The Industrial Internet of Things (IIoT) involves the use of large numbers of sensing nodes, which should meet the requirements for industrial use, such as real-time performance monitoring and high reliability and stability. However, owing to single-station allocation, inappropriate mobile charger (MC) allocation and unreasonable route planning, conventional methods may lead to local blockages, incomplete charging coverage, and high energy consumption because of additional movement. Hence, we propose a multistation-based collaborative charging strategy, termed MCCS, to overcome these problems. In MCCS, the energy sources are static charging stations. Furthermore, MCs that consist of primary and senior chargers act as the transmission media. Specifically, the senior chargers, which are charged by the stations, transmit energy to the primary chargers, which then transmit energy to the sensor nodes. The following steps are involved in MCCS. To begin with, MCCS divides the sensor nodes into various categories based on a self-organizing feature mapping neural network in order to ensure appropriate primary charger allocation. Next, a genetic algorithm is used to generate the optimal routes for the MCs. Finally, MCCS allocates the senior chargers and sets up the charging stations. Simulations were conducted to evaluate MCCS, which exhibited better performance in terms of efficiency, energy consumed in movement, and charging energy loss as compared with existing strategies.
