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Trust and Opportunity Based Routing Framework in Wireless Sensor Network Using Hybrid Optimization Algorithm - 2020

Research Area:  Wireless Sensor Networks

Abstract:

Opportunistic routing (OR) is the area of research in the trending scenario as the traditional methods for ensuring secure routing in the Wireless Sensor Networks (WSNs) suffer from the security and reliability issues. Though the existing multi-hop routing protocols are cheap and require less-intensive deployment, security is a major concern. The paper proposes the OR in WSNs using the hybrid optimization algorithm named, Monarch-Cat Swarm Optimization (M-CSO) which is the integration of Monarch Butterfly Optimization in Cat Swarm Optimization. The framework operates on two crucial aspects: one is to select the secure nodes and the other is to choose opportunistic nodes among selected secure nodes. The selection of secure nodes through the tolerant constant is based on the parameters of trust, connectivity, and QoS. The first two parameters are direct whereas, for QoS, Link Life Time and delay are considered to define it. Secondly, opportunistic nodes are optimally chosen through proposed M-CSO, based on the fitness parameters trust, distance, delay, and connectivity. The effectiveness of M-CSO based routing framework is evaluated using the performance metrics with 50 and 100 nodes in the presence of the attacks DoS, and Blackhole. The maximal detection rate, maximal throughput, minimum delay and minimum distance of the proposed M-CSO are 74.95, 77.7, 6.7 s and 102.67, respectively which is far better than competing methods Trust aware routing framework, Simple Opportunistic Adaptive Routing and Ant Colony Optimization for secured routing protocol. Moreover, M-CSO shows better performance as compared to the existing trust and opportunity based routing and optimization schemes.

Author(s) Name:  Pritesh A. Patil,Rajkumar S. Deshpande,Pradeep B. Mane

Journal name:  Wireless Personal Communications

Conferrence name:  

Publisher name:  Springer

DOI:  10.1007/s11277-020-07579-6

Volume Information:  volume 115, pages 415–437 (2020)