IET Smart Cities最新文献

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 R² 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.

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.

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.

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 [R²]), 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.

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.

The rendering of urban 3D scenes involves a large number of models. In order to render scenes more efficiently, the main solution is to build a level of detail model (LOD). This may have the problem of building fragmentation, while relying on building a level of detail model (LOD) alone cannot meet the accuracy and fluency of large-scale scene visualisation. Effective and reasonable data organisation has important research significance for the authors to achieve accurate and fast rendering of scenes. Therefore, the authors propose a large-scale city model data organisation method considering building distribution to solve the above problems. This method first classifies the buildings in the scene at macro-, meso- and microscales and records the classification using R-trees. Then an adaptive quadtree is used to construct the data index of the city model. Finally, the data organisation of the large-scale 3D city model is achieved by using the information of each node of the R-tree as a constraint and combining with the adaptive quadtree. The results show that the method not only ensures the integrity of the user's area of interest but also can improve the efficiency of 3D scene construction.

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.

An innovative approach to the collection of unplanned municipal waste through the integration of an Internet of Things (IoT) enabled system in urban settings is presented. Despite significant strides in waste management optimisation, traditional systems have largely overlooked the management of occasional or seasonal waste such as green waste, wild dump, and construction debris. The authors seek to address this gap by deploying an IoT-enabled system to optimise resource utilisation and efficiency. Building on existing infrastructures for real-time tracking of waste collection circuits, equipment, and bin filling levels, the system incorporates an additional module to manage unpredictable waste categories. The system collects field data leveraging existing resources with minimal investment. To manage the sporadic nature of these waste types, the system employs a flexible approach with the use of sensors and algorithms for dynamic route planning and waste collection. Using the city of Tangier, Morocco, as a case study, a comprehensive methodology for waste location capture, GIS mapping, priority-based route identification, scenario testing, and operational cost estimation is implemented. A modified version of the Contraction Hierarchies algorithm is applied to compute optimal waste collection paths, ensuring timely and efficient waste removal while minimising environmental impact. The findings from this research promise significant implications for municipal waste collection, particularly in developing countries, opening new possibilities for sustainable waste management practices in smart cities.

Fixed cycle traffic lights primarily regulate road traffic, in which traffic light control systems are for specific lanes or crossings in urban areas. Also, not being appropriately installed can prolong the congestion delay and unnecessarily long wait times for crossing intersections, which can cause emergency vehicles to become stuck at intersections. Adaptive signal timing management technique that is more computationally viable than current fixed cycle signal control systems and can improve network-wide traffic operations by reducing traffic delay and energy consumption. Even though specific adaptive control systems exist, there is no mechanism to communicate with emergency vehicles, which is crucial for smart cities. Motivated by this problem, a novel framework, Emergency Vehicle Adaptive Traffic Light (EVATL), is proposed for smart cities where an adaptive mode of operation for traffic lights is employed with emergency vehicle communication, improving their functioning and reducing overall congestion delay. EVATL detects emergency vehicle location using GPS with the Internet of Things(IoT), which integrates with traffic signals and works adaptively according to vehicle density at the traffic signal using YOLOv8. So, the primary goal of the proposed EVATL is to prioritise an emergency vehicle while simultaneously integrating adaptive traffic signals for smart cities. A GUI is developed for evaluating the proposed model by creating different scenarios for an adaptive traffic light and emergency vehicle communication. While analysing the simulation results of the proposed model EVATL, a clear improvement can be seen in the wait time of vehicles at a traffic light with the timely detection of an emergency vehicle at a set distance.