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Future Internet
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IoT, Edge, and Cloud Computing in Smart Cities
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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: 31 March 2025 | Viewed by 5089

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

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Published Papers (5 papers)

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Research

24 pages, 7635 KiB  
Article
Improved Adaptive Backoff Algorithm for Optimal Channel Utilization in Large-Scale IEEE 802.15.4-Based Wireless Body Area Networks
by Mounib Khanafer, Mouhcine Guennoun, Mohammed El-Abd and Hussein T. Mouftah
Future Internet 2024, 16(9), 313; https://doi.org/10.3390/fi16090313 - 29 Aug 2024
Viewed by 409
Abstract
The backoff algorithm employed by the medium access control (MAC) protocol of the IEEE 802.15.4 standard has a significant impact on the overall performance of the wireless sensor network (WSN). This algorithm helps the MAC protocol resolve the contention among multiple nodes in [...] Read more.
The backoff algorithm employed by the medium access control (MAC) protocol of the IEEE 802.15.4 standard has a significant impact on the overall performance of the wireless sensor network (WSN). This algorithm helps the MAC protocol resolve the contention among multiple nodes in accessing the wireless medium. The standard binary exponent backoff (BEB) used by the IEEE 802.15.4 MAC protocol relies on an incremental method that doubles the size of the contention window after the occurrence of a collision. In a previous work, we proposed the adaptive backoff algorithm (ABA), which adapts the contention window’s size to the value of the probability of collision, thus relating the contention resolution to the size of the WSN in an indirect manner. ABA was studied and tested using contention window sizes of up to 256. However, the latter limit on the contention window size led to degradation in the network performance as the size of the network exceeded 50 nodes. This paper introduces the Improved ABA (I-ABA), an improved version of ABA. In the design of I-ABA we observe the optimal values of the contention window that maximize performance under varying probabilities of collision. Based on that, we use curve fitting techniques to derive a mathematical expression that better describes the adaptive change in the contention window. This forms the basis of I-ABA, which demonstrates scalability and the ability to enhance performance. As a potential area of application for I-ABA, we target wireless body area networks (WBANs) that are large-scale, that is, composed of hundreds of sensor nodes. WBAN is a major application area for the emerging Internet of Things (IoT) paradigm. We evaluate the performance of I-ABA based on simulations. Our results show that, in a large-scale WBAN, I-ABA can achieve superior performance to both ABA and the standard BEB in terms of various performance metrics. Full article
(This article belongs to the Special Issue IoT, Edge, and Cloud Computing in Smart Cities)
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17 pages, 1231 KiB  
Article
Dynamic Graph Representation Learning for Passenger Behavior Prediction
by Mingxuan Xie, Tao Zou, Junchen Ye, Bowen Du and Runhe Huang
Future Internet 2024, 16(8), 295; https://doi.org/10.3390/fi16080295 - 15 Aug 2024
Viewed by 373
Abstract
Passenger behavior prediction aims to track passenger travel patterns through historical boarding and alighting data, enabling the analysis of urban station passenger flow and timely risk management. This is crucial for smart city development and public transportation planning. Existing research primarily relies on [...] Read more.
Passenger behavior prediction aims to track passenger travel patterns through historical boarding and alighting data, enabling the analysis of urban station passenger flow and timely risk management. This is crucial for smart city development and public transportation planning. Existing research primarily relies on statistical methods and sequential models to learn from individual historical interactions, which ignores the correlations between passengers and stations. To address these issues, this paper proposes DyGPP, which leverages dynamic graphs to capture the intricate evolution of passenger behavior. First, we formalize passengers and stations as heterogeneous vertices in a dynamic graph, with connections between vertices representing interactions between passengers and stations. Then, we sample the historical interaction sequences for passengers and stations separately. We capture the temporal patterns from individual sequences and correlate the temporal behavior between the two sequences. Finally, we use an MLP-based encoder to learn the temporal patterns in the interactions and generate real-time representations of passengers and stations. Experiments on real-world datasets confirmed that DyGPP outperformed current models in the behavior prediction task, demonstrating the superiority of our model. Full article
(This article belongs to the Special Issue IoT, Edge, and Cloud Computing in Smart Cities)
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28 pages, 5606 KiB  
Article
FIVADMI: A Framework for In-Vehicle Anomaly Detection by Monitoring and Isolation
by Khaled Mahbub, Antonio Nehme, Mohammad Patwary, Marc Lacoste and Sylvain Allio
Future Internet 2024, 16(8), 288; https://doi.org/10.3390/fi16080288 - 8 Aug 2024
Viewed by 662
Abstract
Self-driving vehicles have attracted significant attention in the automotive industry that is heavily investing to reach the level of reliability needed from these safety critical systems. Security of in-vehicle communications is mandatory to achieve this goal. Most of the existing research to detect [...] Read more.
Self-driving vehicles have attracted significant attention in the automotive industry that is heavily investing to reach the level of reliability needed from these safety critical systems. Security of in-vehicle communications is mandatory to achieve this goal. Most of the existing research to detect anomalies for in-vehicle communication does not take into account the low processing power of the in-vehicle Network and ECUs (Electronic Control Units). Also, these approaches do not consider system level isolation challenges such as side-channel vulnerabilities, that may arise due to adoption of new technologies in the automotive domain. This paper introduces and discusses the design of a framework to detect anomalies in in-vehicle communications, including side channel attacks. The proposed framework supports real time monitoring of data exchanges among the components of in-vehicle communication network and ensures the isolation of the components in in-vehicle network by deploying them in Trusted Execution Environments (TEEs). The framework is designed based on the AUTOSAR open standard for automotive software architecture and framework. The paper also discusses the implementation and evaluation of the proposed framework. Full article
(This article belongs to the Special Issue IoT, Edge, and Cloud Computing in Smart Cities)
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25 pages, 8965 KiB  
Article
Multi-Agent Dynamic Fog Service Placement Approach
by Nerijus Šatkauskas and Algimantas Venčkauskas
Future Internet 2024, 16(7), 248; https://doi.org/10.3390/fi16070248 - 13 Jul 2024
Viewed by 508
Abstract
Fog computing as a paradigm was offered more than a decade ago to solve Cloud Computing issues. Long transmission distances, higher data flow, data loss, latency, and energy consumption lead to providing services at the edge of the network. But, fog devices are [...] Read more.
Fog computing as a paradigm was offered more than a decade ago to solve Cloud Computing issues. Long transmission distances, higher data flow, data loss, latency, and energy consumption lead to providing services at the edge of the network. But, fog devices are known for being mobile and heterogenous. Their resources can be limited, and their availability can be constantly changing. A service placement optimization is needed to meet the QoS requirements. We propose a service placement orchestration, which functions as a multi-agent system. Fog computing services are represented by agents that can both work independently and cooperate. Service placement is being completed by a two-stage optimization method. Our service placement orchestrator is distributed, services are discovered dynamically, resources can be monitored, and communication messages among fog nodes can be signed and encrypted as a solution to the weakness of multi-agent systems due to the lack of monitoring tools and security. Full article
(This article belongs to the Special Issue IoT, Edge, and Cloud Computing in Smart Cities)
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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
Cited by 1 | Viewed by 1204
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)
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