Abstract
In this paper, we optimize the performance of an energy harvesting wireless sensor network (EH-WSN) with linear physical topology. We present an optimization framework to minimize the summation of packet dropping probability subject to the data queue stability and quality of monitoring constraints of the nodes. The optimization is performed using joint optimal power allocation and routing. We formulate the problem as a stochastic optimization problem. Then, by transforming it into a standard time average optimization problem, we apply the Lyapunov drift plus penalty theorem to obtain an objective value which O(1/V) is within vicinity of the optimal value, where V > 0 is a tradeoff factor between the achievable V objective value and queue backlog size. The results confirm the effectiveness of the proposed Lyapunovbased algorithm in minimizing the sum packet dropping probability under the mentioned constraints.
Highlights
Energy harvesting (EH) is an innovative solution for the battery depletion problem in wireless sensor networks (WSNs)
A good use case of EH-WSNs is in the structural health monitoring, where the sensors are embedded inside the infrastructures and frequent recharging and replacement of nodes’ batteries are very hard if not impossible [3]
In this paper, we considered an energy harvesting wireless sensor network operating in TDMA fashion with linear physical topology
Summary
Energy harvesting (EH) is an innovative solution for the battery depletion problem in wireless sensor networks (WSNs). The implementation of a fully operational protocol stack for IP-enabled WSNs confirms the effectiveness of this new solution to extend the life-time of the WSNs [1]. A good use case of EH-WSNs is in the structural health monitoring, where the sensors are embedded inside the infrastructures and frequent recharging and replacement of nodes’ batteries are very hard if not impossible [3]. In conventional WSNs, researchers try to maximize the life-time of the network by optimizing different design aspects, such as joint routing and node placement [4] or sleep-awake scheduling [5]. EH-WSNs require new energy management methods that can cope with the random nature of energy availability
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