Open Access

ARTICLE

Heterogeneous Task Allocation Model and Algorithm for Intelligent Connected Vehicles

Neng Wan^1,2, Guangping Zeng^1,*, Xianwei Zhou¹

1 School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China
2 School of Mechanical and Transportation, Southwest Forestry University, Kunming, 650224, China

* Corresponding Author: Guangping Zeng. Email:

(This article belongs to the Special Issue: Advanced Communication and Networking Technologies for Internet of Things and Internet of Vehicles)

Computers, Materials & Continua 2024, 80(3), 4281-4302. https://doi.org/10.32604/cmc.2024.054794

Received 07 June 2024; Accepted 07 August 2024; Issue published 12 September 2024

Abstract

With the development of vehicles towards intelligence and connectivity, vehicular data is diversifying and growing dramatically. A task allocation model and algorithm for heterogeneous Intelligent Connected Vehicle (ICV) applications are proposed for the dispersed computing network composed of heterogeneous task vehicles and Network Computing Points (NCPs). Considering the amount of task data and the idle resources of NCPs, a computing resource scheduling model for NCPs is established. Taking the heterogeneous task execution delay threshold as a constraint, the optimization problem is described as the problem of maximizing the utilization of computing resources by NCPs. The proposed problem is proven to be NP-hard by using the method of reduction to a 0–1 knapsack problem. A many-to-many matching algorithm based on resource preferences is proposed. The algorithm first establishes the mutual preference lists based on the adaptability of the task requirements and the resources provided by NCPs. This enables the filtering out of un-schedulable NCPs in the initial stage of matching, reducing the solution space dimension. To solve the matching problem between ICVs and NCPs, a new many-to-many matching algorithm is proposed to obtain a unique and stable optimal matching result. The simulation results demonstrate that the proposed scheme can improve the resource utilization of NCPs by an average of 9.6% compared to the reference scheme, and the total performance can be improved by up to 15.9%.

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Keywords

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