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Artificial Intelligence Methodologies for Networked Sensors in Smart Cities

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

Deadline for manuscript submissions: closed (15 January 2021) | Viewed by 26543

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Special Issue Editors

Dr. Burak Kantarci

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Guest Editor

School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada
Interests: Internet of Things; cybersecurity; crowdsensing and social networks; artificial intelligence; connected vehicles; digital health (d-health); sustainable ICT
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Sema Oktug

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Guest Editor

Department of Computer Engineering, Istanbul Technical University, Maslak 34469 Istanbul, Turkey
Interests: wireless networks (sensor, mesh, and cognitive); issues related to Internet of Things; performance modelling of computer networks
Special Issues, Collections and Topics in MDPI journals

Dr. Tolga Soyata

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Guest Editor

Department of Electrical and Computer Engineering, State University of New York (SUNY)- Albany, NY, USA
Interests: cyber physical systems; digital health (d-health); High-Performance Medical Data processing and visualization

Special Issue Information

Dear Colleagues,

Two main drivers of smart cities, large-scale sensing-systems and big data concepts, aim to integrate everyday services and artificial intelligence (AI), with the goal of minimizing human intervention. Services such as transportation, utility, public safety, public health, and environmental health are some of the services that utilize AI-based methodologies to realize sustainable cities. Various challenges need to be addressed before AI integration with networked sensors in smart city services is widely adopted.

With this Special Issue on ‘’Artificial Intelligence Methodologies for Networked Sensors in Smart Cities’’, we aim to provide a high-quality collection of recent developments on the tools and platforms for analysis and simulations, as well as practical test beds for the integration of AI-assisted smart sensing-concepts with smart city applications. This Special Issue solicits submissions from scientists, engineers, manufacturers, and service providers, who present novel work contributions of research and innovation that co-utilize AI and sensors in smart cities. The articles that this issue seeks must be original work or comprehensive reviews, which have not been published, or submitted for publication elsewhere; topics of interest include, but are not limited to, the following:

AI models for ubiquitous sensing and internet of things
AI-assisted sensing-solutions for smart utilities
Sensing systems in smart power
Intelligent sensing-systems in lighting
Participatory and opportunistic sensing-solutions for smart cities
AI in security, privacy, and trust in smart cities sensing-systems
Analytics platforms using AI methodologies for multi-sensory data in smart cities
Machine learning-based design of social sensing: citizens as sensors in smart cities
IoT-driven sensing-solutions for smart health services
Applied AI in the control of nano-bio sensing-systems for the smart environment
AI-based virtualization of IoT network functions
Intelligent protocols for self-organizing network management in smart city deployments
Data plane and control plane design issues in ultra-dense smart city topologies
Software-driven flow management in smart city applications
Global sensor deployment case studies in smart cities
Self-healing sensor deployments and redundant sensor hardware design in smart cities
Ethical issues in the application of AI methods on sensory data
Medical cyber physical systems: integrating AI and medical sensing for decision support

Dr. Burak Kantarci
Prof. Dr. Sema Oktug
Dr. Tolga Soyata
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. Sensors 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 2600 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

artificial Intelligence
machine learning
deep neural networks
sensors
smart cities
wireless sensor networks
actuators
Internet of Things
social sensing
cyber-physical systems

Published Papers (4 papers)

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Research

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18 pages, 2033 KiB

Open AccessArticle

Devising Digital Twins DNA Paradigm for Modeling ISO-Based City Services

by Hawazin Faiz Badawi, Fedwa Laamarti and Abdulmotaleb El Saddik

Sensors 2021, 21(4), 1047; https://doi.org/10.3390/s21041047 - 04 Feb 2021

Cited by 5 | Viewed by 2747

Abstract

Digital twins (DTs) technology has recently gained attention within the research community due to its potential to help build sustainable smart cities. However, there is a gap in the literature: currently no unified model for city services has been proposed that can guarantee interoperability across cities, capture each city’s unique characteristics, and act as a base for modeling digital twins. This research aims to fill that gap. In this work, we propose the DT-DNA model in which we design a city services digital twin, with the goal of reflecting the real state of development of a city’s services towards enhancing its citizens’ quality of life (QoL). As it was designed using ISO 37120, one of the leading international standards for city services, the model guarantees interoperability and allows for easy comparison of services within and across cities. In order to test our model, we built DT-DNA sequences of services in both Quebec City and Boston and then used a DNA alignment tool to determine the matching percentage between them. Results show that the DT-DNA sequences of services in both cities are 46.5% identical. Ground truth comparisons show a similar result, which provides a preliminary proof-of-concept for the applicability of the proposed model and framework. These results also imply that one city performs better than the other. Therefore, we propose an algorithm to compare cities based on the proposed DT-DNA and, using Boston and Quebec City as a case study, demonstrate that Boston has better services towards enhancing QoL for its citizens. Full article

(This article belongs to the Special Issue Artificial Intelligence Methodologies for Networked Sensors in Smart Cities)

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19 pages, 4167 KiB

Open AccessArticle

Non-Technical Loss Detection in Power Grids with Statistical Profile Images Based on Semi-Supervised Learning

by Jiangteng Li and Fei Wang

Sensors 2020, 20(1), 236; https://doi.org/10.3390/s20010236 - 31 Dec 2019

Cited by 16 | Viewed by 2658

Abstract

In order to keep track of the operational state of power grids, the world’s largest sensor system, smart grid, was built by deploying hundreds of millions of smart meters. Such a system makes it possible to discover and make quick response to any hidden threat to the entire power grid. Non-technical losses (NTLs) have always been a major concern for their consequent security risks as well as immeasurable revenue loss. However, various causes of NTL may have different characteristics reflected in the data. Accurately capturing these anomalies faced with such a large scale of collected data records is rather tricky as a result. In this paper, we proposed a new methodology of detecting abnormal electricity consumptions. We did a transformation of the collected time-series data which turns it into an image representation that could well reflect users’ relatively long term consumption behaviors. Inspired by the excellent neural network architecture used for objective detection in computer vision, we designed our deep learning model that takes the transformed images as input and yields joint features inferred from the multiple aspects the input provides. Considering the limited amount of labeled samples, especially the abnormal ones, we used our model in a semi-supervised fashion that was brought about in recent years. The model is tested on samples which are verified by on-field inspections and our method showed significant improvement for NTL detection compared with the state-of-the-art methods. Full article

(This article belongs to the Special Issue Artificial Intelligence Methodologies for Networked Sensors in Smart Cities)

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13 pages, 1660 KiB

Open AccessArticle

Road Environment Semantic Segmentation with Deep Learning from MLS Point Cloud Data

by Jesús Balado, Joaquín Martínez-Sánchez, Pedro Arias and Ana Novo

Sensors 2019, 19(16), 3466; https://doi.org/10.3390/s19163466 - 08 Aug 2019

Cited by 68 | Viewed by 7078

Abstract

In the near future, the communication between autonomous cars will produce a network of sensors that will allow us to know the state of the roads in real time. Lidar technology, upon which most autonomous cars are based, allows the acquisition of 3D geometric information of the environment. The objective of this work is to use point clouds acquired by Mobile Laser Scanning (MLS) to segment the main elements of road environment (road surface, ditches, guardrails, fences, embankments, and borders) through the use of PointNet. Previously, the point cloud was automatically divided into sections in order for semantic segmentation to be scalable to different case studies, regardless of their shape or length. An overall accuracy of 92.5% has been obtained, but with large variations between classes. Elements with a greater number of points have been segmented more effectively than the other elements. In comparison with other point-by-point extraction and ANN-based classification techniques, the same success rates have been obtained for road surfaces and fences, and better results have been obtained for guardrails. Semantic segmentation with PointNet is suitable when segmenting the scene as a whole, however, if certain classes have more interest, there are other alternatives that do not need a high training cost. Full article

(This article belongs to the Special Issue Artificial Intelligence Methodologies for Networked Sensors in Smart Cities)

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Review

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29 pages, 867 KiB

Open AccessReview

Federated Learning in Smart City Sensing: Challenges and Opportunities

by Ji Chu Jiang, Burak Kantarci, Sema Oktug and Tolga Soyata

Sensors 2020, 20(21), 6230; https://doi.org/10.3390/s20216230 - 31 Oct 2020

Cited by 126 | Viewed by 13353

Abstract

Smart Cities sensing is an emerging paradigm to facilitate the transition into smart city services. The advent of the Internet of Things (IoT) and the widespread use of mobile devices with computing and sensing capabilities has motivated applications that require data acquisition at a societal scale. These valuable data can be leveraged to train advanced Artificial Intelligence (AI) models that serve various smart services that benefit society in all aspects. Despite their effectiveness, legacy data acquisition models backed with centralized Machine Learning models entail security and privacy concerns, and lead to less participation in large-scale sensing and data provision for smart city services. To overcome these challenges, Federated Learning is a novel concept that can serve as a solution to the privacy and security issues encountered within the process of data collection. This survey article presents an overview of smart city sensing and its current challenges followed by the potential of Federated Learning in addressing those challenges. A comprehensive discussion of the state-of-the-art methods for Federated Learning is provided along with an in-depth discussion on the applicability of Federated Learning in smart city sensing; clear insights on open issues, challenges, and opportunities in this field are provided as guidance for the researchers studying this subject matter. Full article

(This article belongs to the Special Issue Artificial Intelligence Methodologies for Networked Sensors in Smart Cities)

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