Information theoretical optimization gains in energy adaptive data gathering and relaying in cognitive radio sensor networks

Abstract

Cognitive radio (CR) technology helps mitigate wireless resource scarcity problem by dynamically changing frequency spectrum, power and modulation type. Opportunistic spectrum access increases the network capability and quality. Recently, CR applied to wireless sensor networks (WSNs) generated the paradigm of cognitive radio sensor networks (CRSNs) overcoming the challenges posed by event-driven traffic demands of WSNs. To realize advantages of CRSN, spectrum and power allocation, and routing must be jointly considered to maximize the information capacity, resource utilization and the lifetime. In this paper, power and rate adaptation problem is analyzed for a multi-hop CRSN in an information theoretical (IT) capacity maximization framework combined with energy adaptive (EA) mechanisms and utilization of sensor data information correlations (ICs). CRSN characteristics, i.e., fast data aggregation, bursty traffic and node failures, are considered. The capacity optimization problem is defined analytically and practical local schemes are presented showing the superiority of objective functions utilizing ICs and EA mechanisms in terms of the resulting maximum information rate at sink, i.e., R-max, lifetime, and energy utilization. Furthermore, dependence of performance on total bandwidth and various relay energy distributions is explored observing the logarithmic dependence of R-max on total bandwidth.