Paper ID: 2410.12798 • Published Sep 29, 2024
Design of an Efficient Fan-Shaped Clustered Trust-Based Routing Model with QoS & Security-Aware Side-Chaining for IoV Deployments
Sadaf Ravindra Suryawanshi, Praveen Gupta
TL;DR
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The rapid expansion of Internet of Vehicles (IoV) deployments has
necessitated the creation of efficient and secure routing models to manage the
massive data traffic generated by interconnected devices & vehicles. For IoV
deployments, we propose a novel fan-shaped trust-based routing model with
Quality of Service (QoS) and security-aware side-chaining. Our method employs
temporal levels of delay, throughput, Packet Delivery Ratio (PDR), and energy
consumption to determine optimal routing paths, thereby ensuring efficient data
transmissions. We employ the Bacterial Foraging Optimizer (BFO) algorithm to
manage side-chains within the network, which dynamically adjusts side-chain
configurations to optimize system performance. The technique of fan-shaped
clustering is used to group nodes into efficient clusters, allowing for more
efficient communication and resource utilization sets. Extensive
experimentation and performance analysis are utilized to evaluate the proposed
model. Existing blockchain-based security models have been significantly
improved by our findings. Our model achieves a remarkable 9.5% reduction in
delay, a 10.5% improvement in throughput, a 2.9% improvement in PDR, and a 4.5%
reduction in energy consumption compared to alternative approaches. In
addition, we evaluate the model's resistance to Sybil, Masquerading, and
Flooding attacks, which are prevalent security threats for IoV deployments.
Even under these attack scenarios, our model provides consistently higher QoS
levels compared to existing solutions, ensuring uninterrupted and reliable data
transmissions. In IoV deployments, the proposed routing model and side-chaining
management approach have numerous applications and use-cases like Smart cities,
industrial automation, healthcare systems, transportation networks, and
environmental monitoring.