Industrial Internet of Things inevitably leads to the implementation of highly data-intensive devices, where the associated sensing nodes accelerate the energy consumption rate, which ultimately produces an energy bottleneck. To address this issue, this paper proposes a dynamic collaborative charging algorithm that acts on both the mobile nodes and the static nodes in a sensing node network. The proposed scheme is to design a collaborative group of charging robots that can rendezvous with the sensing nodes. The group includes aerial charging vehicles – able to charge the underpowered mobile nodes, and terrestrial charging vehicles, which charge their targeted static nodes. The aim of this study is to optimize the charging effect and the energy cost in the rendezvous process. This approach consists of two sub-algorithms: a charging algorithm for mobile nodes and a charging algorithm for static nodes. The charging algorithm for mobile nodes is designed so that each underpowered mobile node can be charged by a dedicated aerial charging vehicle. For this purpose, a deep learning model is trained to divide the underpowered mobile nodes into appropriate clusters, each of which is equipped with a mobile base station. The rendezvous process is then constructed as a mixed continuous/discrete optimization problem, which is solved by using firefly algorithm. In addition, the charging algorithm for static nodes ensures that the terrestrial charging vehicles traverse their routes, charging static nodes as they proceed. This traversing process was formulated as a multi-objective optimization problem, solved by using genetic algorithm. Through various experiments and case studies, the results have demonstrated both the feasibility and the efficiency of the proposed algorithms.
