Particle Swarm Optimization in the Presence of Malicious Users in Cognitive IoT Networks with Data

With the increasing applications in the domains of ubiquitous and context-aware computing, Internet of Things (IoT) is gaining importance. The study to efficiently exploit and manage a spectrum resources for industrial IoT (IIoT) applications is currently in the interest of research community. As increasing number of IIoT devices is heading towards the future-connected society with the cost of high system complexity, to meet the growing demands of wireless communication in future, cognitive IoT (CIoT) technology is considered as a choice. Reliable detection of the vacant spectrum holes is a vital task in the CIoT network with data. However, the performance of spectrum sensing severely degraded with the existence of malicious users (MUs) which falsifies the sensing results by reporting false data to the fusion center (FC). In this paper, we focus on the use of particle swarm optimization (PSO) to safeguard the cooperative spectrum sensing (CSS) from the negative effects caused by the MUs. The effectiveness of the proposed scheme is verified numerically in various scenarios with different types of MUs through analysis and simulations.

[1]  Iván García-Magariño,et al.  Internet of Things for Healthcare Using Effects of Mobile Computing: A Systematic Literature Review , 2019, Wirel. Commun. Mob. Comput..

[2]  Junsu Kim,et al.  A Genetic Algorithm-Based Soft Decision Fusion Scheme in Cognitive IoT Networks with Malicious Users , 2020, Wirel. Commun. Mob. Comput..

[3]  Ángel G. Andrade,et al.  Comparing particle swarm optimization variants for a cognitive radio network , 2013, Appl. Soft Comput..

[4]  Sanjay Kumar Dhurandher,et al.  A Contract Theory Approach-Based Scheme to Encourage Secondary Users for Cooperative Sensing in Cognitive Radio Networks , 2020, IEEE Systems Journal.

[5]  Zhang Jie,et al.  The Parallel Algorithm Based on Genetic Algorithm for Improving the Performance of Cognitive Radio , 2018, Wirel. Commun. Mob. Comput..

[6]  Ijaz Mansoor Qureshi,et al.  Differential Evolution Based Reliable Cooperative Spectrum Sensing in the Presence of Malicious Users , 2020, Wirel. Pers. Commun..

[7]  Zhu Han,et al.  Catch Me if You Can: An Abnormality Detection Approach for Collaborative Spectrum Sensing in Cognitive Radio Networks , 2010, IEEE Transactions on Wireless Communications.

[8]  Geoffrey Ye Li,et al.  Soft Combination and Detection for Cooperative Spectrum Sensing in Cognitive Radio Networks , 2008, IEEE GLOBECOM 2007 - IEEE Global Telecommunications Conference.

[9]  Habib Ullah Khan,et al.  Towards Energy-Efficient Framework for IoT Big Data Healthcare Solutions , 2020, Sci. Program..

[10]  Bao-Shuh Paul Lin,et al.  The Roles of 5G Mobile Broadband in the Development of IoT, Big Data, Cloud and SDN , 2016 .

[11]  Junsu Kim,et al.  Support Vector Machine-Based Classification of Malicious Users in Cognitive Radio Networks , 2020, Wirel. Commun. Mob. Comput..

[12]  Linbo Zhai,et al.  A Spectrum Access Based on Quality of Service (QoS) in Cognitive Radio Networks , 2016, PloS one.

[13]  Rozeha A. Rashid,et al.  Efficient In-Band Spectrum Sensing Using Swarm Intelligence for Cognitive Radio Network , 2015, Canadian Journal of Electrical and Computer Engineering.

[14]  Insoo Koo,et al.  Mitigation of Adverse Effects of Malicious Users on Cooperative Spectrum Sensing by Using Hausdorff Distance in Cognitive Radio Networks , 2015, J. Inform. and Commun. Convergence Engineering.

[15]  Ijaz Mansoor Qureshi,et al.  Suppression of Mutual Interference in Noncontiguous Orthogonal Frequency Division Multiplexing Based Cognitive Radio Systems , 2017, Wirel. Commun. Mob. Comput..

[16]  Habib Ullah Khan,et al.  Security Analysis of IoT Devices by Using Mobile Computing: A Systematic Literature Review , 2020, IEEE Access.

[17]  Junsu Kim,et al.  Boosted Trees Algorithm as Reliable Spectrum Sensing Scheme in the Presence of Malicious Users , 2020 .

[18]  Trung Quang Duong,et al.  Editorial: Wireless Communications and Networks for 5G and Beyond , 2019, Mob. Networks Appl..

[19]  Yuanhua Fu,et al.  Bayesian-Inference-Based Sliding Window Trust Model Against Probabilistic SSDF Attack in Cognitive Radio Networks , 2020, IEEE Systems Journal.

[20]  Xiaoyan Ning,et al.  Defending Against Massive SSDF Attacks From a Novel Perspective of Honest Secondary Users , 2019, IEEE Communications Letters.

[21]  H. Asfandyar,et al.  Enhanced Cooperative Spectrum Sensing in Cognitive Radio Network Using Flower Pollination Algorithm , 2019, 2019 International Conference on Electrical, Communication, and Computer Engineering (ICECCE).

[22]  N. Gul,et al.  Performance Comparison of Hard Decision Schemes in the Presence of Malicious Users , 2019, 2019 International Conference on Electrical, Communication, and Computer Engineering (ICECCE).

[23]  Ijaz Mansoor Qureshi,et al.  History based forward and feedback mechanism in cooperative spectrum sensing including malicious users in cognitive radio network , 2017, PloS one.

[24]  Mohsen Guizani,et al.  Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications , 2015, IEEE Communications Surveys & Tutorials.

[25]  Venugopal V. Veeravalli,et al.  Cooperative Spectrum Sensing and Detection for Cognitive Radio , 2007, IEEE GLOBECOM 2007 - IEEE Global Telecommunications Conference.

[26]  Tharmalingam Ratnarajah,et al.  On the Performance of Cooperative Spectrum Sensing in Random Cognitive Radio Networks , 2018, IEEE Systems Journal.

[27]  Ijaz Mansoor Qureshi,et al.  Defense against Malicious Users in Cooperative Spectrum Sensing Using Genetic Algorithm , 2018 .

[28]  Majid Khabbazian,et al.  Malicious User Detection in a Cognitive Radio Cooperative Sensing System , 2010, IEEE Transactions on Wireless Communications.

[29]  Anant Sahai,et al.  Cooperative Sensing among Cognitive Radios , 2006, 2006 IEEE International Conference on Communications.

[30]  Mahamod Ismail,et al.  Improved Detection Performance of Cognitive Radio Networks in AWGN and Rayleigh Fading Environments , 2013 .

[31]  Fotis Foukalas,et al.  5G: The Convergence of Wireless Communications , 2015, Wirel. Pers. Commun..

[32]  Mohammad Ghanbarisabagh,et al.  A Novel Evolutionary-Based Cooperative Spectrum Sensing Mechanism for Cognitive Radio Networks , 2014, Wirel. Pers. Commun..

[33]  Ashraf Tammam,et al.  Influence of relaying malicious node within cooperative sensing in cognitive radio network , 2019, Wirel. Networks.

[34]  Junsu Kim,et al.  Robust Spectrum Sensing via Double-Sided Neighbor Distance Based on Genetic Algorithm in Cognitive Radio Networks , 2020, Mob. Inf. Syst..

[35]  Ghassane Aniba,et al.  Equal Gain Combining for Cooperative Spectrum Sensing in Cognitive Radio Networks , 2014, IEEE Transactions on Wireless Communications.

[36]  Insoo Koo,et al.  A sequential cooperative spectrum sensing scheme based on cognitive user reputation , 2012, IEEE Transactions on Consumer Electronics.

[37]  Qihui Wu,et al.  Cognitive Internet of Things: A New Paradigm Beyond Connection , 2014, IEEE Internet of Things Journal.

[38]  Erik G. Larsson,et al.  Spectrum Sensing for Cognitive Radio : State-of-the-Art and Recent Advances , 2012, IEEE Signal Processing Magazine.

[39]  Simon Haykin,et al.  Cognitive radio: brain-empowered wireless communications , 2005, IEEE Journal on Selected Areas in Communications.

[40]  Priyanka Das,et al.  Optimization of probability of false alarm and probability of detection in cognitive radio networks using GA , 2015, 2015 IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS).

[41]  Alagan Anpalagan,et al.  Internet of Things (IoT) in 5G Wireless Communications , 2016, IEEE Access.

[42]  Kemal Tepe,et al.  Technical Issues on Cognitive Radio-Based Internet of Things Systems: A Survey , 2019, IEEE Access.

[43]  Amir Ghasemi,et al.  Spectrum sensing in cognitive radio networks: requirements, challenges and design trade-offs , 2008, IEEE Communications Magazine.