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.