# On-Demand Charging Management Model and Its Optimization for Wireless Renewable Sensor Networks
###### Abstract
Reference [25] observed that PSO is quick at locating a promising region, but cannot refine the solution. Exploration refers to the characteristics of particle swarm optimization that allow it to describe favorable regions in the search space, whereas exploitation refers to the ability of the PSO algorithm to enhance solutions within these favorable regions. Extensive studies show that PSO's ability to find a global optimum mostly depends on exploration and exploitation tendencies [26; 27; 28]. Exploration is beneficial for a multimodal search but compromises convergence speed in the multimodal search, while exploitation is more beneficial for a unimodal search. The contributions of [26; 27; 28; 29] demonstrated that these properties could be adjusted by carefully choosing the value of inertia weight (\(\omega\)) in the particle swarm optimization algorithm. The standard PSO is the version of the PSO that is used today. Techniques that have been successfully implemented for modifying the algorithm include enhanced strategies [30], hybrid strategies [31; 32; 33] dynamic neighborhood topology [34; 35; 36] and time-varying parameters [37; 38; 39; 40], etc. Our paper is the first that aims to solve this problem by investigating the mobile charging request strategy called the energy renewable management system, based on particle swarm optimization (ERMS-PSO), and we examine four metrics together in parallel, which are vacation time (of WMCD) efficiency, the total distance traveled by the WMCD, the total energy consumed by the WMCD by traveling through the target nodes and the total number of cycles. Unlike [17] during each cycle, where they considered the maximum energy (Emax) and minimum energy (Emin) in the nodes and optimized the node's energy consumption and vacation time ratio, for our strategy, we took into account the total power consumption, total distance traveled and wireless mobile charging device's vacation time ratio as performance metrics and aimed to investigate the mobile charging request strategy called the energy renewable management system, which is based on particle swarm optimization (ERMS-PSO). In this new strategy, we introduced two sets of energies, called emin (our minimum energy) and ethresh (our threshold energy), where, when the first node reaches our emin, it will inform the base station (BS), also known as the sink node, which is responsible for collecting all information from sensor nodes and sending it to the end-user. The base station will calculate all nodes' energy that fall under ethresh and send a WMCD to change them in one cycle. Our emin must be greater than the Emin (general minimum energy in the node) and our ethresh must be greater than emin but less than the general Emax. The following are the main contributions and innovations of this paper:  
1. First, we investigate the operation of a sensor network and propose an on-demand energy-saving strategy called the energy renewable management system (ERMS) for keeping the network operational for a long time by examining the wireless mobile charging device vacation time efficiency, the total distance traveled by the WMCD, the total energy consumed and the total number of cycles. 2. Secondly, based on the presented strategy, a heuristic algorithm called particle swarm optimization (PSO) is developed and successfully implemented for solving the energy replenishing problem of the wireless sensor network and developing a suitable fitness function for achieving the said objectives. 3. This work aims to solve the problem of wireless energy transfer by investigating the mobile charging request strategy, where two sets of variables are introduced--emin, ethresh--to help manage the energy in the node and levels of charging of WMCD. We finally compare the results from the proposed algorithm with other notable algorithms. We propose a charging scheme that can improve the current designs while decreasing the effect of their limits. The remaining part of this work is arranged as follows.