Fair and Low Complexity Node Selection in Energy Harvesting Wireless Sensor Networks

The use of energy harvesting in wireless sensor networks is an emerging wireless communication technology with a wide range of applications. Maximizing the number of samples collected by the sensor nodes and transmitted to the sink is a key element in order to minimize uncertainties for those applications. This work considers energy harvesting sensor nodes that are transmitting to a nonenergy harvesting sink. Using a zero-forcing (ZF) receiver, the sink selects the largest possible set of transmitting sensor nodes to maximize the received quantity of information while the selected transmissions should satisfy a given quality of service defined by signal-to-noise ratio and certain fairness constraint. The maximization problem is formulated as an integer nonlinear program and it is proved to be NP-hard. Thus, two low complexity and efficient heuristic algorithms are proposed to solve this problem. Two other variants are also proposed in order to improve the system fairness. We demonstrate via simulations in a node selection context that the proposed algorithms which consider the energy state of the system better exploit the full system resources compared to state-of-the-art algorithms which only consider channel conditions. Interestingly, simulation results show that the performance of the proposed algorithms varies as a function of the energy availability. Hence, they are adapted to the energy harvesting context.