Alaa Sabry Awwad

Department of History

College of Basic Education / Haditha - University of Anbar

A wireless sensor network comprises numerous sensors extended over a geographical region. These are employed in large-scale disciplines such as queue tracking, armed forces implementations, ecological implementations, and others. This technique remains a trial to concentrate upon the attack identification alongside the employment of deep learning and optimization schemes. Initially, a system paradigm will be launched and the nodes will be deployed haphazardly centered upon the network’s dimension. Through an energy-related timer, comparison sets are created. The transmission probability will be later analyzed by considering the spatial comparison, the link’s quality amidst cluster head (CH) and cluster member (CM) node, and the node’s residual energy of the network. The trust administration will be used by the CH choosing. When the node comprises the parameters for trust coverage, the node will be selected as CH. When this circumstance remains unsatisfied, this will be selected as CM. Cluster paths’ (CP) optimum range for effectual data transfer will be performed through optimum CP calculation by employing multi-dimensional trust criteria alongside Dempster-Shaft theory wherein the distance and residual energy remain the chief limitations. When the optimum and trusted path remains selected, the attack’s classification and identification will be performed by Cascaded Hermite Laguerre Neural Network (CHLNNet). This proffered methodology will be correlated with 3 advanced methodologies concerning diverse criteria. Consequently, the proffered CHLNNet methodology attains 31.4% of routing overhead, 23% of end-to-end delay, 78.6% of energy efficiency, 94.8% of throughput, 28.2% of average latency, and 91.4% of malicious detection rate.