Optimizing Vehicle-to-Edge Mapping with Load Balancing for Attack-Resilience in IoV

TL;DR

This paper proposes a load-balanced, attack-resilient vehicle-to-edge mapping strategy in IoV, utilizing deep reinforcement learning to maintain service continuity and optimize resource use amid attacks and mobility.

Contribution

It introduces a novel DRL-based approach for optimal vehicle-to-edge mapping that enhances attack-resilience, load balancing, and delay minimization in IoV networks.

Findings

Improved service availability during attacks.

Reduced delay with optimized mapping.

Effective load balancing across edge nodes.

Abstract

Attack-resilience is essential to maintain continuous service availability in Internet of Vehicles (IoV) where critical tasks are carried out. In this paper, we address the problem of service outage due to attacks on the edge network and propose an attack-resilient mapping of vehicles to edge nodes that host different types of service instances considering resource efficiency and delay. The distribution of service requests (of an attack-affected edge node) to multiple attack-free edge nodes is performed with an optimal vehicle-to-edge (V2E) mapping. The optimal mapping aims to improve the user experience with minimal delay while considering fair usage of edge capacities and balanced load upon a failure over different edge nodes. The proposed mapping solution is used within a deep reinforcement learning (DRL) based framework to effectively deal with the dynamism in service requests and vehicle mobility. We demonstrate the effectiveness of the proposed mapping approach through extensive simulation results using real-world vehicle mobility datasets from three cities.