IET Smart Cities最新文献

LoRa has proven to be an ideal solution for Internet of Things networks and applications that require long-distance communications, such as those related to smart cities or precision agriculture. Its low cost combined with the wide availability of LoRa-compatible devices make it possible to easily deploy a large number of sensors capable of collecting and transmitting key information for new services and applications. However, the process of adding new devices into a Long Range Wide Area Network (LoRaWAN) network represents a significant challenge on a large scale, as each device must be individually configured and manually registered to join the network. This manual approach is costly and impractical when it comes to deploying a very large number of devices. To address this problem, this paper proposes two deployment strategies (semi-automatic and automatic) to simplify and streamline the process of activating and registering LoRaWAN devices. These strategies facilitate the deployment of large-scale devices in smart cities, and their adoption can significantly enhance the deployment of LoRaWAN devices. Experimental results clearly demonstrate the benefits of our solution. Specifically, for 500 devices, the semi-automatic deployment is 3.75 times more efficient, and the automatic deployment is an impressive 394.87 times faster than the manual deployment.

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.