A consensus-based cooperative Spectrum sensing technique for CR-VANET
| Autor: | Sahar Zargarzadeh, Neda Moghim, Behrouz Shahgholi Ghahfarokhi |
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| Rok vydání: | 2021 |
| Předmět: |
Vehicular ad hoc network
Computer Networks and Communications Computer science Real-time computing Bandwidth (signal processing) 020206 networking & telecommunications 020302 automobile design & engineering 02 engineering and technology Open spectrum Data aggregator Cognitive radio 0203 mechanical engineering 0202 electrical engineering electronic engineering information engineering Entropy (information theory) Fading Intelligent transportation system Software |
| Zdroj: | Peer-to-Peer Networking and Applications. 14:781-793 |
| ISSN: | 1936-6450 1936-6442 |
| Popis: | Internet of Things (IoT) is expected to empower all aspects of the Intelligent Transportation System (ITS), the main goal of which is to improve transportation safety. However, due to high demands by the increasing number of associated vehicles, the allocated bandwidth of ITS is inadequate. Cognitive Radio (CR) technology can be used as a solution for this high demand level. In CR, the pre-allocated spectrum bands are sensed to find the existing holes, caused by the absence of primary users. Cooperative spectrum sensing is an efficient tool for the detection of free spectrum bands that increase the probability of correct detection. In this paper, a distributed cooperative spectrum sensing technique is proposed using the consensus algorithm which is a distributed data aggregation mechanism whereby each vehicle combines the results received from its neighbors’ spectrum sensing. The combined results are repeatedly shared and combined such that all vehicles reach the same results. In vehicular networks, due to the vehicle’s movement, the number of its neighbors changes dynamically. Therefore, considering the vehicle’s mobility is essential in the spectrum sensing process. The consensus algorithm which is a data aggregation method is used to increase the probability of correct detection, and thus to reduce the number of collisions in the spectrum acquisition process. In our method, each vehicle accurately selects a number of its neighbors dynamically, and involves them in the decision-making process. Moreover, separate weights determined based on the entropy of their information are assigned to the sensing results of the selected neighbors. In this way, even if the vehicles are affected by fading or shadowing, they can make more accurate decisions using the sensing results received from other vehicles. The simulation results of the proposed method show that it increases the probability of correctly detecting free spectrum bands as well as convergence speed. |
| Databáze: | OpenAIRE |
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