PROJECT TITLE :

Combining Solar Energy Harvesting with Wireless Charging for Hybrid Wireless Sensor Networks - 2018

ABSTRACT:

The appliance of wireless charging technology in ancient battery-powered wireless sensor networks (WSNs) grows rapidly recently. Although previous studies indicate that the technology can deliver energy reliably, it still faces regulatory mandate to supply high power density while not incurring health risks. In particular, in clustered WSNs there exists a mismatch between the high energy demands from cluster heads and therefore the comparatively low energy provides from wireless chargers. Fortunately, solar energy harvesting can provide high power density while not health risks. However, its reliability is subject to weather dynamics. During this Project, we have a tendency to propose a hybrid framework that combines the 2 technologies - cluster heads are equipped with solar panels to scavenge solar energy and the rest of nodes are powered by wireless charging. We divide the network into 3 hierarchical levels. On the primary level, we tend to study a discrete placement problem of how to deploy solar-powered cluster heads that can minimize overall price and propose a distributed 1:61(one+?) 2 -approximation algorithm for the placement. Then, we tend to extend the discrete problem into continuous area and develop an iterative algorithm primarily based on the Weiszfeld algorithm. On the second level, we have a tendency to establish an energy balance within the network and explore how to take care of such balance for wireless-powered nodes when daylight is unavailable. We tend to additionally propose a distributed cluster head re-selection algorithm. On the third level, we initial consider the tour designing drawback by combining wireless charging with mobile knowledge gathering in a joint tour. We then propose a polynomial-time scheduling algorithm to find appropriate hitting points on sensors' transmission boundaries for knowledge gathering. For wireless charging, we offer the mobile chargers more flexibility by allowing partial recharge when energy demands are high. The problem turns out to be a Linear Program. By exploiting its specific structure, we propose an efficient algorithm which will achieve near-optimal solutions. Our extensive simulation results demonstrate that the hybrid framework will reduce battery depletion by twenty percent and save vehicles' moving value by 25 p.c compared to previous works. By permitting partial recharge, battery depletion will be further reduced at a slightly increased cost. The results conjointly recommend that we tend to will reduce the quantity of high-value mobile chargers by deploying more low-value solar-powered sensors.


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