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

Abstract

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 work by using a computationally efficient numerical approach. The proposed optimization approach minimizes the probability of 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 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 for defining 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-ofthe-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.

Robust and Low‐Complexity Cooperative Spectrum Sensing via Low‐Rank Matrix Recovery in Cognitive Vehicular Networks

“…Many envisioned applications in vehicular networks that are related to safety would need high reliable connectivity. Therefore, some preliminary work in [15][16][17][18][19][20] has focused on increasing the robustness of cooperative spectrum sensing in cognitive vehicular networks. However, it is hard to implement these methods in practical CVNs environment due to their complexity, especially under heavy traffic conditions.…”

“…In this paper, the spatial correlation between neighboring vehicles is exploited by message passing. In [16], an entropy-based voting algorithm was proposed to decide whether a channel is available with its one-hop neighbor vehicles. In [10], a weighting function based on the distance between the vehicle and its neighbor is established for the sake of evaluating neighbors' credibility with reference to the aggregated spectrum sensing decisions.…”

“…The aforementioned methods [15][16][17][18][19] have played a vital role in fostering new strategies for robust spectrum sensing in CVNs. However, many of the methods in these papers are trust-based, which utilize historical information on malicious vehicles' behavior.…”

Analysis of Spectrum Sensing over Imperfect Channel conditions in Cognitive Vehicular Networks

“…Spectrum sensing in dynamic vehicular environments have been researched in some preliminary works [4], [9], [10], [11], [12]. The authors in [4] proposed a novel adaptive sensing coordinated mechanism, in which the central node merely assist and coordinate the SVUs to better acquire the availability of spectrum holes instead of completely controlling the sensing and access.…”

“…The authors in [10] presented an asynchronous collaborative sensing framework in which the tagged vehicle collects energy measurements labeled with time and location information from collaborative SVUs and assigns weights based on their storing time and location. The authors in [11] proposed a distributed collaborative sensing scheme based on adaptive decision threshold for sensing and voting scheme for connected vehicles. An integrated overview of spectrum sensing in cognitive vehicular environments can be discovered in [12].…”