
[45] |  Detects malicious behaviour and cyber-attacks in IoT devices having limited communication and computation resources.
[46] |  Various attack models for IoT devices are investigated along with IoT security solutions based on ML techniques.
[152] |  Helps in storing and processing a large volume of wearable sensor data in IoT.
[155] |  Addresses the issues with the security and privacy in the edge intelligence model for IoT.
[156] |  Presents reinforcement and deep learning based solutions to combat different types of security attacks in IoT models.
[157] |  Designs a two-way trustworthy communication model in AI-enabled IoT frameworks.
[158] |  The security of IoT devices is achieved by detecting spam with the help of ML, where five ML-based schemes are evaluated using different metrics.

To improve the security of IoT devices, a machine learning algorithm is used
in [150] within the IoT gateway. By using artificial neural networks, we can
detect anomalies in the data intelligently. The results presented in [150]
correctly identify invalid data points in IoT systems. By using ML, Denial of
Service (DoS) detection is performed in [151], where it is observed that DoS
traffic attack can be identified efficiently from customer IoT devices. To
restrict the computational overhead, limited feature set is used, which is
necessary for the middlebox deployment and real-time classification. Five
different classifiers, i.e., KNN, Support vector machine with linear kernel
(LSVM), Decision tree using Gini impurity scores (DT), Random Forest using
Gini impurity scores (RF), and Neural Network (NN) are tested on both normal
and DoS attack traffic datasets. The results show an accuracy of higher than
$0.99$ on all the aforementioned algorithms. An anomaly detection framework is
proposed in [45] for detecting malicious behaviours and cyber-attacks in IoT
devices having limited communication and computation resources. The proposed
mechanism can be implemented in any device despite the size and is more
suitable for devices running specific type of applications. ML models and
statistical techniques can be used in detecting any type of attacks on IoT
devices, and the behavioural models of these IoT devices can be learned
easily. It is seen that the proposed approach performs better in comparison to
other traditional models.

Various attack models for IoT devices are investigated in [46] along with IoT
security solutions based on ML techniques which are learning-based including
access control, secure offloading, IoT authentication, and malware detection
to protect data privacy. The use of the transfer learning at the beginning of
the learning process reduces the risk of choosing bad security defence
policies. New ML techniques having low communication and computational
overhead enhance the security of IoT systems. A backup security solution
should be incorporated and designed in order to provide reliability and
security to the system. A three-tier architecture is proposed in [152], which
helps in storing and processing a large volume of wearable sensor data. Tier-1
helps in extracting data from IoT devices. Tier-2 stores a large volume of
data in cloud. Tier-3 develops a logistic regression-based model for heart
diseases.

In order to ensure the flexibility of data access and data security in the
IoT, the scheme proposed in [155] addresses the issues with the security and
privacy in the edge intelligence (EI) model for the IoT, where ML models are
trained in EI. The work [156] presents a survey of various classes of cyber-
attacks in different IoT systems. Moreover, this work presents reinforcement
and deep learning based solutions to combat different types of security
attacks in several IoT models. The authors in [157] design a two-way
trustworthy communication model in AI-enabled IoT frameworks. The model
incorporates both rating and trust information more thoroughly, which can
alleviate the sparse trust problem in the IoT. In [158], the security of IoT
devices is achieved by detecting spam with the help of ML, where five ML-based
schemes are evaluated using different metrics considering a large set of input
features. Each model calculates a spam score considering the input features,
and this score reflects the trustworthiness of devices under various
parameters. Table 21 highlights existing works which consider the integration
of the IoT and AI concentrating on the security.

###  4.2 Privacy

Various attack models for IoT devices are presented in [46], which address the
IoT authentication and malware detection. The use of transfer learning at the
beginning of learning process reduces the risk of choosing bad security
defense policies. An approach is described in [153], which helps in protecting
the privacy of users whenever a resource-constrained IoT device runs ML
applications based on NN using cloud computing. To improve the privacy,
partial processing is done at the client side such that it would be hard to
retrieve the original data again. It is observed that the proposed approach
has a shorter prediction time compared to other local predictions. However,
there is a chance in increasing the time required for the computation, which
affects the performance of the system.

Table 22: Contributions of Existing Works Integrating IoT and AI, and Concentrating on Privacy Item |  Contributions
---|---
[46] |  Addresses IoT authentication and malware detection using transfer learning.
[153] |  Helps in protecting the privacy of users whenever a resource-constrained IoT device runs ML applications using cloud computing.
[47] |  Using deep learning, the number of tasks in edge servers are risen along with achieving QoS requirements.
[154] |  Predicts whether or not a person is in stress, by exploiting both IoT and ML.

To maintain user privacy in the IoT platform, deep learning as well as edge
computing are used in [47]. Due to the multiple layers in deep learning, the
accurate information can be extracted from the large amount of raw sensor data
generated from the IoT devices. Edge computing helps in reducing the traffic
of the network to cloud servers. From the performance analysis, it is observed
that the proposed solution can rise the number of tasks in edge servers along
with achieving QoS requirements. In [154], using person’s heart beat rate, the
authors have tried to predict whether or not a person is in stress, by
exploiting both the IoT and ML. The IoT informs patients about their stress
conditions and ML algorithms are used to predict the condition of the patient.
Table 22 highlights existing works which consider the integration of the IoT
and AI concentrating on the privacy.

###  4.3 Link Quality, Fault detection, and Data Rate

The work [159] proposes a methodology to dynamically estimate the link quality
in cloud communications, considering locations of nodes. For this purpose,
several machine learning techniques are implemented, such as decision tree,
linear regression, neural networks, random forest, etc. Authors in [160] show
that fault detection in data driven predictive analysis in remote locations
can be achieved using the support vector machine algorithm and low bandwidth
IoT. The scheme proposed in [161] utilizes the self-organization based map
neural network to group IoT devices considering their data rates. For each of
the group, a slot is allocated for channel access.

###  4.4 Sentiment Analysis

A learning-based mechanism is proposed in [162] to analyze sentiment of text
in IoT frameworks, and for this purpose, a self-attention prototype is used to
predict sentiment features. The work [163] discusses sentiment analysis of big
data considering its features, definition, and decision value. Here, the
challenges of the impact of the IoT on sentiment analysis of big data are also
highlighted.

##  5 Integration of IoT with Blockchain and AI

In this section, we discuss existing research works which integrate the IoT
with blockchain and AI technologies. In this regard, the limitations of the
related works are also discussed. Tables 23 and 24 highlight the strengths and
weaknesses of the related works which consider the integration of the IoT with
blockchain and AI.

Sharing of authentic information using AI-capable machines in the IoT is very
crucial for the risk management with personal accountability. A probabilistic
proof system is introduced in [164] using blockchain for cybersecurity
satisfying economic and legal demands of the real-world business. The
decentralized system helps in proving the trustworthiness of partners by a
secure search. Here, the minimal external attacks or losses are expected due
to the nonparticipation of third parties. The symmetric distribution of
authentic information as well as overcoming the vulnerabilities of the adverse
selection in the IoT contribute to an open marketplace on using security
reports for trading rights [164]. The IoT is a transformational technology
that minimizes human intervention with the aid of modern technologies like AI
and blockchain. In spite of authentication of devices, the security remains a
major issue which can be improved by using blockchain’s decentralized and
atomizing mechanisms, and transactions. Problems of network traffic, identity
authentication, etc., can be overcomed by using smart contracts which also
prevent tampering of data. Blockchain-based IoT systems can be used for supply
chain management supporting access management systems, authorized payment
generation, etc. AI, on the other hand, uses machine learning algorithms to
adapt accordingly. Few concerns are that the blockchain system is not scalable
and there is a lack of challenges for developing IoT applications [165].

The MEC-based sharing system offers spatio-temporal services without the need
of a central verification using smart contracts, as discussed in [33]. AI and
cognitive computing are successful in providing reasoning by following the
workflows and identifying or validating any changes found. Data transactions
are advanced via blockchain, IoT, 5G device-to-device (D2D) communication
capability, MEC, and fog computing. Phenomena of interest can be found by
analyzing massive amount of data with the support of different types of data
science advancements. Blockchain can store immutable data securely in a
decentralized repository which can be shared using global identity, and has
the potential to detect and eliminate fraudulent activities [33]. A software
artifact, known as ‘‘Smart Things’’ [36], is supported with AI features and is
integrated with C Language Integrated Production System (CLIPS) to perform
data analysis using RESTful micro services which make it self-inferencable and
self-monitorable. ‘‘Smart Things’’ manages the real-time communication with
other resources via Multichain, a permission-based blockchain protocol, to
reduce the communication cost. A decentralized fog architecture is designed
and implemented to distribute management tasks. In [36], the results
illustrate that, to enable autonomous features, hybrid solutions should be
adopted in IoT networks.

In [166], a data marketplace is implemented on a blockchain-based platform
using Swarm as a distributed storage system, where technologies such as the
IoT, blockchain, and AI can interact and collaborate to create a decentralized
platform, such that data can be stored and accessed easily by multiple
parties. Thus, the proposed mechanism increases the service quality. It uses a
voting mechanism to eliminate unreliable data providers. Blockchain provides
various economic, technical, and user-facing benefits but it does not support
safety-critical applications due to the lack of some form of encryption. Data
replication is to be handled by the vendors. A paid-sharing architecture of
the knowledge market is proposed in [30] for making the knowledge tradable
using edge-AI enabled IoT devices, where blockchain helps in ensuring security
as well as efficiency in the market using a new consensus mechanism, known as
Proof-of-Trading (PoT), that consumes less resources. A game-based knowledge
strategy is proposed as incentives for the market, where it is observed that
an optimal pricing strategy helps buyers with the high quality knowledge and
encourages sellers to learn more data. Using Machine Learning, a hierarchical
market model is introduced, as the extracted knowledge can be reproduced by it
with almost no costs [30].

Blockchain is used to facilitate illegal market activities. To overcome it, a
self-redactable blockchain (SRB) is proposed in [29] for building a trust
layer for the IoT, using revocable chameleon hash (RCH) to create
disincentives to manage any data-driven business and support execution of
chain redaction securely as well as efficiently. To obtain redaction,
additional metadata and costs are introduced, which can be compensated by the
merits of the fast verification and storage saving provided by the same [29].
Smart logistics solution is proposed in [167], where logistics planner and
smart contracts are used to secure trust, provide visibility, and help in
condition-monitoring of assets using distributed ML in supply chain
management. It demonstrates liability, transparency, accountability, optimal
planning, and traceability, for handling assets by different parties involved
in the logistics of supply chain management with the help of the IoT,
blockchain, ML, and Big Data.

The IoT and CPS maintain visibility and global trackability of supply chain by
gathering information in real time. Manufacturers of these smart devices apply
their own standards making interoperability more challenging leading to an
overcomplicated system. Various cybersecurity risks are present due to the
increase in interconnectivity leading to security breaches in sectors like
finance. A framework is proposed in [168] using the IoT and CPS objects to
incorporate forensic-readiness for investigations and post-incident; and a
prototype is maintained to detect anomaly with the help of Bluetooth proximity
monitoring. The proposed scheme ensures confidentiality, integrity,
availability, authenticity, and safety [168]. Using the IoT and AI, a
proposition is made in [169] to operate Halal food chain on blockchain with
respect to its architecture, application, and technological components to
safeguard data. Blockchain reduces chances of tampering with data, facilitates
audit, provides security and compliance but its immutable nature makes
compliance to new data privacy challenging [169].

Table 23: Integration of IoT with Blockchain and AI Item |  Strength |  Weakness
---|---|---
Wohlgemuth et al. [164] |  (1) Symmetric distribution of authentic information as well as overcoming the vulnerabilities of adverse selection in IoT, (2) Minimal external attacks or losses are expected due to the nonparticipation of third parties |  Responsiveness of the blockchain architecture in the presence of learning overhead is not in details
Jindal et al. [165] |  (1) Blockchain-based IoT systems support access management systems and identity automatic currency exchange systems, authorized payment generation etc., (2) AI strengthens the proposed mechanism to adapt accordingly by using machine learning algorithms |  (1) Blockchain system is not scalable, (2) Lack of professionals for developing IoT applications
Rahman et al. [33] |  (1) MEC-based sharing system offers spatio-temporal services without the need of central verification using smart contracts, (2) AI and cognitive computing are useful in providing reasoning by following the workflows and identifying or validating any changes found |  (1) Protocol overhead and learning convergence are not highlighted, (2) Lack of implementation details
Samaniego et al. [36] |  (1) Software artifact is supported with AI features and is integrated with CLIPS system to perform data analysis automatically, (2) Permission-based blockchain protocol to reduce the communication cost, (3) Decentralized fog architecture to distribute management tasks, (4) Manages real-time communication with other resources |  (1) Training constraints and the scope of improvement of the learning are not in details, (2) Lack of details of the proposed approach
Özyilmaz et al. [166] |  (1) Voting mechanism to eliminate unreliable data providers, (2) Provide various economic, technical and user-facing benefits, (3) Blockchain and AI interact and collaborate to create a decentralized and trustless platform |  (1) Does not support safety-critical applications due to the lack of some form of encryption, (2) Data replication is to be handled by the vendors
Lin et al. [30] |  (1) Blockchain helps in ensuring security as well as efficiency in the market, (2) Architecture of knowledge market is proposed for making knowledge tradable using edge-AI, (3) Consumes less resources, (4) Optimal pricing strategy helps buyers with high quality knowledge and encourages sellers to learn more data, (5) Knowledge can be reproduced with almost no costs |  (1) Design complexity can increase the overhead of protocol implementation and execution, (2) Scope of improvement of the trained model is not highlighted
Huang et al. [29] |  (1) Self-redactable blockchain is proposed for building a trust layer for IoT, (2) Execution of chain redaction securely as well as efficiently, (3) Fast verification and storage saving |  (1) Additional metadata and costs are introduced, (2) Convergence of learning in practical scenarios is not in details, (3) Lack of responsiveness analysis
Arumugam et al. [167] |  (1) Helps in condition-monitoring of assets using distributed ML framework in supply chain management, (2) Demonstrates liability, transparency, accountability, optimal planning and traceability, for handling assets by different parties |  (1) Storage management is not presented, (2) Lack of details of the execution flow of the proposed model, (3) Adaptability details and constraints are not highlighted
Ahmadi-Assalemi et al. [168] |  (1) Incorporates forensic-readiness for investigations and post-incident, (2) A prototype is maintained to detect anomaly with the help of Bluetooth proximity monitoring, (3) Ensures confidentiality, integrity, availability, authenticity as well as safety |  (1) Handling cybersecurity needs to be detailed, (2) Lack of implementation details of the prototype along with management of learning overhead
Table 24: Integration of IoT with Blockchain and AI (contd.) Item |  Strength |  Weakness
---|---|---
Chandra et al. [169] |  (1) An increase in trust throughout the supply chain is noted, (2) Blockchain reduces chances of tampering with data, facilitate audit, provide security and compliance |  (1) Immutable nature of blockchain makes compliance to new data privacy challenging, (2) Storage management and implementation constraints are not highlighted
Shen et al. [31] |  (1) Using blockchain, a secure and reliable platform is built for sharing data between data providers, (2) Stores data in distributed ledger without involving a third-party, (3) Secure and efficient training algorithm, (4) Ensures confidentiality of data for data analysis |  (1) Lack of details of communication overhead in data sharing, (2) Implementation constraints, (3) Convergence analysis of the learning, (4) Storage management issues are not highlighted
Wang et al. [35] |  (1) Helps in acquiring large amount of information and processing redundant and private sensor data, (2) Blockchain guarantees reliability, security as well as the robustness of the system, (3) CNN is used for real-time monitoring of data, (4) Ensures the security of the data and tolerance, (5) IPFS and edge computing reduces the storage and transmission cost |  (1) Detailed analysis of latency and responsiveness needs to be presented, (2) Protocol implementation complexity is not in details
Zhao et al. [170] |  (1) Based on reputation crowdsourcing and provides better services, (2) An incentive mechanism is designed to increase the participation of customers in crowdsourcing tasks, (3) ML helps in predicting the consumption behaviours and customers requirements of the system, (4) Blockchain helps in increasing the security and privacy of the system |  (1) Communication overhead and delay in crowdsourcing are not highlighted, (2) Scopes of improvement in training and convergence are not in details
Chen et al. [171] |  (1) Helps to ensure privacy as swell as data fusing, (2) All the resources can be exchanged between clouds and blockchain through trusted environment, and transactions are recorded, (3) The use of smart contracts helps in providing trustworthy and impartial environment |  (1) Lack of details of communication overhead in exchanging information, (2) Storage management issues are not highlighted
Ramachandran et al. [172] |  (1) Transparency and security features are achieved while transferring data, (2) Helps cars to drive automatically, (3) Installation cost is comparatively cheaper |  (1) Learning convergence is not in details, (2) Scope of minimization in response time and latency
Singla et al. [173] |  (1) Smart home IoT system is used to generate customizations based on the activity prediction of users, (2) Distributed association rule mining is applied to generate the rules of the activities from the users’ device logs |  Details of the mechanism and implementation constraints are not highlighted
Outchakoucht et al. [174] |  (1) Ensures distributed aspect which in turn increases the privacy of the model, (2) Machine Learning focuses on the dynamic as well as optimized aspect of the model, (3) Helps in security |  (1) Lack of details of the adaptability issues, (2) Responsiveness analysis is not in details

Based on blockchain, a privacy preserving SVM scheme, known as SecureSVM, is
proposed in [31] to encrypt the IoT data. Using blockchain, a secure and
reliable platform is built for sharing data between data providers, and data
is stored in a distributed ledger without involving a third-party. By
employing a homomorphic cryptosystem, a secure, efficient, and accurate
training algorithm is constructed such that a secure comparison and polynomial
multiplication are achieved, which help in ensuring the confidentiality of
data for data analysis [31]. For smart homes, the privacy-preserving
distributed mechanism proposed in [175] enables users to manage energy usages
parallelly in an optimized way. Moreover, a blockchain system is designed for
developing a smart contract for IoT devices to support an energy management
system.

In [35], a video surveillance system is proposed based on Inter Planetary File
System (IPFS) technology, permissioned blockchain, convolution neural networks
(CNNs), and edge computing. The edge computing helps in acquiring a large
amount of information and processing redundant data. IPFS is used for storing
massive video data. Blockchain guarantees the reliability, security as well as
the robustness of the system, whereas CNN is used for real-time monitoring of
data. The system ensures the security of the data and tolerance. IPFS and edge
computing combinely reduce the storage and transmission costs [35].

Table 25: Contributions of Existing Works Integrating IoT, Blockchain, and AI Item |  Contributions
---|---
[164] |  A probabilistic proof system is introduced using blockchain for cybersecurity satisfying economic and legal demands of real-world business.
[33] |  Data transactions are advanced via blockchain, IoT, 5G D2D communication capability, MEC, and fog computing.
[36] |  Proposes a decentralized fog architecture that manages real-time communications and reduces the communication cost.
[166] |  A data marketplace is implemented on a blockchain-based platform, where data can be stored and accessed easily by multiple parties.
[30] |  A paid-sharing architecture of knowledge market is proposed using edge-AI enabled IoT devices, where Proof-of-Trading (PoT) consumes less resources.
[29] |  A self-redactable blockchain is proposed for building a trust layer for IoT to manage any data-driven business and support execution of chain redaction securely as well as efficiently.
[167] |  Smart logistics solution is proposed, where smart contracts are used to secure trust, provide visibility, and help in condition-monitoring of assets using distributed ML.
[168] |  A framework is proposed using IoT and CPS to incorporate forensic-readiness for investigations and post-incident, and a prototype is maintained to detect anomaly with the help of Bluetooth proximity monitoring.
[169] |  Using IoT and AI, a proposition is made to operate Halal food chain on blockchain with respect to its architecture, application, and technological components to safeguard data.
[31] |  By employing a homomorphic cryptosystem, a secure, efficient, and accurate training algorithm is constructed.
[175] |  A blockchain system is designed for developing a smart contract for IoT devices to support an energy management system.
[35] |  A video surveillance system is proposed based on Inter Planetary File System technology, permissioned blockchain, convolution neural networks, and edge computing.
[170] |  ML helps in predicting the consumption behaviours and customers’ requirements of the system, and blockchain helps in increasing the security and privacy of the system.
Table 26: Contributions of Existing Works Integrating IoT, Blockchain, and AI (contd.) Item |  Contributions
---|---
[176] |  Develops a FL system to assist home device manufacturers to prepare (train) a ML model based on data provided by customers.
[177] |  Presents a framework of blockchain-based FL systems for failure estimation in IIoT to ensure verifiable integrity of data.
[171] |  Helps to ensure the privacy as swell as data fusing.
[172] |  A system with an autonomous feature is proposed using Raspberry Pi, machine learning, and image processing.
[173] |  Generates customizations based on the activity prediction of users for IoT devices.
[174] |  Ensures distributed, dynamic, and optimized aspects of the model and increases the security of the system.
[178] |  Intelligently handles massive decentralized applications in IoT frameworks.
[179] |  An AI-based layered hierarchical model is proposed to deploy a distributed blockchain-enabled SDN framework for IoT.
[180] |  Applies a self-recovery mechanism to impose robustness against maliciousness and device failures.

A federated learning (FL) system is presented in [170] for IoT manufacturers,
where the proposed system is based on reputation crowdsourcing and provides
better services than the existing related functionalities. Various
technologies, such as blockchain, distributed storage, mobile edge computing,
and federated learning, are used to construct the system. An incentive
mechanism is designed to increase the participation of customers in
crowdsourcing tasks. ML helps in predicting the consumption behaviours and
customers’ requirements of the system with the help of the federated
technology, whereas blockchain helps in increasing the security and privacy of
the system. To design a smart home model, authors in [176] develop a FL system
to assist home device manufacturers to prepare (train) a ML model based on
data provided by customers. Here, Manufacturers can estimate customers’ future
consumption and requirement behaviors. The work [177] presents a framework of
blockchain-based FL systems for failure estimation in IIoT, which ensures
verifiable integrity of data. Moreover, to address the issue of data
heterogeneity in IIoT, a federated averaging-based FL algorithm is proposed,
that considers the distance between negative and positive classes of each data
set.

An IoT service platform is proposed in [171] based on Joint Cloud Computing
(JCC) to help ensuring the privacy as swell as data fusing. All the resources
can be exchanged between clouds and blockchain through a trusted environment,
where all the transactions are recorded. All trades are done using smart
contracts which help in providing trustworthy and impartial environments. To
an existing electric car, a system with an autonomous feature is proposed in
[172] using Raspberry Pi, machine learning, and image processing. With the
help of blockchain, transparency and security features are achieved while
transferring data. The system helps the car to drive automatically and the
installation cost of the proposed model is comparatively cheaper.

In [34], a survey of different technologies, such as blockchain, ML, and
artificial intelligence, is conducted in order to address various security
issues such as layer-wise issues and Confidentiality, Integrity, Availability
(CIA) faced in IoT systems. Several research challenges of these technologies
are also highlighted in [34]. By using Machine leaning models, a smart home
IoT system is used in [173] to generate customizations based on the activity
prediction of users for IoT devices. In [173], blockchain is used such that
the model can run in a decentralized and secure manner. The distributed
association rule mining is also applied in order to generate the rules of the
activities from the user’s device logs. A fully distributed and dynamic
security policy is proposed in [174] focusing on the access control in the
IoT. Blockchain ensures the distributed aspect which in turn increases the
privacy of the model. Whereas, ML, particularly reinforcement learning,
focuses on the dynamic and optimized aspect of the model, which helps in the
security part of the system. In addition, the architecture of the proposed
framework is well exposed and explained.

To intelligently handle massive decentralized applications in IoT frameworks,
Edgence [178] is designed to use edge and cloud computing to communicate with
IoT devices and end-users. In this case, an in-built blockchain is used to
realize self-supervision and self-governing of edge clouds. In [179], an AI-
based layered hierarchical model is proposed to deploy a distributed
blockchain-enabled SDN framework for the IoT, that ensures secure network
communication and cluster-head selection via the identification of rouge
switches. The work [181] addresses the privacy and security challenges of
edge-computing-based IoT, and accordingly highlights the characteristics of
blockchain, which are suited for IoT scenarios. The proposed framework is
based on deep reinforcement learning and specifies the procedure for
transactions between an edge node and IoT device. In [182], to propose an
approach for an effective identification of faults in the network, an IoT
model and edge-data verification mechanism are designed using blockchain and
random forest algorithm. The authors in [180] design a protocol that relies on
peer-to-peer communications to allow interactions among IoT devices organized
in a distributed way. The protocol applies a self-recovery mechanism to impose
robustness against maliciousness and device failures. The protocol also uses a
reputation model to monitor the devices and the quality of their service
delivery, such that service delivery reputations can be leveraged for the
selection of future devices.

Tables 25 and 26 highlight existing works which consider the integration of
the IoT with blockchain and AI.

##  6 Challenges and Future Reseacrh Scopes in the Integration of IoT with
Blockchain and AI

There are several issues in integrating blockchain and AI with the IoT, and
these issues need to be addressed in order to fully exploit the utilities of
blockchain and AI in the IoT paradigm. In this section, we present the
challenges in applying blockchain and AI in the IoT to design secure,
scalable, intelligent, and robust IoT systems. These challenges will open new
research scopes in designing smart IoT models.

###  6.1 Challenges in Application of Blockchain in IoT

Different challenges of applying blockchain in IoT are discussed as given in
the following.

####  6.1.1 Storage-Constraint in Blockchain Implementation

The storage issue becomes serious when blockchain is integrated with the IoT
[183, 96, 123]. With a high capacity of storage, blockchain nodes cannot
handle the challenge of increasing data amount. Furthermore, the IoT platform
can have heterogeneous devices having various storage capacities, and thus the
storage issues become more complicated while blockchain is associated with the
IoT [12]. Therefore, the integration of blockchain and IoT devices with a low
capacity will become a great challenge. In the IIoT, the application of
blockchain requires a massive storage space, which is a great challenge in a
resource-constrained IIoT architecture. The work [184] addresses this issue by
proposing a hierarchical blockchain storage architecture where the major
portion of the blockchain is stocked in the cloud. Since an edge-based
architecture can provide a faster response time than a cloud-based structure,
it will be beneficial to use the storage of an edge architecture to implement
blockchain in a resource-constrained IIoT.

####  6.1.2 Integrity

Blockchain technology can address the challenges related to traceability and
integrity, which are due to the complex supply chain management of today’s
world. However, blockchain does not provide a solution of the trust problem
that is associated with the data in the IoT [185]; and therefore, this problem
leads to a research challenge of efficiently handling the trust problem with
an optimization approach.

####  6.1.3 Privacy and Security

The IoT has unprecedented scalability and security challenges due to the high
magnitude of data exchanged by devices in IoT platforms. By coupling
blockchains with IoT applications, some of these challenges are addressed in
[186]. However, the design of a secure and scalable blockchain along with a
resource optimization in the IoT is still a great challenge. The current
Datagram Transport Layer Security (DTLS) and Transport Layer Security (TLS)
are complex or heavy, especially for the IoT architecture with a massive
number of devices [183]; and thus, there is a challenge to design proper
security mechanisms as a part of a lower network layer while merging the IoT
with blockchain.

The primary concerns over the IoT platform are storage and secure data
transaction. Blockchain can be an effective solution by utilizing its secure
and decentralized structure. There are challenges of the implementation of
blockchain over hardware-constrained devices [187]. Since sensors are often
used as IoT devices to detect the environment, a lot of sensitive data is
collected in IoT platforms. Moreover, IoT devices can be highly exploited by
attackers who maliciously use the IoT paradigm to launch various severe
attacks [12]. Therefore, it is required to design effective secure
communication protocols, attack-defense mechanisms, and secure authentication
techniques when integrating the IoT with blockchain.

####  6.1.4 Management of Devices

IoT architecture can have numerous sensors, and the management of the devices
which are connected to the sensors remains a technical challenge forever. This
challenge increases further while blockchain technology is merged with the IoT
[187]. Therefore, an efficient design architecture is required while merging
blockchain and the IoT in order to manage a huge number of sensors and devices
connected in the IoT. This design can significantly help the mobile
communication and satellite monitoring.

####  6.1.5 Scalability

Fog computing which is an extension of IoT-oriented solutions has the
requirements for decentralization and distribution. In this regard, the
integration of blockchain with IoT can provide a solution for decentralization
and distribution, as well as can help fog computing to overcome the
deficiencies in security and privacy [188]. Therefore, an efficient design
architecture integrating fog computing and blockchain is required to utilize
the power of fog computing based IoT solutions. Scalability is one of the
primary challenges of merging blockchain with fog computing [188].

####  6.1.6 Consensus Mechanism

IoT applications have high maintenance costs and weakness to support time-
critical usages. These problems can be solved by proposing an appropriate
distributed consensus mechanism [12]. The IoT platform has a huge number of
various types of devices, and the communication cost becomes extremely high
when every node in blockchain needs to broadcast its data and status.
Therefore, it is required to overcome these obstacles and develop an efficient
consensus mechanism to increase the overall throughput in the network while
blockchain is combined with the IoT.

####  6.1.7 Transaction Fees and Time

One challenging issue in merging blockchain with the IoT implementation is the
fees associated with the transactions in blockchain. These fees are created to
reward miners but unfortunately, many cryptocurrencies allow to implement
market-based fees, and therefore, the fees can become expensive [13]. Another
problematic issue for blockchain is transaction time [13]. Bitcoin has a
stable time span of ten minutes to create new blocks. However, the transaction
time is highly dependent on the congestion levels in the network [189]. Hence,
there is a large scope of research in the blockchain-based IoT payment system
consisting of many micro-transactions which need to be finished within
seconds.

####  6.1.8 Transaction Throughput

The heterogeneity of devices in the IoT platform implies different
communication protocols, like Bluetooth, Wi-Fi, etc., and due to this
heterogeneity, the cost of providing consensus in blockchain becomes expensive
[12]. It is also non-trivial to impose a synchronization among the huge number
of devices in the IoT. Consequently, improving the transaction throughput
becomes challenging. Therefore, it is required to design proper communication
protocols and good consensus techniques to improve the transaction throughput
while the IoT and blockchain are combined together. The communication
protocols should provide effective synchronization among various devices in
the IoT.

###  6.2 Challenges in Application of AI in IoT

To develop an efficient intelligent IoT system, we need to identify the
research challenges in the direction of integrating AI and the IoT together.
Therefore, different challenges in applying AI in the field of the IoT are
presented next.

####  6.2.1 Training of AI-based Model

The appropriate training of a ML model is required to make the system
intelligent such that the system can take the proper decision dynamically
based on the demand [190]. In this regard, the appropriate dataset needs to be
applied and the dataset should be large enough to facilitate the learning
process, by providing a sufficient information about the environment. In an
IoT framework, different types of devices are deployed and various ML
algorithms can be used to serve different purposes. Thus, it is a challenge to
generate the appropriate and sufficient volume of datasets to efficiently
train different ML algorithms, such that the whole system can adapt to the
environment in order to provide several functionalities in the IoT. The
performance of an intelligent system highly depends on the training procedure
that is carried out to train the corresponding ML model. Therefore, the
challenge is to perform the training procedure effectively and efficiently.
Moreover, in distributed networks, the training of ML-based mechanisms is far
challenging [32].

####  6.2.2 Determination of Convergence

A primary challenge in exploiting intelligence in a model is the proper
analysis of convergence of the learning of ML-based models [191]. The question
of convergence is mainly raised in reinforcement learning where the system
gains knowledge dynamically as the number of runs of the model increases
[192]. In this case, the convergence time influences the nature of
adaptability of the ML module in different network scenarios [193]. As the
convergence time decreases, the ML module takes less time to gain knowledge
about the environment. Therefore, our target is to minimize the convergence
time of the learning of an intelligent system. Moreover, the convergence with
the appropriate learning (knowledge) about the network is also important.
Since an IoT model consists of a large number of varieties of devices, it is
crucial to take care of the convergence of ML algorithms which are applied in
the IoT framework [48, 193].

####  6.2.3 Availability of Data for Training

Data plays the primary role to train any ML-based model [194]. To implement an
AI-based IoT framework in a real case scenario, it is required to collect the
data from client sides or through the actual positioning of sensors in the
IoT. In this case, the designers of ML algorithms need to interact with domain
experts to get the data. In addition, the collected data should have enough
large volume so that the proposed ML model can be well-trained to solve the
purposes of using this model in an IoT architecture. Therefore, the collection
of the appropriate and sufficient large volume of data to train a ML model is
a great challenge.

####  6.2.4 Storage Management

An AI-based system needs to store a huge volume of information such that,
based on it, the system can take decision dynamically, and thus intelligence
can be imposed in the system. Such high volume of information is required
because it is difficult for a ML-based system to take decision on which
information will be needed in future; and thus, the system stores all the data
and information, which are obtained during the training of the model [195].
Therefore, it is a challenge to efficiently handle the storage management
while AI is used to make a system intelligent. In this case, the IoT yields a
huge scope of research since a large number of varieties of devices are used
in the IoT, where the edge-based implementation further provides a great
challenge due to the engagement of end-users having low storage capabilities.

####  6.2.5 Development of Smart Cities

The idea of development of smart cities using proper biometric and AI
recognition technologies can be developed further, along with an explainable
AI for supporting high level human decisions [196]. The biometric solutions
are used for police investigations, law enforcement and access to different
facilities which will promote the safety in a much efficient way. Examples of
such concepts have been tested by various countries and it has been found
that, a proper exploitation of AI in developing smart cities will be really
beneficial. For example, the city of Tigre in Argentina uses a command control
centre with biometrics and video analytics, which led to the drop of city’s
car theft by $80\%$ [196]. The AI is expected to give a decision for making a
certain decision, and thus AI emerges the explainable AI field in the
application of the IoT. This type of innovation can be achieved by focusing on
the safety, security, and efficiency, which can be achieved by properly by
using AI technologies in high levels of the IoT data exchange [196]. In
addition, AI can be exploited in developing smart cities with the help of an
edge computing architecture which can enhance the network performance and ease
the development of a distributed architecture at client sides [197].

####  6.2.6 Cooking Innovations with AI

Using IoT devices and special deep learning application, food calorie,
category estimation, and training of robots for kitchen tasks are still a very
underrated topic and should be explored further. By using IoT and AI
technologies, it is possible to create a smart kitchen which can be monitored
via mobile devices; and it will reduce the amount of physical labour that is
put into the kitchen [198].

####  6.2.7 IoT Device Security Based on Machine Learning

The IoT integrates devices in a network so that they can be used for executing
different types of applications. It is really important to protect such
devices from hackers such that the user privacy is maintained. Attacks like
phishing, DoS, eavesdropping, and jamming can be prevented by using a variety
of supervised and unsupervised learning algorithms which fall under artificial
intelligence [46]. Moreover, we can use exposure indicators and predictive
analytics for threats detection in such IoT networks. An efficient threat
exposure and evaluation framework needs to be designed in order to improve
preventive capabilities in the IoT, by combining AI-based tools for the
continuous evaluation of security functionalities [199].

####  6.2.8 Development of Integrated Circuit (IC) Technology with AI

The ICs used for the IoT integrating AI need massive capabilities and smart
integration. The ICs should be capable of concurrent engineering and multi-
level co-optimization such that it can achieve the high performance, good
yield, and low cost [200].

####  6.2.9 Human Centric AI

The IoT uses huge data points and applies artificial intelligence to learn
from individual user’s actions and then use the knowledge gained from the
learning to take appropriate decisions. Most of the AI models are purely based
on mathematics, and this makes the AI model a black box, where users trust
their personal experiences. This type of AI-based system learns from the
inputs provided by users, and continuously improves with each new input. In
the integration of artificial intelligence and IoT devices, the goal is to
understand human emotion, human language, and behaviour; and thus, we can
bridge the gap between machines and humans [201].

####  6.2.10 AI-Enabled IoT for 5G Network Complexity

The convergence of AI-enabled IoT, the network complexity analysis, and 5G
allow for designing, prototyping, and enabling new systems that can adapt to
various network scenarios. IoT systems in the 5G provide support for the
applications in transportation, public safety, health, energy, and food
productions. As the number of IoT devices connected to the Internet is
increasing, the scalability and computability of these devices are becoming a
challenge which needs approaches related to the network complexity analysis
and designing new computational models [202].

####  6.2.11 Wireless Networks

The IoT requires continuous connectivity, dynamic service mobility, and
extreme security when connected in a wireless network [203]. Artificial
intelligence can play an important role in the underlying network
infrastructure. In this case, the main challenges are designing of -- (i) AI-
based approaches for handling bandwidth, spectrum, and latency constraints in
wireless networks, (ii) AI-based security or caching requirement models with
Big data, and (iii) an efficient architecture coping up with the network
complexity in wireless environments. The generic needs of a wireless network
specific design in the IoT can be implemented using the help of AI.

####  6.2.12 Edge Computing on IoT Data Using AI

IoT devices are continuously used and a huge amount of IoT data is generated.
In order to extract insights from such high volume of data, the processing of
data must happen in the place where it was generated [204]. This type of
process is called edge computing and it is a challenge as the AI algorithms
need a lot of processing power which may not be available in the edge devices
like laptops, mobile phones, etc. One particular way of architecture that can
be used is a centralized approach-based AI model. In this way, it can be a
possible option to reduce the overhead of executing AI applications in edge
nodes. However, the next challenge will be to minimize the communication
overhead between the central node and other edge devices.

##  7 Conclusion

Blockchain is a distributed immutable ledger system that can be used for
implementing cryptocurrency with security; and AI is used to make an
intelligent system. Therefore, these two mechanisms can strengthen
functionalities of an IoT system. This survey presents an extensive study of
the existing research works which have applied blockchain and AI in designing
IoT-based applications. We first discuss about the IoT, blockchain, and AI,
such that their potentialities can be understood while studying the impacts of
applying blockchain and AI in IoT paradigms. We present a detailed discussion
on the impacts of blockchain and AI in IoT-based applications which have been
proposed in several existing works. In this direction, we also highlight the
limitations of existing literature which have utilized blockchain and AI in
the IoT. This survey also identifies the research challenges to integrate
blockchain and AI with the IoT. These challenges open a fertile area for the
future research scopes to exploit the capabilities of blockchain and AI in
building IoT-based applications; and meaningful explorations of such
challenges can lead to the development of smart IoT applications ensuring
security, privacy, and trustworthy.

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