A Voting-Based Distributed Cooperative Spectrum Sensing Strategy for Connected Vehicles.

In this paper, we propose a novel distributed cooperative sensing algorithm for connected vehicles. The adaptive energy detection threshold, which is used to decide whether the channel is busy, is optimized in this paper by using a computationally efficient numerical approach. The proposed optimization approach minimizes the probability of an incorrect detection as a function of both the probability of false alarm and the probability of missed detection. By considering both probabilities when computing the probability of incorrect detection, to the best of the authors' knowledge, the proposed approach is novel. During the energy detection process, large-scale fading, multipath fading, Doppler effect, and transmission errors affecting the control messaging process are accounted for in order to provide a practical solution for connected vehicles. Once the available channels have been detected, the vehicles share this information using broadcast control messages. Each vehicle evaluates the available channels by voting on the information received from one-hop neighbors, where the credibility of each neighbor is weighted during the voting process. An interdisciplinary approach referred to as entropy-based weighting is used to define the neighbor, as well as the vehicle's own credibility. The voting mechanism is switched between the proposed voting mechanism and the traditional voting approach obtained from the current state of the art in order to maintain a balance between the computational cost/latency and robust spectrum sensing. Experimental results show that by using the proposed distributed cooperative spectrum sensing mechanism, spectrum detection error converges to zero. [ABSTRACT FROM AUTHOR]