IET Smart Cities最新文献

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.

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.

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.

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.

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.

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.

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.

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.

Numerous strategies have been proposed for the detection and prevention of non-technical electricity losses due to fraudulent activities. Among these, machine learning algorithms and data-driven techniques have gained prominence over traditional methodologies due to their superior performance, leading to a trend of increasing adoption in recent years. A novel two-step process is presented for detecting fraudulent Non-technical losses (NTLs) in smart grids. The first step involves transforming the time-series data with additional extracted features derived from the publicly available State Grid Corporation of China (SGCC) dataset. The features are extracted after identifying abrupt changes in electricity consumption patterns using the sum of finite differences, the Auto-Regressive Integrated Moving Average model, and the Holt-Winters model. Following this, five distinct classification models are used to train and evaluate a fraud detection model using the SGCC dataset. The evaluation results indicate that the most effective model among the five is the Gradient Boosting Machine. This two-step approach enables the classification models to surpass previously reported high-performing methods in terms of accuracy, F1-score, and other relevant metrics for non-technical loss detection.

Developing sustainable smart cities (SSCs) is crucial to modern urban growth, as recognised in various international policies and literature. With Abu Dhabi as a focus, this research aims to identify and evaluate the primary obstacles that hinder the creation of intelligent and sustainable cities. By categorising and ranking these barriers, the study seeks to prioritise the most significant hindrances to smart city development. The research analysed 31 barriers, classified them into six groups, and examined them through existing literature. Semi-structured interviews with stakeholders responsible for implementing the SSC strategy provided additional valuable insights. The study used the Partial Least Squares Path Modelling method to prioritise the selected barriers. The results showed that the most significant barriers to SSC development were in the Economic category, followed by Technology, Governance, Social, Legal, Ethical, and Environmental barriers. This research provides valuable insights for policymakers and the Abu Dhabi government to eliminate obstacles that hinder SSC development initiatives.

Advanced Driver Assistance Systems (ADAS) have gained substantial attention in recent years. However, the integration mechanism of multiple functions within ADAS remains unexplored, and the full potential of its functionality remains underutilised. This paper presents a novel multi-functional integrated High-level Automated Driving Assistance System that combines the Cruise Control (CC), Adaptive Cruise Control (ACC), Automated Emergency Brake (AEB), and Automated Lane Change (ALC) functions. The presented system utilises a hierarchical framework. The extension multi-mode switch strategy is established as the superior module and the Event-Triggered Model Predictive Controller (ETMPC) is designed as the inferior controller. The CC, ACC, and ALC functions are effectively utilised to enhance traffic efficiency, while the AEB function ensures driving safety. To address the time constraints of conventional Model Predictive Control, an event-trigger mechanism is proposed to reduce computational load. Simulations are conducted using the CarSim and Matlab platforms. The study results demonstrate significant improvements in both safety and traffic efficiency compared to conventional ADAS strategies. Furthermore, the proposed ETMPC method significantly reduces the frequency of solving Optimisation Problems and decreases online computation costs.

Energy profligacy and appliance degradation are the apex reasons accounting for the continuous rise in power wastage and high energy bills. The decline in energy conservation and management in residences has been largely attributed to the financial implications of using intrusive methods. This work aimed to resolve the challenges of intrusive load monitoring by introducing artificial intelligence and machine learning to optimise load monitoring. To solve this challenge, a non-intrusive approach was proposed where modalities for load prediction and classification were achieved with a Bagging regressor and a modified multiclass K-Nearest Neighbour algorithms. This developed supervised learning models produced a 0.9624 R² score and 78.24% accuracy for prediction and classification, respectively, when trained and tested on a Dutch Residential Energy Dataset. This work seeks to provide a cost-effective approach to the optimisation of energy using steady state active power features. Essentially, the adoption of this non-intrusive technique for load monitoring would effectively aid customers on the distribution network save cost on energy bills, facilitate the detection of faulty appliances, provide recommendations for smart homes and buildings with the required information for efficient decision making and planning of energy needs. In the long term, easing the pressure on power generation to meet demand would translate to reduction in carbon emissions based on a wide-scale implementation of this proposed system. Hence, these are important parameters in realising the development of smart sustainable cities and sustainable energy systems in this current industrial revolution.

With the recent advances in smartphones and Internet technologies, ride-hailing services (such as Uber and Didi) have emerged and changed the travel modes that residents use. An important issue within this area is how ride-hailing services influence public transit usage. The majority of the research regarding this topic has focused on situations in large cities and has not reached a unanimous consensus among scholars. In particular, the role of ride-hailing services in small- and medium-sized cities may be different from the role of these services in large cities. In this paper, we choose 22 small- and medium-sized cities in China as samples with a research time window spanning from 2011 to 2016 to examine the impact of the introduction of ride-hailing services on public transit usage. The results of the synthetic control method, as well as other robustness checks, show that (1) the introduction of ride-hailing services to China's small- and medium-sized cities significantly increases public transit usage; (2) the effect of the introduction of ride-hailing services on public transit usage in small- and medium-sized cities is “proactive” for approximately 1 year; and (3) the positive effect of ride-hailing services on public transit usage in small- and medium-sized cities weakens over time. This study enriches the literature on the impact of ride-hailing services on the urban transportation system by specifically taking small- and medium-sized cities as the research scope. The above findings are of great significance to the urban transport department's formulation of ride-hailing policies and the operation layout of public transit operators in small- and medium-sized cities.