IET Smart Cities最新文献

{"title":"A machine learning‐based approach for wait‐time estimation in healthcare facilities with multi‐stage queues","authors":"Amjed Al-mousa, Hamza Al‐Zubaidi, Mohammad Al‐Dweik","doi":"10.1049/smc2.12079","DOIUrl":"https://doi.org/10.1049/smc2.12079","url":null,"abstract":"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.","PeriodicalId":34740,"journal":{"name":"IET Smart Cities","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140373438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel two-stage method to detect non-technical losses in smart grids","authors":"Sufian A. Badawi,&nbsp;Maen Takruri,&nbsp;Mahmood G. Al-Bashayreh,&nbsp;Khouloud Salameh,&nbsp;Jumana Humam,&nbsp;Samar Assaf,&nbsp;Mohammad R. Aziz,&nbsp;Ameera Albadawi,&nbsp;Djamel Guessoum,&nbsp;Isam ElBadawi,&nbsp;Mohammad Al-Hattab","doi":"10.1049/smc2.12078","DOIUrl":"10.1049/smc2.12078","url":null,"abstract":"<p>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.</p>","PeriodicalId":34740,"journal":{"name":"IET Smart Cities","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/smc2.12078","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140379773","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 case study on the barriers towards achieving sustainable smart city for Abu Dhabi","authors":"Rahaf Ajaj,&nbsp;Mohanad Kamil Buniya,&nbsp;Ibrahim Yahaya Wuni,&nbsp;Omar Sedeeq Yousif","doi":"10.1049/smc2.12077","DOIUrl":"10.1049/smc2.12077","url":null,"abstract":"<p>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.</p>","PeriodicalId":34740,"journal":{"name":"IET Smart Cities","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/smc2.12077","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140244418","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 comprehensive high-level automated driving assistance system with integrated multi-functionality","authors":"Aijing Kong,&nbsp;Peng Hang,&nbsp;Yu Tang,&nbsp;Xian Wu,&nbsp;Xinbo Chen","doi":"10.1049/smc2.12076","DOIUrl":"10.1049/smc2.12076","url":null,"abstract":"<p>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.</p>","PeriodicalId":34740,"journal":{"name":"IET Smart Cities","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/smc2.12076","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140429134","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":"An artificial intelligence-based non-intrusive load monitoring of energy consumption in an electrical energy system using a modified K-Nearest Neighbour algorithm","authors":"Benjamin Kommey,&nbsp;Elvis Tamakloe,&nbsp;Jerry John Kponyo,&nbsp;Eric Tutu Tchao,&nbsp;Andrew Selasi Agbemenu,&nbsp;Henry Nunoo-Mensah","doi":"10.1049/smc2.12075","DOIUrl":"10.1049/smc2.12075","url":null,"abstract":"<p>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 <i>R</i>² 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.</p>","PeriodicalId":34740,"journal":{"name":"IET Smart Cities","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/smc2.12075","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139602529","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":"The effect of ride-hailing services on public transit usage in China's small- and medium-sized cities: A synthetic control method analysis","authors":"Jun Zhong,&nbsp;Huan Zhou,&nbsp;Yan Lin,&nbsp;Fangxiao Ren","doi":"10.1049/smc2.12074","DOIUrl":"10.1049/smc2.12074","url":null,"abstract":"<p>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.</p>","PeriodicalId":34740,"journal":{"name":"IET Smart Cities","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/smc2.12074","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139384765","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 smart city evaluation: A people-oriented analysis method","authors":"Yufei Fang,&nbsp;Zhiguang Shan","doi":"10.1049/smc2.12073","DOIUrl":"https://doi.org/10.1049/smc2.12073","url":null,"abstract":"<p>Smart cities integrate information technology with urban transformation, making it crucial to systematically evaluate their development level and effectiveness. Recent years have seen increased attention towards smart city evaluations worldwide, but there is still research space for theoretical models, technical methods, and practical applications. To address this gap, this study proposes an efficiency evaluation model for smart cities and a smart city user demand analysis model. It answers two research questions: how to configure investments in different aspects of smart city for a better user experience, and how to judge the extent and specific points of public demand in various sectors of a smart city. By analysing evaluation data, this study accurately identifies the development direction and construction focus of smart cities, supports targeted optimisation and improvement strategies, enhances user experience, and provides rationalised suggestions for a dynamic revision of smart city evaluation indicators.</p>","PeriodicalId":34740,"journal":{"name":"IET Smart Cities","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/smc2.12073","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140031843","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":"Tiny machine learning on the edge: A framework for transfer learning empowered unmanned aerial vehicle assisted smart farming","authors":"Ali M. Hayajneh,&nbsp;Sami A. Aldalahmeh,&nbsp;Feras Alasali,&nbsp;Haitham Al-Obiedollah,&nbsp;Sayed Ali Zaidi,&nbsp;Des McLernon","doi":"10.1049/smc2.12072","DOIUrl":"10.1049/smc2.12072","url":null,"abstract":"<p>Emerging technologies are continually redefining the paradigms of smart farming and opening up avenues for more precise and informed farming practices. A tiny machine learning (TinyML)-based framework is proposed for unmanned aerial vehicle (UAV)-assisted smart farming applications. The practical deployment of such a framework on the UAV and bespoke internet of things (IoT) sensors which measure soil moisture and ambient environmental conditions is demonstrated. The key objective of this framework is to harness TinyML for implementing transfer learning (TL) using deep neural networks (DNNs) and long short-term memory (LSTM) ML models. As a case study, this framework is employed to predict soil moisture content for smart agriculture applications, guiding optimal water utilisation for crops through time-series forecasting models. To the best of authors’ knowledge, a framework which leverages UAV-assisted TL for the edge internet of things using TinyML has not been investigated previously. The TL-based framework employs a pre-trained data model on different but similar applications and data domains. Not only do the authors demonstrate the practical deployment of the proposed framework but they also quantify its performance through real-world deployment. This is accomplished by designing a custom sensor board for soil and environmental sensing which uses an ESP32 microcontroller unit. The inference metrics (i.e. inference time and accuracy) are measured for different ML model architectures on edge devices as well as other performance metrics (i.e. mean square error and coefficient of determination [<i>R</i>²]), while emphasising the need for balancing accuracy and processing complexity. In summary, the results show the practical feasibility of using drones to deliver TL for DNN and LSTM models to ultra-low performance edge IoT devices for soil humidity prediction. But in general, this work also lays the foundation for further research into other applications of TinyML usage in many different aspects of smart farming.</p>","PeriodicalId":34740,"journal":{"name":"IET Smart Cities","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/smc2.12072","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139267319","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":"Monocular-based collision avoidance system for unmanned aerial vehicle","authors":"Abdulrahman Javaid,&nbsp;Asaad Alduais,&nbsp;M. Hashem Shullar,&nbsp;Uthman Baroudi,&nbsp;Mustafa Alnaser","doi":"10.1049/smc2.12067","DOIUrl":"10.1049/smc2.12067","url":null,"abstract":"<p>Obstacle avoidance based on a monocular camera is a challenging task due to the lack of 3D information for Unmanned Aerial Vehicle. Recent methods based on Convolutional Neural Networks for monocular depth estimation and obstacle detection become widely used. However, collision avoidance with depth estimation usually suffers from long computational time and low avoidance success rate. A new collision avoidance system is proposed which uses monocular camera and intelligent algorithm to avoid obstacles on real time processing. Several experiments have been conducted on crowded environments with several object types. The results show outstanding performance in terms of obstacles avoidance and system response time compared to contemporary approaches. This makes the proposed approach of high potential to be integrated in crowded environments.</p>","PeriodicalId":34740,"journal":{"name":"IET Smart Cities","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/smc2.12067","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135290657","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":"Leveraging IoT data stream for near-real-time calibration of city-scale microscopic traffic simulation","authors":"Mozhgan Pourmoradnasseri,&nbsp;Kaveh Khoshkhah,&nbsp;Amnir Hadachi","doi":"10.1049/smc2.12071","DOIUrl":"10.1049/smc2.12071","url":null,"abstract":"<p>The emergence of smart cities is set to transform transportation systems by leveraging real-time traffic data streams to monitor urban dynamics. This complements traditional microscopic simulation methods, offering a detailed digital portrayal of real-time traffic conditions. A framework for near-real-time city-scale traffic demand estimation and calibration is proposed. By utilising Internet of Things (IoT) sensors on select roads, the framework generates microscopic simulations in congested networks. The proposed calibration method builds upon the standard bi-level optimization formulation. It presents a significant computational advantage over available methods by (i) formulating the optimization problem as a bounded variable quadratic programming, (ii) acquiring sequential optimization technique by splitting computations into short time frames while considering the dependency of the demand in successive time frames, (iii) performing parallel simulations for dynamic traffic assignment in corresponding time frames using the open source tool Simulation of Urban MObility (SUMO), and (iv) feeding traffic count data of each time frame as a stream to the model. The approach accommodates high-dimensional real-time applications without extensive prior traffic demand knowledge. Validation in synthetic networks and Tartu City case study showcases scalability, accuracy, and computational efficiency.</p>","PeriodicalId":34740,"journal":{"name":"IET Smart Cities","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/smc2.12071","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135928747","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}