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Smart Cities
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Intelligent Edge Computing for Smart Cities
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Intelligent Edge Computing for Smart Cities

A special issue of Smart Cities (ISSN 2624-6511). This special issue belongs to the section "Cloud Computing".

Deadline for manuscript submissions: closed (15 February 2022) | Viewed by 31940

Special Issue Editors

Dr. Kolomvatsos Kostas

E-Mail Website
Guest Editor
Informatics and Telecommunications, University of Athens, 106 79 Athens, Greece
Interests: intelligent systems; distributed systems; distributed machine learning; computational intelligence; soft computing
Special Issues, Collections and Topics in MDPI journals
Dr. Christos Anagnostopoulos

E-Mail Website
Guest Editor
School of Computing Science, University of Glasgow, Lilybank Gardens, Glasgow G12 8QQ, UK
Interests: distributed machine learning; stochastic optimization; mobile computing; inferential analytics at the edge
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) has pervaded our daily life by making things interconnected through the Internet, as well as smarter, distributed, and more autonomous. The development of intelligent applications in IoT has gained significant attention in recent years. The Cloud provides many benefits to IoT; however, it faces some accessibility challenges. The unstable connection between the Cloud and mobile devices is expected to prevent providers from achieving optimal performance. Motivated to solve these problems, Edge Computing (EC) has appeared to decrease latency and support the massive machine type of communications. EC, however, faces various challenges and open issues, and we need more efforts to deliver the envisioned autonomous intelligent edge and intelligent mesh. The future intelligent mesh will involve numerous autonomous entities capable of understanding their internal status, the status of the environment and their peers, and of taking action to efficiently serve the desired applications. It becomes obvious that EC can provide significant advantages to Smart Cities (SCs), assisting them in supporting novel, efficient, and high-quality services. SCs can provide an intelligent platform involving a set of edge nodes enabling third-party developers to create new applications. The key is the potential for local processing at the edge of the network for data collected by IoT devices. Hence, the intelligence of an SC is distributed around the city in a number of nodes instead of having it centralized in a back-end system. Based on this approach, the SC infrastructure will always be present and always leading to a reliable platform for facilitating citizens’ lives. This Special Issue aims at revealing novel solutions towards a new intelligent edge mesh in SCs, bringing together scientists to discuss future research directions in the domain. 

Dr. Kolomvatsos Kostas
Dr. Christos Anagnostopoulos
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. Smart Cities is an international peer-reviewed open access semimonthly 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 2000 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

  • Distributed Artificial Intelligence (DAI)
  • Computational intelligence (CI) solutions for EC in SCs
  • Distributed machine learning (ML)
  • Distributed decision making
  • Bio-inspired models
  • Software-defined models and management
  • Intelligent data management
  • Intelligent tasks management
  • Heterogeneity management
  • Interoperability with future networks

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Published Papers (6 papers)

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Research

21 pages, 1846 KiB  
Article
Data-Driven Analytics Task Management Reasoning Mechanism in Edge Computing
by Christos Anagnostopoulos, Tahani Aladwani, Ibrahim Alghamdi and Konstantinos Kolomvatsos
Smart Cities 2022, 5(2), 562-582; https://doi.org/10.3390/smartcities5020030 - 24 Apr 2022
Cited by 10 | Viewed by 2821
Abstract
Internet of Things (IoT) applications have led to exploding contextual data for predictive analytics and exploration tasks. Consequently, computationally data-driven tasks at the network edge, such as machine learning models’ training and inference, have become more prevalent. Such tasks require data and resources [...] Read more.
Internet of Things (IoT) applications have led to exploding contextual data for predictive analytics and exploration tasks. Consequently, computationally data-driven tasks at the network edge, such as machine learning models’ training and inference, have become more prevalent. Such tasks require data and resources to be executed at the network edge, while transferring data to Cloud servers negatively affects expected response times and quality of service (QoS). In this paper, we study certain computational offloading techniques in autonomous computing nodes (ANs) at the edge. ANs are distinguished by limited resources that are subject to a variety of constraints that can be violated when executing analytical tasks. In this context, we contribute a task-management mechanism based on approximate fuzzy inference over the popularity of tasks and the percentage of overlapping between the data required by a data-driven task and data available at each AN. Data-driven tasks’ popularity and data availability are fed into a novel two-stages Fuzzy Logic (FL) inference system that determines the probability of either executing tasks locally, offloading them to peer ANs or offloading to Cloud. We showcase that our mechanism efficiently derives such probability per each task, which consequently leads to efficient uncertainty management and optimal actions compared to benchmark models. Full article
(This article belongs to the Special Issue Intelligent Edge Computing for Smart Cities)
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27 pages, 1760 KiB  
Article
Towards a Novel Air–Ground Intelligent Platform for Vehicular Networks: Technologies, Scenarios, and Challenges
by Swapnil Sadashiv Shinde and Daniele Tarchi
Smart Cities 2021, 4(4), 1469-1495; https://doi.org/10.3390/smartcities4040078 - 9 Dec 2021
Cited by 10 | Viewed by 3504
Abstract
Modern cities require a tighter integration with Information and Communication Technologies (ICT) for bringing new services to the citizens. The Smart City is the revolutionary paradigm aiming at integrating the ICT with the citizen life; among several urban services, transports are one of [...] Read more.
Modern cities require a tighter integration with Information and Communication Technologies (ICT) for bringing new services to the citizens. The Smart City is the revolutionary paradigm aiming at integrating the ICT with the citizen life; among several urban services, transports are one of the most important in modern cities, introducing several challenges to the Smart City paradigm. In order to satisfy the stringent requirements of new vehicular applications and services, Edge Computing (EC) is one of the most promising technologies when integrated into the Vehicular Networks (VNs). EC-enabled VNs can facilitate new latency-critical and data-intensive applications and services. However, ground-based EC platforms (i.e., Road Side Units—RSUs, 5G Base Stations—5G BS) can only serve a reduced number of Vehicular Users (VUs), due to short coverage ranges and resource shortage. In the recent past, several new aerial platforms with integrated EC facilities have been deployed for achieving global connectivity. Such air-based EC platforms can complement the ground-based EC facilities for creating a futuristic VN able to deploy several new applications and services. The goal of this work is to explore the possibility of creating a novel joint air-ground EC platform within a VN architecture for helping VUs with new intelligent applications and services. By exploiting most modern technologies, with particular attention towards network softwarization, vehicular edge computing, and machine learning, we propose here three possible layered air-ground EC-enabled VN scenarios. For each of the discussed scenarios, a list of the possible challenges is considered, as well possible solutions allowing to overcome all or some of the considered challenges. A proper comparison is also done, through the use of tables, where all the proposed scenarios, and the proposed solutions, are discussed. Full article
(This article belongs to the Special Issue Intelligent Edge Computing for Smart Cities)
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35 pages, 4907 KiB  
Article
A Comprehensive Framework for Analyzing IoT Platforms: A Smart City Industrial Experience
by Mahdi Fahmideh, Jun Yan, Jun Shen, Davoud Mougouei, Yanlong Zhai and Aakash Ahmad
Smart Cities 2021, 4(2), 588-622; https://doi.org/10.3390/smartcities4020031 - 28 Apr 2021
Cited by 8 | Viewed by 4321
Abstract
The compliance of IoT platforms to quality is paramount to achieve users’ satisfaction. Currently, we do not have a comprehensive set of guidelines to appraise and select the most suitable IoT platform architectures that meet relevant criteria. This paper is a tentative response [...] Read more.
The compliance of IoT platforms to quality is paramount to achieve users’ satisfaction. Currently, we do not have a comprehensive set of guidelines to appraise and select the most suitable IoT platform architectures that meet relevant criteria. This paper is a tentative response to this critical knowledge gap where we adopted the design science research approach to develop a novel evaluation framework. Our research, on the one hand, stimulates an unbiased competition among IoT platform providers and, on the other hand, establishes a solid foundation for IoT platform consumers to make informed decisions in this multiplicity. The application of the framework is illustrated in example scenarios. Moreover, lessons learned from applying design science research are shared. Full article
(This article belongs to the Special Issue Intelligent Edge Computing for Smart Cities)
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23 pages, 1549 KiB  
Article
Concept Drift Adaptation Techniques in Distributed Environment for Real-World Data Streams
by Hassan Mehmood, Panos Kostakos, Marta Cortes, Theodoros Anagnostopoulos, Susanna Pirttikangas and Ekaterina Gilman
Smart Cities 2021, 4(1), 349-371; https://doi.org/10.3390/smartcities4010021 - 14 Mar 2021
Cited by 51 | Viewed by 9255
Abstract
Real-world data streams pose a unique challenge to the implementation of machine learning (ML) models and data analysis. A notable problem that has been introduced by the growth of Internet of Things (IoT) deployments across the smart city ecosystem is that the statistical [...] Read more.
Real-world data streams pose a unique challenge to the implementation of machine learning (ML) models and data analysis. A notable problem that has been introduced by the growth of Internet of Things (IoT) deployments across the smart city ecosystem is that the statistical properties of data streams can change over time, resulting in poor prediction performance and ineffective decisions. While concept drift detection methods aim to patch this problem, emerging communication and sensing technologies are generating a massive amount of data, requiring distributed environments to perform computation tasks across smart city administrative domains. In this article, we implement and test a number of state-of-the-art active concept drift detection algorithms for time series analysis within a distributed environment. We use real-world data streams and provide critical analysis of results retrieved. The challenges of implementing concept drift adaptation algorithms, along with their applications in smart cities, are also discussed. Full article
(This article belongs to the Special Issue Intelligent Edge Computing for Smart Cities)
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15 pages, 3985 KiB  
Article
TreeVibes: Modern Tools for Global Monitoring of Trees for Borers
by Iraklis Rigakis, Ilyas Potamitis, Nicolaos-Alexandros Tatlas, Stelios M. Potirakis and Stavros Ntalampiras
Smart Cities 2021, 4(1), 271-285; https://doi.org/10.3390/smartcities4010017 - 27 Feb 2021
Cited by 16 | Viewed by 5246
Abstract
Is there a wood-feeding insect inside a tree or wooden structure? We investigate several ways of how deep learning approaches can massively scan recordings of vibrations stemming from probed trees to infer their infestation state with wood-boring insects that feed and move inside [...] Read more.
Is there a wood-feeding insect inside a tree or wooden structure? We investigate several ways of how deep learning approaches can massively scan recordings of vibrations stemming from probed trees to infer their infestation state with wood-boring insects that feed and move inside wood. The recordings come from remotely controlled devices that sample the internal soundscape of trees on a 24/7 basis and wirelessly transmit brief recordings of the registered vibrations to a cloud server. We discuss the different sources of vibrations that can be picked up from trees in urban environments and how deep learning methods can focus on those originating from borers. Our goal is to match the problem of the accelerated—due to global trade and climate change— establishment of invasive xylophagus insects by increasing the capacity of inspection agencies. We aim at introducing permanent, cost-effective, automatic monitoring of trees based on deep learning techniques, in commodity entry points as well as in wild, urban and cultivated areas in order to effect large-scale, sustainable pest-risk analysis and management of wood boring insects such as those from the Cerambycidae family (longhorn beetles). Full article
(This article belongs to the Special Issue Intelligent Edge Computing for Smart Cities)
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15 pages, 3263 KiB  
Article
A Predictive Vehicle Ride Sharing Recommendation System for Smart Cities Commuting
by Theodoros Anagnostopoulos
Smart Cities 2021, 4(1), 177-191; https://doi.org/10.3390/smartcities4010010 - 27 Jan 2021
Cited by 15 | Viewed by 4369
Abstract
Smart Cities (or Cities 2.0) are an evolution in citizen habitation. In such cities, transport commuting is changing rapidly with the proliferation of contemporary vehicular technology. New models of vehicle ride sharing systems are changing the way citizens commute in their daily movement [...] Read more.
Smart Cities (or Cities 2.0) are an evolution in citizen habitation. In such cities, transport commuting is changing rapidly with the proliferation of contemporary vehicular technology. New models of vehicle ride sharing systems are changing the way citizens commute in their daily movement schedule. The use of a private vehicle per single passenger transportation is no longer viable in sustainable Smart Cities (SC) because of the vehicles’ resource allocation and urban pollution. The current research on car ride sharing systems is widely expanding in a range of contemporary technologies, however, without covering a multidisciplinary approach. In this paper, the focus is on performing a multidisciplinary research on car riding systems taking into consideration personalized user mobility behavior by providing next destination prediction as well as a recommender system based on riders’ personalized information. Specifically, it proposes a predictive vehicle ride sharing system for commuting, which has impact on the SC green ecosystem. The adopted system also provides a recommendation to citizens to select the persons they would like to commute with. An Artificial Intelligence (AI)-enabled weighted pattern matching model is used to assess user movement behavior in SC and provide the best predicted recommendation list of commuting users. Citizens are then able to engage a current trip to next destination with the more suitable user provided by the list. An experimented is conducted with real data from the municipality of New Philadelphia, in SC of Athens, Greece, to implement the proposed system and observe certain user movement behavior. The results are promising for the incorporation of the adopted system to other SCs. Full article
(This article belongs to the Special Issue Intelligent Edge Computing for Smart Cities)
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