On-Demand Charging Management Model and Its Optimization for Wireless Renewable Sensor Networks

Abstract

Nowadays, wireless energy transfer (WET) is a new strategy that has the potential to essentially resolve energy and lifespan issues in a wireless sensor network (WSN). We investigate the process of a wireless energy transfer-based wireless sensor network via a wireless mobile charging device (WMCD) and develop a periodic charging scheme to keep the network operative. This paper aims to reduce the overall system energy consumption and total distance traveled, and increase the ratio of charging device vacation time. We propose an energy renewable management system based on particle swarm optimization (ERMS-PSO) to achieve energy savings based on an investigation of the total energy consumption. In this new strategy, we introduce two sets of energies called emin (minimum energy level) and ethresh (threshold energy level). When the first node reaches the emin, it will inform the base station, which will calculate all nodes that fall under ethresh and send a WMCD to charge them in one cycle. These settled energy levels help to manage when a sensor node needs to be charged before reaching the general minimum energy in the node and will help the network to operate for a long time without failing. In contrast to previous schemes in which the wireless mobile charging device visited and charged all nodes for each cycle, in our strategy, the charging device should visit only a few nodes that use more energy than others. Mathematical outcomes demonstrate that our proposed strategy can considerably reduce the total energy consumption and distance traveled by the charging device and increase its vacation time ratio while retaining performance, and ERMS-PSO is more practical for real-world networks because it can keep the network operational with less complexity than other schemes.

Multi-Objective Optimization with Mayfly Algorithm for Periodic Charging in Wireless Rechargeable Sensor Networks

“…As some nodes close to the base station could use a few times less energy than nodes farther away, it is unnecessary to charge each node in the cycle. This paper employs the strategy used and is well explained in our previous work [19]. Equation (1) shows how the energy consumption of node i have to be equal to the MC's energy supply, according to the energy conservation principle [1].…”

“…Our work goal is to resolve the WET by studying the charging request strategy. We use the same proposed energy levels from our previous work, which are e min , and e thresh [19] and are calculated using Equations ( 4) and (5). Normally maximum energy in the node is E max , while the minimum one is E min .…”

“…The implementation of our strategy lies in two basic parts. The optimization of the first part of P total is the first step, while the second one is to develop a cooperative scheme to shorten the MC's travel distance as explained in our previous work [19] where the flow balance limitation for each node i is shown in (Equation ( 8)).…”

“…Most existing studies [16][17][18] in wireless rechargeable sensor networks build a charging route for MC and deploy it to charge the sensor nodes by traveling along the obtained path. To keep MC's charging path as short as possible, the shortest Hamiltonian cycle is always chosen, and MC charges each sensor node in turn all along the Hamiltonian cycle [19,20]. The WSN has a BS that works as a sink node to collect information from different nodes and send it to the end-user.…”

“…Unlike our previous work [19], where we studied the node's energy consumption by considering total power consumption, total distance traveled, and the vacation time ratio of the mobile charger as performance metrics, in this paper, we introduce a new algorithm called mayfly algorithm (MA) [36]. MA is a new algorithm that has not been used before for energy harvesting in WRSN and is a type of optimization algorithm that can accomplish this objective.…”