# Swarm Intelligence Techniques for Mobile Wireless Charging
###### Abstract
The idea of network partitioning (i.e., splitting the network into smaller sizes to further reduce the transmission range) aims to minimize the data/charging request flooding problem often witnessed with multi-hop clustering, to minimize the travelling cost of the MCs (as a result of a high movement energy consumption), and to optimize the energy efficiency of the network. Besides, the proposed approach will curb the unnecessary energy transmission delays inherent in the traditional approach and it will also optimize the WPT efficiency of the MCs. In contrast to other approaches, our approach exploits multiple mobile agents, utilizing swarm intelligence-based techniques for energy provisioning to the sensor-nodes. We also investigate some key tradeoffs, which are critical to efficient wireless energy transmission and are seemingly overlooked by other studies. To ensure and optimize energy-efficient route planning and coordination/charging strategies, the paper proposes swarm intelligence-based techniques for the MCs. This is based on their advantages of having both local and global knowledge of the network environment through the way they conduct their search operations, by employing both exploration and exploitation strategies. These two key components of swarm intelligence algorithms are also known as intensification and diversification [11]. In exploitation, information is obtained locally from the existing problem to provide new solutions that can be better than the current solutions. Here, the process of obtaining information is local and, thus, the information itself is also local. The advantage of this technique is that it can lead to very significant convergence rates. However, its drawback is that it may be stuck in a local optimality. Conversely, in exploration, information is obtained globally because the space can be explored more efficiently to provide more diversified solutions than the current solutions. Thus, the search is essentially on a global scale. The advantage is that it is not likely to be stuck locally, and the global-wise optimality is easily accessible. However, its drawbacks are that it has slow convergence and much of the computational efforts are wasted, since many new solutions can be far from the global optimality [11]. This, therefore, necessitates a final balance for the algorithm to achieve a good performance. However, achieving such a balance is a critical challenge of several algorithms, which resort to optimization techniques. In problem-solving, a common practice is to find an optimal solution within a limited time-interval. One of the techniques that is being used to prolong the lifetime of a WSN is the energy harvesting (EH) technique [12; 13; 14; 15; 16; 17]. Although the EH technique can be used to extend the lifetime of the network and solve the node energy restriction problem to some extent, it still has many deficiencies, bordering on environmental limitations [1; 9]. Hence, its success in prolonging the network lifetime remains limited in its operation, as well as providing a performance bottleneck, owing to its environmental dependence. This is, perhaps, one of the major challenges limiting its large-scale deployment for LS-WSN applications. As such, an energy-constrained sensor-node has several limitations in its operational performance, including its computational capabilities, its sensing reliability, and its communication/transmission range. However, nodes can potentially choose to rely on using super batteries to prolong their operational lifetimes, but this will bring an increase in weight and cost implications as a result of higher power requirements [1]. WPT leverages on these shortcomings to present itself as an alternative source of energy replenishment to the energy-constrained sensor-nodes. The recent breakthrough in WPT technology reveals its high potential to address the fundamental energy requirements of a WSN. Thus, WPT technology is currently being used as a new approach to solve the sensor-node energy problem.