The Internet of Vehicles (IoV) is a fundamental paradigm for enabling intelligent transportation systems and promoting high-quality services and applications that require a tremendous amount of data processing resources. In this paper, we consider a computational offloading problem on an Integrated Aerial-Ground (IAG) Edge Computing (EC) architecture, where each task is modeled as a chain of dependent subtasks. To solve the problem, a V2X-based Computation and Communication-efficient Multitask Offloading Approach (CCMTOA) is proposed in which mutual information is exchanged between users, allowing one to effectively solve the multitask multilayer network selection problem. The parameters of vehicle mobility are estimated using a realistic intelligent mobility model. The numerical results with varying VU density show the effectiveness of the proposed method over the benchmark approaches.

Integrated Aerial-Ground Computation Offloading for Dependency-Aware IoV Multitask Services / Shinde, Swapnil Sadashiv; Tarchi, Daniele. - ELETTRONICO. - (2024), pp. 317-322. (Intervento presentato al convegno 2024 IEEE International Mediterranean Conference on Communications and Networking (MeditCom) tenutosi a Madrid, Spain nel 08-11 July 2024) [10.1109/meditcom61057.2024.10621308].

Integrated Aerial-Ground Computation Offloading for Dependency-Aware IoV Multitask Services

Tarchi, Daniele
2024

Abstract

The Internet of Vehicles (IoV) is a fundamental paradigm for enabling intelligent transportation systems and promoting high-quality services and applications that require a tremendous amount of data processing resources. In this paper, we consider a computational offloading problem on an Integrated Aerial-Ground (IAG) Edge Computing (EC) architecture, where each task is modeled as a chain of dependent subtasks. To solve the problem, a V2X-based Computation and Communication-efficient Multitask Offloading Approach (CCMTOA) is proposed in which mutual information is exchanged between users, allowing one to effectively solve the multitask multilayer network selection problem. The parameters of vehicle mobility are estimated using a realistic intelligent mobility model. The numerical results with varying VU density show the effectiveness of the proposed method over the benchmark approaches.
2024
2024 IEEE International Mediterranean Conference on Communications and Networking (MeditCom)
2024 IEEE International Mediterranean Conference on Communications and Networking (MeditCom)
Madrid, Spain
08-11 July 2024
Shinde, Swapnil Sadashiv; Tarchi, Daniele
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1381089
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