The field of edge computing and vehicular networks is rapidly evolving, with a focus on optimizing resource allocation, task offloading, and routing protocols to improve system performance and reliability. Researchers are exploring innovative approaches, such as multi-objective optimization, deep reinforcement learning, and graph theory, to address the challenges of dynamic network topology, limited resources, and stringent latency requirements. Noteworthy papers in this area include: Local Ratio based Real-time Job Offloading and Resource Allocation in Mobile Edge Computing, which proposes an approximation algorithm to jointly optimize job offloading and resource allocation. Betweenness Centrality Based Dynamic Source Routing for Flying Ad Hoc Networks in Marching Formation, which introduces a routing protocol that exploits the concept of betweenness centrality to measure the importance of relay nodes.
Advances in Edge Computing and Vehicular Networks
Sources
Local Ratio based Real-time Job Offloading and Resource Allocation in Mobile Edge Computing
Betweenness Centrality Based Dynamic Source Routing for Flying Ad Hoc Networks in Marching Formation
Bandwidth Reservation for Time-Critical Vehicular Applications: A Multi-Operator Environment
QR-MO: Q-Routing for Multi-Objective Shortest-Path Computation in 5G-MEC Systems
Partitioned Task Offloading for Low-Latency and Reliable Task Completion in 5G MEC
A Reliable and Efficient 5G Vehicular MEC: Guaranteed Task Completion with Minimal Latency
Multi-Agent Deep Reinforcement Learning for Safe Autonomous Driving with RICS-Assisted MEC
Bandwidth Allocation for Cloud-Augmented Autonomous Driving
Sequential Task Assignment and Resource Allocation in V2X-Enabled Mobile Edge Computing
DATA-WA: Demand-based Adaptive Task Assignment with Dynamic Worker Availability Windows
Enhancing Mobile Crowdsensing Efficiency: A Coverage-aware Resource Allocation Approach
A Multi-Objective Simultaneous Routing, Facility Location and Allocation Model for Earthquake Emergency Logistics