# Artificial-Intelligence-Based Charger Deployment in Wireless Reshargeable Sensor Networks
###### Abstract  
To extend a network's lifetime, wireless rechargeable sensor networks are promising solutions. Chargers can be deployed to replenish energy for the sensors. However, deployment cost will increase when the number of chargers increases. Many metrics may affect the final policy for charger deployment, such as distance, the power requirement of the sensors and transmission radius, which makes the charger deployment problem very complex and difficult to solve. In this paper, we propose an efficient method for determining the field of interest (FoI) in which to find suitable candidate positions of chargers with lower computational costs. In addition, we designed four metaheuristic algorithms to address the local optima problem. Since we know that metaheuristic algorithms always require more computational costs for escaping local optima, we designed a new framework to reduce the searching space effectively. The simulation results show that the proposed method can achieve the best price-performance ratio. wireless rechargeable sensor network (WSN); wireless sensor network (WSN); power consumption; network planning; artificial intelligence; internet of things +
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problem [9; 10; 11; 12]. Some scholars think that all of the sensors should not work at the same time because it will cause power to be wasted unnecessarily. Scheduling sensor working time can make efficient use of the power of sensors. However, the lifetime of the WSN still depends on the upper bound of a battery's capacity 12; this is a fact which cannot be changed. In order to solve the power problem, the development of wireless charging technology has been proposed for wireless rechargeable sensor networks (WRSNs) [13; 14; 15; 16]. A WRSN is composed of charger, sensor and relay node; the charger is the main power source in such an environment. Each WRSN has its own suitable environment so that the user is able to choose specialized charging equipment according to his requirements. Of course, different charger equipment has different wireless charging methods. For example, wireless charging technology of mobile phone uses electromagnetic induction. WRSNs can be roughly divided into two types: those that operate in indoor environments, and those that operate in outdoor environments. The deployment strategy of the chargers needs to consider various impacts from those different environments. Most studies discuss outdoor scenarios; too few studies consider indoor scenarios. Actually, wireless sensor networks are very helpful indoors as well [17; 18]; for example, they can help factories control production for better quality and provide disaster prevention and relief. Life will become more convenient if sensors never run out of power. This is one of the main reasons why the wireless rechargeable sensor network is increasingly popular. In this paper, the deployment scenario is the indoor environment; it entails a complex problem because the charger deployment strategy will be affected by the sensors' positions, RF interference and the charger's efficiency, since those metrics have very frequent trade-off relationships. We have to balance each factor and then make sure the resultant deployment strategy can provide more charging efficiency. In previous work, we proposed the moveable-charger-based algorithm (MCBA) [19] to achieve a good charger deployment strategy. First, find the candidate nodes for charger deployment through mapping. Calculate the number of sensors that each candidate node can cover. Then, select candidate nodes in order from large to small by sorting until all sensors are covered. However, MCBA still has some defects, such as the MCBA being a greedy-based algorithm, so it has more of an opportunity to fall into the local optimum. The charger deployment has been proven to be an NP-hard problem.