Joint utility optimization for wireless sensor networks with energy harvesting and cooperation In wireless sensor networks (WSNs), the limited battery capacity restricts the lifetime of the sensor nodes and thus degrades the system performance. The energy harvesting and cooperation techniques are promising solutions to prolonging the battery life, by collecting energy from ambient environment and exchanging energy among sensor nodes. This paper studies the joint utility maximization problem for WSNs in consideration of energy harvesting and cooperation. We first derive an upper bound on the Lyapunov drift for the network stability, and then formulate the optimization as a stochastic optimization problem. Furthermore, we propose an energy harvesting and energy transfer, data transmission, power control, routing and scheduling (EDPR) online algorithm by combining Lyapunov optimization technique with driftpluspenalty method and perturbation technique. It contributes to optimal utility in a distributed manner along with a balanced tradeoff between network utility and queue backlog, with no need for any statistical information about dynamic systems and no concern of curse of dimensionality under large queue backlog. Simulation results also show the practicality of the proposed algorithm in real implementation since data transmission has a linear relationship with battery life.

Achieving NearOptimal Fairness in Energy Harvesting Wireless Sensor Networks This paper tackles a singlehop wireless sensor network (WSN) where a fusion center (FC) receives data from M energy harvesting (EH) wireless sensors. Each sensor stores its harvested energy losslessly in an infinite capacity battery. In each time slot, K sensors are selected for data transmission over K mutual orthogonal channels. FC knows neither battery states of sensors, nor statistics of EH processes at sensors. FC just knows outcomes of previous transmission attempts. A sensor transmits data if it is scheduled and has enough energy in its battery. FC aims to maximize fairness among EH sensor nodes over the time horizon under the proportionate progress (Pfairness) by using Jainxe2x80x99s Fairness Index. Uniforming Random Ordered Policy (UROP) and Myopic Policy (MP) are suggested as solutions to the fairness problem. It is shown both analytically and numerically that under infinite capacity battery assumption, UROP achieves asymptotically optimal performance in fairness under quite general EH process over infinite time horizon. Even with finite capacity batteries, UROP can achieve nearly optimality in fairness over finite time horizons whereas the MP is generally suboptimal.