# Swarm Intelligence Techniques for Mobile Wireless Charging
###### Abstract
This technique has been applied in various networks, including the wireless rechargeable sensor networks (WRSNs), characterized by the presence of multiple sensor-nodes (stationary or mobile nodes) and a few high-energy mobile nodes, called MCs, that exploit the WPT technologies to replenish the sensor-nodes based on some charging request priorities. This technology has been found to be more controllable and stable compared to other sources of sensor-energy replenishments [9]. Research efforts on wireless energy transmission reveals that efficient WPT has been achieved by using technologies, such as magnetic resonant coupling, inductive coupling, and EM radiation [1; 10; 19; 20]. To show the pervasiveness of WPT technologies, it has been demonstrated by a considerable number of companies and several international organizations, capitalizing on WPT ideas, to maximize the use of these technologies. Examples of companies applying WPT ideas include: WiTricity, Ossia, Energous, Proxi, and Powercast [21], as well as the Wireless Power Consortium (WPC), the Power Matters Alliance (PMA), and the Alliance for Wireless Power (A4 WP) [22]. These technologies and industries lead the way towards an emerging technology and a new paradigm of energy replenishment for WSNs, namely, the Wireless Rechargeable Sensor Networks (WRSNs), which demonstrate the practical approaches for efficient energy replenishment for sensors via wireless charging techniques. WRSNs are an emerging technology consisting of sensors and wireless mobile chargers (MCs). The technology appears to provide an advanced solution to overcome the challenges of battery limitations and is widely applied in many applications, such as in environmental monitoring/surveillance, fire and explosion monitoring, and the medical field [23]. Inspired by the recent breakthroughs in WPT technologies, this study aims to investigate the challenges of efficient wireless charging (WPT) using swarm intelligence-based approaches. Our contributions to this work can be further highlighted as follows. To improve the MC wireless charging efficiency, we identify some key tradeoffs that are critical to efficient wireless charging, which include: (1) determining the efficient coordination processes and charging strategies for the MCs; (2) determining the optimal amount of energy for the MCs, given the overall available network energy; and (3) determining the best algorithm for node partitioning that minimizes the transmission range of the MCs. To address each of the above tradeoffs, we first formulate the wireless charging problem and show that it is computationally hard. To solve the complex problem of scheduling multiple MCs in LS-WSNs, we propose a node-partition algorithm, based on cluster centroids, that adaptively partitions the entire network into several clusters and regions. We also consider some strategies for efficient wireless charging in WRSNs that efficiently partition the network regions into subregions and assign each subregion to MCs based on some charging-request priorities. We then provide, via simulations, some energy-efficient charging and coordination protocols that operate under three assumptions of the network knowledge level: global network knowledge (GNK), local network knowledge (LNK), and no network knowledge (NNK). These factors need to be considered when devising the efficient coordination and charging strategies of MCs. Our detailed simulation experiments, using three representative SI-based algorithms, show the efficacy of the proposed techniques in terms of the various evaluation metrics. While we appreciate the interesting research efforts and contributions to the wireless charging problem, especially the scheduling operations of a single MC, we note that these approaches are limited and are currently not feasible for LS-WSN scenarios. In contrast to the most notable approaches, we propose a distributed method of wireless charging which utilizes the local and global network knowledge.