IET Smart Cities最新文献

{"title":"Domestic smart metering infrastructure and a method for home appliances identification using low-rate power consumption data","authors":"Ioannis Paraskevas,&nbsp;Maria Barbarosou,&nbsp;Richard Fitton,&nbsp;William Swan","doi":"10.1049/smc2.12009","DOIUrl":"10.1049/smc2.12009","url":null,"abstract":"<p>The deployment of domestic smart metering infrastructure in Great Britain provides the opportunity for identification of home appliances utilising non-intrusive load monitoring methods. Identifying the energy consumption of certain home appliances generates useful insights for the energy suppliers and for other bodies with a vested interest in energy consumption. Consequently, the domestic smart metering system, which is an integral part of the smart cities' infrastructure, can also be used for home appliance identification purposes taking into account the limitations of the system. In this article, a step-by-step description on accessing data directly from the domestic Smart Meter via an external Consumer Access Device is described, as well as an easy-to-implement method for identifying commonly used home appliances through their power consumption signals sampled at a rate similar to the rate available by the domestic smart metering system. The experimental results indicate that the combination of time domain with frequency domain features extracted either from the 1D/2D Discrete Fourier Transform or the Discrete Cosine Transform provides improved recognition performance compared to the case where the time domain or the frequency domain features are used separately.</p>","PeriodicalId":34740,"journal":{"name":"IET Smart Cities","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2021-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/smc2.12009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47392553","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 survey on blockchain-enabled smart grids: Advances, applications and challenges","authors":"Chao Liu,&nbsp;Xiaoshuai Zhang,&nbsp;Kok Koeng Chai,&nbsp;Jonathan Loo,&nbsp;Yue Chen","doi":"10.1049/smc2.12010","DOIUrl":"10.1049/smc2.12010","url":null,"abstract":"<p>Electric power grid infrastructure has revolutionized our world and changed the way of living. So has blockchain technology. The hierarchical electric power grid has been shifting from a centralized structure to a decentralized structure to achieve higher flexibility and stability, and blockchain technology has been widely adopted in the energy sector to deal with grid management, billing, metering, and so on, because of its nature of decentralization. Here, the aim is to provide a multi-dimensional review on the technological advances of the blockchain in smart grids. Its corresponding applications based on these advances, including company projects and use cases, are summarized. Furthermore, the security threat issues in smart grids, Ethereum Virtual Machine (i.e. the operating environment of consensus mechanisms), and smart contracts are analysed, with a brief conclusion to manifest the prior tasks in building secure blockchain-based infrastructures in smart grids. As such, the challenges and features of different protocols and their applicability in each use case are identified to provide an insightful guide for future research studies.</p>","PeriodicalId":34740,"journal":{"name":"IET Smart Cities","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2021-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/smc2.12010","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45657986","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":"Lightweight Frame Scrambling Mechanisms for End-to-End Privacy in Edge Smart Surveillance","authors":"Alem Fitwi, Yu Chen, Sencun Zhu","doi":"10.36227/TECHRXIV.14402504.V1","DOIUrl":"https://doi.org/10.36227/TECHRXIV.14402504.V1","url":null,"abstract":"As smart surveillance becomes popular in today's smart cities, millions of closed circuit television (CCTV) cameras are ubiquitously deployed that collect huge amount of visual information. All these raw visual data are often transported over a public network to distant video analytic centers. This increases the risk of interception and the spill of individuals' information into the wider cyberspace that causes privacy breaches. The edge computing paradigm allows the enforcement of privacy protection mechanisms at the point where the video frames are created. Nonetheless, existing cryptographic schemes are computationally unaffordable at the resource constrained network edge. Based on chaotic methods we propose three lightweight end-to-end (E2E) privacy-protection mechanisms: (1) a Dynamic Chaotic Image Enciphering (DyCIE) scheme that can run in real time at the edge; (2) a lightweight Regions of Interest (RoI) Masking (RoI-Mask) scheme that ensures the privacy of sensitive attributes on video frames; and (3) a novel lightweight Sinusoidal Chaotic Map (SCM) as a robust and efficient solution for enciphering frames at edge cameras. Design rationales are discussed and extensive experimental analyses substantiate the feasibility and security of the proposed schemes.","PeriodicalId":34740,"journal":{"name":"IET Smart Cities","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2021-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44394450","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":"Smart city development in Taiwan","authors":"Jang-Hwa Leu,&nbsp;Bo-Chi Lin,&nbsp;Yen-Yi Liao,&nbsp;Dai-You Gan","doi":"10.1049/smc2.12008","DOIUrl":"10.1049/smc2.12008","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <p>This article outlines Taiwan’s experience in developing smart cities, including visions, implementation strategies and application cases. To take global trends and local needs into account, Taiwan has applied a dual development model that combines top-down (theme-based)/bottom-up (needs-based) approaches for a synergy effect in balancing innovations and local needs. Furthermore, a public–private partnership program has been adopted to prompt collaboration between central/local authorities with local businesses.Meanwhile, Taiwan uses a private finance initiative program and a global marketing strategy for strengthening the scalability and sustainability of smart city solutions. Three visions in the project help achieve the transformation to ensure smarter urban governance, more comprehensive industrial business models and better livelihoods of residents. Moreover, this article also presents five application cases in the top-down approach and four application cases in the bottom-up approach with proven track records which covered eight industry sectors: agriculture, healthcare, education, mobility, retail, energy, governance and environment.</p>\u0000 </section>\u0000 </div>","PeriodicalId":34740,"journal":{"name":"IET Smart Cities","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2021-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/smc2.12008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41917587","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":"Automatic heart disease class detection using convolutional neural network architecture-based various optimizers-networks","authors":"Marwa Fradi,&nbsp;Lazhar Khriji,&nbsp;Mohsen Machhout,&nbsp;Abdulnasir Hossen","doi":"10.1049/smc2.12003","DOIUrl":"10.1049/smc2.12003","url":null,"abstract":"<p>Early heart disease class detection is of great interest to reduce the mortality rate. In this context, computational techniques have been proposed to solve this issue. Thus, here, a deep learning architecture is proposed to automatically classify the patient’s Electrocardiogram (ECG) signal into a specific class according to the ANSI–AAMI standards. The proposed methodology is a multi-stage technique. The first stage combines an R–R peak extraction with a low-pass filter applied on the ECG data for noise removal. The second stage shows the proposed convolutional neural network-based fully connected layers architecture, using different network optimizers. Different ECG databases, including challenging tasks, have been used for validation purpose. The whole system is implemented on both CPU and GPU for complexity analysis. For the predicted improved PTB data set, the classification accuracy results achieve 99.37%, 99.15% and 99.31% for training, validation and testing, respectively. Besides, for the MIT-BIH-database, the training, validation and testing accuracies are 99.5%, 99.06% and 99.34%, respectively. A top F1-score of 0.99 is obtained. Experimental results show a high achievement compared to the state-of-the-art models where the implementation of GPU confirms the low computational complexity of the system.</p>","PeriodicalId":34740,"journal":{"name":"IET Smart Cities","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2021-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/smc2.12003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46328632","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":"Improving Access and Mental Health For Youth Using Smart Technologies","authors":"Cheryl Forchuk,&nbsp;Sandra Fisman,&nbsp;Jeffrey P. Reiss,&nbsp;Kerry Collins,&nbsp;Julie Eichstedt,&nbsp;Abraham Rudnick,&nbsp;Wanrudee Isaranuwatchai,&nbsp;Jeffrey S. Hoch,&nbsp;Xianbin Wang,&nbsp;Daniel Lizotte,&nbsp;Shona Macpherson,&nbsp;Richard Booth","doi":"10.1049/smc2.12006","DOIUrl":"10.1049/smc2.12006","url":null,"abstract":"<p>The overall objective of this research is to evaluate the use of a mobile health smartphone application (app) to improve the mental health of youth between the ages of 14 and 25 years, with symptoms of anxiety and/or depression. This project includes 122 youth who are accessing outpatient mental health services at one of three hospitals and two community agencies. The youth and care providers are using the Smart technology to enhance care. The technology uses mobile questionnaires (Qnaires^TM) to help promote self-assessment and track changes to support the plan of care. The youth were provided a smartphone and talk/text/data plan, if needed. The majority of participants identified themselves as Caucasian (73.5%). Expectedly, the demographics revealed that Anxiety Disorders and Mood Disorders were highly prevalent within the sample (73.6% and 66.9% respectively). Findings from the qualitative summary established that both staff and youth found having a smartphone and data plan beneficial. Demographic variables such as age, sex, mental health and physical health did not predict which youth were more likely to use the application.</p>","PeriodicalId":34740,"journal":{"name":"IET Smart Cities","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2021-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/smc2.12006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43827844","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 Plug&Play approach for modeling and simulating applications in the era of internet of social things","authors":"Zakaria Maamar,&nbsp;Mohamed Sellami,&nbsp;Fatma Masmoudi,&nbsp;Muhammad Asim,&nbsp;Abdul Haseeb,&nbsp;Thar Baker,&nbsp;Fadwa Yahya","doi":"10.1049/smc2.12005","DOIUrl":"10.1049/smc2.12005","url":null,"abstract":"<p>This article presents an approach to model and simulate Plug&amp;Play social things. Confined into silos, existing (not social) things are restricted to basic operations like sensing and actuating, which deprive them from participating in the satisfaction of complex business applications. Contrarily, social things are expected to engage in collaborative scenarios and to tap into specific relations that connect them to peers when achieving these scenarios. These relations are referred to as complimentary, antagonism, and competition, and allow to develop networks of things. To capitalize on such networks, the approach to model and simulate Plug&amp;Play social things puts forward four stages that are referred to as connecting to demystify social relations between things, influencing to examine the impact of social relations on things, playing to make things perform while considering influence, and incentivizing to reward things based on their performance. A smart system for elderly the care centers has been developed to showcase the technical doability of Plug&amp;Play social things. The system is an integrated development environment allowing IoT engineers to define the collaboration of social things, thanks to a set of drag&amp;drop operations.</p>","PeriodicalId":34740,"journal":{"name":"IET Smart Cities","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2021-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/smc2.12005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47964120","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":"Guest editorial: Selected papers from the International Conference on Smart Living and Public Health","authors":"Hamdi Aloulou,&nbsp;Mohamed Jmaiel,&nbsp;Mounir Mokhtari,&nbsp;Bessam Abdulrazak,&nbsp;Slim Kallel","doi":"10.1049/smc2.12007","DOIUrl":"10.1049/smc2.12007","url":null,"abstract":"<p>The International Conference on Smart Living and Public Health (ICOST, www.icost-society.org) provides a premier venue for the presentation and discussion of research in the design, development, deployment, and evaluation of artificial intelligence (AI) for health, smart urban environments, assistive technologies, chronic disease management, and coaching and health telematics systems. ICOST focuses on analysing the impact of ICTs on public health and the wellbeing of citizens all over the world. For more than a decade and a half, the ICOST conference has succeeded in bringing together a community from different continents and has raised awareness about frail and dependent people's quality of life in our societies.</p><p>This special issue presents extended versions of selected papers from the 18th edition of the ICOST conference. The issue contains four papers presented at the conference on Biomedical and Health Informatics, Internet of Things and AI solutions for E-health and Wellbeing Technologies topics.</p><p>Khriji et al. in their paper entitled “Automatic heart disease class detection using convolutional neural network architecture-based various optimizers-networks” propose a deep learning architecture for automatic classification of the patient's electrocardiogram (ECG) signal into a specific class according to American National Standards Institute – Association for the Advancement of Medical Instrumentation standards. This enables automatic arrhythmia heart disease detection at an early stage, which is of high interest because it helps to reduce the mortality rate for cardiac disease patients. The proposed approach is validated through different ECG databases. Experimental results show high achievement compared with state-of-the-art models. Implementation on graphical processing units confirms the low computational complexity of the system and its possible use in detecting disease events in real time, which makes it a good candidate for portable health care devices.</p><p>Ben Ida et al. in their paper “Adaptative vital signs monitoring system based on the early warning scoring approach in smart hospital context” present an edge-based early warning score (EWS) that respects a risk evaluation approach named NEWS2. The proposed approach allows the prediction of patients' risk level based on collected vital signs data. The paper proposes an adaptative configuration of the vital signs monitoring process depending on variations in the patient’s health status and the medical staff’s decisions. The authors also propose an intelligent notification mechanism that reduces the delay of medical staff intervention in case of risk detection.</p><p>Sellami et al. in their paper entitled “A Plug&amp;Play Approach for Modelling and Simulating Applications in the Era of Internet of Social Things” presents an approach to model and simulate Plug&amp;Play social things. Social things engage in collaborative scenarios that expose specific relations connectin","PeriodicalId":34740,"journal":{"name":"IET Smart Cities","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2021-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/smc2.12007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45318444","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":"Adaptative vital signs monitoring system based on the early warning score approach in smart hospital context","authors":"Imen Ben Ida,&nbsp;Moez Balti,&nbsp;Sondes Chaabane,&nbsp;Abderrazak Jemai","doi":"10.1049/smc2.12004","DOIUrl":"10.1049/smc2.12004","url":null,"abstract":"<p>Changes in vital signs are an important indicator of physiological decline and provide opportunities for early recognition and intervention. The collected vital signs data can be evaluated using several approaches such as the Early warning score (EWS) approach to predict the risk level of patients. By exploring the Internet of things (IoT), vital signs monitoring solutions are automated based on various medical devices and sensors. However, there is a lack of efficient tools that enable an adaptative monitoring depending on the patient situations. This article explores the IoT technologies to provide an EWS system in smart hospital situation. The proposed solution presents an adaptative configuration of the vital signs monitoring process depending on the patient’s health status variation and the medical staff decisions. Also, an intelligent notification mechanism that reduces the delay of the medical staff intervention in the case of risk detection is proposed.</p>","PeriodicalId":34740,"journal":{"name":"IET Smart Cities","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2021-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/smc2.12004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48703715","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":"Orchestration-based mechanism for sampling adaptation in sensing-based applications","authors":"H. Harb,&nbsp;H. Baalbaki,&nbsp;C. Abou Jaoude,&nbsp;A. Jaber","doi":"10.1049/smc2.12002","DOIUrl":"10.1049/smc2.12002","url":null,"abstract":"<p>Currently, the world witnesses a boom in the sensing-based applications where the number of connected devices is becoming higher than the people. Such small sensing devices are now deployed in billions around the world, collecting data about the surroundings and reporting them to the data analysis centres. This fact allows a better understanding of the world and helps to reduce the effects of potential risks. However, while the benefits of such devices are real and significant, sensing-based applications face two major challenges: big data collection and restricted power of sensor battery. In order to overcome these challenges, data reduction and sampling sensor adaptation techniques have been proposed to reduce data collection and to save the sensor energy. The authors propose an orchestration-based mechanism (OM) for adapting the sampling rate of the sensors in the network. OM is two-fold: first, it proposes a data transmission model at the sensor level, based on the clustering and Spearman coefficient, in order to reduce the amount of data transmitted to the sink; second, it proposes a sampling rate mechanism at the cluster-head level that allows searching the similarity between data collected by the neighbouring sensors, and then to adapt their sensing frequencies accordingly. A set of simulations on real sensor data have been conducted to evaluate the efficiency of OM, in terms of data reduction and energy conservation, compared to other exiting techniques.</p>","PeriodicalId":34740,"journal":{"name":"IET Smart Cities","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2021-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/smc2.12002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43659905","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}