|Haipeng Dai||Nanjing University & State Key Laboratory for Novel Software Technology, P.R. China|
|Yang Zhao||Nanjing University, P.R. China|
|Guihai Chen||Shanghai Jiao Tong University, P.R. China|
|Wanchun Dou||Nanjing University, P.R. China|
|Chen Tian||Nanjing University, P.R. China|
|Xiaobing Wu||University of Canterbury, New Zealand|
|Tian He||University of Minnesota, USA|
One critical issue for wireless power transfer is to avoid human health impairments caused by electromagnetic radiation (EMR) exposure. The existing studies mainly focus on scheduling wireless chargers so that (expected) EMR at any point in the area doesn't exceed a threshold Rt. Nevertheless, they overlook the EMR jitter that leads to exceeding of Rt even if the expected EMR is no more than Rt. This paper studies the fundamental problem of RObustly SafE charging for wireless power transfer (ROSE), that is, scheduling the power of chargers so that the charging utility for all rechargeable devices is maximized while the probability that EMR anywhere doesn't exceed Rt is no less than a given confidence. We first build our empirical probabilistic charging model and EMR model. Then, we present EMR approximation and area discretization techniques to formulate ROSE into a Second-Order Cone Program, and the first redundant second-order cone constraints reduction algorithm to reduce the computational cost, and therefore obtain a (1 −)-approximation centralized algorithm. Further, we propose a (1 −)-approximation fully distributed algorithm scalable with network size for ROSE. Simulations and field experiments show that our algorithms can outperform comparison algorithms by 480.19%.