Haythem Bany Salameh
ABSTRACT
Integrating Cognitive Radio (CR) technology in Internet-of-Things (IoT) devices allows efficient large-scale deployment of IoT systems. Recently, research efforts are shifted toward adopting CR in IoT as a response for the spectrum scarcity problem. Unfortunately, CR Networks (CRNs) share the same security weaknesses with traditional wireless networks. CR communication is also vulnerable to jamming attacks which can significantly affect network performance, consume network resources and results in delays, that make it less suitable for IoT time-critical systems. Routing in CR-based IoT networks, in general, considered as a challenging issue. Under the jamming attack, routing becomes even more challenging. In this paper, we introduce a new jamming-aware routing and channel assignment protocol that deals with proactive jamming attacks in CR-based IoT networks without requiring extra resources. The proposed protocol attempts at improving the overall packet delivery ratio in the network while considering the primary user's activities, multi-channel fading and jamming behavior. The proposed protocol consists of three phases: route discovery, channel assignment, and path selection. The channel assignment problem along each path is formulated as an optimization problem with the objective of maximizing the end-to-end probability of success. This problem is shown to be an uni-modular problem, which can be solved in polynomial-time using linear programming techniques. Compared to reference protocols, simulation results reveal that the proposed protocol significantly improves network performance in terms of packet delivery ratio.
- l Ridhawi, Ismaeel, et al. "A Profitable and Energy-Efficient Cooperative Fog Solution for IoT Services." IEEE Transactions on Industrial Informatics (2019).Google Scholar
- . Balasubramanian, F. Zaman, M. Aloqaily, I. A. Ridhawi, Y. Jararweh and H. B. Salameh, "A Mobility Management Architecture for Seamless Delivery of 5G-IoT Services," ICC 2019 - 2019 IEEE International Conference on Communications (ICC), Shanghai, China, 2019, pp. 1--7.Google Scholar
- . Singh, G. Tripathi, and A. J. Jara, "A survey of Internet-of-Things: Future vision, architecture, challenges and services,"s in Proc. IEEE World Forum Internet Things, Mar. 2014, pp. 287--292.Google ScholarCross Ref
- . A. Khan, M. H. Rehmani, and A. Rachedi, $“$When Cognitive Radio Meets the Internet of Things,$"$ IEEE 12th Int'l. Wireless Commun. $&$ Mobile Computing Conf., Paphos, Cyprus, Sept. 5--9 2016.Google Scholar
- loqaily, Moayad, et al. "An intrusion detection system for connected vehicles in smart cities." Ad Hoc Networks 90 (2019): 101842.Google ScholarCross Ref
- Lu, W. Wang, and C. Wang, $“$Modeling, evaluation and detection of jamming attacks in time-critical wireless applications,$"$ IEEE Transactions on Mobile Computing, Vol. 13, No. 8, pp. 1746--1759, 2014.Google ScholarCross Ref
- . Mourougayane and S. Srikanth, $“$Intelligent jamming threats to cognitive radio based strategic communication networks - a survey,$"$ in 2015 3rd International Conference on Signal Processing, Communication and Networking (ICSCN), March 2015, pp. 1--6.Google Scholar
- Bany Salameh, S. Almajali, M. Ayyash and H. Elgala, $“$Spectrum assignment in cognitive radio networks for internet-of-things delaysensitive applications under jamming attacks,$"$ IEEE Internet of Things Journal, vol. 5, no. 3, pp. 1903--1913, June 2018.Google Scholar
- toum, Safa, Burak Kantarci, and Hussein T. Mouftah. "Detection of known and unknown intrusive sensor behavior in critical applications." IEEE Sensors Letters 1.5 (2017): 1--4.Google ScholarCross Ref
- alehi, M., & Boukerche, A. (2019). Secure opportunistic routing protocols: methods, models, and classification. Wireless Networks, 25(2), 559--571.Google Scholar
- . Rawi and K. Yau, $“$Route selection for minimizing interference to primary users in cognitive radio networks: A reinforcement learning approach,$"$ in Proc. IEEE Symp. Comput. Intell. Commun. Syst. Netw., Singapore, 2013, pp. 24--30.Google Scholar
- . Khalife, S. Ahuja, N. Malouch, and M. Krunz, $“$Probabilistic path selection in opportunistic cognitive radio networks$"$, in Proc. IEEE GlobeCom, 2008, pp. 1--5.Google ScholarCross Ref
- El-Sherif and A. Mohamed, $“$Joint routing and resource allocation for delay minimization in cognitive radio based mesh networks,$"$ IEEE Trans. Wireless Commun., vol. 13, no. 1, pp. 186--197, Jan. 2014.Google ScholarCross Ref
- . Kaushik, Y. Yoganandam i S.K. Sahoo, $“$Quality and Availability of spectrum based routing for Cognitive radio enabled IoT networks$"$, Journal of Communications Software and Systems, vol.14, br. 3, str. 239--248, 2018.Google ScholarCross Ref
- . Chowdhury and M. Di Felice, $“$Search: A routing protocol for mobile cognitive radio ad-hoc networks,$"$ in Sarnoff Symposium, 2009. SARNOFF '09. IEEE, 2009, pp. 1--6.Google Scholar
- . Ji, M. Yan, R. Beyah and Z. Cai, "Semi-Structure Routing and Analytical Frameworks for Cognitive Radio Networks," in IEEE Transactions on Mobile Computing, vol. 15, no. 4, pp. 996--1008, 1.Google Scholar
- . Otoum, B. Kantarci and H. Mouftah, "Empowering Reinforcement Learning on Big Sensed Data for Intrusion Detection," ICC 2019 - 2019 IEEE International Conference on Communications (ICC), Shanghai, China, 2019, pp. 1--7.Google Scholar
- . Otoum, B. Kantarci and H. Mouftah, "Empowering Reinforcement Learning on Big Sensed Data for Intrusion Detection," ICC 2019 - 2019 IEEE International Conference on Communications (ICC), Shanghai, China, 2019, pp. 1--7.Google Scholar
- toum, Safa, Burak Kantarci, and Hussein T. Mouftah. "On the feasibility of deep learning in sensor network intrusion detection." IEEE Networking Letters 1.2 (2019): 68--71.Google ScholarCross Ref
- Manogna and K. Naik, $“$Detection of Jamming Attack in Cognitive Radio Networks$"$, International Journal of Recent Advances in Engineering $&$ Technology (IJRAET), Vol. 2, Issue 6, pp. 69--72, 2014.Google Scholar
- . Xiao, T. Chen, J. Liu, and H. Dai, $“$Anti-jamming transmission stackelberg game with observation errors,$"$ Communications Letters, IEEE, vol. 19, no. 6, pp. 949--952, June 2015.Google ScholarCross Ref
- . Adem and B. Hamdaoui, $“$Jamming resiliency and mobility management in cognitive communication networks$"$, in Proc. of the IEEE International Conference on Communications (ICC), May 2017Google ScholarCross Ref
- hamayseh, Yaser, et al. "Dynamic framework to mining Internet of Things for multimedia services." Expert Systems (2019): e12404.Google Scholar
- loqaily, Moayad, et al. "Data and service management in densely crowded environments: Challenges, opportunities, and recent developments." IEEE Communications Magazine 57.4 (2019): 81--87.Google ScholarCross Ref
- . Wu, B. Wang, and K. Liu, $“$Optimal defense against jamming attacks in cognitive radio networks using the Markov decision process approach$"$, in Proc. of the IEEE Globecom Conference, 2010Google Scholar
- Bany Salameh, $"$ Rate-Maximization Channel Assignment Scheme for Cognitive Radio Networks,$"$ in IEEE Global Telecommunications Conference GLOBECOM'10, Miami, FL, 2010, pp. 1--5.Google Scholar
Index Terms
- Secure Routing in Multi-hop IoT-based Cognitive Radio Networks under Jamming Attacks
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