IET Smart Cities最新文献

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.

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.

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.

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.

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.

Secure data transmission and efficient network performance are both key aspects of the modern Internet. Traditionally, Transport Layer Security (TLS)/Transmission Control Protocol (TCP) has been used for reliable and secure networking communications. In the past decade, Quick User Datagram Protocol (UDP) Internet Connections QUIC has been designed and implemented on UDP, attempting to improve security and efficiency of Internet traffic. Real-world platform investigations are carried out in this paper to evaluate TLS/TCP and QUIC/UDP in maintaining communication, security and efficiency under three different types of popular cyber-attacks. A set of interesting findings, including delay, loss, server CPU utilisation and server memory usage are presented to provide a comprehensive understanding of the two protocol stacks in performing malicious traffic. More specifically, in terms of the efficiency in achieving short delays and low packet loss rates with limited CPU and memory resources, QUIC/UDP performs better under Denial of Service attacks but TLS/TCP overtakes QUIC/UDP when handling MitM attacks. In terms of security, the implementation of TCP tends to be more secure than QUIC, but QUIC traffic patterns are harder to learn using machine learning methods. We hope that these insights will be informative in protocol selection for future networks and applications, as well as shedding light on the further development of the two protocol stacks.

Digital technologies have been contributing to providing quality health care to patients. One aspect of this is providing accurate wait times for patients waiting to be serviced at healthcare facilities. This is naturally a complex problem as there is a multitude of factors that can impact the wait time. However, the problem becomes even more complex if the patient's journey requires visiting multiple stations in the hospital; such as having vital signs taken, doing an ultrasound, and seeing a specialist. The authors aim to provide an accurate method for estimating the wait time by utilising a real dataset of transactions collected from a major hospital over a year. The work employs feature engineering and compares several machine learning-based algorithms to predict patients' waiting times for single-stage and multi-stage services. The Random Forest algorithm achieved the lowest root mean squared error (RMSE) value of 6.69 min among all machine learning algorithms. The results were also compared against a formula-based system used in the industry, and the proposed model outperformed the existing model, showing improvements of 25.1% in RMSE and 18.9% in MAE metrics. These findings indicate a significant improvement in the accuracy of predicting waiting times compared to existing techniques.

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.