Multicast-Energy-Cooperation-Assisted Time-Efficient Data Collection Scheduling in WSNs

In wireless sensor networks (WSNs), enabling nodes to harvest energy from the environment and facilitating energy sharing among nodes through wireless power transfer (WPT) technology, known as energy cooperation, can alleviate energy scarcity issues and effectively prolong the lifespan of WSNs. Although previous research has investigated various forms of energy cooperation, recent developments have underscored the potential of Multicast Energy Cooperation (M-EC) in supporting efficient multinode energy sharing. This approach leverages the broadcast nature of wireless signals, potentially offering greater efficiency compared to traditional point-to-point style Unicast Energy Cooperation (U-EC). In this article, We focus on the M-EC Assisted Data Collection paradigm for energy harvesting-WSNs (EH-WSNs) and investigate the underlying M-EC assisted data collection scheduling (MECADCS) problem, aiming to minimize the data collection completion time by jointly optimizing the schedule decisions for energy cooperation and data collection. We formulate the MECADCS problem as a mixed integer nonlinear programming (MINLP) problem and establish its NP-hardness. We also simplified the MECADCS problem into a mixed integer linear programming (MILP) formulation via piecewise linear approximation, yet solving it using existing mature MILP solvers is still computationally expensive. To promptly return good solutions, we propose an efficient greedy-based data transmission scheduling algorithm (GDTS),heuristically determines energy cooperation and data transmission schedules and achieves a computational speedup of $10^{4}$ times compared to exact solvers. Simulation results demonstrate that GDTS significantly reduces the data collection completion time compared to both algorithms without energy cooperation and those utilizing U-EC.