Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.7
3.4
Journals
Future Internet
Special Issues
IoT, Edge, and Cloud Computing in Smart Cities
share announcement

IoT, Edge, and Cloud Computing in Smart Cities

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Network Virtualization and Edge/Fog Computing".

Deadline for manuscript submissions: 20 July 2024 | Viewed by 545

Special Issue Editors

Dr. Stefano Rinaldi

E-Mail Website
Guest Editor
Department of Information Engineering, University of Brescia, 25123 Brescia, Italy
Interests: instrumentation and measurement; industrial real-time network; wireless sensor network; smart sensors; communication systems for smart grids; time synchronization; Linux-embedded programming; embedded systems; power quality; smart grids; energy systems; smart building; energy management system; electric vehicles; vehicle-to-vehicle communication
Special Issues, Collections and Topics in MDPI journals
Dr. Alan Oliveira De Sá

E-Mail Website
Guest Editor
Department of Informatics, Faculty of Science, University of Lisbon, 1749-016 Lisbon, Portugal
Interests: cybersecurity; cyber-physical systems; control systems; intelligent systems; artificial intelligence

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT), Edge Computing, and Cloud Computing have brought in a new era of urban development, creating the concept of Smart Cities. In this Special Issue, we investigate the complex interplay of these cutting-edge technologies in the urban landscape, exploring the multifaceted dimensions of their integration and impact on the future of urban living. This Special Issue provides a comprehensive platform for exploring the symbiotic relationships between these technologies and their role in improving efficiency, sustainability, and overall quality of life in modern urban environments. As our cities grow into complex ecosystems of interconnected devices, intelligent sensors, and advanced computing infrastructures, the potential for innovation in areas such as transportation, healthcare, energy management, and public services becomes clearer.

This Special Issue features contributions from top experts, researchers, and practitioners in the field, providing a wide range of perspectives and insights. From theoretical frameworks to practical applications, the papers in this collection are aimed at explaining the current state-of-the-art, addressing emerging challenges, and propose novel solutions that take advantage on the synergies of IoT, Edge, and Cloud Computing in the context of Smart Cities.

This Special Issue aims to present the latest research advances in IoT, Edge, and Cloud Computing technologies and their application in Smart Cities. Practical applications of these technologies in real-world Smart City scenarios are welcome.

Dr. Stefano Rinaldi
Dr. Alan Oliveira De Sá
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • cyber-security
  • edge-to-cloud integration
  • urban governance and technology integration
  • data analytics
  • intelligent transportation systems
  • sustainable urban development
  • federated learning
  • cyber-physical systems

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

15 pages, 518 KiB  
Article
A Hybrid Multi-Agent Reinforcement Learning Approach for Spectrum Sharing in Vehicular Networks
by Mansoor Jamal, Zaib Ullah, Muddasar Naeem, Musarat Abbas and Antonio Coronato
Future Internet 2024, 16(5), 152; https://doi.org/10.3390/fi16050152 - 28 Apr 2024
Viewed by 290
Abstract
Efficient spectrum sharing is essential for maximizing data communication performance in Vehicular Networks (VNs). In this article, we propose a novel hybrid framework that leverages Multi-Agent Reinforcement Learning (MARL), thereby combining both centralized and decentralized learning approaches. This framework addresses scenarios where multiple [...] Read more.
Efficient spectrum sharing is essential for maximizing data communication performance in Vehicular Networks (VNs). In this article, we propose a novel hybrid framework that leverages Multi-Agent Reinforcement Learning (MARL), thereby combining both centralized and decentralized learning approaches. This framework addresses scenarios where multiple vehicle-to-vehicle (V2V) links reuse the frequency spectrum preoccupied by vehicle-to-infrastructure (V2I) links. We introduce the QMIX technique with the Deep Q Networks (DQNs) algorithm to facilitate collaborative learning and efficient spectrum management. The DQN technique uses a neural network to approximate the Q value function in high-dimensional state spaces, thus mapping input states to (action, Q value) tables that facilitate self-learning across diverse scenarios. Similarly, the QMIX is a value-based technique for multi-agent environments. In the proposed model, each V2V agent having its own DQN observes the environment, receives observation, and obtains a common reward. The QMIX network receives Q values from all agents considering individual benefits and collective objectives. This mechanism leads to collective learning while V2V agents dynamically adapt to real-time conditions, thus improving VNs performance. Our research finding highlights the potential of hybrid MARL models for dynamic spectrum sharing in VNs and paves the way for advanced cooperative learning strategies in vehicular communication environments. Furthermore, we conducted an in-depth exploration of the simulation environment and performance evaluation criteria, thus concluding in a comprehensive comparative analysis with cutting-edge solutions in the field. Simulation results show that the proposed framework efficiently performs against the benchmark architecture in terms of V2V transmission probability and V2I peak data transfer. Full article
(This article belongs to the Special Issue IoT, Edge, and Cloud Computing in Smart Cities)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop