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BIO-INSPIRED BASED SEGMENTATION AND USER AUTHENTICATED KEY MANAGEMENT FOR IOT NETWORKS
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Data exchanging and gathering is greatly achieved by several interconnected physical objects or smart devices over the Internet are termed as Internet of Things (IoT). A generic IoT network called Hierarchical IoT Network (HIoTN) inclusive of the organized different nodes in a hierarchy as gateway node, cluster head nodes and sensing nodes. In HIoTN of generic IoT networking environment for a particular application, user direct access in real-time data from the sensing nodes is necessitated. Recent work introduces a User Authenticated Biometric Key Management Protocol (UABKMP) for IoT network. Hence this proposed work exhibits new region of interest based segmentation algorithm with base procedure of Modified Bat optimization (MBO) algorithm hybrid with Active Contour Model. In the MBO algorithm the parameters of the bat is tuned via the use of the Brownian Distribution. Finally an Authenticated Key Management (AKM) is proposed for IoT network. The Real-Or-Random (ROR) model is incorporated in network for proving the scheme formal security and also ensures the informal security being protected from several probable attacks.
Keywords
Hierarchical IoT Network, Authentication, Key Management, Security, Segmentation, Modified Bat Optimization, Active Contour Model, Iris.
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- J. Mitola and G.Q. Maguire, “Cognitive Radio: Making Software Radios More Personal”, IEEE Personal Communications, Vol. 6, No. 6, pp. 13-18, 1999.
- S. Haykin, “Cognitive Radio: Brain-Empowered Wireless Communications”, IEEE Journal on Selected Areas in Communications, Vol. 23, No. 2, pp.201-220, 2005.
- A. Ghasemi and E.S. Sousa, “Fundamental Limits of Spectrum-Sharing in Fading Environments”, IEEE Transactions on Wireless Communications, Vol. 6, No. 2, pp. 649-658, 2007.
- D. Niyato and E. Hossain, “Competitive Spectrum Sharing in Cognitive Radio Networks: A Dynamic Game Approach”, IEEE Transactions on Wireless Communications, Vol. 7, No. 7, pp. 2651-2660, 2008.
- S. Hu and Z. Yang, “Cognitive Medium Access Control Protocols for Secondary Users Sharing a Common Channel with Time Division Multiple Access Primary Users”, Proceedings of International Conference on Wireless Communication and Mobile Computing, pp. 1-13, 2012.
- R.B. Lopez, S.M. Sanchez, M.G. Fernandez, R.D. Souza and H. Alves, “Genetic Algorithm Aided Transmit Power Control in Cognitive Radio Networks”, Proceedings of International Conference on Cognitive Radio Oriented Wireless Networks, pp. 61-66, 2014.
- P. Tiwari and S. Saha, “Co-Channel Interference Constrained Spectrum Allocation with Simultaneous Power and Network Capacity Optimization using PSO in Cognitive Radio Network”, Proceedings of International Conference on Advance Networks and Telecommunication Systems, pp. 1-3, 2015.
- M. Zaheer, M. Uzma, A. Asif and I.M. Qureshi, “Interference Control in Cognitive Radio using Joint Beamforming and Power Optimization by Applying Artificial Bee Colony”, Proceedings of International Conference on Multi-Topic, pp. 1-6, 2016.
- S. Zhang and A.S. Hafid, “Impact of Heterogeneous Fading Channels in Power Limited Cognitive Radio Networks”, IEEE Transactions on Cognitive Communications and Networking, Vol. 9, No. 2, pp.1-14, 2017.
- C. Sun, Y.D. Alemseged, H.A. Tran and H. Harada, “Transmit Power Control for Cognitive Radio Over a Rayleigh Fading Channel”, IEEE Transactions on Vehicular Technology, Vol. 59, No. 4, pp. 1847-1857, 2010.
- A. Bagayoko, I. Fijalkow and P. Tortelier, “Power Control of Spectrum-Sharing in Fading Environment with Partial Channel State Information”, IEEE Transactions on Signal Processing, Vol. 59, No. 5, pp. 2244-2256, 2011.
- X. Kang, R. Zhang, Y.C. Liang and H.K. Garg, “Optimal Power Allocation Strategies for Fading Cognitive Radio Channels with Primary User Outage Constraint”, IEEE Journal on Selected Areas in Communications, Vol. 29, No. 2, pp. 374-383, 2011.
- D. Shiung and Y.Y. Yang, “Rate Enhancement for Cognitive Radios using the Relationship between Transmission Rate and Signal-to-Interference Ratio Statistics”, IET Communications, Vol. 7, No. 18, pp. 2044-2053, 2019.
- G. Ozcan and M.C. Gursoy, “Optimal Power Control for Underlay Cognitive Radio Systems with Arbitrary Input Distributions”, Proceedings of International Conference on Global Communications, pp. 1-14, 2015.
- G. Yang, B. Li, X. Tan and X. Wang, “Adaptive Power Control Algorithm in Cognitive Radio Based on Game Theory”, IET Communication, Vol. 9, No. 15, pp. 1807- 1811, 2015.
- C. Sun, Y.D. Alemseged, H.A. Tran and H. Harada, “Transmit Power Control for Cognitive Radio Over a Rayleigh Fading Channel”, IEEE Transactions on Vehicular Technology, Vol. 59, No. 4, pp. 1847-1857, 2010.
- B. Sklar, “Rayleigh Fading Channels in Mobile Digital Communication Systems Part I: Characterization”, IEEE Communication Magazine, Vol. 35, No. 9, pp. 136-146, 1997.
- T.S. Rappaport, “Wireless Communications-Principles and Practice”, Prentice-Hall, 2002.
- D. Shiung and Y.Y Yang, “Rate Enhancement for Cognitive Radios using the Relationship between Transmission Rate and Signal-to-Interference Ratio Statistics”, IET Communications, Vol. 7, No. 18, pp. 2044-2053, 2013.
- R. Combes and A. Proutiere, “Dynamic Rate and Channel Selection in Cognitive Radio Systems”, IEEE Journal on Selected Areas in Communications, Vol. 33, No. 5, pp. 910- 920, 2015.
- G. Yang, B.Li, X. Tan and X. Wang, “Adaptive Power Control Algorithm in Cognitive Radio based on Game Theory”, IET Communication, Vol. 9, No. 15, pp. 1807- 1811, 2015.
- P. Sandeep, “A Comparative Analysis of Optimization Techniques in Cognitive Radio (QoS)”, International Journal of Engineering and Advanced Technology, Vol. 6, No. 3, pp. 97-10, 2017.
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