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Edge Computing for Resource Sharing and Sensing in IoT Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 816

Special Issue Editors


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Guest Editor
Departamento de Automática, Universidad da Alcalá, Alcala de Henares, Spain
Interests: SDN; NFV; routing; Ethernet; IoT; 5G; data center networks
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Departamento de Automática, Universidad da Alcalá, Alcala de Henares, Spain
Interests: computer networks; communication networks; SDN; NFV; IoT; P4
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Departamento de Automática, Universidad da Alcalá, Alcala de Henares, Spain
Interests: computer networks; network architecture; communication networks

E-Mail Website
Guest Editor
Departamento de Automática, Universidad da Alcalá, Alcala de Henares, Spain
Interests: network; exploration; multipath; SDN; disjoint paths

Special Issue Information

Dear Colleagues, 

The rapid proliferation of IoT devices has created unprecedented opportunities for distributed intelligence and real-time sensing at the network edge. However, the massive heterogeneity and limited resources of IoT systems pose major challenges for efficient data processing, communication, and coordination. Edge computing has emerged as a key enabler to bridge these gaps, bringing computation closer to data sources while reducing latency and bandwidth demands. This Special Issue aims to explore innovative mechanisms for resource sharing, cooperative sensing, and intelligent orchestration across heterogeneous edge environments. We invite original research and visionary contributions that address novel architectures, protocols, and algorithms enabling collaborative, adaptive, and energy-efficient edge systems for IoT applications.

Dr. Elisa Rojas
Prof. Dr. Isaias Martinez Yelmo
Dr. Joaquin Alvarez Horcajo
Dr. Diego López-Pajares
Guest Editors

Manuscript Submission Information

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Keywords

  • edge computing
  • resource sharing and discovery
  • cooperative and distributed sensing
  • IoT systems and architectures
  • edge intelligence and orchestration
  • heterogeneous networks
  • federated and collaborative learning at the edge
  • low-latency communication
  • energy-efficient edge systems
  • 5G and 6G networks

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Published Papers (1 paper)

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Research

26 pages, 1169 KB  
Article
HyAR-PPO: Hybrid Action Representation Learning for Incentive-Driven Task Offloading in Vehicular Edge Computing
by Wentao Wang, Mingmeng Li and Honghai Wu
Sensors 2026, 26(6), 1743; https://doi.org/10.3390/s26061743 - 10 Mar 2026
Viewed by 524
Abstract
Vehicular Edge Computing (VEC) can effectively guarantee the service experience of user vehicles, but resource-limited Roadside Units (RSUs) may face insufficient computing capacity during task peak periods. Utilizing Assisting Vehicles (AVs) with idle resources to share computing power can alleviate the pressure on [...] Read more.
Vehicular Edge Computing (VEC) can effectively guarantee the service experience of user vehicles, but resource-limited Roadside Units (RSUs) may face insufficient computing capacity during task peak periods. Utilizing Assisting Vehicles (AVs) with idle resources to share computing power can alleviate the pressure on RSUs. However, existing studies often fail to adequately incentivize selfish assisting vehicles to contribute resources and frequently lack a global optimization perspective from the overall system welfare. To address these challenges, this paper proposes an incentive-driven utility-balanced task offloading framework that aims to maximize social welfare while jointly optimizing resource allocation and profit pricing. Specifically, we first formulate the resource allocation as a Mixed-Integer Nonlinear Programming (MINLP) problem. To solve this problem, we introduce hybrid action representation learning to VEC for the first time and propose the HyAR-PPO algorithm to jointly optimize discrete offloading decisions and continuous resource allocation. This algorithm maps heterogeneous hybrid actions to a unified latent representation space through a Variational Autoencoder for the solution. Subsequently, equilibrium prices among user vehicles, Computation Service Providers (CSPs), and assisting vehicles are determined through Nash bargaining games, satisfying individual rationality constraints and achieving Pareto-optimal fair profit distribution. Experimental results demonstrate that the proposed framework can effectively coordinate multi-party interests. Compared with mainstream methods, the approach based on hybrid action representation learning achieves a significant improvement in social welfare, with its advantages being more pronounced in medium-to-large-scale scenarios. Full article
(This article belongs to the Special Issue Edge Computing for Resource Sharing and Sensing in IoT Systems)
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