Research On Placement And Scheduling Of Chargers In Wireless Rechargeable Sensor Networks

In traditional wireless sensor networks (WSNs), sensor nodes get power from their equipped batteries, however, the limited energy stored shortens the nodes’lifetime and make the working efficiency seriously affected. With Wirelss Power Transmission’s (WPT) rapid development and becoming more maturer, WSNs began to adopt the wire-less charging to power the nodes which promote the birth and development of wireless rechargeable sensor networks (WRSNs). These newly-developed sensor networks are widely used because of their longer lifecycle and the convenience of charging. But unfortunately, the WPT comes with high intensity of electromagnetic radiation (EMR) and enormous waste of energy which makes this technology difficult to promote in real life.Research of this paper focus on the placement of static chargers and the schedul-ing of mobile chargers to optimize charging utility and enhance overall network per-formance. For static chargers’ placement, we take the led in triggering the EMR issue into consideration when making plans, which makes this work have a strong theoreti-cal and practical significance. In the design of mobile charger scheduling schemes, we propose a charging and scheduling schemes for stochastic events capture in WRSNs based on previous work but wipe off some unreasonable assumptions, which has a guiding significance to real application.Totally speaking, we make following contributions in this work. First of all, a comprehensive investigation on the topic of static charger placement, mobile charger scheduling and safe charging problem is conducted. Secondly, on the basis of ful-ly research, for the first time we give a scheme PESA, a wireless charger Placement scheme that guarantees EMR SAfety for every location on the plane, in which we aim-s to find locations to place the chargers so that the charging utility of all devices is maximized and there is no location where the aggregate EMR exceeds a given safety threshold. Thirdly, in PESA, we discretize the whole charging area and formulate the problem into the Multidimensional 0/1 Knapsack Problem(MDK). Then we propose a fast approximation algorithm to the MDK problem, what’s more, we optimize our scheme to improve speed by double partitioning the area. We prove that the output of our algorithm is better than (1-∈) of the optimal solution to PESA with a smaller EMR threshold and a larger EMR coverage radius. We conducted both simulations and field experiments to evaluate the performance of our scheme. Our experimental results show that in terms of charging utility, our algorithm outperforms the prior art by up to 45.7%. Fourthly, we give a charging and scheduling scheme CHASE, in which our goal is to jointly design the charging scheme and sensors schedules to maximize the quality of monitoring. Finally, we give a approximation algorithm to solve the problem and conduct simulations to validate our solutions. The result show that our scheme can achieve 37% lower energy consumption and 53% higher energy efficiency than basic algorithm.