# Swarm Intelligence Techniques for Mobile Wireless Charging
## 2 Related Works and Comparison
### Swarm Intelligence (SI)-Based Techniques  
SI is a novel field of artificial intelligence, concerned with the study of biological species with self-organized behaviors that can be witnessed in social living beings, including ants, fishes, birds, termites, etc. The Collins Dictionary defines SI as \"_an artificial-intelligence approach to problem-solving utilizing algorithms based on the self-organized collective behaviors of social insects_\". SI algorithms are described as heuristics that target and solve complex problems by imitating the swarm behaviors observed in biological systems [24]. Accordingly, systems relying on SI exhibit many useful characteristics, including self-organization and adaptivity. In [24], the authors presented an approach and the system architecture for the efficient parallelization of the swarm algorithms. The proposed method exploited the space partitioning approach, which assigned the various network regions to different servers. The goal was to show the efficacy of the proposed approach in terms of the high degree of scalability in a wide set of scenarios. The performance of the proposed approach was evaluated using the ant-based spatial clustering and sorting algorithm. The primary concepts of SI protocols are its self-organization, where the protocols are designed to provide solutions to many complex problems [25]. The past years have witnessed the development of several routing protocols for WSNs, based on the principles of SI, and the inspiration of the foraging behaviors of ant and bee colonies [26]. Thus, the authors [26] presented an extensive survey of SI-based routing protocols for WSNs. In [27], the authors presented BeeSwarm, an SI-based energy-efficient hierarchical routing protocol for WSNs. The approach consists of three phases, namely, the set-up phase, the route discovery phase, and the data transmission phase. The robustness of the system lies in the integration of the three phases, which invariably forms the major aspect of the protocol. The authors in [28] presented an SI-based centralized clustering solution for large WSNs and compared to other state-of-the-art algorithms using some evaluation parameters (the scalability, the energy consumption, and the data delivery rate). The result of this comparison showed that the SI-based centralized clustering approach outperformed the other algorithms in terms of the performance metrics. The authors in [29] presented a review of SI algorithms for feature selection, which provides solutions to various optimization problems. The survey gave insight to researchers on the possible design approach for a specific feature selection problem. In [30], authors studied the deployment optimization of an unmanned aerial vehicle (UAV) for the data collection platform of IoT devices and proposed a coding scheme based on the SI algorithm. The focus of this approach was to minimize the energy consumption of the UAV. A comprehensive review can be found in [31; 32; 33]. However, we note that none of the techniques in the literature have exploited SI-based approaches for energy provisioning to sensor-nodes. Our approach investigates the challenges of efficient mobile wireless charging in distributed LS-WSNs, using SI-based techniques.