Volume 12 , Issue 1 , PP: 110-128, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Ahmad Raza Khan 1 *
Doi: https://doi.org/10.54216/JISIoT.120109
As the Internet and computer technology develop, more gadgets are linked wirelessly, expanding the Internet of Things (IoT). IoT is a huge network of sensors and gateways that links them. IoT devices generate images, music, video, digital signals, and more by interacting with their surroundings. To exchange resources and information, all IoT equipment and apps may connect to the Internet. Everything is connected in our world. Due to the broad deployment and massive size of IoT devices, access control of device resources is problematic. Obtaining IoT device resources unlawfully will have major implications since they include personal and sensitive information. Many systems and situations employ access control technologies to secure resources. Discriminatory, identity-based, and MAC access control schemes are traditional (mandatory access control). However, these centralized methods have single-point failure, scalability issues, poor dependability, and low throughput. IoT devices may belong to several organizations or people, be mobile, and function badly, making centralized access management problematic. Another innovative data management solution is blockchain, which uses distributed storage to stabilize data. A transaction writes the data reading or modification record into a block, and the blocks are connected as a chain using a hash to maintain data integrity. It synchronizes data between nodes via a peer-to-peer network and consensus process, assuring data consistency for blockchain network participants. Zero Trust-Based Blockchain, an open source blockchain development platform, offers more efficient consensus methods, larger throughputs, smart contracts, and support for different organizations and ledgers. Proposed work build the fabric-IoT access control system using Zero Trust-Based Blockchain to apply blockchain technology to IoT access control in this study. Distributed processing and storage for IoT data may solve these critical issues with blockchain. Thus, developing distributed IoT-based e-healthcare services using blockchain technology may have been feasible. FabricIoT can keep records, handle dynamic access control, and solve the IoT access control problem using distributed architecture.
Blockchain , Zero Trust Architecture , Control Framework , Security , Internet of Things.
[1] Renuka, N., and Satya Sairam, M. (2019). ‘‘Peak-to-average power ratio performance of transform precoding-based orthogonal frequency division multiplexing offset quadrature amplitude modulation.’’ In Smart Intelligent Computing and Applications, pp. 241–248. Springer, Singapore.
[2] Raghunatharao, D., Prasad, T. J., & Prasad, M. G. (2020). Optimal pilot-based channel estimation in cognitive radio. Wireless Personal Communications, 114(4), 2801–2819. https://doi.org/ 10.1007/s11277-020-07504-x
[3] Salih, B. M., Badr, B., Baghdadi, A., & Francine, K. (2022, May). Quality of service optimization in OFDM-based cognitive radio network. In 2022 19th International Multi-Conference on Systems, Signals & Devices (SSD) (pp. 1731-1736). IEEE.
[4] Sedighi, S, Taherpour, A, Gazor, S & Khattab, T 2017, ‘Eigen value- based multiple antenna spectrum sensing: higher order moments’,IEEE Transactions on Wireless Communications, vol.16, pp.1168-1184.
[5] Shaat, M., & Bader, F. (2010). Computationally efficient power allocation algorithm in multicarrier-based cognitive radio networks: OFDM and FBMC systems. EURASIP Journal on Advances in Signal Processing, 2010, 1-13.
[6] Shuangfei Z Y Cheng, W Lu & Z Zhang 2016, ‘Deep Structured Energy Based εodels for Anomaly Detection’, Proceedings of the 33rd International Conference on Machine Learning, New York, NY, USA.
[7] Siddiqi, M. Z., Wuttisittikulkij, L., Mirza, I. S., Chaudhary, S., & Paranianifard, A. (2022, May). Adaptive Modulation and Power Allocation in Green Cognitive Radio Networks. In 2022 19th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) (pp. 1-4). IEEE.
[8] Sidhu, G. A. S., Gao, F., Wang, W., & Chen, W. (2013). Resource allocation in relay-aided OFDM cognitive radio networks. IEEE Transactions on Vehicular Technology, 62(8), 3700-3710.
Signal processing, vol. 120, pp. 385-408.
[9] Simsir S & Taspinar 2020, ‘A novel discrete cuckoo search algorithm‐ based selective mapping technique to minimize the PAPR universal filtered multicarrier signal’, International Journal of Communication Systems, vol.33,no.18,p.e4640
[10] Singh, S & Patra, SK 2014, ‘Partial transmit sequence based PAPR reduction for OFDM using Best harmony search evolutionary algorithm’, Proceedings of the 8th International Conference on Bio inspired Information and Communications Technologies, pp. 75-80
[11] Sofotasios, PC, Mohjazi, L, Muhaidat, S, Al-Qutayri, M & Karagiannidis, GK 2016, ‘Energy detection of unknown signals over cascaded fading channels’, IEEE Antennas and Wireless Propagation Letters, vol. 15, pp. 135-138.
[12] Spooner, Cε & Nicholls, RB 2009, ‘Spectrum sensing based on spectral correlation’, Cognitive Radio Technology, vol. 2, pp. 593-634.
[13] Sultana, A., Zhao, L., & Fernando, X. (2016, September). Power allocation using geometric water filling for OFDM-based cognitive radio networks. In 2016 IEEE 84th Vehicular Technology Conference (VTC-Fall) (pp. 1-5). IEEE.
[14] Sun, C, Zhang, W & δetaief, KB 2007, ‘Cluster-based cooperative spectrum sensing in cognitive radio systems’, In 2007 IEEE international conference on communications, pp. 2511-2515.
[15] Suresh Dannana, Babji Prasad Chapa & Gottapu Sasibhushana Rao 2019, ‘Spectrum Sensing for OFDε Cognitive Radio using εatched Filter Detection’, International Journal of Recent Technology and Engineering (IJRTE), ISSN: 2277-3878, vol. 8, no. 2, pp. 1-6.
[16] Sutton, PD, Nolan, KE & Doyle, δE 2008, ‘Cyclostationary signatures in practical cognitive radio applications’, IEEE Journal on selected areas in Communications, vol. 26, no. 1, pp. 13-24.
[17] Tamilselvi, T., Rajendran, V., & Bharathy, G. T. (2022, January). Comparative analysis of CP-OFDM and F-OFDM schemes for cognitive networks. In AIP Conference Proceedings (Vol. 2385, No. 1, p. 060002). AIP Publishing LLC
[18] Tan, CE & Wassell, IJ 2003 ‘Data bearing peak reduction carriers for OFDε systems’, the Fourth Pacific Rim Conference on Multimedia, Proceedings of the 2003 Joint, vol. 2, pp. 854-858.
[19] Taspinar, N & simsir, 2019, ‘Dual symbol optimization‐based partial transmit sequence technique for PAPR reduction in WOLA‐OFDM waveform’, International Journal of Communication Systems, vol. 32, no. 14, p. e4081.
[20] Teo, Zhong, & Ng, BCh 2010, ‘An Iterative Threshold Selection Algorithm for Cooperative Sensing in a Cognitive Radio Network’, IEEE Symposium on New Frontiers in Dynamic Spectrum, pp. 1-8.
[21] Tosato, F, Sandell, M & Tanahashi, ε 2016, ‘Tone reservation for PAPR reduction: An optimal approach through sphere encoding’, 2016 IEEE International Conference on Communications (ICC), pp. 1-6.
[22] Vadivelu, R, Sankaranarayanan, K & Vijayakumari, V 2014, ‘εatched filter based spectrum sensing for cognitive radio at low signal to noise ratio’, Journal of Theoretical and Applied Information Technology, vol. 62, no. 1, pp. 107-113.
[23] Van der Neut, N, Maharaj, BT, De Lange, F, González, GJ, Gregorio, F & Cousseau, J 2014, ‘PAPR reduction in FBεC using an ACE- based linear programming optimization’, EURASIP Journal on Advances in Signal Processing, vol. 1, pp. 1-21.
[24] Vangala, S & Sundru, A 2016, ‘Adaptive Clipping Active Constellation Extension for PAPR Reduction of OFDM/OQAM System’, Procedia Computer Science, vol. 93, pp. 617-623.
[25] Ahmad. R. khan, (2024). “Dynamic Load Balancing in Cloud Computing: An Optimized RL-Based Clustering with Multi-Objective Optimized Task Scheduling”, International journal Processes.
[26] Ahmad. R. Khan (2022). “Using virtualized multimedia tools for video conferencing solution integrated in teaching and learning environment”, Journal of Discrete Mathematical Sciences and Cryptography, 25:3, 801-815, DOI: 10.1080/09720529.2021.2014137.
[27] Khan AR (2022). Secure PAAS environment over hybrid cloud using load-balanced Docker containers. International Journal of Advanced and Applied Sciences, 9(3): 133-141.