Enhancing Network Lifespan Longevity Through Optimal Routing in Wireless Sensor Networks

In the role of addressing the critical challenge as energy imbalance in Sensory Networks, this research study manages the reduction in overall network lifetime caused by uneven energy consumption of nodal points. The existing energy aware approaches that struggle with nodal point replacement difficulties and overlook key network parameters, the proposed system Bat Algorithm integrated with Particle Swarm Optimization (BA-PSO) gives a solution and introduces a density-aware optimal clustering method. This approach ensures reliable data-packet transmission by integrating power balancing and Optimized data-packet Routing for Secured Data Transmission through cluster based network structure. The method linearly enhances the overall lifetime of deployed Sensory Networks by employing a balanced energy conservation technique. Unlike traditional Ant Colony Optimization, the proposed system selects the optimal cluster head to extend the network's lifespan. The work introduces various strategies for optimal edge disjoint routing, utilizing Bat Algorithm integrated with Particle Swarm Optimization to select the best routes. In the Quality of Service parameters, including data reliability, network-processing capacity, network bandwidth, and Power Conservation are considered for optimal selection of routing in Sensor network. By grouping sensor data based on similarity values and grouping them, the proposed system achieves optimal data transmission while efficiently managing power consumption.