P ro of 1 Combining Solar Energy Harvesting with 2 Wireless Charging for Hybrid Wireless 3 Sensor Networks

5 Abstract—The application Q1 of wireless charging technology in traditional battery-powered wireless sensor networks (WSNs) grows 6 rapidly recently. Although previous studies indicate that the technology can deliver energy reliably, it still faces regulatory mandate to 7 provide high power density without incurring health risks. In particular, in clustered WSNs there exists a mismatch between the high 8 energy demands from cluster heads and the relatively low energy supplies from wireless chargers. Fortunately, solar energy harvesting 9 can provide high power density without health risks. However, its reliability is subject to weather dynamics. In this paper, we propose a 10 hybrid framework that combines the two technologies cluster heads are equipped with solar panels to scavenge solar energy and the 11 rest of nodes are powered by wireless charging. We divide the network into three hierarchical levels. On the first level, we study a 12 discrete placement problem of how to deploy solar-powered cluster heads that can minimize overall cost and propose a distributed 13 1:61ð1þ Þ-approximation algorithm for the placement. Then, we extend the discrete problem into continuous space and develop an 14 iterative algorithm based on the Weiszfeld algorithm. On the second level, we establish an energy balance in the network and explore 15 how to maintain such balance for wireless-powered nodes when sunlight is unavailable. We also propose a distributed cluster head 16 re-selection algorithm. On the third level, we first consider the tour planning problem by combining wireless charging with mobile data 17 gathering in a joint tour. We then propose a polynomial-time scheduling algorithm to find appropriate hitting points on sensors’ 18 transmission boundaries for data gathering. For wireless charging, we give the mobile chargers more flexibility by allowing partial 19 recharge when energy demands are high. The problem turns out to be a Linear Program. By exploiting its particular structure, we 20 propose an efficient algorithm that can achieve near-optimal solutions. Our extensive simulation results demonstrate that the hybrid 21 framework can reduce battery depletion by 20 percent and save vehicles’ moving cost by 25 percent compared to previous works. By 22 allowing partial recharge, battery depletion can be further reduced at a slightly increased cost. The results also suggest that we can 23 reduce the number of high-cost mobile chargers by deploying more low-cost solar-powered sensors.