Overlapping task offloading and resource allocation via multi-RSU collaborative load balancing in vehicular edge computing

Cao, D., Peng, C., Peng, B., Liu, P., Sherratt, R. S. ORCID: https://orcid.org/0000-0001-7899-4445 and Wang, J. (2026) Overlapping task offloading and resource allocation via multi-RSU collaborative load balancing in vehicular edge computing. IEEE Transactions on Consumer Electronics. ISSN 0098-3063 doi: 10.1109/TCE.2026.3689497

Abstract/Summary

Vehicular Edge Computing (VEC) reduces latency by offloading vehicle generated tasks to Roadside Unit (RSU), unlocking vast opportunities for in vehicle electronics commercial services. Existing studies on task offloading in multi-RSU scenarios suffer from two major gaps. First, the similar sub-tasks across different tasks themselves have not been sufficiently investigated. This oversight leads to redundant computing, thereby undermining system efficiency. Second, and more critically, the load imbalance stemming from the uneven distribution of vehicles is further exacerbated by the neglect of similar sub-tasks. This paper focuses on redundant computation of similar sub-tasks in multi-RSU scenarios. We characterize similar sub-tasks of overlapping tasks, thereby capturing the redundancy that exists across these overlapping tasks. We then model the offloading of similar sub-tasks as a multi-objective Mixed Integer Nonlinear Program (MINLP) problem that simultaneously minimizes latency, energy consumption, and load imbalance. The original problem is approximated by a weighted-sum multi-objective problem, and we prove by contradiction that any optimal solution to this weighted-sum problem is a Pareto-optimal solution to the original multi-objective problem. To solve the constructed MINLP, we propose the Multi-RSU Distributed Shared Offloading (MRDSO) scheme. In this scheme, discrete variables are fixed based on Benders partitioning theorem, reducing the problem to a linear-programming sub-problem of the continuous variables. By proving the convexity of this sub-problem, we have demonstrated that a global optimum exists for the original MINLP problem. We then design two algorithms to solve it. Finally, experimental results confirm that this scheme reduces average system computing latency and energy consumption while maintaining multi-RSU load balance. Compared to the existing SSO, LAGO, RO, the proposed MRDSO has at least 54.8% improvement in the system cost.

Item Type	Article
URI	https://centaur.reading.ac.uk/id/eprint/129630
Identification Number/DOI	10.1109/TCE.2026.3689497
Refereed	Yes
Divisions	Life Sciences > School of Biological Sciences > Biomedical Sciences Life Sciences > School of Biological Sciences > Department of Bio-Engineering
Publisher	IEEE
Download/View statistics	View download statistics for this item