Keywords
edge computing
blockchain
deep reinforcement learning
task offloading
DDQN
practical Byzantine fault tolerance
Kubernetes
Hyperledger Fabric
privacy preservation
How to Cite
Abstract
The existing methods do not effectively meet the security and performance demands for Internet of Vehicles (IoV) applications. They also do not provide low-latency, secure edge-computing solutions for end-users in vehicular environments. The study presented in this paper proposes a blockchain-based edge computing framework that utilises Double Deep Q-Network (DDQN) for reinforcement learning and lightweight Practical Byzantine Fault Tolerance (PBFT) consensus for simultaneously optimising latency, energy consumption, and security. For efficient microservice orchestration and task off-loading, the containerised architecture utilises Kubernetes with Hyperledger Fabric. The experiments conducted in urban, suburban, and highway scenarios confirmed that the proposed framework outperformed baseline algorithms with end-to-end latency reduction of 30–45% while also lowering energy consumption by up to 55% under moderate-to-heavy loads. With less than 1.2 seconds per block on the blockchain consensus, the system also maintained task completion rates exceeding 95% during peak conditions. The framework demonstrates consistent performance across various vehicular densities and consumes zero-knowledge proofs with attribute-based encryption for data against cybersecurity threats. These results confirm that the integration of DDQN and blockchain technology effectively tackles primary obstacles IoV faces by providing secure edge computing for next generation vehicular networks.
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