IET Smart Cities最新文献

Fossil-fuelled vehicles are being replaced by electric vehicles (EVs) around the world due to environmental pollution and high fossil fuel price. On the one hand, the electrical grid is faced with some challenges when too many EVs are improperly integrated. On the other hand, using massive unexploited capacity of the battery storage in too many EVs makes these challenges to opportunities. This unused capacity can be employed for the grid ancillary services and trading peer-to-peer (P2P) energy. However, the preference of EV users is one of the most important factors, which has to be considered within the scheduling process of EVs. Therefore, this paper proposes a stochastic model for EVs bidirectional smart charging taking into account the preferences of EV users, P2P energy trading, and providing ancillary services of the grid based on blockchain mechanism. Considering the preferences of EV users makes the proposed scheduling model adaptive against changing operating conditions. The presented model is formulated as an optimisation problem aiming at optimal management of EV battery state of charge and energy placement of several services considering the provision of ancillary services and contributing to P2P transactions. To evaluate the proposed model, real-world data collected from Tehran city are used as input data of simulation. Numerical results demonstrate the efficacy of the presented model. Simulation results show that considering the preferences of EV users in the proposed model can enhance the total income provided by the EV energy-planning model such that it could balance the charging cost. Moreover, this advanced user-based smart charging model increases P2P energy transactions amongst EVs and raises the ancillary services facility to the grid.

Traffic speed prediction is an integral part of an Intelligent Transportation System (ITS) and the Internet of Vehicles (IoV). Advanced knowledge of average traffic speed can help take proactive preventive steps to avoid impending problems. There have been studies for traffic speed prediction in which data has been decomposed into components using various decomposition techniques such as empirical mode decomposition, wavelets, and seasonal decomposition. As far as the authors are aware, no research has used additively decomposed components as input features. In this study, we used additive decomposition on 21,843 samples of traffic speed data. We implemented two statistical techniques designed for double seasonality (i) Double Seasonal Holt-Winter, and (ii) Trigonometric seasonality, Box-Cox transformation, autoregressive integrated moving average errors, trend, and Seasonal components (TBATS), and five machine learning (ML) techniques, (i) Multi-Layer Perceptron, (ii) Convolutional-Neural Network, (iii) Long Short-Term Memory, (iv) Gated Recurrent Unit and (v) Convolutional-Neural Network-LSTM. Machine learning techniques are used in univariate mode with raw time series as features and then with decomposed components as features in multivariate mode. This study demonstrates that using decomposed components (trend, seasonal, and residual), as features, improves prediction results for multivariate ML techniques. This becomes a significant advantage when no other features are available.

Unmanned Aerial Vehicles (UAVs) are offering many global industry sectors the opportunity to adopt more sustainable business models. They offer innovative ways of managing resources and water and offer newer opportunities to address key challenges in many areas like border surveillance, precision agriculture and search and rescue missions. All these new applications areas tend to require the cooperation of groups, or “swarms” of UAVs to provide collaborative sensing and processing solutions. These new scenarios impose new requirements in terms of safety, coordination, and operation management. This paper provides an overview of some of the technical challenges that multirotor UAVs are still facing in terms of aerial coordination and interaction. In this regard, it focusses on recent developments available in the literature and presents some contributions realised during the past few years by the authors addressing UAV interaction to achieve collision-free flights and swarm-based missions. Based on the analysis provided in this work, the paper is able to provide insight into the challenges still open that need to be solved in order to enable effective UAV-based solutions to support sustainable aerial services.

An optimal day-ahead operation of a microgrid (MG) based on an energy hub (EH) that is an industrial building, is presented in this paper. The proposed EH includes wind turbine (WT), photovoltaic (PV), triple generation that is combined cooling, heat and power, and salt water desalination. The purpose of solving problem is to lessen the operational and pollution costs limited to several technical restrictions. The EH takes into account plug in electric vehicle (PEV) and an ice storage conditioner (ISC) and together with a thermal energy storage system that is a supplementary energy storage system (ESS). Particularly, the performance and efficacy of the EH operational and pollution costs are studied by considering a solar--powered compressed air energy storage (SPCAES) that is a novel rechargeable and developing ESS. The proposed model takes into account the uncertain behaviour of PV and WT generations together with the thermal, electrical, and cooling demands, which deal with a robust optimisation approach. The suggested robust mix integer linear problem model is figured out using the CPLEX solver in general algebraic modelling system software. The proposed framework is implemented on the industrial building located in the industrial city of Kaveh, Iran. The simulation results show that using ESSs including SPCAES, ISC, and PEVs reduce the total costs (operation and emission costs) by 2.42% in the day-ahead energy management.

Smart computing has a big role to play in the development of the smart cities. The advanced networking paradigms such as programmable and virtual networks, growth in communication technologies like 5G, and use of advanced computing infrastructure such as fog/edge/cloud computing can all contribute in the applications related to smart cities. With the penetration of Internet of things (IoT) devices in smart city applications, smart computing has become all the more relevant in terms of managing and processing the data. The applications in smart cities can be computing-intensive, network-intensive, disk-intensive, data-intensive etc., which require different solution sets in order to effectively solve the issues pertaining to these applications. The computing and communication aspects in smart computing are of great relevance in the wider domain of smart cities, which consists of smart metering, smart homes, smart building, smart industries, connected vehicles, and tackles various problems ranging from data reporting to providing smart services.

Hence, the aim of this special issue is to bring together researchers working in the broad area of smart cities using smart computing. A number of authors from the 2nd International Workshop on Smart Computing for Smart Cities were invited to expand and submit their papers to this special issue alongside papers received from our open call for papers. The summary of each paper is provided below. Please read the full papers if you are interested in more details.

Nassar and Simon, in their paper ‘Wavelet-attention-based traffic prediction for smart cities’, proposed a wavelet-attention based traffic prediction system to predict the temporal correlations between the traffic flow and the weather factors, so as to reduce the traffic congestion problem in smart cities. Often the traffic is affected by external factors, such as weather, which makes traffic prediction more complicated. Their proposed paper modelled the interactions between traffic and these external factors such as temperature, visibility, wind speed, rain, and humidity, based on which the important features are then calculated and compared to each other to get the attention weights that describe the importance of each external factor on traffic.

Fitwi et al., in their paper ‘Lightweight frame scrambling mechanisms for end-to-end privacy in edge smart surveillance’, investigated a very interesting and timely problem of smart surveillance and provided a lightweight frame scrambling mechanism for ensuring end-to-end privacy in such systems. The authors rightly pointed out that existing cryptographic schemes are computationally expensive if data is to be processed at the resource constrained network edge devices. Keeping this in mind, they designed a lightweight sine-cosine chaotic map solution for enciphering frames at edge cameras and ran dynamic chaotic image enciphering scheme in real time at the edge along with a lightweight r

The relationship between smart cities and communities is anchored on the way organisations leverage technology to impact the quality of living in a city, state, or country. The emergence of smart cities has been welcomed as one of the significant breakthroughs in improving the public sector. In particular, generation of revenue, utilisation of technology, and formulation of policies are used to deal with challenges related to smart city infrastructure. Leadership is one area that has faced strategic challenges with the development of smart cities. This study aims to showcase connectivism between smart cities and communities, challenges associated with smart cities, requirements for modern leadership, and opportunities related to smart cities to improve the community.

The evolution of the Internet of Things (IoT) has increased the number of connected devices in the network. This has shifted the focus from IP-based network architecture towards content-centric networking (CCN). CCN eliminates the need for address-content binding in the conventional IP-based networks and allows the content to be accessed based on the name instead of the physical location. Named data networking (NDN) is a promising technique that can fulfil the increasing demand for connected devices through the CCN approach. NDN distributes the content on the network and focusses on the security of the content rather than the communication channel. However, the increase in traffic due to the escalation in the number of connected devices can lead to congestion in the network. The content distribution approach on the nodes is generalised and suitable for small networks. In the case of larger networks, an optimal approach is required to decide the optimal location to store the required content. However, a linear search approach is used to search (or lookup) the content in the assigned cache of the NDN node. In this work, the authors have combined the software-defined networking (SDN) with the NDN approach to overcome the above-highlighted challenge. Thus, the authors have designed an optimal content storage and indexing approach based on NDN-SDN coalesce in the IoT ecosystem. The proposed approach includes different phases, (a) a hashing-based content searching approach is formulated to reduce the look-up time of the content, (b) a red-black tree-based content storage approach is introduced for optimal utilisation of the assigned cache memory of the different NDN nodes, and (c) SDN controller facilitates automated network management and helps to administer the network requirements centrally and locate the content accordingly. The proposed approach was validated through the simulation experiments concerning network delay, packet rate, throughput, and cache hit ratio. The results obtained show the effectiveness of the proposed approach.

Our built environment is characterized by large, ever-expanding and highly complex cities. The spatial extent of the interconnected systems that serve these cities leads to higher vulnerability to disruption. On the other hand, climate change and political instability have noticeably increased the frequency of natural and human-induced hazards. Recalling that risk is the product of vulnerability and hazard, it is evident that large cities are experiencing unprecedented levels of risk. While major investments and numerous research, development and implementation efforts have been dedicated to address natural and human-induced risk to large cities, there is still a lack of system-of-systems level considerations and a comprehensive, interdependent vision for creating cities that respond effectively to severe disruptions. On this note, the authors envision the city of the future, its features and its operational modes. The requirements of creating such smart and sustainable, hence optimally resilient, cities dictate research-to-implementation consequences. A high-level view of these requirements and their implications on research and development is provided.

Since 2008, the development of China's smarter cities has experienced four phases: Exploration and practice phase, normative adjustment phase, strategic breakthrough phase and all-round development phase. A number of innovative practices such as the city brain and ‘unified online government service’ have provided the world with Chinese solutions for smart city construction. This study explains the concept and connotation of innovative smarter cities, summarizes the development status of China's innovative smarter cities, analyses and judges the seven development trends in the construction of innovative smarter cities, analyses the shortcomings and deficiencies, and puts forward policy suggestions to promote the development. It has an important reference value for comprehensively understanding the development concept and overall development status of China's innovative smarter cities and clarifying the next development direction.