Research Article
Optimizing Fuzzy Adaptive Based Metaheuristics ABC to Detect Malicious Node to improve the performance of WSN
@INPROCEEDINGS{10.4108/eai.16-4-2022.2318174, author={Virendra Tiwari and Akhilesh A. Waoo}, title={Optimizing Fuzzy Adaptive Based Metaheuristics ABC to Detect Malicious Node to improve the performance of WSN}, proceedings={Proceedings of The International Conference on Emerging Trends in Artificial Intelligence and Smart Systems, THEETAS 2022, 16-17 April 2022, Jabalpur, India}, publisher={EAI}, proceedings_a={THEETAS}, year={2022}, month={6}, keywords={wireless sensor network (wsn) clustering artificial bee colony (abc) sinkhole attacks swarm intelligence security}, doi={10.4108/eai.16-4-2022.2318174} }- Virendra Tiwari
Akhilesh A. Waoo
Year: 2022
Optimizing Fuzzy Adaptive Based Metaheuristics ABC to Detect Malicious Node to improve the performance of WSN
THEETAS
EAI
DOI: 10.4108/eai.16-4-2022.2318174
Abstract
The Wireless Sensor Networks comprises a set of sensors situated at different locations, usually employed for the gathering of data and tracking applications like monitoring the movement of autonomous nodes in both static and dynamic environments. However, the design of WSN has some the vulnerabilities mainly; its security problems nowadays are found hot research topics in many applications by the researchers. To prevent sinkhole attacks, this paper is doing some research on it and will try to deploy an enhancing and Optimized Artificial Bee Colony algorithm based on fuzzy logic to improve sinkhole detection via packet delivery rate, packet drop, energy exchange, and throughput in a wireless sensor network. Lastly, a simulation using NS2 is performed to evaluate the effectiveness and accuracy by showing initial results and the expected outcome of the proposed model, and the executed simulation result shows that the proposed model could work to some extent.