Advanced generative adversarial network for optimizing layout of wireless sensor networks.

The best layout design related to the sensor node distribution represents one among the major research questions in Wireless Sensor Networks (WSNs). It has a direct impact on WSNs' cost, detection capabilities, and monitoring quality. The optimization of several conflicting objectives, including as load balancing, coverage, cost, lifetime, connection, and energy consumption of sensor nodes, is necessary for layout optimization. Layout optimization represents an NP-hard combinatorial issue. A number of meta-heuristic optimization strategies have been put out to address this issue in the past ten years. Nevertheless, these methods only addressed a subset of the objectives-combinations of energy consumption, count of sensor nodes, area coverage, and lifetime-or they offered computationally costly solutions. Therefore, this research paper presents a layout optimization problem using novel intelligent deep learning-based optimization methodology. Here, the major objective is to cover numerous objectives associated with optimal layouts of homogeneous WSNs that involves connectivity, coverage, energy consumption, lifetime, and the number of sensor nodes. The layout optimization problem is handled by the novel Advanced Generative Adversarial Network (AGAN), where the parameter tuning is performed by the nature inspired optimization algorithm called Piranha Foraging Optimization Algorithm (PFOA), with the consideration of deriving the objective function. Simulation findings revealed that the proposed novel AGAN-PFOA generated optimal Pareto front of non-dominated solutions having better hyper-volumes as well as spread of solutions than the state-of-the-art solutions. The proposed AGAN-PFOA for the WSN layout optimization problem in terms of PDR, coverage, energy consumption, lifetime, alive node count, delay, and routing overhead is 61.46%, 15.12%, 12.67%, 65.91%, 70.59%, 44.88%, and 68.86% better than the existing methods respectively.