Enhancing Data Security in IoT Networks with Blockchain-Based Management and Adaptive Clustering Techniques

Ajmeera Kiran, Prasad Mathivanan, Miroslav Mahdal (), Kanduri Sairam, Deepak Chauhan and Vamsidhar Talasila
Additional contact information
Ajmeera Kiran: Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad 500043, India
Prasad Mathivanan: School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, India
Miroslav Mahdal: Department of Control Systems and Instrumentation, Faculty of Mechanical Engineering, VSB-Technical University of Ostrava, 17. Listopadu 2172/15, 70800 Ostrava, Czech Republic
Kanduri Sairam: Department of Electronics and Communication Engineering, NITTE Deemed to Be University, Udupi 574110, India
Deepak Chauhan: School of Computing, Graphic Era Hill University, Dehradun 248002, India
Vamsidhar Talasila: Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur 522502, India

Mathematics, 2023, vol. 11, issue 9, 1-14

Abstract: The rapid proliferation of smart devices in Internet of Things (IoT) networks has amplified the security challenges associated with device communications. To address these challenges in 5G-enabled IoT networks, this paper proposes a multi-level blockchain security architecture that simplifies implementation while bolstering network security. The architecture leverages an adaptive clustering approach based on Evolutionary Adaptive Swarm Intelligent Sparrow Search (EASISS) for efficient organization of heterogeneous IoT networks. Cluster heads (CH) are selected to manage local authentication and permissions, reducing overhead and latency by minimizing communication distances between CHs and IoT devices. To implement network changes such as node addition, relocation, and deletion, the Network Efficient Whale Optimization (NEWO) algorithm is employed. A localized private blockchain structure facilitates communication between CHs and base stations, providing an authentication mechanism that enhances security and trustworthiness. Simulation results demonstrate the effectiveness of the proposed clustering algorithm compared to existing methodologies. Overall, the lightweight blockchain approach presented in this study strikes a superior balance between network latency and throughput when compared to conventional global blockchain systems. Further analysis of system under test (SUT) behavior was accomplished by running many benchmark rounds at varying transaction sending speeds. Maximum, median, and lowest transaction delays and throughput were measured by generating 1000 transactions for each benchmark. Transactions per second (TPS) rates varied between 20 and 500. Maximum delay rose when throughput reached 100 TPS, while minimum latency maintained a value below 1 s.

Keywords: blockchain; Internet of Things (IoT); Evolutionary Adaptive Swarm Intelligent Sparrow Search (EASISS); Network Efficient Whale Optimization (NEWO); data security; clustering techniques (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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