Radiation Constrained Scheduling of Wireless Charging Tasks

• Published in: IEEE/ACM transactions on networking 2018-02, Vol.26 (1), p.314-327
• Main Authors: Dai, Haipeng, Ma, Huizhen, Liu, Alex X., Chen, Guihai
• Format: Article
• Language: English
• Subjects: Algorithms; approximation algorithm; Computer simulation; distributed algorithm; Electromagnetic radiation; Electronic equipment; Energy conservation; Greedy algorithms; Inductive charging; Linear programming; Optimization; Radiation; Regularization; Rocks; Safety; Scaling; Scheduling; Task scheduling; Wireless communication; Wireless power transfer; Wireless power transmission
• ISSN: 1063-6692; 1558-2566
• DOI: 10.1109/TNET.2017.2786463

This paper studies the problem of R adiation c O nstrained scheduling of wireless C harging tas K s (ROCK), that is, given wireless charging tasks with required charging energy and charging deadline for rechargeable devices, scheduling the power of wireless chargers to maximize the overall effective charging energy for all rechargeable devices, and further to minimize the total charging time, while guaranteeing electromagnetic radiation (EMR) safety, i.e., no point on the considered 2-D area has EMR intensity exceeding a given threshold. To address ROCK, we first present a centralized algorithm. We transform ROCK from nonlinear problem to linear problem by applying two approaches of area discretization and solution regularization, and then propose a linear programming-based greedy test algorithm to solve it. We also propose a distributed algorithm that is scalable with network size by presenting an area partition scheme and two approaches called area-scaling and EMR-scaling, and prove that it achieves effective charging energy no less than (1-\varepsilon) of that of the optimal solution, and charging time no more than that of the optimal solution. We conduct both simulation and field experiments to validate our theoretical findings. The results show that our algorithm achieves 94.9% of the optimal effective charging energy and requires 47.1% smaller charging time compared with the optimal one when {\varepsilon } \geq 0.2 , and outperforms the other algorithms by at least 350.1% in terms of charging time with even more effective charging energy.