Wireless Sensor Networks (WSNs) have been widely deployed in hostile locations for environmental monitoring. Sensor placement and energy management are the two main factors that should be focused due to certain limitations in WSNs. The nodes in a sensor network might not stay charged when energy draining takes place; therefore, increasing the operational lifespan of the network is the primary purpose of energy management. Recently, major research interest in WSN has been focused with the essential aspect of localization. Several types of research have also taken place on the challenges of node localization of wireless sensor networks with the inclusion of range-free and range-based localization algorithms. In this work, the optimal positions of Sensor Nodes (SNs) are determined by proposing a novel Hybrid M-ACO – PSO (HMAP) algorithm. In the HMAP method, the improved PSO utilizes learning strategies for estimating the relay nodes' optimal positions. The M-ACO assures the data conveyance. A route discovers when it relates to the ideal route irrespective of the possibility of a system that includes the nodes with various transmission ranges, and the network lifetime improves. The proposed strategy is executed based on the energy, throughput, delivery ratio, overhead, and delay of the information packets.
Because wireless sensor networks (WSNs) have low-constrained batteries, optimizing the network lifetime is a primary challenge. Rechargeable batteries are a solution to prolong the lifetime of a sensor node instead of restricting their functionalities to save energy. Wireless energy transmitters have the added benefit of providing a charger for the batteries of the sensor nodes in the WSN. However, scheduling one or more charging vehicles efficiently to recharge multiple sensor nodes is challenging. In this context, this paper provides a solution to recharge the sensor nodes using charging vehicle scheduling in WSNs through a mixed linear programming approach. Initially, we identify a heuristic value of each sensor node based on their residual energy, distance from a charging vehicle, available data packets, and other metrics. Further, a set of nodes is recharged by identifying the best charging vehicle to prolong their lifetimes, as well as the lifetime of the network as a whole. We simulated the proposed approach using a Python simulator, tested using different performance metrics, and compared using the recently published works. We notice the superior performance of the proposed work under various metrics in time and query-driven WSNs.
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