Cooperative Sensing in Cognitive Radio Ad Hoc Networks

Cognitive radio technology can largely enhance spectrum utilization efficiency by dynamic spectrum access. In cognitive radio, spectrum sensing is essential to protect the transmission of primary users (PUs). To improve the sensing accuracy, cooperative sensing has been introduced in the literature. However, there are still some challenges on cooperative sensing, especially in Cognitive Radio Ad Hoc Networks (CRAHNs) in which a centralized coordinator does not exist. In this paper, we deal with the challenges of cooperative sensing in CRAHNs, with focus on sensing data fusion and security. An overview of existing research efforts is given first, and thus, the research challenges are identified and discussed in details. To solve those challenges, we propose a cooperative sensing scheme for CRAHNs. In order to reduce the communication overhead, we partition the secondary users (SUs) to several clusters, and in each cluster, a cluster head is selected to serve as the representative for the cluster. An efficient consensus-based method with security consideration is proposed to obtain the accurate final sensing result. Extensive simulation is conducted based on real scenarios to evaluate the performance of the proposed scheme.

[1]  Ian F. Akyildiz,et al.  Reinforcement learning for cooperative sensing gain in cognitive radio ad hoc networks , 2013, Wirel. Networks.

[2]  Hyung Seok Kim,et al.  Distributed cooperative spectrum sensing in cognitive radio for ad hoc networks , 2013, Comput. Commun..

[3]  Ming Li,et al.  Vulnerability and protection for distributed consensus-based spectrum sensing in cognitive radio networks , 2012, 2012 Proceedings IEEE INFOCOM.

[4]  Pramod K. Varshney,et al.  Data Falsification Attacks on Consensus-Based Detection Systems , 2017, IEEE Transactions on Signal and Information Processing over Networks.

[5]  Hsiao-Hwa Chen,et al.  Cognitive radio networks with asynchronous spectrum sensing and access , 2015, IEEE Network.

[6]  Jin Wei,et al.  Energy-Efficient Distributed Spectrum Sensing for Wireless Cognitive Radio Networks , 2010, 2010 INFOCOM IEEE Conference on Computer Communications Workshops.

[7]  Joseph R. Cavallaro,et al.  Trust-Aware Consensus-Inspired Distributed Cooperative Spectrum Sensing for Cognitive Radio Ad Hoc Networks , 2016, IEEE Transactions on Cognitive Communications and Networking.

[8]  Zheng Wang,et al.  Distributed Cooperative Spectrum Sensing Based on Weighted Average Consensus , 2011, 2011 IEEE Global Telecommunications Conference - GLOBECOM 2011.

[9]  Ian F. Akyildiz,et al.  CRAHNs: Cognitive radio ad hoc networks , 2009, Ad Hoc Networks.

[10]  Zhiqiang Li,et al.  Distributed consensus-based security mechanisms in cognitive radio mobile ad hoc networks , 2012, IET Commun..

[11]  Huifang Chen,et al.  Cooperative Spectrum Sensing With M-Ary Quantized Data in Cognitive Radio Networks Under SSDF Attacks , 2017, IEEE Transactions on Wireless Communications.

[12]  Nicola Marchetti,et al.  Decentralized Cooperative Spectrum Sensing for Ad-Hoc Disaster Relief Network Clusters , 2010, 2010 IEEE 71st Vehicular Technology Conference.

[13]  Lijun Qian,et al.  Belief Propagation and Quickest Detection-Based Cooperative Spectrum Sensing in Heterogeneous and Dynamic Environments , 2017, IEEE Transactions on Wireless Communications.

[14]  Mohsen Guizani,et al.  Home M2M networks: Architectures, standards, and QoS improvement , 2011, IEEE Communications Magazine.

[15]  Xuemin Shen,et al.  Cooperative heterogeneous framework for spectrum harvesting in cognitive cellular network , 2015, IEEE Communications Magazine.

[16]  Laurence T. Yang,et al.  Optimal data fusion of collaborative spectrum sensing under attack in cognitive radio networks , 2014, IEEE Network.