# A Zigbee Based Cost-Effective Home Monitoring System Using WSN

Garapati Venkata Krishna Rayalu12, Paleti Nikhil Chowdary13, Manish Nadella14,
Dabbara Harsha15, Pingali Sathvika16, B.Ganga Gowri17  1Amrita School Of
Artificial Intelligence, Coimbatore, Amrita Vishwa Vidyapeetham, India  Email:
<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>
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###### Abstract

WSNs are vital in a variety of applications, including environmental
monitoring, industrial process control, and healthcare. WSNs are a network of
spatially scattered and dedicated sensors that monitor and record the physical
conditions of the environment.Significant obstacles to WSN efficiency include
the restricted power and processing capabilities of individual sensor nodes
and the issues with remote and inaccessible deployment sites. By maximising
power utilisation, enhancing network effectiveness, and ensuring adaptability
and durability through dispersed and decentralised operation, this study
suggests a comprehensive approach to dealing with these challenges. The
suggested methodology involves data compression, aggregation, and energy-
efficient protocol. Using these techniques, WSN lifetimes can be increased and
overall performance can be improved. In this study we also provide methods to
collect data generated by several nodes in the WSN and store it in a remote
cloud such that it can be processed and analyzed whenever it is required.

###### Index Terms:

Wireless Sensor Networks, Zig-bee, Energy efficiency,Cost Effective.

##  I Introduction

Wireless Sensor Networks (WSNs) are networks of inexpensive, low-power, small
devices with wireless communication, sensors, and microcontrollers. According
to [1] these networks have various applications, including environmental
monitoring, industrial process control, and healthcare. WSNs play a crucial
role in wildlife conservation by detecting and tracking animal movements,
monitoring crop health in agriculture [2], and ensuring the stability of
buildings and bridges [3]. Additionally, WSNs can assist in home monitoring
and support elderly individuals.

Small, battery-operated sensor nodes form the basis of Wireless Sensor
Networks (WSNs), which use wireless communication to monitor and collect data
from their surroundings. These sensor nodes continuously sense environmental
conditions and gather data. The nodes form a network with different
topologies, such as star, mesh, or cluster-tree, and communicate with one
another via wireless protocols. Energy-efficient communication protocols such
as Zigbee and LoWPANs, along with techniques like data compression, have been
used to improve network efficiency [4]. The distributed and decentralized
nature of WSNs, enabling local decision-making based on gathered data,
enhances adaptability and robustness.

Considering the existing research, studies have focused on developing and
enhancing energy-efficient protocols for WSNs [1]. Other research has explored
techniques such as data compression, aggregation, and hierarchical routing to
improve overall network effectiveness [3]. The impact of distributed and
decentralized operation on adaptability and robustness has also been
investigated [4]. However, there is a lack of a comprehensive strategy that
simultaneously addresses power and computational constraints, remote
deployment challenges, energy efficiency, and decentralized operation.
Therefore, the Zigbee protocol is chosen as a solution.

Objectives of our work is to put to use an energy-efficient protocol that
optimizes computing resources and power usage in WSNs.Address challenges
associated with remote and inaccessible deployment sites.Improve network
efficiency through data aggregation. Design a distributed and decentralized
strategy to enhance the robustness and adaptability of WSNs.

To achieve these goals, we propose a unique methodology that combines multiple
approaches and readily available algorithms specifically tailored for WSNs.
This methodology takes into account factors such as remote deployment sites,
limitations of individual sensor nodes, and the need for energy-efficient
operations. It leverages the benefits of distributed and decentralized
decision-making to enhance adaptability and resilience.

The following sections in this paper section 2 outlines the setup, utilizing
XBee modules and Arduino-integrated sensors for data collection. In Section 3,
we develop a user-friendly GUI for efficient data visualization. Section 4
focuses on collecting and securely storing the data on the cloud, while
implementing continuous monitoring for detecting data changes.

##  II Related Works

The table of literature review Table I provides a summary of the key findings
from the relevant research literature.

TABLE I: Literature Review Ref no |  Title |  Author |  Methodology |  Results
---|---|---|---|---
[5] |  Xbee-Based WSN Architecture for Monitoring of Banana Ripening Process Using Knowledge-Level Artificial Intelligent Technique |  S. Altaf, S. Ahmad, M. Zaindin, M.W. Soomro |  In order to track banana ripening, the article suggests an Xbee wireless sensor network (WSN) design. In order to improve analysis and decision-making in this process, artificial intelligence techniques are used. However, it is devoid of precise methodological information. |  According to the findings, a wireless sensor network based on Xbees and knowledge-level AI were able to accurately classify bananas into three states: normal, rotten, and unknown.
[6] |  Elderly Infrared Body Temperature Telemonitoring System with XBee Wireless Protocol |  T. H. Y. Ling, L. J. Wong |  The research describes an XBee wireless protocol-based elderly infrared body temperature telemonitoring system. It makes it possible to track and remotely monitor an elderly person’s body temperature. The citation, however, is devoid of precise methodological information. |  The study used an XBee wireless protocol-based telemonitoring system to successfully monitor elderly people’s body temperatures.
[7] |  Ambient Assisted Living Environment Towards Internet of Things Using Multifarious Sensors Integrated with XBee Platform |  Nagender Suryadevara, Sean Kelly, S.C. Mukhopadhyay |  The study focuses on exploiting IoT technology to enhance the quality of life in assisted living settings by using the XBee platform and sensors to build an ambient assisted living environment. But no precise methodological information was offered. |  Using the XBee platform and many sensors, the study successfully developed an ambient assisted living environment. Through better monitoring and assistance, this integration increased the quality of life for residents of assisted living facilities.
[8] |  A Smart Monitoring System for Campus Using Zigbee Wireless Sensor Networks |  Alaa Allahham, Md Arafatur Rahman |  The study describes a smart campus monitoring system based on Zigbee. It keeps track of temperature, humidity, light output, and sound levels using wireless sensor networks. The citation did not, however, provide any information regarding the process. |  The study was successful in putting in place a smart campus monitoring system based on Zigbee. It makes real-time environmental parameter monitoring and data collecting possible, facilitating effective campus infrastructure management.
[4] |  Performance Analysis of Data Transmission on a Wireless Sensor Network Using the XBee Pro Series 2B RF Module |  I Gusti Made Ngurah Desnanjaya et al. |  The XBee Pro Series 2B RF module used in the article is used to analyze data transmission on a wireless sensor network. It assesses this module’s effectiveness and dependability without presenting a thorough methodology or analysis. |  The design and execution of wireless sensor networks are influenced by the examination of the XBee Pro Series 2B RF module’s data transmission capabilities.

In [5] Altaf et al. describes a wireless monitoring system for banana ripening
using knowledge-level artificial intelligence algorithms and XBee-based WSN
architecture. The system incorporates XBee modules for wireless communication
between sensor nodes and a central controller, providing real-time data
collecting. Throughout the ripening process, a variety of sensors are used to
measure the temperature, humidity, and gas concentration levels. In order to
determine the ideal ripening conditions, the collected data is processed using
knowledge-level artificial intelligence techniques that incorporate
professional knowledge and rules. The proposed system offers an efficient and
optimized approach to monitor and control the banana ripening process
wirelessly, facilitating improved quality and productivity in the industry.

In [6] Ling et al. describes an XBee wireless protocol-based elderly infrared
body temperature tele-monitoring system. The technology uses infrared body
temperature sensors to remotely check on elderly people’s body temperatures.
The XBee protocol is used to wirelessly send the temperature data obtained,
allowing for real-time monitoring and analysis. With the aid of this device,
older patients’ health can be monitored discreetly and conveniently, enabling
early identification of aberrant temperature levels and fast intervention when
required. However, detailed information regarding the affiliations of the
authors or more data regarding the reference are not available.

In [7] Sean et al. examines the idea of ambient assisted living (AAL) and how
the Internet of Things (IoT) is integrated into it. For the purpose of
creating an intelligent environment for assisted living, the authors suggest a
system that makes use of a range of sensors combined with the XBee platform.
These sensors keep an eye on a number of variables, including temperature,
humidity, light, motion, and gas concentrations, and they provide real-time
data for assessing people’s safety and well-being. With the help of the XBee
platform, wireless connectivity and communication are made possible, allowing
for smooth data transfer between the sensors and the main system. The system
aims to improve people’s quality of life by offering a smart living
environment that fosters independence, security, and comfort, especially for
the elderly or people with disabilities.

In [8] Allahham et al. provides a clever monitoring system for university
settings that makes use of Zigbee WSNs. The system incorporates a network of
wireless sensors placed throughout the campus to gather information on the
climate, security, and energy use. These sensors enable real-time monitoring
and analysis by wirelessly transmitting the data to a centralised monitoring
system. The solution offers effective and dependable wireless communication
while minimising power consumption by utilising Zigbee technology. The
suggested system intends to better campus administration, boost safety
precautions, maximise energy use, and offer useful information for making
decisions. However, the reference does not provide any detailed information
regarding the affiliations of the writers.

In [4] Desnanjaya et al. presents a performance evaluation of the XBee Pro
Series 2B RF module-based WSN for data transmission. The study focuses on
measuring the efficiency and reliability of data transmission in a WSN
context. The XBee Pro Series 2B RF module will be used in tests to monitor
important performance parameters like packet loss, throughput, and latency.
They examine how several elements, including distance, hop count, and
interference, affect the effectiveness of the WSN. The analysis’s findings
shed light on the XBee Pro Series 2B RF module’s advantages and disadvantages
in terms of data transmission in a WSN configuration.

In [9] Maneesha V. Ramesh et al., and [10] M Shyama et al. the hindrance faced
by WSN’s were explored which gave us their significance of these factors
affect a WSN network.

In [11] Sanjeev Kumar Shah et al., the study gave insights about the flexible
and extensible architecture to integrate WSN and IoT, and in [12] V.Sanjay
Kumar et al., the authors describe the effectiveness of sensory networks in
safeguarding humans and were able to implement a smart surveillance system.

##  III Methodology

Hardware elements such as the Jetson Nano, Arduino microcontrollers, XBee
modules, and other sensors are combined to build a smart home application. The
Arduino microcontrollers operate as bridges between the Jetson Nano and the
sensor nodes, while the Jetson Nano serves as the main controller. The Jetson
Nano, Arduino microcontrollers, and sensor nodes can all communicate
wirelessly with each other using XBee modules, which also makes it easier to
send and receive control signals and data. The system’s sensors gather
information on variables related to the environment, including vibration, soil
moisture, water levels, human presence, distance, temperature, humidity,
flame, gas concentration, light intensity, sound intensity, and level of light
and sound. Within the smart home system, LED lights, buzzers, and a 7-color
LED provide visual and aural feedback.The collected data can be processed and
analyzed by the Jetson Nano for decision-making and control of the connected
devices.

The hardware setup includes the following components:

  * •

Jetson Nano: The Jetson Nano serves as the main controller and interface for
the smart home system. It provides powerful computing capabilities and acts as
the central hub for data processing and decision-making.

  * •

Arduino: Four Arduino microcontrollers are utilized in the project. These
microcontrollers serve as intermediaries between the Jetson Nano and the
different sensor nodes, facilitating data acquisition and communication.

  * •

Breadboard: Four breadboards are used to provide a platform for connecting and
prototyping the various hardware components, enabling their integration into
the system.

  * •

Jumper Wires: Jumper wires are employed to establish electrical connections
between different components, ensuring the proper flow of data and signals
within the system.

  * •

XBee Modules: Three XBee modules are utilized to enable wireless communication
between the Jetson Nano, Arduino microcontrollers, and the sensor nodes. These
modules facilitate the transmission of control signals and data exchange
within the smart home application.

  * •

Sensors: A variety of sensors are incorporated into the system to gather data
on different environmental parameters. These include:

    * –

Flame sensor: Detects the presence of fire or flames.

    * –

Gas sensor: Measures the concentration of gases in the environment.

    * –

Photoresistor sensor: Detects and measures light levels.

    * –

Big sound sensor: Captures and analyzes sound intensity.

    * –

Temperature and humidity sensor: Measures temperature and humidity levels.

    * –

PIR sensor: Detects human presence based on infrared radiation.

    * –

Ultrasonic sensor: Measures distance by emitting and receiving ultrasonic
waves.

    * –

Shock sensor: Detects sudden vibrations or movements.

    * –

Soil and moisture sensor: Measures moisture levels in the soil.

    * –

Water level sensor: Monitors the water level in tanks or containers.

  * •

Additional Components: The setup also includes LED lights, buzzers, and a
7-color LED for visual and auditory feedback.

TABLE II: Comparative Discussion Feature |  Zigbee | WiFi | LoRa | Bluetooth
---|---|---|---|---
Focus |  Home Automation | High-Speed data | Low-power long range | Short range communication
Battery Life |  Longer battery life | Shorter battery life | Very long battery life | Shorter battery life
Range |  100 m | 100 m | Several km | 10m
Bandwidth |  Max 250 kbps | Upto several Gbps | Max 27 kbps | Max 24 kbps
Advantages |  Low power mesh networks | High data rate , easy setup | Long range, low power | Ease of use, low cost, compatability.
Frequency Band |  2.4GHz, 915MHz, 868MHz | 2.4GHz, 5GHz | 433MHz, 868MHz, 915MHz | 2.4GHz
Maximum Nodes |  65,000 | 200 - 300 | Upto Millions | 7
Security |  AES128 encryption | WPA/WPA2 encryption | AES encryption, spread spectrum | AES128 encryption
Latency |  15 ms - 100 ms | A few ms to several 100 ms | 200 - 4000 ms | 1 ms - 10 ms
Scalability |  Highly Scalable | Scalable | Highly Scalable | Moderate Scalable
Ease Of Intergration |  Moderate | Easy | Moderate | Easy
Cost |  Moderate | Moderate to high | Low | Low to moderate
Mobility |  No | Yes | No | Yes
Data Rate |  Variable, depends on network size and configuration | Upto several Gbps | Upto 50 Gpbs | Upto 2 Mbps
Frequency Hoping |  Yes | No | Yes | No
Interference Tolerance |  Good | Poor | Good | Poor
Device Complexity |  Low | High | Low | Low
Bit error rate |  $10^{-}6$ \- $10^{-}9$ |  $10^{-}6$ \- $10^{-}9$ |  $10^{-}5$ \- $10$ | NIL

###  III-A Xbee and its Role in WSN

XBee is a wireless communication module that uses the Zigbee protocol. Zigbee
is a low-power, low-range wireless protocol suitable for home automation. It
operates in the 2.4 GHz ISM band and provides low power consumption and high
security. XBee modules can be used for wireless communication, creating
networks, and remotely controlling devices. In a wireless sensor network
(WSN), XBee modules connect sensor nodes to a central controller like
Raspberry Pi or Jetson Nano. They enable data transmission and remote control
of devices within the smart home. XBee modules can also be configured as a
mesh network, ensuring robust and fault-tolerant communication. XBee S2C
documentation and datasheet is available in the link given here
(https://www.digi.com/resources/library/data-sheets/ds_xbee-s2c-802-15-4).

Figure 1: XBee Module

The methodology involves connecting and configuring these hardware components
according to the desired system architecture. The Arduino microcontrollers
interface with the sensors and XBee modules to collect sensor data and
transmit it to the Jetson Nano. The Jetson Nano then processes and analyzes
the data, utilizing machine learning algorithms if necessary, to make
decisions and control the connected devices within the smart home.

The XBee modules enable wireless communication between the Jetson Nano and the
Arduino microcontrollers, as well as between the Arduino microcontrollers and
the sensor nodes. This wireless communication ensures seamless data exchange
and control signal transmission throughout the smart home system.

It is important to note that, unlike traditional datasets, the data in this
project is collected in real time from the interconnected Zigbee modules. The
collected data is then utilized for analysis and decision-making within the
smart home application.

This methodology involves setting up the hardware components, configuring the
Arduino microcontrollers and XBee modules, and establishing wireless
communication between them. The sensors are integrated into the system to
collect real-time data, which is processed by the Jetson Nano for decision-
making and control. This hardware-based approach enables the development of a
functional smart home application with the capability to monitor and control
various aspects of the home environment.

###  III-B Setup Description

In this section, we describe the implementation of our work. The proposed
project involves using two Xbee modules to transmit data wirelessly from one
location to another. The Xbee modules will be connected to multiple sensors
and Arduino microcontrollers, which will collect data from the sensors and
send it to the Xbee modules for transmission. The Image 2 the architecture of
a smart home system in which Bee is connected to sensors in every room. These
sensors collect data on temperature, humidity, air quality, and other
environmental factors.

Figure 2: Setup Architecture

We use a library called software serial library in our project enables us to
use any digital pin on the micro controller as an RX or TX pin, which provides
flexibility in terms of pin usage and allows us to communicate with other
devices using serial communication without being limited to the hardware
serial pins. Additionally, the library provides a number of useful functions
for reading and writing data to the software serial port, making it easy to
implement serial communication in our project.

  * •

Living Room : a sound sensor to measure sound levels, a photoresistor to
measure ambient light levels, and two pins for a disco light effect. The sound
level is read using the sound sensor, the photoresistor is read to measure
ambient light levels, and the DHT sensor is used to measure temperature and
humidity. The function takes a string as input and sends it over the serial
connection by breaking it down into bytes and sending each byte one at a
time.The image depicts the sensors used in this room 3.

Figure 3: Living Room

If the ambient light level is greater than 500, the disco light effect is
activated and the two pins are set to opposite states. if the sound level is
greater than 30, the led pin is turned on for 2 seconds. Finally, the loop
function waits for 1 second before repeating.

  * •

Kitchen : uses two analog sensors, a flame sensor and a gas sensor, and a
buzzer for an auditory output. The flame sensor and gas sensor are used to
measure the values of flame and gas in the surrounding. If the flame sensor
value is less than 800 or the gas sensor value is greater than 600, the buzzer
sounds for 1 second. Finally, the loop function waits for 1 second before
repeating.The image depicts the sensors used in this room 4.

Figure 4: Kitchen

  * •

Porch : Sensors used are ultrasonic sensor to measure distance, a PIR sensor
to detect motion, and a shock sensor to detect shocks. It also uses a LED and
a buzzer for visual and auditory output. The distance is measured using the
ultrasonic sensor, the PIR sensor is read to detect motion, and the shock
sensor is utilized to detect shocks. If a shock is detected, the LED is turned
on. The distance, motion detection, and shock-sensor state are then printed to
the Serial console and sent over the serial connection to another device using
the ”send message” function. The function takes a string as input and sends it
over the serial connection by breaking it down into bytes and sending each
byte one at a time. The image depicts the sensors used in this room 5.

Figure 5: Porch

  * •

Terrace garden : We use a DHT11 sensor to measure temperature and humidity, an
analog sensor to measure the moisture level of the soil, and another sensor to
measure the water level in a tank. It also uses a buzzer for an auditory
output. The moisture sensor and water level sensor are read to measure the
values of moisture and water level. The DHT sensor is used to measure the
temperature and humidity. If the water level sensor value is greater than 600,
the buzzer sounds for 1 second. Finally, the loop function waits for 1 second
before repeating. The image depicts the sensors used in this room 6.

Figure 6: Terrace Garden

The data will then be received by the second Xbee module, which can be
connected to another Jetson Nano for further processing or storage (MongoDB
Cluster).

Overall, the system will allow for wireless monitoring and data collection
from multiple sensors in different locations using Xbee wireless communication
technology and Arduino microcontroller boards.

###  III-C Cloud Integration

In this scenario, a Wireless Sensor Network (WSN) is being set up using XBee
radios, a Jetson Nano single-board computer, and MongoDB Atlas. The XBee
radios are used to wirelessly transmit sensor data from the sensor nodes to
the Jetson Nano, which acts as a gateway to the MongoDB Atlas database.MongoDB
Atlas is a cloud-based, fully managed version of MongoDB, which is a popular
NoSQL database. The sensor data collected by the XBee radios are stored in
MongoDB Atlas, where it can be easily queried, analyzed, and visualized.

The Jetson Nano is programmed to communicate with the XBee radios and collect
sensor data. It also uses the PyMongo library to interact with MongoDB Atlas.
The Jetson Nano can be configured to establish a connection with the MongoDB
Atlas server and insert the sensor data into a specific database and
collection. The sensor data stored in MongoDB Atlas can be accessed and
visualized using various tools such as MongoDB Compass, a graphical user
interface for MongoDB, or by using the MongoDB query language (MQL) to create
custom queries and aggregations.

###  III-D GUI

Streamlit is a Python library that allows you to create interactive web apps
for machine learning and data science. To deploy a Streamlit app that uses
data collected from Xbee transmitted to Jetson board and stored in MongoDB
Atlas,the following steps are to be followed. The app starts by connecting to
the MongoDB database using the pymongo library and the MongoDB connection
string. It then presents the user with a dropdown menu to select a room to
analyze, which sets the collection to retrieve data from House.py . The app
then presents the user with another dropdown menu to select a field to
analyze. It then displays the current time when the data was retrieved, and a
line chart of the time series data for the selected field using the st.line-
chart() function. Next, we define a function get-data() that retrieves data
from the selected collection, converts the timestamp field to a datetime
object, sets it as the index and returns the current time and dataframe. This
function is called to initialize the data when the app starts. We create a
Streamlit app that connects to a MongoDB database, allows the user to select a
room and field to analyze, retrieves and displays the data in a line chart,
and provides the ability to refresh the data.

##  IV Results and Discussions

We have successfully combined Jetson Nano, Arduino microcontrollers, XBee
modules, and numerous sensors. The main controller and data processing
component is a Jetson Nano, while the bridges to the sensor nodes are Arduino
microcontrollers. Wireless connection is made possible via XBee modules,
making it easier to exchange control signals and send and receive data. A wide
variety of sensors are used in the system to collect data on environmental
factors, and LED lights, buzzers, and a 7-color LED are used to provide visual
and acoustic feedback. Real-time monitoring and control are made possible by
the Jetson Nano’s processing and analysis of the sensor data. In order to link
sensor nodes to the Jetson Nano and enable remote device control, XBee modules
create a wireless sensor network. MongoDB Atlas is used to store and manage
sensor data, making it simple to query, analyse, and visualise. MongoDB is
accessed through a graphical user interface (GUI) created with Streamlit,
which offers an interactive platform for real-time analysis and visualisation
of the sensor data. Overall, using this methodology allows for the creation of
a smart home system that is functional and has monitoring, controlling, and
analytical capabilities.

Table II discusses that zigbee is suitable for home automation with low-power
mesh networks and a maximum of 65,000 nodes. WiFi offers high-speed data
transmission and easy setup, but with shorter battery life and limited range.
LoRa excels in low-power, long-range communication, while Bluetooth provides
short-range communication, ease of use, and compatibility. The choice of
wireless technology depends on factors such as range, power consumption, data
rate, security, and scalability.

##  V Conclusion and Futureworks

We have successfully integrated Jetson Nano, Arduino microcontrollers, XBee
modules, and various sensors to create a comprehensive smart home system. It
enables real-time monitoring, control, and analysis of environmental variables
through wireless communication, data processing, and cloud integration.

Potential future works might include implementing a secured sytem as Zigbee
networks are vulnerable to various types of cyber-attacks, so future work in
this area will focus on developing more secure Zigbee protocols and systems.
Edge computing: Zigbee networks can be integrated with edge computing
architectures to enable data processing and decision-making at the network
edge, which can lead to low latency and more efficient use of network
resources. The data collected can be used for implementing Machine learning
based prediction Big Data Analytics: Zigbee networks generate large amounts of
data, future work will focus on developing efficient algorithms to process,
analyze and extract useful information from these data

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