Partially Cooperative Scalable Spectrum Sensing in Cognitive Radio Networks Under SDF Attacks

Abstract

Massive Internet of Things (IoT) connectivity requires addressing spectrum congestion caused by spectrum scarcity in wireless communications. Over the past decade, cognitive radio has been proposed as a promising solution to utilize the licensed spectrum efficiently. Conventional spectrum sensing approaches are complex and require statistical information about the behavior of licensed users, which is impractical. To overcome this limitation, several reinforcement learning (RL)- based spectrum sensing approaches have been proposed that are also highly adaptable to the dynamics of IoT environments. Additionally, cooperative RL-based spectrum sensing approaches have been widely used because they are more accurate than noncooperative approaches. However, the advantage comes at the cost of scalability due to increased information sharing overhead. Furthermore, cooperative spectrum sensing (CSS) approaches suffer from attacks on the network, such as sensing data falsification (SDF) attacks, which deteriorate sensing accuracy dramatically. In this paper, we present a scalable, partially CSS algorithm that is highly resilient to SDF attacks. The novelty of the proposed algorithm lies in partial cooperation through coalition formation, which reduces sensing and information sharing overhead while improving sensing accuracy. Moreover, the algorithm learns to adapt the sensing participation percentage and selects the most rewarding channel for sensing to maximize rewards while minimizing energy consumption. The proposed algorithm outperforms state-of-the-art CSS algorithms in terms of sensing accuracy and overheard. Contrary to centralized CSS algorithms, the proposed algorithm’s performance is directly proportional to the number of devices; hence, it is suitable for massive connectivity.