Smart and Sustainable Approaches for Optimizing Performance of Wireless Networks E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
SMART AND SUSTAINABLE APPROACHES FOR OPTIMIZING PERFORMANCE OF WIRELESS NETWORK
Explores the intersection of sustainable growth, green computing and automation, and performance optimization of 5G wireless networks
Smart and Sustainable Approaches for Optimizing Performance of Wireless Networks explores how wireless sensing applications, green computing, and Big Data analytics can increase the energy efficiency and environmental sustainability of real-time applications across areas such as healthcare, agriculture, construction, and manufacturing.
Bringing together an international team of expert contributors, this authoritative volume highlights the limitations of conventional technologies and provides methodologies and approaches for addressing Quality of Service (QOS) issues and optimizing network performance. In-depth chapters cover topics including blockchain-assisted secure data sharing, smart 5G Internet of Things (IoT) scenarios, intelligent management of ad hoc networks, and the use of Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) techniques in smart healthcare, smart manufacturing, and smart agriculture.
- Covers design, implementation, optimization, and sustainability of wireless and sensor-based networks
- Discusses concepts of sustainability and green computing as well as their relevance to society and the environment
- Addresses green automation applications in various disciplines such as computer science, nanoscience, information technology (IT), and biochemistry
- Explores various smart and sustainable approaches for current wireless and sensor-based networks
- Includes detailed case studies of current methodologies, applications, and implementations
Smart and Sustainable Approaches for Optimizing Performance of Wireless Networks: Real-time Applications is an essential resource for academic researchers and industry professionals working to integrate sustainable development and Information and Communications Technology (ICT).
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Seitenzahl: 567
Veröffentlichungsjahr: 2022
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Table of Contents
Cover
Title Page
Copyright
About the Editors
List of Contributors
1 Analysis and Clustering of Sensor Recorded Data to Determine Sensors Consuming the Least Energy
1.1 Introduction
1.2 The Working of WSNs and Sensor Nodes
1.3 Classification of WSNs
1.4 Security Issues
1.5 Energy Consumption Issues
1.6 Commonly Used Standards and Protocols for WSNs
1.7 Effects of Temperature and Humidity on the Energy of WSNs
1.8 Proposed Methodology
1.9 Conclusion
References
2 Impact of Artificial Intelligence in Designing of 5G
2.1 5G – An Introduction
2.2 5G and AI
2.3 AI and 5G
2.4 Challenges and Roadmap
2.5 Mathematical Models
2.6 Conclusion
References
3 Sustainable Paradigm for Computing the Security of Wireless Internet of Things: Blockchain Technology
3.1 Introduction
3.2 Research Background
3.3 Related Work
3.4 Research Methodology
3.5 Comparison of Various Existing Solutions
3.6 Discussion of Research Questions
3.7 Future Scope of Blockchain in IoT
3.8 Conclusion
References
4 Cognitive IoT‐Based Health Monitoring Scheme Using Non‐Orthogonal Multiple Access
4.1 Introduction
4.2 Related Work
4.3 System Model and Implementation
4.4 Simulation Results
4.5 Conclusion
4.A Appendix
References
5 Overview of Resource Management for Wireless Ad Hoc Network
5.1 Introduction
5.2 Mobile Ad Hoc Network (MANET)
5.3 Vehicular Ad Hoc Network (VANET)
5.4 Wireless Mesh Network (WMN)
5.5 Wireless Sensor Network (WSN)
5.6 Intelligent Resource Management Concerns in WANET
5.7 Future Research Directions
5.8 Conclusion
References
6 A Survey: Brain Tumor Detection Using MRI Image with Deep Learning Techniques
6.1 Introduction
6.2 Background
6.3 Related Work
6.4 Gaps and Observations
6.5 Suggestions
6.6 Conclusion
References
7 Challenges, Standards, and Solutions for Secure and Intelligent 5G Internet of Things (IoT) Scenarios
7.1 Introduction
7.2 Safety in Wireless Networks: Since 1G to 4G
7.3 IoT Background and Requirements
7.4 Non 5G Standards Supporting IoT
7.5 5G Advanced Security Model
7.6 Safety Challenges and Resolution of Three‐Tiers Structure of 5G Networks
7.7 Conclusion and Future Research Directions
References
8 Blockchain Assisted Secure Data Sharing in Intelligent Transportation Systems
8.1 Introduction
8.2 Intelligent Transport System
8.3 Blockchain Technology
8.4 Blockchain Assisted Intelligent Transportation System
8.5 Future Research Perspectives
8.6 Conclusion
References
9 Utilization of Agro Waste for Energy Engineering Applications: Toward the Manufacturing of Batteries and Super Capacitors
9.1 Introduction
9.2 Super Capacitors and Electrode Materials
9.3 Related Works in the Utilization of Agro‐Waste for Energy Engineering Applications
9.4 Inferences from Works Related with Utilization of Coconut, Rice Husk, and Pineapple Waste for Fabrication of Super Capacitor
9.5 Factors Contributing in the Fabrication of Super Capacitor from Agro‐Waste
9.6 Conclusion
Acknowledgment
References
10 Computational Intelligence Techniques for Optimization in Networks
10.1 Introduction Focussing on Pedagogy of Impending Approach
10.2 Relevant Analysis
10.3 Broad Area of Research
10.4 Problem Identification
10.5 Objectives of the Study
10.6 Methodology to be Adopted
10.7 Proposed/Expected Outcome of the Research
References
11 R&D Export and ICT Regimes in India
11.1 Introduction
11.2 Artificial Intelligence the Uptake of Infrastructure Development
11.3 Future Analysis and Conclusion
References
12 Metaheuristics to Aid Energy‐Efficient Path Selection in Route Aggregated Mobile Ad Hoc Networks
12.1 Introduction
12.2 Framework
12.3 Clustering
12.4 Ant Colony Optimization
12.5 Methodology
12.6 Results
12.7 Discussion
12.8 Conclusion
References
13 Knowledge Analytics in IOMT‐MANET Through QoS Optimization for Sustainability
13.1 Introduction
13.2 Related Work
13.3 Proposed Neoteric Nature Inspired IWD Algorithm for ZRP
13.4 Simulation Results
13.5 Conclusion and Future Work
References
14 Appraise Assortment of IoT Security Optimization
14.1 Introduction
14.2 Literature Review
14.3 Analysis of Traditional Security Mechanisms in IOT
14.4 Conclusion and Future Scope
References
15 Trust‐Based Hybrid Routing Approach for Securing MANET
15.1 Introduction
15.2 Literature Review
15.3 Gaps and Objectives from the Literature Review
15.4 Methodology to be Adopted
15.5 Comparison Analysis
15.6 Conclusion and Future Scope
References
16 Study of Security Issues on Open Channel
16.1 Introduction
16.2 Wireless Attacks
16.3 Securing Wireless Transmissions
16.4 Proposed Model for Securing the Client Over the Channel
16.5 Conclusion
References
Index
End User License Agreement
List of Tables
Chapter 1
Table 1.1 Accuracy report – k‐Means.
Table 1.2 Clusters – k‐Means.
Chapter 3
Table 3.1 Research questions of systematic study.
Table 3.2 Inclusion and exclusion criteria table.
Table 3.3 Keywords of research.
Table 3.4 Analysis of existing security solution of smart home IoT.
Chapter 5
Table 5.1 IEEE standard 802.11 comparison.
Table 5.2 Applications of MANET.
Chapter 6
Table 6.1 Comparison of CT and MRI in the assessment of brain tumors.
Table 6.2 Analysis of work proposed for human brain data for detection of ab...
Chapter 7
Table 7.1 Security evaluation from 1G to 4G.
Table 7.2 Comparison of non 5G standards of IoT.
Table 7.3 Summary of safety challenges and resolutions of three‐tier structu...
Chapter 8
Table 8.1 Comparison of consensus protocols.
Table 8.2 Traditional traffic system vs. ITS.
Chapter 9
Table 9.1 Fabrication of super capacitor from coconut shell waste.
Table 9.2 Fabrication of super capacitor from rice husk waste.
Table 9.3 Fabrication of super capacitor from pineapple waste.
Chapter 10
Table 10.1 Hybrid/secured routing protocols.
Chapter 11
Table 11.1 Features of artificial intelligence.
Chapter 12
Table 12.1 Simulation parameters while simulating NS2.
Table 12.2 Ant colony optimization
Chapter 13
Table 13.1 Simulation structure.
Table 13.2 Packet delivery ratio.
Table 13.3 End‐to‐End Delay.
Table 13.4 Throughput.
Table 13.5 Normalized routing load.
Table 13.6 Packet loss ratio.
Chapter 14
Table 14.1 Literature survey of IoT security.
Table 14.2 Analysis of traditional security mechanisms in IoT and their atta...
Chapter 15
Table 15.1 Contrast study of basic routing protocols of MANET [3, 14].
List of Illustrations
Chapter 1
Figure 1.1 Proposed methodology.
Figure 1.2 Correlation matrix of features.
Figure 1.3 (a) Mean Humidity vs. Voltage; (b) Mean Temp (temperature) vs. Vo...
Figure 1.4 k‐Elbow Visualizer.
Figure 1.5 Accuracy vs. n_neighbors.
Figure 1.6 (a) Clustered plot of Mean Humidity vs. Voltage; (b) clustered pl...
Figure 1.7 Confusion matrix (top); classification report (bottom).
Chapter 2
Figure 2.1 Growth of 5G connections worldwide.
Figure 2.2 Functional architecture of 5G networks. Source: Based on Zhang, e...
Figure 2.3 Network slicing of 5G networks.
Figure 2.4 Intelligent 5G network management by AI.
Figure 2.5 Improved 5G end‐to‐end system by On‐device AI.
Figure 2.6 Personalized shopping through boundless AR.
Figure 2.7 Entirely disseminated AI with lifetime learning.
Figure 2.8 Distributed learning AI model over 5G Network (part 1 of 2).
Figure 2.9 Distributed learning AI model over 5G Network (part 2 of 2).
Chapter 3
Figure 3.1 Growth of connected devices.
Figure 3.2 Security requirement in wireless IoT system.
Figure 3.3 Wireless IoT system architecture [9, 11–13].
Figure 3.4 Types of blockchain.
Figure 3.5 Key characteristics of blockchain.
Figure 3.6 Steps of literature review [31, 32].
Chapter 4
Figure 4.1 Communication linkage of a cognitive IoT‐based health monitoring ...
Figure 4.2 Average effectual throughput for both HRC and MRC devices at nume...
Figure 4.3 Average effectual energy efficiency for both HRC and MRC devices ...
Figure 4.4 Average interference throughput for both HRC and MRC devices at v...
Figure 4.5 Average interference energy efficiency for both HRC and MRC devic...
Chapter 5
Figure 5.1 Information exchange for traditional approach.
Figure 5.2 Information exchange in cross layer approach.
Figure 5.3 Cross layer design categories.
Figure 5.4 Broadcast TV spectrum.
Figure 5.5 3G Broadband spectrum. Source: Based on D.B. Johnson and D.A. Mal...
Figure 5.6 Wireless LAN spectrum. Source: Based on Lei Chen and Wendi B. Hei...
Figure 5.7 WANET taxonomy.
Figure 5.8 Ad hoc mode Vs infrastructure mode.
Figure 5.9 Mobile ad hoc network.
Figure 5.10 VANET.
Figure 5.11 Mesh network.
Figure 5.12 Wireless mesh network architecture. Source: Based on A.A. Pirzad...
Figure 5.13 (a) Broadband WMN. (b) Community networking.
Figure 5.14 Wireless sensor network.
Figure 5.15 Environment monitoring through sensors.
Chapter 6
Figure 6.1 Classification of MRI brain image segmentation methods.
Chapter 7
Figure 7.1 Network evaluation toward 5G.
Figure 7.2 5G security model.
Chapter 8
Figure 8.1 Area of implementation of blockchain based ITS.
Figure 8.2 Services provided by ITS.
Chapter 9
Figure 9.1 World total primary energy supply. Source: Based on Sindhu, N. et...
Figure 9.2 Plot comparison of global temperature anomalies with global carbo...
Figure 9.3 Block diagram representing the categorization of existing capacit...
Figure 9.4 Ragone plot for various electrical energy storage devices.
Chapter 10
Figure 10.1 Various complex networks for smart world.
Figure 10.2 Security aspects in complex networks.
Figure 10.3 Routing protocols in wireless sensor networks.
Figure 10.4 Methodology flowchart.
Chapter 11
Figure 11.1 Market survey report for AI and ML use worldwide until 2027. Sou...
Figure 11.2 Smart energy management.
Figure 11.3 Challenges of smart energy management.
Figure 11.4 AI‐SI‐IoT architecture [1].
Figure 11.5 Structure of decision support system.
Figure 11.6 Running prediction based on heart rate [1].
Figure 11.7 Emerging technology system.
Figure 11.8 Number of IoT devices predicted to be installed by 2025.
Chapter 12
Figure 12.1 Proposed algorithm.
Figure 12.3 Flowchart for ACO Algorithm.
Figure 12.2 Algorithm.
Chapter 13
Figure 13.1 IoMT environment.
Figure 13.2 Objective function IWDRA.
Figure 13.3 Varying PDR with dynamic nodes.
Figure 13.4 X‐Graph for PDR with dynamic nodes.
Figure 13.5 Varying End‐to‐End Delay with dynamic nodes.
Figure 13.6 X‐Graph for End‐to‐End Delay with dynamic nodes.
Figure 13.7 Varying throughput with dynamic nodes.
Figure 13.8 X‐Graph for throughput with dynamic nodes.
Figure 13.9 Varying NRL with dynamic nodes.
Figure 13.10 X‐Graph for NRL with dynamic nodes.
Figure 13.11 Varying PLR with dynamic nodes.
Figure 13.12 X‐Graph for PLR with dynamic nodes.
Chapter 14
Figure 14.1 Rise in Internet usage from 2002–2020.
Figure 14.2 Components of Internet of Things.
Chapter 15
Figure 15.1 Example of ad‐hoc network.
Figure 15.2 Flowchart of the proposed methodology.
Chapter 16
Figure 16.1 Authentication process.
Figure 16.2 Use of VPN on the router's access point to increase the security...
Guide
Cover Page
Table of Contents
Title Page
Copyright
About the Editors
List of Contributors
Begin Reading
Index
End User License Agreement
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Smart and Sustainable Approaches for Optimizing Performance of Wireless Networks
Real‐time Applications
Edited by
Sherin ZafarJamia HamdardNew DelhiIndia
Mohd Abdul AhadJamia HamdardNew DelhiIndia
Syed Imran AliUniversity of Technology and Applied SciencesMAl MusannahSultanate of Oman
Deepa MehtaSenior Data Scientist Great Learning
M. Afshar AlamJamia HamdardNew DelhiIndia
This edition first published 2022
© 2022 John Wiley & Sons Ltd
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The right of Sherin Zafar, Mohd Abdul Ahad, Syed Imran Ali, Deepa Mehta, and M. Afshar Alam to be identified as the authors of the editorial material in this work has been asserted in accordance with law.
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Library of Congress Cataloging‐in‐Publication Data
Names: Zafar, Sherin, editor. | Ahad, Mohd Abdul, editor. | Ali, Syed Imran, editor. | Mehta, Deepa, 1981- editor. | Alam, M. Afshar, editor.
Title: Smart and sustainable approaches for optimizing performance of wireless networks : real-time applications / edited by Sherin Zafar, Mohd Abdul Ahad, Syed Imran Ali, Deepa Mehta, M. Afshar Alam.
Description: Hoboken, NJ : Wiley, 2022. | Includes bibliographical references and index.
Identifiers: LCCN 2021040689 (print) | LCCN 2021040690 (ebook) | ISBN 9781119682509 (cloth) | ISBN 9781119682523 (adobe pdf) | ISBN 9781119682530 (epub)
Subjects: LCSH: Network performance (Telecommunication) | 5G mobile communication systems.
Classification: LCC TK5105.5956 .S63 2022 (print) | LCC TK5105.5956 (ebook) | DDC 004.6–dc23
LC record available at https://lccn.loc.gov/2021040689
LC ebook record available at https://lccn.loc.gov/2021040690
Cover Design: Wiley
Cover Image: © Krunja/Shutterstock
About the Editors
Dr. Sherin Zafar is Assistant Professor Computer Science & Engineering in the School of Engineering Sciences & Technology, Jamia Hamdard, with a decade of successful experience in teaching and research management. She specializes in Wireless Networks, Soft Computing, Network Security, Data Visualization, Design Thinking, and Machine learning, etc. and has a great profile in Scopus, Mendeley, Google Scholar, Research Gate, and Publons. She has about a half century of papers published in Scopus, SCI, and peer reviewed journals and a double century of papers reviewed by the Editorial Board and Editor in Chief of many reputed and Scopus indexed journals. She has a published patent and one patent granted under her name, Co‐Pi for the FIST project of DST and Pi for a completed project for Unnant Bharat Abhiyan. A strong believer in the power of positive thinking in the workplace, Dr Sherin regularly develops internship and career campaigns for students through Internshala and Epoch (Literary and Cultural Society) Groups and has guided a huge number of graduates, postgraduate, and PhD students. She has been session chair for 7+ international conferences, keynote speaker, resource person for 1000+ webinars and FDPs for renowned institutions, AICTE STTP, and for AICTE ATAL FDP. Sherin enjoys a good Netflix and Cricket binge but can also be found on long drives along country roads.
Dr. Mohd Abdul Ahad is currently working in the Department of Computer Science and Engineering, School of Engineering Sciences and Technology, Jamia Hamdard. He has a rich experience of more than 14 years in the field of Computer Science and Engineering. He obtained his PhD in the field of Big Data Architecture. His research areas include Big Data Architecture, Distributed Computing, IoT, and Sustainable Computing. He has published several research papers in various international journals of repute in Q1 and Q2 categories. His cumulative Impact factor for the last three years is 49.8 (as per Clarivate/JCR). He has chaired several sessions at international conferences of Springer, Elsevier, etc. He is a Certified Microsoft Innovative Educator and a certified Google Educator. He is a life member of the Indian Society of Technical Education (ISTE), as well as an active Senior Member of IEEE. He is also on the review board of several prestigious journals such as Journal of Networks and Computer Applications, Elsevier, Sustainable Cities and Society, Elsevier, Journal of Ambient Intelligence and Humanized Computing (JAIHC), Springer, and Computers in Biology, Elsevier, etc.
Dr. Syed Imran Ali is currently working in the Department of Engineering, Computer Engineering Section, University of Technology and Applied Sciences Al‐Musannah, Sultanate of Oman.
He has 16 years of academic and research experience and worked at various reputed institution in India and Oman. He obtained his B. Tech, M. Tech and PhD degree in the field of Computer Engineering.
Syed Imran Ali's research areas include Image Processing, Artificial Intelligence, Networks, and Security. He has published 19 research papers in various international journals of repute including IEEE and Scopus indexed journals.
He published a book on Artificial Intelligence in 2007 and another book on wireless sensor networks in 2021.
He is a life member of the Computer Society of India and he is also on the editorial review boards of several prestigious journals.
Dr. Deepa Mehta, Senior Data Scientist in Great Learning, has also been associated with KR Mangalam University for Research Industry Interactions & Incubation. She has a PhD in Computer Science Engineering from Manav Rachna International Institute of Research and Studies. Her research areas are Machine Learning and Artificial Intelligence. She is an expert in the area of Computer Networks and Machine Learning and has taught subjects in this area for 8+ years in reputed colleges and universities. She has authored more than 20 research papers in journals and national/international conferences. She has organized several successful workshops and Winter Schools in innovative and neoteric areas. She is also a certified trainer of Python.
Deepa Mehta's current research interests are on sustainable energy using the Artificial Intelligence and Machine Learning techniques. She maintains an upward learning curve by adapting to the new technologies and applying the new technologies to achieve the sustainable environment goal.
Prof. M. Afshar Alam is the Vice Chancellor, Jamia Hamdard and also Professor and Dean in SEST, Jamia Hamdard. He has a rich teaching and research experience of more than 24 years. He has guided more than 25 PhD students. He has published more than 130 research paper in various international journals and conferences of repute. Prof Afshar is also a member of several high‐powered committees of Government of India Agencies such as DST, UGC, SERB, MHRD, etc. He has achieved a huge number of awards of national and international importance.
List of Contributors
Prashant AbbiDepartment of Information Science and EngineeringR V College of Engineering
Ayesha Hena AfzalDepartment of Computer Science and EngineeringFETMRIIRSHaryanaIndia
Shakil AhmedDepartment of Electrical and Computer EngineeringIowa State UniversityAmesIowaUSA
M. Afshar AlamDepartment of Computer Science and EngineeringSchool of Engineering Sciences and TechnologyJamia HamdardNew DelhiIndia
Mohammed AmjadDepartment of Computer EngineeringJamia Millia IslamiaNew DelhiIndia
Khushi AroraDepartment of Computer Science and EngineeringR V College of Engineering
K.B. AshwiniDepartment of Master's in Computer ApplicationsR V College of Engineering
BanuroopaDepartment of Computer ScienceKarpagam Academy at Higher EducationCoimbatoreTamilnadu
Bharat BhushanSchool of Engineering and TechnologySharda UniversityNoidaUttar PradeshIndia
Satrupa BiswasDepartment of Information Communication and TechnologyManipal UniversityMAHE
Siddhartha Sankar BiswasDepartment of Computer Science and EngineeringSchool of Engineering Sciences and TechnologyJamia HamdardNew DelhiIndia
Md Mudassir ChaudharyDepartment of Computer Science and EngineeringSchool of Engineering Sciences and TechnologyJamia HamdardNew DelhiIndia
V. ChayapathyDepartment of Electrical & Electronics EngineeringR V College of Engineering
Mehajabeen FatimaSIRT BhopalMadhya PradeshIndia
Ashu GautamMRIIRS FaridabadHaryanaIndia
Aju Mathew GeorgeDepartment of Civil EngineeringAmal Jyorhi College of EngineeringKanjirapallyKeralaIndia
Majumder Fazle HaiderDepartment of Electrical and Electronic EngineeringBRAC UniversityBangladesh
Nida IftekharDepartment of Computer Science and EngineeringSchool of Engineering Sciences and TechnologyJamia HamdardNew DelhiIndia
Christina JaneDepartment of ECEMar Ephraem College of EngineeringElavuvilaiTamil NaduIndia
Lijo JosephDepartment of EEEAmal Jyothi College of EngineeringKanjirapallyKeralaIndia
Maheswari KDepartment of MathematicsKumaraguru College of TechnologyCoimbatoreTamilnadu
Chalapathiraju KanumuriRV College of EngineeringBengaluruKarnatakaIndia
&
S.R.K.R Engineering CollegeBhimavaramAndhra PradeshIndia
Samia KhanDepartment of Computer Science and EngineeringSchool of Engineering Sciences and TechnologyJamia HamdardNew DelhiIndia
Afreem KhursjeedIIIT BhopalMadhya PradeshIndia
Avinash KumarSchool of Engineering and TechnologySharda UniversityNoidaUttar PradeshIndia
S.N. KumarDepartment of EEEAmal Jyothi College of EngineeringKanjirapallyKeralaIndia
Praveen Kumar GuptaDepartment of BiotechnologyR V College of EngineeringBengaluruIndia
Gunjan MadaanDeloitte Consulting India Private LimitedBengaluruIndia
CH. Renu MadhaviRV College of EngineeringBengaluruIndia
Rashima MahajanMRIIRS FaridabadHaryanaIndia
Ayasha MalikNoida Institute of Engineering Technology Greater NoidaNoidaUttar PradeshIndia
Deepa MehtaDepartment of EEEGD Goenka UniversitySohnaHaryanaNew DelhiIndia
MohankumarDepartment of Computer ScienceKarpagam Academy at Higher EducationCoimbatoreTamilnadu
Md Tabrez NafisDepartment of Computer Science and EngineeringSchool of Engineering Sciences and TechnologyJamia HamdardNew DelhiIndia
Nishitha R. P.Department of EEEAmal Jyothi College of EngineeringKanjirapallyKeralaIndia
Ashiqur Rahman RahulDepartment of Electrical and Electronic EngineeringBRAC UniversityBangladesh
Danish Raza RizviDepartment of Computer Science and EngineeringJamia Millia IslamiaNew DelhiIndia
Akhil SabujDepartment of EEEAmal Jyothi College of EngineeringKanjirapallyKeralaIndia
Saifur Rahman SabujDepartment of Electrical and Electronic EngineeringBRAC UniversityBangladesh
and
Department of Electronics and Control EngineeringHanbat National UniversityKorea
Neha SharmaDepartment of DEEEGD Goenka UniversitySohnaHaryanaIndia
Safdar TanweerDepartment of Computer Science and EngineeringSchool of Engineering Sciences and TechnologyJamia HamdardNew DelhiIndia
M.J. VidyaDepartment of Electronics and Instrumentation EngineeringR V College of Engineering
Sana ZebaDepartment of Computer EngineeringJamia HamdardNew DelhiIndia
Editors:
Mohd Abdul AhadDepartment of Computer Science and EngineeringSchool of Engineering Sciences and TechnologyJamia HamdardNew DelhiIndia
Syed Imran AliUniversity of Technology and Applied SciencesAl MusannahSultanate of Oman
Sherin ZafarDepartment of Computer Science and EngineeringSchool of Engineering Sciences and TechnologyJamia HamdardNew DelhiIndia
1Analysis and Clustering of Sensor Recorded Data to Determine Sensors Consuming the Least Energy
Prashant Abbi1, Khushi Arora2, Praveen Kumar Gupta3, K.B. Ashwini4, V. Chayapathy5, and M.J. Vidya6
1 Department of Information Science and Engineering, R V College of Engineering, Bengaluru, India
2 Department of Computer Science and Engineering, R V College of Engineering, Bengaluru, India
3 Department of Biotechnology, R V College of Engineering, Bengaluru, India
4 Department of Master's in Computer Applications, R V College of Engineering, Bengaluru, India
5 Department of Electrical & Electronics Engineering, R V College of Engineering, Bengaluru, India
6 Department of Electronics and Instrumentation Engineering, R V College of Engineering, Bengaluru, India
1.1 Introduction
Wireless sensor networks (WSNs) [1] are wireless networks that configure themselves and are used to observe environmental or physical circumstances, such as pollutants, motion, vibration, pressure, temperature, sound, etc. Through the network, the data is returned to a principal position. The data is observed and analyzed at this station. The station behaves as an interface between the network and its users. The essential information can be retrieved from the station by raising queries and consolidating and assembling the required results. Usually, a WSN comprises a sizable number of sensor nodes, sometimes as high as hundreds of thousands of nodes. The mode of communication between the sensor nodes is typically through radio signals [2].
Currently, there are numerous areas where these networks can be applied, assisted by the vast open research on WSNs [3]. A few of these application areas, among others, are monitoring, tracking, automation, surveillance, military applications, and agriculture. In all such applications, one of the main objectives for the design of the sensor node and network is to keep the WSN working, functional, and efficient for as long as possible [4]. Thus, the way the network is formed is a key element. The topology is usually defined on the basis of the context and environment of the network application.
WSNs allow new areas of implementation, and need unconventional prototypes for protocol design, due to various restrictions [5]. Due to prerequisites such as the low complexity of devices along with low energy consumption and a long lifetime of the network, a suitable balance between signal and data processing capacity and communication must be found and established. During the last decade, this has motivated huge research activities, industrial investments, and standardization processes in this area [6–11].
1.2 The Working of WSNs and Sensor Nodes
Sensor nodes have sensory devices that allow data sensing, while transceivers help them communicate [12]. When a stimulus is detected, sensor nodes, known as sources, generate data packets, and the corresponding sensor information is typically recorded via the accessible “gateways” in the topology. They then transfer them using the network to one or more special nodes, known as sinks or base stations. If a high transmission power is used on the node that is transmitting, direct communication would be possible. However, in largely established networks, lots of energy would be used, and high transmission power alone would not be adequate to reach the sink. This complication can be controlled by multihop communication, i.e. sensors acting as both data routers and data generators [13].
A WSN is assembled using computing and sensing devices, power components, and radio transceivers. In a WSN, the individual nodes have inherently restrained resources, i.e. the processing speed, communication bandwidth, and storage capacity are limited [14]. Once the sensor nodes are set, they are supposed to organize themselves into a suitable network infrastructure, usually along with multihop communication. This is followed by the onboard sensors collecting the required information. Wireless sensor devices also perform specified commands or provide sensing samples as a response to queries raised by a control site. The mode of operation of the sensor nodes might be either event driven or continuous. The Global Positioning System (GPS) and the algorithms used for native positioning could be used to procure information about positioning and location [15]. Wireless Sensor and Actuator Networks are essentially wireless sensor devices that are fitted with actuators to “act” on specific given conditions.
The wireless sensor network's design complexity and intricacy is dependent on the requirements of the application, the utilization or consumption power, the number of nodes, the sensor lifespan, the data that has to be collected, its timing, the environment, and the context, geography, and location of usage of the sensor [16–21].
Cable Mode Transition (CMT) [22] is a proposed algorithm for the determination of the least number of sensors and the optimum architectures needed to remain active to sustain the k‐coverage [6, 23, 24] of a terrain along with the k‐connectivity [25–29] of the network. Essentially, based only on native information, it assigns phases of idleness for the cable sensors without impacting the connectivity and coverage requirements of the network. Another proposition for WSNs is a network structure for data collection that is delay‐aware [30–33]. The objective of this proposal is to reduce delays to the maximum extent possible in the data collection processes in WSNs. This results in extension of the lifespan of the network. A third proposition considers the relay nodes to alleviate the geometric insufficiencies of the network, and uses algorithms based on Particle Swarm Optimization (PSO) to detect the ideal sink position in relation to the relay nodes to overcome the lifespan challenge.
Another proposal suggests a geometry‐based solution for finding the ideal sink location to maximize the lifespan of the network. Usually, homogeneous sensor nodes have been considered in the research on WSNs. However, researchers now are focused more on leveraging the use of sensor nodes that are dissimilar in terms of their energy. These are known as heterogeneous WSNs. The provision of fault tolerance with elevated network correspondence in heterogeneous WSNs using a deployment of relay nodes where sensor nodes have different radii of transmission is a major problem in research, which still needs to be, and is being, addressed. Rapid advancements in technology and new network architectures based on heterogeneous devices broaden the scope of possible applications for WSNs and eliminate the present‐day limitations considerably.
1.3 Classification of WSNs
WSNs are often classified into two broad categories – distributed and centralized techniques. Within a distributed system, nodes are self‐governing and therefore the link is merely between neighboring nodes, whereas in a centralized system, one device or appliance controls the network formation. WSNs can also be classified into two categories according to the type of their deployed sensors: static and mobile. Static WSNs are denoted simply as WSNs, while mobile WSNs are abbreviated as MWSNs. This chapter focuses on centralized and distributed networks.
Centralized networks [34, 35]: These are appropriate for those networks wherein the power capacity of processing relies totally on a singular appliance. Thus, this single appliance is responsible not just for the regulation and coordination of the sensed information but also for subjecting it to a series of actions in order to achieve a required result. The data that is sensed is forwarded and provided to the required sink node. The foremost benefits of this method are:
Inside the network, roaming is allowed.
Better application design in terms of the placement of nodes, application awareness, etc. is made possible through context information availability.
Network coverage analysis is simplified.
Energy management is more efficient in centralized schemes.
Centralized networks receive instructions from a singular appliance.
This centralized node is answerable to the network for delivering operational services like node localization, event detection, and effective routing of traffic. An appropriate topology for this technique would be a star topology. Centralized networks are often classified based on the algorithm for processing the information. A few of these categories are mentioned below:
Single sink: Essentially, the objective of the developmental plan is to find a method of reducing the forwarding time for routing the knowledge toward a singular sink. However, a point to be noted is that there is a lack of redundancy in these systems.
Multisink
[36]
: When the assigned tasks are diverted among several nodes, multiple sinks are employed. Coverage, efficient distribution of traffic flows, network density and lifetime, redundancy, and possible energy utilization are a few possible areas where Multisink is applied.
Multiple task devices: According to prominent research works, the utilization of additional network appliances is often responsible for performing selected activities inside the network, like controlling the node movements and defining target nodes, while also having knowledge of the environment that informs the definition of a route in order to optimize the general WSN application performance.
Based on the dynamics of the node roles, a further classification can also be made, between defined operational networks (DON) [36–39], hierarchical networks [40–44], and static networks [45–47].
DON: The action or behavior of the node can be defined during the processing function of the network. The application begins with the successful detection of an event by the node. Thus, the node forwards its data to the required sink node.
Hierarchical networks: Sensors define priorities specific to their role within the network. A lower precedence is seen in nodes that forward traffic, compared with fully functional nodes, which can sense, consolidate, and forward relevant information and data. The network management execution is carried out in a hierarchical way and is clearly designated to support the roles.
Static networks: In these networks, the nodes are usually positioned strategically and in suitable places prior to the launch of the application. The main aim here is to provide a much better and enhanced data collection and processing performance. The network is partitioned into independent clusters. The centroids corresponding to these newly formed clusters are set in place with the positioning of the sinks. Node positions are set based on some measures. The assembled formation is based on Particle Swarm Optimization (PSO).
Distributed networks: In these networks, each node manages the information: decisions are taken locally and restricted to its neighborhood, known as single‐hop neighbors. The foremost characteristics of this type of network are listed below:
The appliances are autonomous.
The information is shared by each node to its neighborhood.
There is no requirement for interconnection devices, such as routers, bridges, etc.
These networks are appropriate for applications that are distributed, like self‐organized systems, multiagent systems, etc.
Targeting harsh environments is made possible, due to their flexibility
Information is specifically forwarded to at least one node.
Distributed techniques are applied when the appliance needs to maintain essential properties, such as the quantity of connections, memory, or energy conservation, or when information processing is ineffective. These techniques have some noteworthy characteristics:
Independence: This is observed in those instances where a user is the only one who has the liberty to choose where and when the knowledge could be stored, altered, or removed. The knowledge saved is completely independent and does not depend on any other devices. Device data is instrumental in supporting important decisions.
Reduced information management: Information is shared by each node to its neighbors, so that additional interconnecting devices are not required, and the management of information is made simpler compared to other network types.
Scalability: Depending on the appliance, more nodes can be added to the network and the architecture can be scaled without changing the overall performance of the network, which suggests that the modifications made do not affect the network as a whole.
Networks that are centralized are easily divided between those that operate with a single sink and those that use multisink environments. These networks may be classified further, based on the application in different categories, as hierarchical networks or application‐based networks, and can be analyzed by topology.
Hierarchical networks: A notable research paper proposes the Adaptive Epidemic Tree Overlay Service (AETOS) [48]. This approach is based on using replacement agents to develop and maintain robust tree topologies on demand. They react to changes in the environment by rewiring their connections. Interactions between the agents and thus across the appliances are managed with the help of an AETOS proxy, with another native agent. The AETOS proposal focuses on virtual tree topologies built on fundamental network infrastructures [49–54].
The tree overlay is influenced by the node, and so is the way that the native software agents interact with each other to ensure that the topology is robust against failure. An optimal number of child nodes should be retained for each node in order to control the processing cost, which is not in the purview of the AETOS. The behavior of every agent in AETOS is cut‐throat, which suggests continuous optimization of its location within the tree by selecting links with sturdier agents, which causes substitutions in the closeness criteria. This in turn suggests a high energy consumption. Every node within the tree, not including the leaves, is presumed to have a spread of child nodes. This demonstrates native adaption and structurally fluid nodes. They coordinate and collectively use an auto‐organization scheme based in AETOS. The researchers failed to consider energy constraints or a multilink environment. There is no restructuring for the main node in the tree.
Application‐based networks [55–61]: These networks are often further sub classified into event‐based networks and routing‐based networks. In an event‐based network, the starting point for execution or formation is events, while in a routing‐based network, the focus is more on finding the most appropriate route. The choice of route is informed by a number of different metrics that evaluate the nodes for their energy level, distance, number of hops, number of visited nodes, etc.
Networks compared by topology [62–68]: The foremost common topologies utilized in sensor networks were considered. A number of metrics such as energy consumption, reliability, and latency are presented as emergent properties of this approach. Other topologies where the node behavior affects the execution of the network were also analyzed.
1.3.1 Benefits and Drawbacks of Centralized Techniques
The chief benefit of centralized approaches is as follows: they are controlled by a singular central appliance which knows the entire neighboring domain and thus the locations of all the stationed appliances. Usually, the event source is known, and thus the knowledge is shipped to a specified sink or target. There are not many reception or transmission disputes since the central appliance manages each node.
The routing is simple to compute and thus the simplest paths are often chosen, keeping in consideration the entire environment. The optimum positions of the nodes and sink, and thus the number of hops, are often computed considering metrics like the space between nodes, the number of nodes within the neighboring domain, and the energy level of each node [69].
One of the disadvantages of this sort of technique is immense energy consumption. Since the nodes have to transfer information, they need to know to which node they are delivering the message. Normally, GPS or other such approaches are implemented on each node to determine their locations, which suggests plenty of processing and energy loss. These approaches normally assume faultless behavior of nodes within the network, but this does not consider memory constraints, which suggest a loss of data. Also, interference and obstacles are not taken into consideration.
In these approaches, the implementation of reconfiguration is simple, but more network resources are required, along with an excessive energy cost. The network does not support a high density of nodes, due to the huge quantity of knowledge produced within the network. Network interactivity is not assured, since during a period of quiet activity for the application it is normal to decide to connect nodes that are active instead of the whole network. Robustness and verification depend on the appliances.
The responsibility for repairing a failure lies with the central device. The rectification of a failure is difficult for a couple of nodes, because full rectification is necessary. The central appliance needs all of the data that is in the neighboring domain for the repair. The network is broken when the central device fails.
1.3.2 Benefits and Drawbacks of Distributed Techniques
The distributed approach is typically used when the system has to handle huge amounts of data, and having some expendability and some verification of the knowledge is convenient. In these approaches, the benefits and drawbacks are defined based on the appliances, the assets, and thus the neighboring domain.
The chief benefits are as follows: The knowledge is native. Therefore, a node only keeps information pertaining to its neighborhood, which may consist of one‐hop, or at maximum two‐hop, neighbors. The algorithms in distributed WSN systems are considered to be scalable. Reconfiguration takes place natively only on the afflicted parts. Since nodes are self‐governing, substitutions are made by each node and are consistent with its place or its work. The prime concerns and information that is accessible within the network are interpreted by every node. Even when the death of a node occurs, the network still remains operational, and thus execution is not affected so much. Distributed techniques allow noisy environments to be handled, including obstacles. Every node reduces energy consumption. Normally, routing starts when an occurrence is detected or when a target is set. This suggests that before the procedure starts, there is no unnecessary energy depletion [70–75].
A few drawbacks of the distributed approach are as follows: Since nodes only have native information, there is no guarantee of connectivity of the entire network. Bottlenecks can arise when multichip transmission is used and there is only a single sink node. A lot of energy is required for the mobility of nodes. When only one sink node is present, the network ceases to function effectively, rendering it useless in that situation.
Some factors can impact the execution of distributed and centralized networks, like the number of hops to reach to a target or a specified appliance, the flow, the volume of retransfers, the volume of linking, and thus the number of appliances [76–78].
1.4 Security Issues
Security issues in WSNs necessarily depend upon understanding what is to be protected. A research paper [79] identified and defined a number of major goals in the field of security in WSNs, the four most important of which are Confidentiality, Authentication, Integrity, and Availability. The potential to hide messages containing confidential data from a passive or indirect attacker as they are communicated through the network is known as Confidentiality [80]. Authentication determines whether the data has truly come from the node that is declared to be the source. Integrity refers to the capability to verify that the data has not been meddled or interfered with while it has been on the network. The question of whether a node has the power to use the network and its resources, and whether they are available for propogating the data, is known as Availability.
A further security goal of Freshness means ensuring that the data received by the receiver is recent, and no attacker can replay old data. This goal is crucial when the nodes of the wireless sensor network use shared keys for data interaction, where a possible adversary could initiate a replay attack or assault using the earlier key while the latest key is being refreshed and communicated to all or any of the nodes within the network [81]. To satisfy the goal of Freshness, certain mechanisms need to be added to every data packet.
In addition to establishing a base set of security standards for wireless sensor networks, the main possible security assaults in the networks are identified in [82].
Routing loop attacks target the knowledge exchanged between nodes. When an attacker changes and replays the routing information, false error messages are generated. Routing loops increase node to node latency and attract or repel the network traffic [83].
Selective forwarding attacks affect the network traffic when the network believes that each of the nodes within the network is a dependable forwarder of messages. In a selective forwarding attack, the malicious or infected nodes drop some of the messages instead of than forwarding them. Once a malicious or infected node reduces the number of messages that it forwards, latency is reduced, and the nearby nodes are deceived into believing that they are on a shorter route. The two factors that the efficiency of this assault depends on are: (i) primarily, the site of the infected node – the nearer it is to the base station, the more traffic it will lure [84]; and (ii) secondarily, the amount of data it drops – when a selective forwarder forwards less data and drops more, its energy level is retained, which enables it to continue to mislead the neighboring nodes.
In sinkhole attacks, the attacker lures the traffic to an infected node [85]. Only if the infected node is located where it can attract most of the data, which is likely to be near to the sink or base station, or by the infected node itself pretending to be the base station, can a sinkhole attack be made [86]. One explanation for why attackers use sinkhole attacks is to make selective forwarding possible, to attract the traffic toward an infected node. WSNs where all the data flows toward one base station are more susceptible to this sort of attack.
Sybil attacks are attacks in which a node in the WSN creates multiple illegitimate identities, either by fabricating them or by stealing the identities of legitimate nodes [87]. These attacks are often used against routing algorithms and topology maintenance; they reduce the effectiveness of fault tolerance schemes like distributed storage and disparity.
1.4.1 Layer‐ or Level‐based Security
The application layer: Data is gathered and managed at this level, therefore it is crucial to ensure the authenticity of the data. A resilient aggregation scheme has been presented in research; it can be implemented in a cluster‐based network in which a cluster leader acts as an assembler [88]. However, this system can only be implemented if the aggregating node is within range of all the source nodes and there is no interceding aggregator between the aggregating node and source nodes. To prove the validity of the aggregation and the reliability of the data, cryptographic techniques are used by the cluster leaders.
The network layer: This level is responsible for routing messages between cluster leaders and the base station, between cluster leaders, between nodes and cluster leaders, and between nodes [34, 35, 89].
The data link layer: This level is responsible for fault identification and rectification, and encoding the data. This level is susceptible to jamming and DoS attacks. Link layer encryption has been introduced by TinySec [90], which is dependent on regulation strategy. However, an attack can still be staged if the attacker has better energy efficiency. A few protocols have anti‐jamming properties that have proven to be viable corrective measures at this level [91].
The physical layer: This level focuses on the transfer connection between forwarding and collecting nodes. Signal strengths, frequency types, information rates, etc. are all considered within this level [92].
1.5 Energy Consumption Issues
To compute the lifespan of a WSN, energy consumption can prove to be a very valuable component, since sensor nodes usually operate on batteries [93]. Occasionally, energy optimization can prove to be much more complicated in WSNs, since it involves not only lowering of the consumption of energy but also elongating the lifespan of the network to the maximum possible [93]. The optimization is often performed by using energy awareness in all of the essential parts of design and operation. This also ensures that energy awareness is built into blocks of interacting sensor nodes, and therefore the entire network, not only within the individual nodes [94].
A sensor node typically comprises four sub‐systems:
Computing subsystem: This architecture comprises a microprocessor (often abbreviated as MCU), which is responsible for the governance and implementation of conveyance protocols, and usually functions in multiple modes for power regulation purposes. As these functioning modes involve the utilization of power, the energy consumption rates of the various modes should be taken into consideration when observing the battery lifespan of each node
[95]
.
Communication subsystem: This subsystem comprises a short‐range radio, which communicates with neighboring nodes and thus the surface world. It is crucial to have the radio totally shut down, instead of keeping it in a mode in which it is idle, when it is not transmitting or receiving, to increase efficiency and reduce power consumption
[96]
.
Sensing subsystem: Actuators and sensors that connect the node to the surface world form the basis of this subsystem
[97]
. Energy consumption is usually lowered by using low‐power components, and by lowering execution speeds to save power (where higher speeds are not critical)
[98]
.
Power supply subsystem: Power is supplied to the node by the battery that is part of this subsystem. The power drawn from the battery has to be monitored frequently, due to the fact that a high current drawn from the battery for an extended period of time would lead to its rapid discharge. Usually, the minimum energy consumption of the node exceeds the nominal rated current capacity of the battery that is used in the sensor node. The lifespan of the battery can be extended by reducing this excess load. This can also be achieved by turning off the sensors every once in a while
[94]
.
To reduce the overall energy consumption of the WSN to the maximum extent possible, differing kinds of algorithms and protocols have been designed and studied. The lifespan of a WSN is often extended remarkably if the appliance layer, the operating system, and thus the protocols of the network are designed to be energy conscious [99]. These protocols and methods need to take into consideration knowledge of the hardware, and must be able to utilize the relevant features of the microprocessors and nodes that transmit and receive information to reduce energy consumption [100]. This might lead to a better range of design and implementation solutions for sensor nodes. Different types of WSNs are a direct consequence of different kinds of sensor nodes. This could also lead to synergy between various sorts of algorithms in the WSN arena [101].
1.6 Commonly Used Standards and Protocols for WSNs
Protocols are crucial parts of the blueprint for sensor networks, mainly concerned with the interactions between sensor nodes. A quick outline of key protocols and standards that are used for wireless interaction is given below.
The standards are split into layers and sublayers. These guide the decisions relating to performing tasks, such as when and how to collect, transfer, dispatch, and process the information on each appliance [102]. In the link layer, information is collected and processed, after which it is sent to a different sensor appliance. Performance management for data transfer and possible fault rectification are provided at this level. It is split into two sublayers: medium access control (MAC) and logical link control (LLC) [103].
The LLC sublayer functions as an intermediary between the MAC sublayer and networking tasks. It is responsible for flow and fault supervision, and for data transfer between appliances on a network. Some standards utilized at this sublevel are 802.11, 802.5, Fiber Distributed Data Interface (often abbreviated as FDDI), and 802.3/Ethernet [104].
The MAC sublayer is responsible for governing the approach to the neighboring domain, including data frame verification, fault correction on transfers, transfer rates, package transfers, management of flow, acknowledgment messages, and so on [105]. In the MAC sublayer, there is an immediate impact on how the node approaches the neighboring domain to access information about the paths. The protocols are divided into two general groups:
Slot‐based or slotted protocols: In this subgroup of protocols, time is partitioned into time gaps known as slots or frames. In these protocols, the node has a state such as Transmit, Receive, or Sleep. The co‐occurrence of these time slots is defined for the governance these states. The co‐occurrence and preservation costs affect the energy consumption and therefore the bandwidth. A number of examples are the
Sensor Medium Access Control
(
S‐MAC
) protocol,
time division multiple access
(), IEEE 802.15.4, and the T‐MAC protocol, among others.
Sampling‐based protocols: A unique feature of these protocols is that they are generally turned off for most of the time, and are turned on only during specific instances, unlike the slot‐based protocols. They search for activity within the channel. In the case that an action is detected, they begin receiving data. When these actions are not detected, they are shut down in order to conserve energy. These types of protocols are generally flexible in nature, and communication is permitted to any other sensor in scope. Communication, however, is not always possible, due to a shortfall of integration. ALOHA, B‐MAC, Wiseman, and the protocol used by the Chipcon CC2500 transceiver are some popular examples of sampling‐based protocols [
106
–
110
].
1.6.1 Slotted Protocols
Slotted protocols are commonly used in WSNs. Some samples of slotted protocols are as follows:
1.6.1.1 Time Division Multiple Access
The time division multiple access (TDMA) protocol is straightforward. It uses the technique of frequency division multiple access (FDMA) to split the radio spectrum into individual frequency channels through the use of a duplex channel. TDMA hence divides these frequency carriers further into smaller time slices. A frame is then formed by a combination of these time intervals. Transmission is structured in frames with a duration of Ti. The length of the interval is defined by T = Ti/N. Information is transferred through a burst of bits, and each conversation employs just one slot. Thus, instead of handling only one conversation, each and every radio frequency (RF) carrier can transport multiple conversations [111–115].
1.6.1.2 ZigBee/802.15.4
The IEEE 802.15.4 has been a standard since the early 2000s. It was created to address the need for a sensor network protocol that consumes less energy and is efficient in a wireless personal area network (WPAN) [116]. It is flexible, and operates on a narrow bandwidth. This protocol is designed to support two kinds of topologies:
Star: this topology is used to implement power networks that consume less energy.
Peer‐to‐peer: this topology is primarily used to implement wide, yet precise, networks
[117]
.
This protocol functions with three types of role. All of these roles have specific functionality as described below:
A
reduced function device
(
RFD
) is restricted to the star topology. These nodes interact solely with the relevant coordinators of the network, instead of adopting the role of a network organizer. These are straightforward devices with restricted resources, interactions, and communication requirements. They communicate solely with FDDs.
A device with the
personal area network
(
PAN
) role acts as a router, and accordingly manages the network load.
A
full function device
(
FFD
) is equipped to perform any task, and is able to communicate with all nodes within the network. The FDD adopts the role of an organizer.
To ensure the security and protection of the frame, the IEEE 802.15.4 standard is able to provide eight sophisticated security levels for this purpose [118–120]. They include:
unsecured;
encryption only;
authentication only; and
encrypted and authenticated.
Each category mentioned is available in three variations, depending on the size of the MAC and whether or not it supports authentication. On enabling the unsecured level, neither message integrity nor data confidentiality is ensured. In the other cases, data encryption is generally performed with the help of a variant of the Advanced Encryption Standard (AES), and the authentication of messages is performed using the AES‐CBC technique. Each packet can carry a particular service payload. Communication with the MAC entity is made possible by using specific primitives to select the security level and the other various parameters that are instrumental to conducting the necessary security procedures [121].
The protocol involves the emission of beacon frames by the network coordinator for WPAN discovery and the detection of devices [122]. Devices are designed to function in two ways:
Non‐beacon‐enabled: An active scan is initiated to send the frames as requested by the command frame.
Beacon‐enabled: The frames are sent in a periodic fashion by the network coordinator. A passive scan is initiated to detect the network [
119
,
123
,
124
].
The prevailing standards give rise to low data transmission and connectivity service cycles. The foremost reason for promoting a replacement protocol (ZigBee/802.15.4) as a standard is to ensure interoperability between devices that are manufactured by different companies. The intermediate nodes and sinks are FFD devices, while the final devices or leaf nodes are RFD devices. The network coordinator is chosen based on having the absolute best average energy level, using the energy detection (ED) procedure. Using different types of environmental sensors, the nodes measure moisture, temperature, and luminosity [125].
The fraction of nodes that are connected in the network is analyzed by mobilizing the sink nodes. A reconfiguration is taken into consideration to analyze the scenarios and run simulations to evaluate the performance. However, the characteristic node features such as noise, message losses, and delays are not considered. Hence, it is increasingly difficult to evaluate if the implementations are economically viable and are optimum, since there is no assurance that the connectivity of the network is consistent [126, 127].
