IET Smart Cities最新文献

The trend towards cities and urbanisation, which increases the number of people living in urban areas, requires local authorities to provide services and natural resources more efficiently and effectively and develop some strategies for a sustainable environment. The more effective use of resources, growing awareness of sustainable environment, climate confidence and motivation to make cities more livable is a new concept called Smart City. In this study, the proposed system supports the garbage collection of the city government and works with low budget, low energy, and free radio frequencies. The Internet of Things (IoT) sensor node is assembled, and the network is set up based on the long range wide area network protocol to connect it to the sample garbage bin and collect data. Instant data collection by this network is carried out through the IoT and is designed based on the collected data. The goal is to build an ideal system for smart garbage collection in cities and support sustainability in cities by integrating with the city government's information systems. The data received from the sensor nodes and the efficiency of the system were demonstrated for local governments. The main outcome of this research is to develop a practical smart city application with minimal resources and support local governments in their daily work. Moreover, how a low power wide area network communication network with a frequency of 868 MHz works in Istanbul (Turkey) will be investigated further and which alternative to cellular networks is the most suitable for excellent communication in smart cities will be studied.

Smart city is one of the current trends of city evolvement. Smart city is a systematic project, which needs a holistic approach. As lots of pilot projects have been undertaken by cities, how to assess the ‘smartness’ of a city is of great importance. This study sums China's smart city standardisation and assessment practice, starting from the national policy evolvement, followed by the standardisation system, assessment and its key procedures and concludes with some possible future works regarding smart city standardisation and assessment.

The use of distributed energy resources (DERs) in a city contributes to the net zero CO₂ of a city. However, the spatially uneven distribution of power demand and surplus electricity causes congestion in the grid system, making wide-area operation difficult. The concept of local energy self-sufficiency via energy management, in which batteries or electric vehicles are charged using power generated by DERs and discharged to neighbouring consumers, is expected to be a way to avoid grid conjunction while maximizing the use of DERs. For efficient local energy self-sufficiency, it is necessary to identify where and when future power surpluses and shortages will occur within a city and optimize battery operation according to demand. Forecasts that focus only on representative points of a city may be less reproducible in diversity in the power demand transition for individual consumers in local parts of cities. Electricity smart meters that monitor power demand every 30 min from each consumer are expected to help predict the spatiotemporal distribution of power demand to achieve efficient local energy self-sufficiency. The significance of reflecting regional characteristics in forecasting spatiotemporal distribution of power demand is demonstrated using actual data obtained by smart meters installed in Japanese cities. The results suggest that the forecast approach, which considers the daily periodicity of power demand and weather conditions, obtains high prediction accuracy in predicting power demand in meshed local areas in the city and derives results precisely reproducing the spatiotemporal behaviours of power demand.

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.

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.

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.

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.