Residual energy aware channel assignment in cognitive radio sensor networks

We investigate the channel assignment problem in a cluster-based multi-channel cognitive radio sensor network in this paper. Due to the inherent power and resource constraints of sensor networks, energy efficiency is the primary concern for network design. An R-coefficient is developed to estimate the predicted residual energy using sensor information (current residual energy and expected energy consumption) and channel conditions (primary user behavior). We examine three channel assignment approaches: Random pairing, Greedy channel search and Optimization-based channel assignment. The last two exploit R-coefficient to obtain a residual energy aware channel assignment solution. Simulation results show that R-coefficient-based approaches lead to better performance in terms of energy consumption and residual energy balance. Optimization-based channel assignment outperforms the other two approaches with respect to network lifetime.