IET Smart Cities最新文献

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.

As smart surveillance has become popular in today's smart cities, millions of closed circuit television 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 centres. This increases the risk of interception and the spill of individuals' information into the wider cyberspace that risks 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, three lightweight end-to-end privacy-protection mechanisms are proposed: (1) a novel lightweight Sine-cosine Chaotic Map, which is a robust and efficient solution for enciphering frames at edge cameras; (2) Dynamic Chaotic Image Enciphering scheme that can run in real time at the edge; (3) a lightweight Regions of Interest Masking scheme that ensures the privacy of sensitive attributes like face on video frames. Design rationales are discussed and extensive experimental analyses substantiate the feasibility and security of the proposed schemes.

Traffic congestion is a problem facing today's world, especially in smart cities where the economy is booming. Solving this issue by upgrading the traffic infrastructure of the city might be very cost-inefficient as well as time-consuming. With the help of recent technologies, traffic can be predicted to give the authorities the time to react before congestion evolves. As traffic is affected by several external factors, such as weather and anomalies (accidents, not expected road closures etc.), understanding the relationship between traffic and these factors can improve the prediction even further. In this study, a new method, the weather-based traffic analysis (hereafter WBTA), is utilised to investigate the temporal correlations between the traffic flow and the exogenous weather factors at different frequencies and time intervals. In addition, a novel method, the wavelet-attention-based calculation (hereafter WABC) is introduced to help to understand the importance of each external factor, compared with the others. Five weather factors (temperature, wind speed, rain, visibility, and humidity) are analysed, weighted, and merged with each other as one auxiliary input to improve traffic prediction accuracy. Based on that, the wavelet-attention-based prediction model is introduced, where the mean squared error is reduced by 32.3% and 24.52% for one future time step prediction, and 14.9% and 18.22% for five, compared with using the traffic time series alone, and with external factors without weights, respectively.

Biodiversity surveys are often required for development projects in cities that could affect protected species such as bats. Bats are important biodiversity indicators of the wider health of the environment and activity surveys of bat species are used to report on the performance of mitigation actions. Typically, sensors are used in the field to listen to the ultrasonic echolocation calls of bats or the audio data is recorded for post-processing to calculate the activity levels. Current methods rely on significant human input and therefore present an opportunity for continuous monitoring and in situ machine learning detection of bat calls in the field. Here, we show the results from a longitudinal study of 15 novel Internet connected bat sensors—Echo Boxes—in a large urban park. The study provided empirical evidence of how edge processing can reduce network traffic and storage demands by several orders of magnitude, making it possible to run continuous monitoring activities for many months including periods which traditionally would not be monitored. Our results demonstrate how the combination of artificial intelligence techniques and low-cost sensor networks can be used to create novel insights for ecologists and conservation decision-makers.

A dynamic digital twin is a feasible solution that can be employed to build real-time connectivity between virtual and physical objects. Industries like manufacturing, aerospace and healthcare utilise dynamic digital twins for simulation, monitoring and control purposes, but recently, this nascent technology has also attracted the interest of urban designers. Due to the novelty of the dynamic digital twin in urban design, this research study addresses the concept of digital twin technology and investigates its applicability in so-called smart city settings. Drawing on results from research interviews and examples from the Digital Twin project in Helsinki city, the research illustrates that solid data infrastructure forms the foundation for urban digital twins and the development of future smart city applications and services. Furthermore, data-enriched digital twins evidently accelerate smart city experimentations and strengthen both learning and knowledge-based decision-making. Digital twins have also proved that they offer an environment in which smart city practitioners can bridge multi-stakeholder urban design teams through one digital platform.