Secondary users (SUs) can detect the states of primary users (PUs) and access the idle spectrum in an opportunistic way in cognitive radio networks (CRNs). With the spread of wireless communication devices, the mobility of both PUs and SUs is ubiquitous. To obtain more accurate spectrum sensing data, SUs must be located within the transmission range of PUs. With the unknown mobility, it is difficult to guarantee the efficiency of spectrum sensing. Focused on this issue, we propose a new scheme, called Particle Swarm Optimization (PSO) -based agent cooperative spectrum sensing (PSOA) in this paper. In this scheme, we deploy multiple mobile agents spreading over the network, to cooperate in spectrum sensing instead of SUs. All agents will move according to the latest global optimal agents of the corresponding target PUs with the fitness function calculated by modified PSO. With the optimal movement, the distribution of agents can guarantee that most PUs are within the detection coverage of PSOA. The evaluation results show that our scheme can save over 80% of sensing time and over 80% of energy consumption (affected by the agents' number and max velocity) than the active searching scheme. PSOA also guarantees the sensing probability of 80% and higher in our simulation.
The ever-increasing demand for vehicular traffic consumption in 5G makes the problem of spectrum scarcity in vehicular networks more serious. In order to solve this problem, cognitive radio (CR) technology has been used in vehicular networks, leading to cognitive vehicular networks (CVNs). However, different from traditional cognitive networks, the high mobility of CVNs makes cooperative spectrum sensing more challenging, and new attacks are frequently emerging. In this paper, we address a speed adjustment attack (SAA) on cooperative sensing in CVNs. In this attack, attackers can affect the spectrum sensing data of their neighbors by dynamically adjusting moving speed. Therefore, this attack can speed up the spread of error-sensing data across the entire network with changing neighbors. The simulation results show that the SAA can mislead the sensing result more quickly without detection. With Historical Data Information detection, it can significantly slow down the convergence time, potentially resulting in algorithmic divergence. INDEX TERMS Cognitive vehicular networks, spectrum sensing, speed adjustment attack.
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