AN INVESTIGATION OF ENHANCED THRESHOLD SENSITIVE STAGNATION ELECTION PROTOCOL FOR HETEROGENEOUS WSN
DOI:
https://doi.org/10.65009/edsae662Keywords:
WSN, Energy Efficiency, Clustering, Heterogeneity, ETSSEP,,Abstract
—It is now possible for wireless sensor networks, often known as WSNs, to carry out tasks that
are becoming more difficult. When it comes to wireless sensor networks, the most significant challenges
include enhancing dependability, conserving resources, extending the lifetime of each node, and
increasing the throughput of both the network and the nodes. In order to improve the network's
dependability while also reducing its energy consumption, clustering is being utilized. It is essential to
have an efficient routing protocol in a cluster in order to maintain reliability and cut down on energy
consumption. When it comes to cluster heads in heterogeneous networks, nodes that have a higher energy
level have a better probability of being cluster heads than nodes that have a lower energy level. It is
crystal clear that the selection of a cluster leader and the delegation of work to that individual would result
in an increase in energy efficiency.
In this article, the Enhance Threshold Sensitive Stable Election Protocol (ETSSEP) is proposed as a
solution for heterogeneous wireless sensor networks. It can be defined as the probability that the election
of a cluster head will change over the course of time. For heterogeneous wireless sensor networks
(WSNs), a number of energy-efficient protocols have been developed in recent years, and the article
provides a description of these protocols.
References
Naveen Sai Bommina, Uppu Lokesh, Nandipati Sai Akash, Dr. Hussain Syed, Dr. Syed Umar,
"Optimizing AI-Driven Security Protocols in IoT Networks Using Metaheuristic Algorithms",
International Journal of Intelligent Systems and Applications in Engineering, IJISAE, 2024, 12(23s),
–3347.
Naveen Sai Bommina, Nandipati Sai Akash, Uppu Lokesh, Dr. Hussain Syed, Dr. Syed Umar, "A
Hybrid Optimization Framework for Enhancing IoT Security via AI-based Anomaly Detection",
International Journal on Recent and Innovation Trends in Computing and Communication, ISSN:
-8169 Volume: 11 Issue: 3.
Habeeb, M. S., & Babu, T. R. (2022). Network intrusion detection system: a survey on artificial
intelligence‐based techniques. Expert Systems, 39(9), e13066.
K. Kartheeban, K. Kalyani, S. K. Bommavaram, D. Rohatgi, M. N. Kathiravan, and S. Saravanan,
“Intelligent Deep Residual Network based Brain Tumor Detection and Classification,” in 2022
International Conference on Automation, Computing and Renewable Systems (ICACRS), Dec. 2022,
pp. 785–790. doi:10.1109/ICACRS55517.2022.10029146.
Nandipati Sai Akash, Uppu Lokesh, Naveen Sai Bommina, Hussain Syed, Syed Umar, "Swarm
Intelligence-Based Hyperparameter Optimization for AI-Powered IoT Threat Detection",
International Journal of Intelligent Systems and Applications in Engineering, (2024), 12(17s), 941.
Uppu Lokesh, Naveen Sai Bommina, Nandipati Sai Akash, Dr. Hussain Syed, Dr. Syed Umar,
"Designing Energy-Efficient and Secure IoT Architectures Using Evolutionary Optimization
Algorithms", International Journal of Applied Engineering & Technology, Vol. 4 No.2, September,
Femi, A. A., & Jeremiah, D. D. (2009). An enhanced Stable Election Protocol (SEP) for clustered
heterogeneous WSN. Department of Information Science, University of Otago, New Zealand.
Brahim Elbhiri, Rachid Saadane, Sanaa El fldhi, Driss Aboutajdine, “Developed Distributed Energy
Efficient Clustering (DDEEC) for heterogeneous wireless sensor networks”, In I/V Communications
and International conference on I-SMAC (IoT in Social, Mobile, Analytics and cloud), 2010.
Naveen Sai Bommina , Nandipati Sai Akash, Uppu Lokesh , Dr. Hussain Syed , Dr. Syed Umar,
"Privacy-Preserving Federated Learning for IoT Devices with Secure Model Optimization",
International Journal of Communication Networks and Information Security (IJCNIS), (2021), 13(2),
–405.
Naveen Sai Bommina, Uppu Lokesh, Nandipati Sai Akash, Dr. Hussain Syed, Dr. Syed Umar,
"Optimized AI Models for Real-Time Cyberattack Detection in Smart Homes and Cities",
International Journal of Applied Engineering & Technology, Vol. 4 No.1, June, 2022.
R. Gnanakumaran, Divya Rohatgi, A K Sampath, Nidhi Nagar, D. Amuthaguka, Raj Kumar Gupta,
"Robust Extreme Learning Machine based Sentiment Analysis and Classification", 2023 5th
International Conference on Smart Systems and Inventive Technology (ICSSIT), (2023), DOI:
1109/ICSSIT55814.2023.10061017.
M. M. Islam, M.A Matin, T. K. Mondol, “Extended Stable Election protocol for Threelevel
Hierarchical Clustered Heterogeneous WSN”, IEEE Conferance on Wireless Sensor System (WSS),
RS Supriya Khaitan, Divya Rohatgi, Sana Nalband, Tejali Mhatre, Shweta Patil, "Enhancing Essay
Grading Efficiency and Consistency through Two-Layer LSTM Models and Attention Mechanisms",
Journal of Information Systems Engineering and Management 10 (2), 191-202.
Habeeb, M. S., & Babu, T. R. (2024). Coarse and fine feature selection for network intrusion detection
systems (IDS) in IoT networks. Transactions on Emerging Telecommunications Technologies, 35(4),
e4961.
Habeeb, M. S., & Babu, T. R. (2024). MS-CFFS: Multistage Coarse and Fine Feature Selection for
Advanced Anomaly Detection in IoT Security Networks. International Journal of Electrical and
Electronics Research, 12(3), 780-790.
Nandipati Sai Akash, Naveen Sai Bommina, Uppu Lokesh, Hussain Syed, Syed Umar, "Optimized
Block Chain-Enabled Security Mechanism for IoT Using Ant Colony Optimization", International
Journal on Recent and Innovation Trends in Computing and Communication, (2023), 11(10), 1226
