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IoT Enabling Technologies for Smart Cities: Challenges and Approaches (Volume II)

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

Deadline for manuscript submissions: closed (10 March 2024) | Viewed by 16509

Special Issue Editors


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Guest Editor
Department of Engineering and Architecture, University of Parma, Parco Area delle Scienze, 181/A, 43124 Parma, Italy
Interests: Internet of Things; smart agriculture; smart cities; big stream; data
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering (DIEE), University of Cagliari, Piazza d’Armi, 09123 Cagliari, Italy
Interests: Internet of Things; smart cities; localization; wireless networking
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Engineering and Architecture, University of Parma, Parco Area delle Scienze, 181/A, 43124 Parma, Italy
Interests: Internet of Things; networking and communication; signal processing; smart systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are happy to launch this follow-up Sensors Special Issue titled "IoT Enabling Technologies for Smart Cities: Challenges and Approaches (Volume II)". Thanks to the many valuable submissions to our previous Special Issue (“IoT Enabling Technologies for Smart Cities: Challenges and Approaches”; link here: https://www.mdpi.com/journal/sensors/special_issues/IETSC), we have already presented some recent developments in this field. The ongoing diffusion of Internet of Things (IoT) technologies is opening new possibilities in different and heterogeneous fields, with remarkable applications being associated with the smart city paradigm, continuously evolving and representing the future of modern cities. On a wider perspective, the smart city concept involves the integration of IoT and Information Communication Technologies (ICT) into different aspects of the city’s management, with the aims of (i) addressing the exponential growth of urbanization and population and (ii) increasing people's life quality. Moreover, the smart city paradigm is also strictly connected to sustainability aspects, taking into account the environmental impact’s reduction of urban activities, the optimized management of energy resources, and the design of innovative services and solutions for citizens. Abiding by this new paradigm, several cities have started a process of strong innovation in different sectors, also on the basis of significant investments. Despite the progress achieved thus far, many challenges are currently open, due to the complexity of sustainable smart city scenarios, which require integration and cooperation between a multitude of actors and technologies.

This Special Issue thus encourages authors, from academia and industry, to submit new research providing a novel insight into the challenges and the approaches in the development of IoT infrastructures for future sustainable smart cities.

The Special Issue topics include, but are not limited to, the following: 

  • IoT technology integration for smart cities;
  • IoT architectures and infrastructures for smart cities;
  • Blockchain technologies applied to smart cities;
  • Machine-learning-based and Artificial-Intelligence-oriented technologies applied to smart cities;
  • Smart urban mobility and transportation in smart cities;
  • IoT-aided localization techniques in smart cities;
  • IoT applications for tourism and education scenarios in smart cities;
  • Edge/cloud-computing-based solutions for IoT smart city data;
  • Big data in smart cities;
  • Smart city services based on IoT data;
  • Sustainable resource management in smart cities;
  • Environmental monitoring in smart cities;
  • Smart waste management in smart cities;
  • Vehicular networking for traffic management in smart cities;
  • Cryptography, security, and privacy issues and challenges in IoT-enabled smart cities;
  • Governance and regulation innovation in smart cities;
  • UAV-supported IoT systems for smart cities;
  • IoT-enhanced smart transport;
  • Artificial Intelligence of Things (AIoT) for smart cities.

Dr. Laura Belli
Dr. Luca Davoli
Dr. Marco Martalò
Prof. Dr. Gianluigi Ferrari 
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.

Published Papers (7 papers)

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Research

13 pages, 1010 KiB  
Communication
A Novel Hybrid Convolutional Neural Network- and Gated Recurrent Unit-Based Paradigm for IoT Network Traffic Attack Detection in Smart Cities
by Brij B. Gupta , Kwok Tai Chui, Akshat Gaurav , Varsha Arya  and Priyanka Chaurasia 
Sensors 2023, 23(21), 8686; https://doi.org/10.3390/s23218686 - 24 Oct 2023
Cited by 4 | Viewed by 1091
Abstract
Internet of Things (IoT) devices within smart cities, require innovative detection methods. This paper addresses this critical challenge by introducing a deep learning-based approach for the detection of network traffic attacks in IoT ecosystems. Leveraging the Kaggle dataset, our model integrates Convolutional Neural [...] Read more.
Internet of Things (IoT) devices within smart cities, require innovative detection methods. This paper addresses this critical challenge by introducing a deep learning-based approach for the detection of network traffic attacks in IoT ecosystems. Leveraging the Kaggle dataset, our model integrates Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRUs) to capture both spatial and sequential features in network traffic data. We trained and evaluated our model over ten epochs, achieving an impressive overall accuracy rate of 99%. The classification report reveals the model’s proficiency in distinguishing various attack categories, including ‘Normal’, ‘DoS’ (Denial of Service), ‘Probe’, ‘U2R’ (User to Root), and ‘Sybil’. Additionally, the confusion matrix offers valuable insights into the model’s performance across these attack types. In terms of overall accuracy, our model achieves an impressive accuracy rate of 99% across all attack categories. The weighted- average F1-score is also 99%, showcasing the model’s robust performance in classifying network traffic attacks in IoT devices for smart cities. This advanced architecture exhibits the potential to fortify IoT device security in the complex landscape of smart cities, effectively contributing to the safeguarding of critical infrastructure Full article
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16 pages, 5771 KiB  
Article
An Intelligent Water Monitoring IoT System for Ecological Environment and Smart Cities
by Shih-Lun Chen, He-Sheng Chou, Chun-Hsiang Huang, Chih-Yun Chen, Liang-Yu Li, Ching-Hui Huang, Yu-Yu Chen, Jyh-Haw Tang, Wen-Hui Chang and Je-Sheng Huang
Sensors 2023, 23(20), 8540; https://doi.org/10.3390/s23208540 - 18 Oct 2023
Cited by 3 | Viewed by 2786
Abstract
Global precipitation is becoming increasingly intense due to the extreme climate. Therefore, creating new technology to manage water resources is crucial. To create a sustainable urban and ecological environment, a water level and water quality control system implementing artificial intelligence is presented in [...] Read more.
Global precipitation is becoming increasingly intense due to the extreme climate. Therefore, creating new technology to manage water resources is crucial. To create a sustainable urban and ecological environment, a water level and water quality control system implementing artificial intelligence is presented in this research. The proposed smart monitoring system consists of four sensors (two different liquid level sensors, a turbidity and pH sensor, and a water oxygen sensor), a control module (an MCU, a motor, a pump, and a drain), and a power and communication system (a solar panel, a battery, and a wireless communication module). The system focuses on low-cost Internet of Things (IoT) devices along with low power consumption and high precision. This proposal collects rainfall from the preceding 10 years in the application region as well as the region’s meteorological bureau’s weekly weather report and uses artificial intelligence to compute the appropriate water level. More importantly, the adoption of dynamic adjustment systems can reserve and modify water resources in the application region more efficiently. Compared to existing technologies, the measurement approach utilized in this study not only achieves cost savings exceeding 60% but also enhances water level measurement accuracy by over 15% through the successful implementation of water level calibration decisions utilizing multiple distinct sensors. Of greater significance, the dynamic adjustment systems proposed in this research offer the potential for conserving water resources by more than 15% in an effective manner. As a result, the adoption of this technology may efficiently reserve and distribute water resources for smart cities as well as reduce substantial losses caused by anomalous water resources, such as floods, droughts, and ecological concerns. Full article
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14 pages, 1004 KiB  
Article
Probabilistic Coverage Constraint Task Assignment with Privacy Protection in Vehicular Crowdsensing
by Zhe Li, Xiaolong Liu, Yang Huang and Honglong Chen
Sensors 2023, 23(18), 7798; https://doi.org/10.3390/s23187798 - 11 Sep 2023
Viewed by 943
Abstract
The increasing popularity of portable smart devices has led to the emergence of vehicular crowdsensing as a novel approach for real-time sensing and environmental data collection, garnering significant attention across various domains. Within vehicular crowdsensing, task assignment stands as a fundamental research challenge. [...] Read more.
The increasing popularity of portable smart devices has led to the emergence of vehicular crowdsensing as a novel approach for real-time sensing and environmental data collection, garnering significant attention across various domains. Within vehicular crowdsensing, task assignment stands as a fundamental research challenge. As the number of vehicle users and perceived tasks grows, the design of efficient task assignment schemes becomes crucial. However, existing research solely focuses on task deadlines, neglecting the importance of task duration. Additionally, the majority of privacy protection mechanisms in the current task assignment process emphasize safeguarding user location information but overlook the protection of user-perceived duration. This lack of protection exposes users to potential time-aware inference attacks, enabling attackers to deduce user schedules and device information. To address these issues in opportunistic task assignment for vehicular crowdsensing, this paper presents the minimum number of participants required under the constraint of probability coverage and proposes the User-Based Task Assignment (UBTA) mechanism, which selects the smallest set of participants to minimize the payment cost while measuring the probability of accomplishing perceived tasks by user combinations. To ensure privacy protection during opportunistic task assignment, a privacy protection method based on differential privacy is introduced. This method fuzzifies the sensing duration of vehicle users and calculates the probability of vehicle users completing sensing tasks, thus avoiding the exposure of users’ sensitive data while effectively assigning tasks. The efficacy of the proposed algorithm is demonstrated through theoretical analysis and a comprehensive set of simulation experiments. Full article
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23 pages, 6261 KiB  
Article
A Probabilistic Digital Twin for Leak Localization in Water Distribution Networks Using Generative Deep Learning
by Nikolaj T. Mücke, Prerna Pandey, Shashi Jain, Sander M. Bohté and Cornelis W. Oosterlee
Sensors 2023, 23(13), 6179; https://doi.org/10.3390/s23136179 - 5 Jul 2023
Cited by 2 | Viewed by 1680
Abstract
Localizing leakages in large water distribution systems is an important and ever-present problem. Due to the complexity originating from water pipeline networks, too few sensors, and noisy measurements, this is a highly challenging problem to solve. In this work, we present a methodology [...] Read more.
Localizing leakages in large water distribution systems is an important and ever-present problem. Due to the complexity originating from water pipeline networks, too few sensors, and noisy measurements, this is a highly challenging problem to solve. In this work, we present a methodology based on generative deep learning and Bayesian inference for leak localization with uncertainty quantification. A generative model, utilizing deep neural networks, serves as a probabilistic surrogate model that replaces the full equations, while at the same time also incorporating the uncertainty inherent in such models. By embedding this surrogate model into a Bayesian inference scheme, leaks are located by combining sensor observations with a model output approximating the true posterior distribution for possible leak locations. We show that our methodology enables producing fast, accurate, and trustworthy results. It showed a convincing performance on three problems with increasing complexity. For a simple test case, the Hanoi network, the average topological distance (ATD) between the predicted and true leak location ranged from 0.3 to 3 with a varying number of sensors and level of measurement noise. For two more complex test cases, the ATD ranged from 0.75 to 4 and from 1.5 to 10, respectively. Furthermore, accuracies upwards of 83%, 72%, and 42% were achieved for the three test cases, respectively. The computation times ranged from 0.1 to 13 s, depending on the size of the neural network employed. This work serves as an example of a digital twin for a sophisticated application of advanced mathematical and deep learning techniques in the area of leak detection. Full article
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21 pages, 31152 KiB  
Article
StreetAware: A High-Resolution Synchronized Multimodal Urban Scene Dataset
by Yurii Piadyk, Joao Rulff, Ethan Brewer, Maryam Hosseini, Kaan Ozbay, Murugan Sankaradas, Srimat Chakradhar and Claudio Silva
Sensors 2023, 23(7), 3710; https://doi.org/10.3390/s23073710 - 3 Apr 2023
Cited by 2 | Viewed by 3432
Abstract
Access to high-quality data is an important barrier in the digital analysis of urban settings, including applications within computer vision and urban design. Diverse forms of data collected from sensors in areas of high activity in the urban environment, particularly at street intersections, [...] Read more.
Access to high-quality data is an important barrier in the digital analysis of urban settings, including applications within computer vision and urban design. Diverse forms of data collected from sensors in areas of high activity in the urban environment, particularly at street intersections, are valuable resources for researchers interpreting the dynamics between vehicles, pedestrians, and the built environment. In this paper, we present a high-resolution audio, video, and LiDAR dataset of three urban intersections in Brooklyn, New York, totaling almost 8 unique hours. The data were collected with custom Reconfigurable Environmental Intelligence Platform (REIP) sensors that were designed with the ability to accurately synchronize multiple video and audio inputs. The resulting data are novel in that they are inclusively multimodal, multi-angular, high-resolution, and synchronized. We demonstrate four ways the data could be utilized — (1) to discover and locate occluded objects using multiple sensors and modalities, (2) to associate audio events with their respective visual representations using both video and audio modes, (3) to track the amount of each type of object in a scene over time, and (4) to measure pedestrian speed using multiple synchronized camera views. In addition to these use cases, our data are available for other researchers to carry out analyses related to applying machine learning to understanding the urban environment (in which existing datasets may be inadequate), such as pedestrian-vehicle interaction modeling and pedestrian attribute recognition. Such analyses can help inform decisions made in the context of urban sensing and smart cities, including accessibility-aware urban design and Vision Zero initiatives. Full article
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14 pages, 2008 KiB  
Article
An IoT Enable Anomaly Detection System for Smart City Surveillance
by Muhammad Islam, Abdulsalam S. Dukyil, Saleh Alyahya and Shabana Habib
Sensors 2023, 23(4), 2358; https://doi.org/10.3390/s23042358 - 20 Feb 2023
Cited by 13 | Viewed by 3602
Abstract
Since the advent of visual sensors, smart cities have generated massive surveillance video data, which can be intelligently inspected to detect anomalies. Computer vision-based automated anomaly detection techniques replace human intervention to secure video surveillance applications in place from traditional video surveillance systems [...] Read more.
Since the advent of visual sensors, smart cities have generated massive surveillance video data, which can be intelligently inspected to detect anomalies. Computer vision-based automated anomaly detection techniques replace human intervention to secure video surveillance applications in place from traditional video surveillance systems that rely on human involvement for anomaly detection, which is tedious and inaccurate. Due to the diverse nature of anomalous events and their complexity, it is however, very challenging to detect them automatically in a real-world scenario. By using Artificial Intelligence of Things (AIoT), this research work presents an efficient and robust framework for detecting anomalies in surveillance large video data. A hybrid model integrating 2D-CNN and ESN are proposed in this research study for smart surveillance, which is an important application of AIoT. The CNN is used as feature extractor from input videos which are then inputted to autoencoder for feature refinement followed by ESN for sequence learning and anomalous events detection. The proposed model is lightweight and implemented over edge devices to ensure their capability and applicability over AIoT environments in a smart city. The proposed model significantly enhanced performance using challenging surveillance datasets compared to other methods. Full article
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22 pages, 447 KiB  
Article
Smart Streets as a Cyber-Physical Social Platform: A Conceptual Framework
by Theo Lynn and Charles Wood
Sensors 2023, 23(3), 1399; https://doi.org/10.3390/s23031399 - 26 Jan 2023
Cited by 4 | Viewed by 1989
Abstract
Streets perform a number of important functions and have a wide range of activities performed in them. There is a small but growing focus on streets as a more generalisable, atomised, and therefore more manageable unit of development and analysis than cities. Despite [...] Read more.
Streets perform a number of important functions and have a wide range of activities performed in them. There is a small but growing focus on streets as a more generalisable, atomised, and therefore more manageable unit of development and analysis than cities. Despite the public realm being one of the largest physical spaces on streets, the impact and potential of digitalisation projects on this realm is rarely considered. In this article, the smartness of a street is derived from the cyber-physical social infrastructure in the public realm, including data obtained from sensors, the interconnection between different services, technologies and social actors, intelligence derived from analysis of the data, and optimisation of operations within a street. This article conceptualises smart streets as basic units of urban space that leverage cyber-physical social infrastructure to provide and enable enhanced services to and between stakeholders, and through stakeholders’ use of the street, generate data to optimise its services, capabilities, and value to stakeholders. A proposed conceptual framework is used to identify and explore how streets can be augmented and create value through cyber-physical social infrastructure and digital enhancements. We conclude with a discussion of future avenues of research. Full article
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