TPEMLB: A novel two-phase energy minimized load balancing scheme for WSN data collection with successive convex approximation using mobile sink

Abstract

Wireless Sensor Networks (WSNs) an energy consumption is a significant problematic due to the limited power resources of sensor nodes. Mobile Sinks (MS) rise the system's flexibility and convenience of data collection. The important role of Load balancing techniques plays in extending network lifetime by uniformly spreading energy usage between sensor nodes. WSN Data Collection for Two-Phase Energy Minimized Load Balancing Scheme (TPEMLB) with Sequential Convex Approximation (SCA), the use of a movable sink, is discovered in this research. The MS collects data from nearby sensors called as sub-sinks along a path as it moves. Data collection throughput is enhanced through efficient data distribution among sub-sinks and a data collection schedule. Geometric programming and SCA methods make an algorithm with guaranteed convergence to meet the problematic task. This research employs a SCA method to discover the best locations for sensor nodes while keeping energy restrictions in mind. Using a series of convex optimization difficulties, this technique repeatedly estimates the optimal sensor node positions that minimize energy consumption while ensuring sufficient coverage of the target region. In the second stage, incorporate a mobile drain that traverses the network intelligently to collect data from sensor nodes. The technique considers sensor node energy levels, data collection rates, and distance from the receptacle to balance network traffic and decrease energy consumption. Subsequently, the proposed model TPEMLB has a higher deployment success rate and efficiency, a more extended network lifetime, lower energy consumption, and better load balancing, and is the preferred solution for the unique challenge.