IET Smart Cities最新文献

{"title":"Guest Editorial: Smart cities 2.0: How Artificial Intelligence and Internet of Things are transforming urban living","authors":"Zheng-Yi Chai,&nbsp;Syed Attique Shah,&nbsp;Dirk Draheim,&nbsp;Sufian Hameed,&nbsp;Muhammad Mazhar Ullah Rathore","doi":"10.1049/smc2.12091","DOIUrl":"https://doi.org/10.1049/smc2.12091","url":null,"abstract":"<p>The evolution of smart cities marks a profound shift in urban life globally, where new technologies enhance efficiency, sustainability, and the quality of life for residents. At the forefront of this transformation are Artificial Intelligence (AI) and the Internet of Things (IoT), driving cities into a new era of innovation. AI and IoT connect devices and infrastructure, enabling cities to process vast amounts of data efficiently. These technologies have already revolutionised various aspects of daily life. IoT, for example, powers intelligent systems in logistics, healthcare, and automotive technology.</p><p>In line with the trend of advancing urban technologies, this Special Issue aims to present the latest advancements and explore the opportunities and challenges of integrating these technologies into city infrastructure. It provides policymakers, urban planners, and stakeholders with critical insights into how these innovations shape the future of our cities. By sharing best practices, we highlight the potential of AI and IoT to foster smarter, sustainable, and more liveable cities. This issue underscores the importance of integrating these technologies into city planning and development, empowering stakeholders to drive positive change and build resilient urban communities.</p><p>The issue contains a curated selection of five papers, each offering groundbreaking insights into how AI and IoT are revolutionising urban living. From air quality prediction to cybersecurity and digital twin cities, these studies showcase diverse applications that are shaping the future of smart cities worldwide.</p><p>All of the papers selected for this Special Issue showcase the diverse and transformative potential of AI and IoT technologies in shaping the future of smart cities. From optimising air quality prediction using advanced hybrid models to enhancing cybersecurity through machine learning-driven approaches, each study contributes unique insights and practical solutions. Additionally, research on digital twin cities, ICT acceptance models, and art-based interventions underscores the interdisciplinary nature of smart city development, emphasising community engagement and sustainable urban planning. These findings collectively highlight the pivotal role of technological innovation in fostering resilience, efficiency, and inclusivity within urban environments. As smart cities continue to evolve, the lessons and advancements presented in this issue provide valuable guidance for policymakers, urban planners, and researchers striving to build more intelligent and liveable cities worldwide.</p>","PeriodicalId":34740,"journal":{"name":"IET Smart Cities","volume":"6 3","pages":"129-131"},"PeriodicalIF":2.1,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/smc2.12091","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142160196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive and dynamic smart traffic light system for efficient management of regular and emergency vehicles at city intersection","authors":"Rafik Zerroug,&nbsp;Zibouda Aliouat,&nbsp;Makhlouf Aliouat,&nbsp;Adel Alti","doi":"10.1049/smc2.12090","DOIUrl":"https://doi.org/10.1049/smc2.12090","url":null,"abstract":"<p>Smart Traffic Light Systems play an important role in urban traffic management. They often rely on cameras and sensors to collect traffic data. However, these methods are limited in terms of vehicle occupancy and queuing. Effective traffic management remains a challenge in urban areas owing to traffic congestion and emergencies. A new system called ADSTLS (Adaptive and Dynamic Smart Traffic Light System) is proposed, which handles traffic management at an intersection and effectively solves the cumbersome problem of traffic congestion while ensuring priority for emergency vehicles. ADSTLS provides fault tolerance to its components and works reliably in most failure situations. Therefore, traffic data is collected from cameras, and useful traffic information is extracted using computer vision and image processing. The proposed system also uses the Weight Chicken Swarm Optimisation algorithm for decision-making to reduce congestion and average vehicle waiting time significantly. ADSTLS was applied to a real case study of EL-Hidhab Setif city intersection. The approach's effectiveness was confirmed by thorough experiments, resulting in a noteworthy decrease in the average vehicle waiting time (31 s) and queue occupation rate (33.82%) across all simulated traffic scenarios. Furthermore, compared to other car types, emergency vehicles usually had much shorter wait times.</p>","PeriodicalId":34740,"journal":{"name":"IET Smart Cities","volume":"6 4","pages":"387-421"},"PeriodicalIF":2.1,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/smc2.12090","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143187088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A hybrid attention-based long short-term memory fast model for thermal regulation of smart residential buildings","authors":"Ashkan Safari,&nbsp;Hamed Kharrati,&nbsp;Afshin Rahimi","doi":"10.1049/smc2.12088","DOIUrl":"10.1049/smc2.12088","url":null,"abstract":"<p>An attention-based long short-term memory (ALSTM)-fast model predictive control (MPC) thermal regulation system for buildings is presented. The proposed system is developed to address the challenges associated with traditional heating, ventilation, and cooling (HVAC) control systems, often designed with fixed setpoints and static control strategies, leading to poor performance and suboptimal energy efficiency. The ALSTM-Fast MPC system, on the other hand, performs the integration of deep learning and optimisation algorithms to predict the thermal behaviour of buildings and optimise the HVAC system control for thermal comfort and energy efficiency. The ALSTM-Fast MPC system was implemented and evaluated on a real-world data collected from a building automation system. Additionally, extensive experiments were conducted to analyse the system's performance. The results demonstrated the system's adaptability to changing thermal dynamics and occupancy patterns and its ability to achieve robust and efficient thermal regulation. As a result, a solution for optimising HVAC control in buildings is provided by the proposed ALSTM-Fast MPC system.</p>","PeriodicalId":34740,"journal":{"name":"IET Smart Cities","volume":"6 4","pages":"361-371"},"PeriodicalIF":2.1,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/smc2.12088","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141659269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A collaborative WSN-IoT-Animal for large-scale data collection","authors":"Hamayadji Abdoul Aziz,&nbsp;Ado Adamou Abba Ari,&nbsp;Arouna Ndam Njoya,&nbsp;Assidé Christian Djedouboum,&nbsp;Alidou Mohamadou,&nbsp;Ousmane Thiare","doi":"10.1049/smc2.12089","DOIUrl":"10.1049/smc2.12089","url":null,"abstract":"<p>In recent years, large-scale data collection systems have developed rapidly in many fields, including agriculture, transport and many others. The internet of things (IoT), whose main platform is wireless sensor networks (WSNs), is behind this development. Comprising thousands of sensors of different kinds, their main purpose is to collect and transmit data. Several data collection techniques have been proposed, including static, mobile and hybrid approaches. The challenges faced by these techniques are considerable, and include energy conservation, planning and trajectory optimisation during data collection, most importantly, the challenges related to the communication between the static sensors generally distributed in a more or less large geographical space and the mobile data collection system (UAV, vehicle, robot etc.). Not to mention the cost, which remains enormous for the agricultural sectors. A hybrid WSN-IoT-Animal that is self-configured to improve data acquisition over large agricultural areas is presented. The main objective and originality of the heterogeneous semi-modern scheme proposed here oscillating between traditional agriculture and precision agriculture is the use of animals as data collection tools. The main contribution here is the design of a simple and efficient model of data collection that is easily accessible by farmers by adapting the available resources. This model describes and adopts a sensor deployment method based on the notion of the hypergraph, which provides adequate coverage and ensures communication between the mobile sink and a subset of peripheral sensors chosen in alternation. Simulation results verify the effectiveness of the proposed protocol in terms of network lifetime compared to other works. In addition, the amount of data received by the mobile sink demonstrates the importance of this approach in terms of connectivity for large-scale data collection.</p>","PeriodicalId":34740,"journal":{"name":"IET Smart Cities","volume":"6 4","pages":"372-386"},"PeriodicalIF":2.1,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/smc2.12089","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141666416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing smart tourism destinations: A case study using bidirectional encoder representations from transformers-based occupancy predictions in torrevieja (Spain)","authors":"José Ginés Giménez Manuel,&nbsp;José Giner Pérez de Lucia,&nbsp;Marco Antonio Celdrán Bernabeu,&nbsp;José Norberto Mazón López,&nbsp;Juan Carlos Cano Escribá,&nbsp;José María Cecilia Canales","doi":"10.1049/smc2.12085","DOIUrl":"10.1049/smc2.12085","url":null,"abstract":"<p>Tourism represents a crucial socio-economic pillar globally, yet the multifaceted challenges it poses necessitate innovative management approaches. The paradigm of smart tourism harnesses advanced data analytics tools to promote both profitability and sustainability in tourist destinations, leading to new levels of destination smartness. Accurate tourist occupancy prediction, particularly in areas dominated by second-home accommodations where traditional hospitality data may be insufficient, plays a key role in optimising tourism management. To address this data gap, our prior research employed ARIMA modelling on Airbnb booking time series and analysed tourism-related Twitter conversations to forecast occupancy levels in Torrevieja (Alicante); a prominent second-home tourism destination in Southeastern Spain. In this extended study, we delve deeper into the realm of social sensing by utilising bidirectional encoder representations from transformers (BERT) for topic modelling. Our methodology involves the processing and analysis of Twitter data to identify prominent themes related to Torrevieja. The findings not only reveal nuanced perceptions and discussions about the destination but also underscore the effectiveness of BERT in capturing intricate topic dynamics. Importantly, this work highlights how the alignment of specific topics with booking patterns can further enhance predictive accuracy for tourist occupancy, presenting a robust toolkit for stakeholders in the tourism sector.</p>","PeriodicalId":34740,"journal":{"name":"IET Smart Cities","volume":"6 4","pages":"422-440"},"PeriodicalIF":2.1,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/smc2.12085","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141703233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Smart city fire surveillance: A deep state-space model with intelligent agents","authors":"A. Rehman,&nbsp;F. Saeed,&nbsp;M. M. Rathore,&nbsp;A. Paul,&nbsp;J.-M. Kang","doi":"10.1049/smc2.12086","DOIUrl":"https://doi.org/10.1049/smc2.12086","url":null,"abstract":"<p>In the realm of smart city development, the integration of intelligent agents has emerged as a pivotal strategy to enhance the efficacy of search methodologies. This study introduces a novel state-space navigational model employing intelligent agents tailored specifically for fire surveillance in urban environments. Central to this model is the fusion of a convolutional neural network and multilayer perceptron, enabling accurate fire detection and localisation. Leveraging this capability, the intelligent agent proactively navigates through the search space, guided by the shortest path to the identified fire location. The utilisation of the A* algorithm as the search mechanism underscores the efficiency and efficacy of our proposed approach. Implemented in Python and Gephi, our method surpasses traditional search algorithms, both informed and uninformed, demonstrating its effectiveness in navigating urban landscapes for fire surveillance. This research study contributes significantly to the field by offering a robust solution for proactive fire detection and surveillance in smart city environments, thereby enhancing public safety and urban resilience.</p>","PeriodicalId":34740,"journal":{"name":"IET Smart Cities","volume":"6 3","pages":"199-210"},"PeriodicalIF":2.1,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/smc2.12086","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142160158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Securing smart cities through machine learning: A honeypot-driven approach to attack detection in Internet of Things ecosystems","authors":"Yussuf Ahmed,&nbsp;Kehinde Beyioku,&nbsp;Mehdi Yousefi","doi":"10.1049/smc2.12084","DOIUrl":"https://doi.org/10.1049/smc2.12084","url":null,"abstract":"<p>The rapid increase and adoption of Internet of Things (IoT) devices have introduced unprecedented conveniences into modern life. However, this growth has also ushered in a wave of cyberattacks targeting these often-vulnerable systems. Smart cities, relying on interconnected sensors, are particularly susceptible to attacks due to the expanded entry points created by these devices. A security breach in such systems can compromise personal data and disrupt entire ecosystems. Traditional security measures are inadequate against the evolving sophistication of cyberattacks. The authors aim to address these challenges by leveraging honeypot data and machine learning to enhance IoT security. The research focuses on three objectives: identifying datasets from IoT-targeted honeypots, evaluating machine learning algorithms for threat detection, and proposing comprehensive security solutions. Real-world cyber-attack datasets from diverse honeypots simulating IoT devices are analysed using various machine learning and neural network algorithms. Results demonstrate significant improvement in cyber-attack detection and mitigation when integrating honeypot data into IoT security frameworks. The authors advance knowledge and provides practical insights for implementing robust security measures in diverse IoT applications, filling a crucial research gap.</p>","PeriodicalId":34740,"journal":{"name":"IET Smart Cities","volume":"6 3","pages":"180-198"},"PeriodicalIF":2.1,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/smc2.12084","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142160188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Smart resilience through IoT-enabled natural disaster management: A COVID-19 response in São Paulo state","authors":"Alessandro S. Santos,&nbsp;Icaro Goncales,&nbsp;Angelina Silva,&nbsp;Rodrigo Neves,&nbsp;Igor Teixeira,&nbsp;Eder Barbosa,&nbsp;Vagner Gava,&nbsp;Olga Yoshida","doi":"10.1049/smc2.12082","DOIUrl":"10.1049/smc2.12082","url":null,"abstract":"<p>Natural disaster management approach establishes stages of prevention, preparation, response, and recovery. With the Internet of Things (IoT), Bigdata, Business Intelligence, and other Information Communication Technologies, data can be gathered to support decisions in stages of the response to natural disaster events. In biological natural disasters, the ICTs can also support efforts to promote social distancing, public health, and economic monitoring to face the threads. São Paulo state used IoT in scenarios to face COVID-19, such as monitoring vehicular interurban mobility, social distancing, and economic activity. Frameworks, strategies, data views, and use cases are presented to support the decision-making process to face this biological natural disaster. The data-driven approach supports several purposes, including the communication of social distancing indices, economic recovery, the progression of contagion, and deaths. It also played a pivotal role in fostering transparency initiatives for society and supporting the crisis committee by facilitating situational analyses, and this approach became standard practice for pandemic response. Studies and innovative visualisation perspectives have produced positive outcomes, guiding the decision-making process through data analysis. Noteworthy use cases were interurban traffic fence monitoring; mapping of virus spreading; tracking the economic impact concerning recovery plans; and, evaluating the effectiveness of public policies.</p>","PeriodicalId":34740,"journal":{"name":"IET Smart Cities","volume":"6 3","pages":"211-224"},"PeriodicalIF":2.1,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/smc2.12082","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141107060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sense (and) the city: From Internet of Things sensors and open data platforms to urban observatories","authors":"Vijay Kumar,&nbsp;Sam Gunner,&nbsp;Maria Pregnolato,&nbsp;Patrick Tully,&nbsp;Nektarios Georgalas,&nbsp;George Oikonomou,&nbsp;Stylianos Karatzas,&nbsp;Theo Tryfonas","doi":"10.1049/smc2.12081","DOIUrl":"10.1049/smc2.12081","url":null,"abstract":"<p>Digitalisation and the Internet of Things (IoT) help city councils improve services, increase productivity and reduce costs. City-scale monitoring of traffic and pollution enables the development of insights into low-air quality areas and the introduction of improvements. IoT provides a platform for the intelligent interconnection of everyday objects and has become an integral part of a citizen's life. Anyone can monitor from their fitness to the air quality of their immediate environment using everyday technologies. With caveats around privacy and accuracy, such data could even complement those collected by authorities at city-scale, for validating or improving policies. The authors explore the hierarchies of urban sensing from citizen-to city-scale, how sensing at different levels may be interlinked, and the challenges of managing the urban IoT. The authors provide examples from the UK, map the data generation processes across levels of urban hierarchies and discuss the role of emerging sociotechnical urban sensing infrastructures, that is, independent, open, and transparent capabilities that facilitate stakeholder engagement and collection and curation of grassroots data. The authors discuss how such capabilities can become a conduit for the alignment of community- and city-level action via an example of tracking the use of shared electric bicycles in Bristol, UK.</p>","PeriodicalId":34740,"journal":{"name":"IET Smart Cities","volume":"6 4","pages":"291-311"},"PeriodicalIF":2.1,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/smc2.12081","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141116388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimising air quality prediction in smart cities with hybrid particle swarm optimization-long-short term memory-recurrent neural network model","authors":"Surjeet Dalal,&nbsp;Umesh Kumar Lilhore,&nbsp;Neetu Faujdar,&nbsp;Sarita Samiya,&nbsp;Vivek Jaglan,&nbsp;Roobaea Alroobaea,&nbsp;Momina Shaheen,&nbsp;Faizan Ahmad","doi":"10.1049/smc2.12080","DOIUrl":"10.1049/smc2.12080","url":null,"abstract":"<p>In smart cities, air pollution is a critical issue that affects individual health and harms the environment. The air pollution prediction can supply important information to all relevant parties to take appropriate initiatives. Air quality prediction is a hot area of research. The existing research encounters several challenges that is, poor accuracy and incorrect real-time updates. This research presents a hybrid model based on long-short term memory (LSTM), recurrent neural network (RNN), and Curiosity-based Motivation method. The proposed model extracts a feature set from the training dataset using an RNN layer and achieves sequencing learning by applying an LSTM layer. Also, to deal with the overfitting issues in LSTM, the proposed model utilises a dropout strategy. In the proposed model, input and recurrent connections can be dropped from activation and weight updates using the dropout regularisation approach, and it utilises a Curiosity-based Motivation model to construct a novel motivational model, which helps in the reconstruction of long short-term memory recurrent neural network. To minimise the prediction error, particle swarm optimisation is implemented to optimise the LSTM neural network's weights. The authors utilise an online Air Pollution Monitoring dataset from Salt Lake City, USA with five air quality indicators for comparison, that is, SO2, CO, O3, and NO2, to predict air quality. The proposed model is compared with existing Gradient Boosted Tree Regression, Existing LSTM, and Support Vector Machine based Regression Model. Experimental analysis shows that the proposed method has 0.0184 (Root Mean Square Error (RMSE)), 0.0082 (Mean Absolute Error), 2002*109 (Mean Absolute Percentage Error), and 0.122 (R2-Score). The experimental findings demonstrate that the proposed LSTM model had RMSE performance in the prescribed dataset and statistically significant superior outcomes compared to existing methods.</p>","PeriodicalId":34740,"journal":{"name":"IET Smart Cities","volume":"6 3","pages":"156-179"},"PeriodicalIF":2.1,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/smc2.12080","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141123495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}