A Scalable Approach to Counter Reliability and Load Balancing in Mobile Wireless Sensor Networks

M-WSN, or Mobile Wireless Sensor Networks, are comprised of even smaller nodes and are run by even smaller batteries. Real-world factors like lint, corrosive substances, harsh weather, etc., can all cause a sensor node to fail. In this case, a trustworthy sensor node that can distribute the load among itself is necessary. Saving energy often wasted on crashes, resending information, and validating information is made possible by establishing reliable communication and load balancing between sensor nodes. This study aims to provide a novel framework that improves WSN capabilities by employing a method never previously utilized in this setting to tackle the issues of reliability and load balancing. For the purpose of load balancing, a novel hybridization of Genetic Algorithm-Particle Swarm Optimization (GA-PSO) is used along with the concept of Artificial Neural Network (ANN) as a deep learning. The proposed framework also uses some characteristics of Ad hoc network, but with fresh terminology to ensure that the packet delivered the sink in a timely fashion and achieves highest battery and network life together with minimized transmission delay. The proposed framework is assessed using delay, energy consumption and throughput as a Quality of Service (QoS) parameters. Here, combination of different bio-inspired techniques with routing approaches are used to achieve better performance of the simulator. The proposed scenario helps to provide better transmission lines together with the transmission node’s range capacity and decreases the rate of energy consumption.