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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: closed (31 March 2025) | Viewed by 15358

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Special Issue Editors

Dr. Stefano Rinaldi

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Guest Editor

Department of Information Engineering, University of Brescia, 25123 Brescia, Italy
Interests: 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
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Dr. Alan Oliveira De Sá

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Department of Informatics, Faculty of Science of the University of Lisbon, 1749-016 Lisbon, Portugal
Interests: cybersecurity; cyber-physical systems; control systems; intelligent systems; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

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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Related Special Issue

IoT, Edge, and Cloud Computing in Smart Cities, 2nd Edition in Future Internet

Published Papers (11 papers)

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Research

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20 pages, 12983 KiB

Open AccessArticle

Towards Safer Cities: AI-Powered Infrastructure Fault Detection Based on YOLOv11

by Raiyen Z. Rakin, Mahmudur Rahman, Kanij F. Borsa, Fahmid Al Farid, Shakila Rahman, Jia Uddin and Hezerul Abdul Karim

Future Internet 2025, 17(5), 187; https://doi.org/10.3390/fi17050187 - 22 Apr 2025

Abstract

The current infrastructure is crucial to metropolitan improvement. Natural factors, aging, and overuse cause these structures to deteriorate, introducing dangers to public well-being. Timely detection of infrastructure failures requires an effective solution. A YOLOv11-based deep learning model has been proposed which analyzes infrastructure and detects faults in civil architecture. The focus of this study is on an image-based approach to infrastructure assessment, which is an alternative to manual visual inspections. Despite not explicitly modeling infrastructure deterioration, the proposed method is designed to automate defect identification based on visual cues. A customized dataset was created with 9116 images collected from various platforms. The dataset was pre-processed, i.e., annotated, and after pre-processing, the proposed model was trained. After training, our proposed model finds defects with greater precision and speed than conventional defect detection techniques. It achieves high performance with precision, recall, F1 score, and mAP in 100 epochs, and is therefore reliable for applications in civil engineering and urban infrastructure monitoring. Finally, the detection results show that the proposed YOLOv11 model works better than other baseline algorithms (YOLOv8, YOLOv9, and YOLOv10) and is more accurate at finding infrastructure problems in real-world scenarios. Full article

(This article belongs to the Special Issue IoT, Edge, and Cloud Computing in Smart Cities)

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24 pages, 2548 KiB

Open AccessArticle

CPCROK: A Communication-Efficient and Privacy-Preserving Scheme for Low-Density Vehicular Ad Hoc Networks

by Junchao Wang, Honglin Li, Yan Sun, Chris Phillips, Alexios Mylonas and Dimitris Gritzalis

Future Internet 2025, 17(4), 165; https://doi.org/10.3390/fi17040165 - 9 Apr 2025

Viewed by 232

Abstract

The mix-zone method is effective in preserving real-time vehicle identity and location privacy in Vehicular Ad Hoc Networks (VANETs). However, it has limitations in low-vehicle-density scenarios, where adversaries can still identify the real trajectories of the victim vehicle. To address this issue, researchers often generate numerous fake beacons to deceive attackers, but this increases transmission overhead significantly. Therefore, we propose the Communication-Efficient Pseudonym-Changing Scheme within the Restricted Online Knowledge Scheme (CPCROK) to protect vehicle privacy without causing significant communication overhead in low-density VANETs by generating highly authentic fake beacons to form a single fabricated trajectory. Specifically, the CPCROK consists of three main modules: firstly, a special Kalman filter module that provides real-time, coarse-grained vehicle trajectory estimates to reduce the need for real-time vehicle state information; secondly, a Recurrent Neural Network (RNN) module that enhances predictions within the mix zone by incorporating offline data engineering and considering online vehicle steering angles; and finally, a trajectory generation module that collaborates with the first two to generate highly convincing fake trajectories outside the mix zone. The experimental results confirm that CPCROK effectively reduces the attack success rate by over 90%, outperforming the plain mix-zone scheme and beating other fake beacon schemes by more than 60%. Additionally, CPCROK effectively minimizes transmission overhead by 67%, all while ensuring a high level of protection. Full article

(This article belongs to the Special Issue IoT, Edge, and Cloud Computing in Smart Cities)

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26 pages, 430 KiB

Open AccessArticle

Practical Comparison Between the CI/CD Platforms Azure DevOps and GitHub

by Vladislav Manolov, Daniela Gotseva and Nikolay Hinov

Future Internet 2025, 17(4), 153; https://doi.org/10.3390/fi17040153 - 31 Mar 2025

Viewed by 388

Abstract

Continuous integration and delivery are essential for modern software development, enabling teams to automate testing, streamline deployments, and deliver high-quality software more efficiently. As DevOps adoption grows, selecting the right CI/CD platform is essential for optimizing workflows. Azure DevOps and GitHub, both under Microsoft, are leading solutions with distinct features and target audiences. This paper compares Azure DevOps and GitHub, evaluating their CI/CD capabilities, scalability, security, pricing, and usability. It explores their integration with cloud environments, automation workflows, and suitability for teams of varying sizes. Security features, including access controls, vulnerability scanning, and compliance, are analyzed to assess their suitability for organizational needs. Cost-effectiveness is also examined through licensing models and total ownership costs. This study leverages real-world case studies and industry trends to guide organizations in selecting the right CI/CD tools. Whether seeking a fully managed DevOps suite or a flexible, Git-native platform, understanding the strengths and limitations of Azure DevOps and GitHub is crucial for optimizing development and meeting long-term scalability goals. Full article

(This article belongs to the Special Issue IoT, Edge, and Cloud Computing in Smart Cities)

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13 pages, 420 KiB

Open AccessArticle

Towards a Decentralized Collaborative Framework for Scalable Edge AI

by Ahmed M. Abdelmoniem , Mona Jaber , Ali Anwar , Yuchao Zhang and Mingliang Gao

Future Internet 2024, 16(11), 421; https://doi.org/10.3390/fi16110421 - 14 Nov 2024

Viewed by 1296

Abstract

Nowadays, Edge Intelligence has seen unprecedented growth in most of our daily life applications. Traditionally, most applications required significant efforts into data collection for data-driven analytics, raising privacy concerns. The proliferation of specialized hardware on sensors, wearable, mobile, and IoT devices has led to the growth of Edge Intelligence, which has become an integral part of the development cycle of most modern applications. However, scalability issues hinder their wide-scale adoption. We aim to focus on these challenges and propose a scalable decentralized edge intelligence framework. Therefore, we analyze and empirically evaluate the challenges of existing methods, and design an architecture that overcomes these challenges. The proposed approach is client-driven and model-centric, allowing models to be shared between entities in a scalable fashion. We conduct experiments over various benchmarks to show that the proposed approach presents an efficient alternative to the existing baseline method, and it can be a viable solution to scale edge intelligence. Full article

(This article belongs to the Special Issue IoT, Edge, and Cloud Computing in Smart Cities)

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24 pages, 7635 KiB

Open AccessArticle

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

Cited by 1 | Viewed by 3599

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 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

Open AccessArticle

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 1086

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 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

Open AccessArticle

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 1685

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 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

Open AccessArticle

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 1144

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 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

Open AccessArticle

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 6 | Viewed by 2254

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 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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Review

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41 pages, 603 KiB

Open AccessReview

Edge and Cloud Computing in Smart Cities

by Maria Trigka and Elias Dritsas

Future Internet 2025, 17(3), 118; https://doi.org/10.3390/fi17030118 - 6 Mar 2025

Viewed by 1180

Abstract

The evolution of smart cities is intrinsically linked to advancements in computing paradigms that support real-time data processing, intelligent decision-making, and efficient resource utilization. Edge and cloud computing have emerged as fundamental pillars that enable scalable, distributed, and latency-aware services in urban environments. Cloud computing provides extensive computational capabilities and centralized data storage, whereas edge computing ensures localized processing to mitigate network congestion and latency. This survey presents an in-depth analysis of the integration of edge and cloud computing in smart cities, highlighting architectural frameworks, enabling technologies, application domains, and key research challenges. The study examines resource allocation strategies, real-time analytics, and security considerations, emphasizing the synergies and trade-offs between cloud and edge computing paradigms. The present survey also notes future directions that address critical challenges, paving the way for sustainable and intelligent urban development. Full article

(This article belongs to the Special Issue IoT, Edge, and Cloud Computing in Smart Cities)

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28 pages, 15575 KiB

Open AccessReview

Architectural Trends in Collaborative Computing: Approaches in the Internet of Everything Era

by Débora Souza, Gabriele Iwashima, Viviane Cunha Farias da Costa, Carlos Eduardo Barbosa, Jano Moreira de Souza and Geraldo Zimbrão

Future Internet 2024, 16(12), 445; https://doi.org/10.3390/fi16120445 - 29 Nov 2024

Viewed by 1056

Abstract

The majority of the global population now resides in cities, and this trend continues to grow. In this context, the Internet of Things (IoT) is crucial in transforming existing urban areas into Smart Cities. However, IoT architectures mainly focus on machine-to-machine interactions, leaving human involvement aside. The Internet of Everything (IoE) includes human-to-human and human–machine collaboration, but the specifics of these interactions are still under-explored. As urban populations grow and IoT integrates into city infrastructure, efficient, collaborative architectures become crucial. In this work, we use the Rapid Review methodology to analyze collaboration in four prevalent computing architectures in the IoE paradigm, namely Edge Computing, Cloud Computing, Blockchain/Web Services, and Fog Computing. To analyze the collaboration, we use the 3C collaboration model, comprising communication, cooperation, and coordination. Our findings highlight the importance of Edge and Cloud Computing for enhancing collaborative coordination, focusing on efficiency and network optimization. Edge Computing supports real-time, low-latency processing at data sources, while Cloud Computing offers scalable resources for diverse workloads, optimizing coordination and productivity. Effective resource allocation and network configuration in these architectures are essential for cohesive IoT ecosystems. Therefore, this work offers a comparative analysis of four computing architectures, clarifying their capabilities and limitations. Smart Cities are a major beneficiary of these insights. This knowledge can help researchers and practitioners choose the best architecture for IoT and IoE environments. Additionally, by applying the 3C collaboration model, the article provides a framework for improving collaboration in IoT and IoE systems. Full article

(This article belongs to the Special Issue IoT, Edge, and Cloud Computing in Smart Cities)

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