Self-Organized Cognitive Sensor Networks: Distributed Channel Assignment for Pervasive Sensing

Abstract

We study the channel assignment strategy in multichannel wireless sensor networks (WSNs) where macrocells and sensor nodes are overlaid. The WSNs dynamically access the licensed spectrum owned by the macrocells to provide pervasive sensing services. We formulate the channel assignment problem as a potential game which has at least one pure strategy Nash equilibrium (NE). To achieve the NE, we propose a stochastic learning-based algorithm which does not require the information of other players' actions and the time-varying channel. Cluster heads as players in the game act as self-organized learning automata and adjust assignment strategies based on their own action-reward history. The convergence property of the proposed algorithm toward pure strategy NE points is shown theoretically and verified numerically. Simulation results demonstrate that the learning algorithm yields a 26% sensor node capacity improvement as compared to the random selection, and incurs less than 10% capacity loss compared to the exhaustive search.

Selective Sensing and Access Strategy to Maximize Throughput in Cognitive Radio Sensor Network

“…Normally, the available spectrum opportunities are found in time [4,7,8,[10][11][12]15] space [14] and frequency domains [18]. In [10][11][12], SUs perform spectrum sensing to find access opportunities in time domain.…”

“…Cognitive radio (CR), which has been introduced as a way to improve the efficiency of spectrum utilization, becomes a research focus in recent years [1–27]. Combined with the advantages of CR and WSNs, cognitive radio sensor network (CRSN) has been studied [1–4, 6–8, 15]. In studies [1, 2], CR has been used in smart grid communication networks.…”

“…In [4], channel sensing and switching were used for SU to access a set of PU channels. In [7, 8], two spectrum sensing methods were studied in CRSN. In [15], both single-user and collaborative spectrum sensing schemes were proposed in cognitive sensor networks, while quantised sensing observation is used.…”