Performance Analysis of a Blockchain-Enabled Adaptive Routing Strategy for Mobile Producer Handoff in Multihomed Named Data Networking
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Abstract
Named Data Networking (NDN) is considered a promising paradigm that enables content-centric communication and in-network caching. However, challenges of mobility, scalability, and security limit its effectiveness and impact in dynamic IoT environments. Existing mobility management approaches and strategies, including anchor-based and anchor-free schemes, are unable to jointly optimise latency, security, and multihoming efficiency, even under highly dynamic conditions. This research work proposes a Mobile Producer Handoff in the Named-data Emulated Mobility framework known as MP-HNEM. This is a blockchain-based adaptive routing strategy that integrates predictive handoff, cross-layer optimisation, and a lightweight Proof-of-Authority consensus mechanism to provide and enhance mobility support in multihomed NDN-based wireless sensor networks. The framework also addresses a crucial gap in secure and latency-aware producer mobility. A hybrid simulation and emulation method is employed using ndnSIM v2.9 and a Mini-NDN to evaluate MP-HNEM performance under varying mobility patterns, trust thresholds, and network densities. We analyzed latency, throughput, packet delivery ratio, energy consumption, and trust validation delay as key metrics. MP-HNEM results show a 42.7% reduction in latency, a 73% increase in throughput, and a 39% reduction in energy consumption compared to baseline schemes. The packet delivery ratio increases by 31.5%, indicating improved reliability across all handoff events. Security analysis shows detection accuracy over 90% and block validation success rates over 98% under mobility conditions. Using ANOVA, we conducted Statistical validation and achieved p < 0.05, confirming the impact and significance of these improvements. The major contributions of this research work are: (i) a blockchain-integrated ARS developed to secure multihoming mobility, (ii) a reinforcement learning-based predictive handoff mechanism for smart support, and (iii) a hybrid validation framework that combines both simulation and emulation procedures. The results indicate that MP-HNEM is a scalable, energy-efficient, and secure mobility solution for NDN-based IoT systems, suitable for applications such as smart healthcare and industrial IoT. Future work intends to focus on real-world deployment and heterogeneous IoT integration.
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