Evolutionary Computing Assisted Wireless Sensor Network Mining for QoS-Centric and Energy-efficient Routing Protocol

The exponential rise in wireless communication demands and allied applications have revitalized academia-industries to develop more efficient routing protocols. Wireless Sensor Network (WSN) being battery operated network, it often undergoes node death-causing pre-mature link outage, data drop and retransmission causing delay and energy exhaustion. Furthermore, the presence of a malicious node to impacts network performance adversely. In this paper, a highly robust and efficient Evolutionary computing assisted WSN routing protocol is developed for QoS and energy-efficiency. Our proposed routing protocol encompasses two key functions Network Condition Aware Node Profiling and Malicious Node Detection (NCAMND) exploits or mines the dynamic node/network parameters to identify malicious node, and Evolutionary Computing assisted Dual-Disjoint Forwarding Path (EC-DDFP) model learns over node/network connectivity and availability information to obtain a dual-disjoint path with no-shared components to ensure QoS centric and energy-efficient routing. Simulation results affirm that the proposed routing protocol achieves higher throughput, low energy consumption, and low delay that confirm its suitability for real-time WSN systems.

Evolutionary Computing Assisted Wireless Sensor Network Mining for QoS-Centric and Energy-efficient Routing Protocol Keywords:

Wireless Sensor Network Network Mining Evolutionary Computing Network Optimization Malicious Node Detection Link , connectivity , availability Dynamic Routing