Artificial Intelligence for Smart Cities

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Smart System Infrastructure and Applications".

Deadline for manuscript submissions: closed (20 June 2023) | Viewed by 5016

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Department of Information Management, Asia Eastern University of Science and Technology, Taipei 22064, Taiwan
Interests: short-term load forecasting; intelligent forecasting technologies (e.g., neural networks, knowledge–based expert systems, fuzzy inference systems, evolutionary computation, etc.); hybrid forecasting models (e.g., hybridizing traditional models with intelligent technologies, or hybridizing two or more different models to form a novel forecasting model); novel intelligent methodologies (chaos theory; cloud theory; quantum theory)
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Special Issue Information

Dear Colleagues,

A smart city uses information and communication technology (ICT) to improve operational efficiency, share information with the public and provide a better quality of government service and citizen welfare. The main goal of a smart city is to optimize city functions and promote economic growth while also improving the quality of life for citizens by using smart technologies and data analysis. The value lies in how this technology is used rather than simply how much technology is available. As artificial intelligence technologies mature, it becomes easier to meet the demands of the development for smart cities.

Artificial intelligence (AI) technologies (e.g., IoT, blockchain, virtual reality, fuzzy inference systems, deep-learning-based neural networks (DNNs), convolutional neural networks, stacked autoencoders, deep reinforcement learning, meta-learning, life-long learning, graph neural networks, meta-heuristic algorithms) have played an important role in enhancing the quality of service operations from smart cities which combine people, processes, and machines, to impact the overall economical productions. Meanwhile, these emerging AI technologies also provide enough support for the connectivity of buildings, data, energy, transport, and governance, which is leading to many innovations across industrial applications to form more mature smart cities.

Hence, there is a demand to further explore the abundant applications of these AI technologies or intelligent solutions to improve/enhance any operating components, such as the quality of manufacturing, supply chain management, and Industry 5.0 for future smart cities. Thus, this Special Issue aims to provide a platform for the discussions of novel scientific and technological insights, principles, algorithms, and experiences in topics including but not limited to:

  • Novel AI-based analytics on data to improve efficiency, product quality and employee safety;
  • Data-driven innovations for demand planning and logistics management;
  • AI-based and green-based supply chains;
  • Applications of IoT for tracking production across entire processes and supply chains;
  • Advanced AI technologies for enhancing the connectivity of any components of smart cities;
  • Hybrid meta-heuristic algorithms with AI technologies in enhancing the connectivity of any components for smart cities;
  • Intelligent solutions for future smart cities;
  • Smart transportation and traffic prediction for smart cities using IoT;
  • Cloud and data analytics for the effective analysis of industrial data;
  • Smart grid and sustainable energy solutions for smart cities;
  • Smart governance and smart education;
  • Smart cloud storage and data access.

Prof. Dr. Wei-Chiang Hong
Guest Editor

Manuscript Submission Information

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Keywords

  • IoT
  • AI
  • smart cities
  • smart transportation
  • industrial data

Published Papers (3 papers)

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Research

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18 pages, 3674 KiB  
Article
Bus Travel Time Prediction Based on the Similarity in Drivers’ Driving Styles
by Zhenzhong Yin and Bin Zhang
Future Internet 2023, 15(7), 222; https://doi.org/10.3390/fi15070222 - 21 Jun 2023
Cited by 2 | Viewed by 1172
Abstract
Providing accurate and real-time bus travel time information is crucial for both passengers and public transportation managers. However, in the traditional bus travel time prediction model, due to the lack of consideration of the influence of different bus drivers’ driving styles on the [...] Read more.
Providing accurate and real-time bus travel time information is crucial for both passengers and public transportation managers. However, in the traditional bus travel time prediction model, due to the lack of consideration of the influence of different bus drivers’ driving styles on the bus travel time, the prediction result is not ideal. In the traditional bus travel time prediction model, the historical travel data of all drivers in the entire bus line are usually used for training and prediction. Due to great differences in individual driving styles, the eigenvalues of drivers’ driving parameters are widely distributed. Therefore, the prediction accuracy of the model trained by this dataset is low. At the same time, the training time of the model is too long due to the large sample size, making it difficult to provide a timely prediction in practical applications. However, if only the historical dataset of a single driver is used for training and prediction, the amount of training data is too small, and it is also difficult to accurately predict travel time. To solve these problems, this paper proposes a method to predict bus travel times based on the similarity of drivers’ driving styles. Firstly, the historical travel time data of different drivers are clustered, and then the corresponding types of drivers’ historical data are used to predict the travel time, so as to improve the accuracy and speed of the travel time prediction. We evaluated our approach using a real-world bus trajectory dataset collected in Shenyang, China. The experimental results show that the accuracy of the proposed method is 13.4% higher than that of the traditional method. Full article
(This article belongs to the Special Issue Artificial Intelligence for Smart Cities)
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16 pages, 3570 KiB  
Article
A Deep Learning Approach to Detect Failures in Bridges Based on the Coherence of Signals
by Francesco Morgan Bono, Luca Radicioni, Simone Cinquemani, Lorenzo Benedetti, Gabriele Cazzulani, Claudio Somaschini and Marco Belloli
Future Internet 2023, 15(4), 119; https://doi.org/10.3390/fi15040119 - 25 Mar 2023
Cited by 2 | Viewed by 1123
Abstract
Structural health monitoring of civil infrastructure, such as bridges and buildings, has become a trending topic in the last few years. The key factor is the technological push given by new technologies that permit the acquisition, storage, processing and visualisation of data in [...] Read more.
Structural health monitoring of civil infrastructure, such as bridges and buildings, has become a trending topic in the last few years. The key factor is the technological push given by new technologies that permit the acquisition, storage, processing and visualisation of data in real time, thus assessing a structure’s health condition. However, data related to anomaly conditions are difficult to retrieve, and, by the time those conditions are met, in general, it is too late. For this reason, the problem becomes unsupervised, since no labelled data are available, and anomaly detection algorithms are usually adopted in this context. This research proposes a novel algorithm that transforms the intrinsically unsupervised problem into a supervised one for condition monitoring purposes. Considering a bridge equipped with N sensors, which measure static structural quantities (rotations of the piers) and environmental parameters, exploiting the relationships between different physical variables and determining how these relationships change over time can indicate the bridge’s health status. In particular, this algorithm involves the training of N models, each of them able to estimate the quantity measured via a sensor by using the others’ N1 measurements. Hence, the system can be represented by the ensemble of the N models. In this way, for each sensor, it is possible to compare the real measurement with the predicted one and evaluate the residual between the two; this difference can be addressed as a symptom of changes in the structure with respect to the condition regarded as nominal. This approach is applied to a real test case, i.e., Candia Bridge in Italy, and it is compared with a state-of-the-art anomaly detector (namely an autoencoder) in order to validate its robustness. Full article
(This article belongs to the Special Issue Artificial Intelligence for Smart Cities)
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Review

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20 pages, 1673 KiB  
Review
Indoor Occupancy Sensing via Networked Nodes (2012–2022): A Review
by Muhammad Emad-Ud-Din and Ya Wang
Future Internet 2023, 15(3), 116; https://doi.org/10.3390/fi15030116 - 22 Mar 2023
Cited by 2 | Viewed by 1369
Abstract
In the past decade, different sensing mechanisms and algorithms have been developed to detect or estimate indoor occupancy. One of the most recent advancements is using networked sensor nodes to create a more comprehensive occupancy detection system where multiple sensors can identify human [...] Read more.
In the past decade, different sensing mechanisms and algorithms have been developed to detect or estimate indoor occupancy. One of the most recent advancements is using networked sensor nodes to create a more comprehensive occupancy detection system where multiple sensors can identify human presence within more expansive areas while delivering enhanced accuracy compared to a system that relies on stand-alone sensor nodes. The present work reviews the studies from 2012 to 2022 that use networked sensor nodes to detect indoor occupancy, focusing on PIR-based sensors. Methods are compared based on pivotal ADPs that play a significant role in selecting an occupancy detection system for applications such as Health and Safety or occupant comfort. These parameters include accuracy, information requirement, maximum sensor failure and minimum observation rate, and feasible detection area. We briefly describe the overview of occupancy detection criteria used by each study and introduce a metric called “sensor node deployment density” through our analysis. This metric captures the strength of network-level data filtering and fusion algorithms found in the literature. It is hinged on the fact that a robust occupancy estimation algorithm requires a minimal number of nodes to estimate occupancy. This review only focuses on the occupancy estimation models for networked sensor nodes. It thus provides a standardized insight into networked nodes’ occupancy sensing pipelines, which employ data fusion strategies, network-level machine learning algorithms, and occupancy estimation algorithms. This review thus helps determine the suitability of the reviewed methods to a standard set of application areas by analyzing their gaps. Full article
(This article belongs to the Special Issue Artificial Intelligence for Smart Cities)
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