Optimized Secure Handover in 5G Networks Using Lightweight Blockchain and Hierarchical Clustering

International Journal of Electronics and Communication Engineering |
© 2025 by SSRG - IJECE Journal |
Volume 12 Issue 7 |
Year of Publication : 2025 |
Authors : Kiran Mannem, Suresh Suggula, Srinivasulu Reddy Battu, Shilpa Bagade |
How to Cite?
Kiran Mannem, Suresh Suggula, Srinivasulu Reddy Battu, Shilpa Bagade, "Optimized Secure Handover in 5G Networks Using Lightweight Blockchain and Hierarchical Clustering," SSRG International Journal of Electronics and Communication Engineering, vol. 12, no. 7, pp. 378-389, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I7P129
Abstract:
Efficient mobility management is essential in contemporary wireless communication systems to maintain continuous and reliable connectivity as users move across the network users. In Fifth-Generation (5G) networks, the widespread use of densely packed small cells results in a higher likelihood of handover events, increasing the chances of issues such as the ping-pong effect and radio link failures. These problems are often linked to poorly optimized handover control parameters. To mitigate these challenges, this study proposes HO-LBlock, a secure and lightweight blockchain-assisted framework designed for efficient handover and mobility management in 5G heterogeneous network environments. The proposed approach is structured around four key phases: network formation, secure authentication, hierarchical clustering, and a hybrid handover mechanism, all designed to enhance both security and efficiency in dynamic 5G environments. A grid-based network is initially constructed to enhance data transmission rates and strengthen connectivity between devices. After that, to amplify security, authentication is performed based on the Colour Code Combination (CCC) method, and the secret key is provided by a lightweight blockchain using the Elliptic Curve SIGNcryption algorithm (ECSIGN). Following this, hierarchical clustering is performed by integrating the Dunn Index and the Improved k-medoids (DUNNI) method to minimize energy consumption by selecting the Cluster Head (CH) and Substitute Cluster Head (SUB CH). Finally, a two-condition-based handover is executed by optimizing the HCPs using a data-driven optimization approach with the Combined Coral Reef Optimization and Improved Q-learning (CCROIQ) algorithm. In this work, a lightweight blockchain is employed to ensure data privacy and security during information transactions. The performance of the proposed framework is evaluated through simulations conducted in Network Simulator 3.26 (NS-3.26), and the results demonstrate its superiority over existing approaches across several critical performance indicators.
Keywords:
Authentication, Clustering, Blockchain, Handover, Heterogeneous network, Mobility management.
References:
[1] Jawad Tanveer et al., “An Overview of Reinforcement Learning Algorithms for Handover Management in 5G Ultra-Dense Small Cell Networks,” Applied Sciences, vol. 12, no. 1, pp. 1-25, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Maraj Uddin Ahmed Siddiqui et al., “Mobility Management Issues and Solutions in 5G-and-Beyond Networks: A Comprehensive Review,” Electronics, vol. 11, no. 9, pp. 1-27, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Kotaru Kiran, and D. Rajeswara Rao, “Analytical Review and Study on Various Vertical Handover Management Technologies in 5G Heterogeneous Network,” Infocommunications Journal, vol. 14, no. 2, pp. 28-38. 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Sajjad Ahmad Khan et al., “Handover Management over Dual Connectivity in 5G Technology with Future Ultra-Dense Mobile Heterogeneous Networks: A Review,” Engineering Science and Technology, an International Journal, vol. 35, pp. 1-16, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Abdelfatteh Haidine et al., Artificial Intelligence and Machine Learning in 5G and beyond: A Survey and Perspectives, Moving Broadband Mobile Communications Forward - Intelligent Technologies for 5G and Beyond, IntechOpen, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Muhammad Mohtasim Sajjad et al., “Inter-Slice Mobility Management in 5G: Motivations, Standard Principles, Challenges, and Research Directions,” IEEE Communications Standards Magazine, vol. 6, no. 1, pp. 93-100, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Vuyo S. Pana, Oluwaseyi P. Babalola, and Vipin Balyan “5G Radio Access Networks: A Survey,” Array, vol. 14, pp. 1-10, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Kang Tan et al., “Machine Learning in Vehicular Networking: An Overview,” Digital Communications and Networks, vol. 8, no. 1, pp. 18-24, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Gaofeng Hong et al., “Decentralized Vehicular Mobility Management Study for 5G Identifier/Locator Split Networks,” Wireless Communications and Mobile Computing, vol. 2022, no. 1, pp. 1-14, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Nirmin Monir et al., “Seamless Handover Scheme for MEC/SDN-Based Vehicular Networks,” Journal of Sensor and Actuator Networks, vol. 11, no. 1, pp. 1-16, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Emre Gures et al., “Machine Learning-Based Load Balancing Algorithms in Future Heterogeneous Networks: A Survey,” IEEE Access, vol. 10, pp. 37689-37717, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Syed Hussain Ali Kazmi et al., “Routing-Based Interference Mitigation in SDN Enabled Beyond 5G Communication Networks: A Comprehensive Survey,” IEEE Access, vol. 11, pp. 4023-4041, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[13] W.T. Alshaibani et al., “Mobility Management of Unmanned Aerial Vehicles in Ultra–Dense Heterogeneous Networks,” Sensors, vol. 22, no. 16, pp. 1-32, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Ibraheem Shayea et al., “Handover Management for Drones in Future Mobile Networks—A Survey,” Sensors, vol. 22, no. 17, pp. 1-36, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Tidiane Sylla et al., “Multi-Connectivity for 5G Networks and Beyond: A Survey,” Sensors, vol. 22, no. 19, pp. 1-32, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Ashok Kumar Das et al., “On the Security of a Secure and Lightweight Authentication Scheme for Next Generation IoT Infrastructure,” IEEE Access, vol. 9, pp. 71856-71867, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Haibin Zhang et al., “Mobility Management for Blockchain-Based Ultra-Dense Edge Computing: A Deep Reinforcement Learning Approach,” IEEE Transactions on Wireless Communications, vol. 20, no. 11, pp. 7346-7359, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Jihyeon Ryu et al., “SMASG: Secure Mobile Authentication Scheme for Global Mobility Network,” IEEE Access, vol. 10, pp. 26907-26919, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Wasan Kadhim Saad et al., “Performance Evaluation of Mobility Robustness Optimization (MRO) in 5G Network With Various Mobility Speed Scenarios,” IEEE Access, vol. 10, pp. 60955-60971, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Tarek Al Achhab, Fariz Abboud, and Abdulkarim Assalem, “A Robust Self-Optimization Algorithm Based on Idiosyncratic Adaptation of Handover Parameters for Mobility Management in LTE-A Heterogeneous Networks,” IEEE Access, vol. 9, pp. 154237-154264, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Sana Amjad et al., “Blockchain Based Authentication and Cluster Head Selection Using DDR-LEACH in Internet of Sensor Things,” Sensors, vol. 22, no. 5, pp. 1-20 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Md. Rafiqul Islam et al., “Cluster-Based Authentication Process in a Smart City,” Security and Communication Networks, vol. 2022, no. 1, pp. 1-14, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Muhammad Usman Iqbal et al., “Improving the QoS in 5G HetNets Through Cooperative Q-Learning,” IEEE Access, vol. 10, pp. 19654-19676, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Vicent Pla et al., Optimal Admission Control Using Handover Prediction in Mobile Cellular Networks, 1st ed., Mobility Management and Quality-Of-Service for Heterogeneous Networks, River Publishers, pp. 1-22, 2009.
[Google Scholar] [Publisher Link]
[25] Randeep Singh et al., “Analysis of Network Slicing for Management of 5G Networks Using Machine Learning Techniques,” Wireless Communications and Mobile Computing, vol. 2022, no. 1, pp. 1-10, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Muhammad Asif Khan et al., “ML-Based Handover Prediction and AP Selection in Cognitive Wi-Fi Networks,” Journal of Network and Systems Management, vol. 30, pp. 1-21, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Ahmad Hassan et al., “Vivisecting Mobility Management in 5G Cellular Networks,” Proceedings of the ACM SIGCOMM 2022 Conference, Amsterdam Netherlands, pp. 86-100, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Qianyu Liu et al., “Autonomous Mobility Management for 5G Ultra-Dense HetNets via Reinforcement Learning With Tile Coding Function Approximation,” IEEE Access, vol. 9, pp. 97942-97952, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Man Chun Chow, and Maode Ma, “A Secure Blockchain-Based Authentication and Key Agreement Scheme for 3GPP 5G Networks,” Sensors, vol. 22, no. 12, pp. 1-26, 2022.
[CrossRef] [Google Scholar] [Publisher Link]