SCAS: Sensing Channel ASsignment for Spectrum Sensing Using Dedicated Wireless Sensor Networks

Spectrum sensing is essential for the success of the cognitive radio networks. In traditional spectrum sensing schemes, Secondary Users (SUs) are responsible for the spectrum sensing which could be very time and resource consuming. It leads to a great deal of inefficiency in spectrum usage and introduces many practical challenges. To tackle these challenges and leverage the spectrum opportunity more efficiently, we propose a new system that provides a spectrum sensing service for SUs using dedicated wireless spectrum sensor networks (WSSNs). In this paper we focus on the sensing channel assignment problem in WSSNs and formulate the problem as a Sensing Effectiveness Maximization Problem (SEMP). We prove that SEMP is NP-complete under the ideal case, and show that the more challenges arises in real environments. To address the issues, we systematically study the design tradeoff and critical factors when maximizing the sensing effectiveness. Based on these study results we propose a Sensing Channel Assignment algorithm (SCAS). We conduct test-bed empirical investigations as well as comprehensive simulations. Performance evaluation results show that for both the scenarios of given deployments and manual deployments, SCAS is able to sense more channels to improve the sensing effectiveness. The improvement is up to 300% and the average improvement is 150% compared with other simple alternatives.