scholarly journals Smart cities and a data-driven response to COVID-19

2020 ◽  
Vol 10 (2) ◽  
pp. 255-259 ◽  
Author(s):  
Philip James ◽  
Ronnie Das ◽  
Agata Jalosinska ◽  
Luke Smith

This commentary describes the rapid development of a COVID-19 data dashboard utilising existing Urban Observatory Internet of Things (IoT) data and analytics infrastructure. Existing data capture systems were rapidly repurposed to provide real-time insights into the impacts of lockdown policy on urban governance.

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3594 ◽  
Author(s):  
Hung Cao ◽  
Monica Wachowicz

Exploring Internet of Things (IoT) data streams generated by smart cities means not only transforming data into better business decisions in a timely way but also generating long-term location intelligence for developing new forms of urban governance and organization policies. This paper proposes a new architecture based on the edge-fog-cloud continuum to analyze IoT data streams for delivering data-driven insights in a smart parking scenario.


Smart Cities ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 894-918
Author(s):  
Luís Rosa ◽  
Fábio Silva ◽  
Cesar Analide

The evolution of Mobile Networks and Internet of Things (IoT) architectures allows one to rethink the way smart cities infrastructures are designed and managed, and solve a number of problems in terms of human mobility. The territories that adopt the sensoring era can take advantage of this disruptive technology to improve the quality of mobility of their citizens and the rationalization of their resources. However, with this rapid development of smart terminals and infrastructures, as well as the proliferation of diversified applications, even current networks may not be able to completely meet quickly rising human mobility demands. Thus, they are facing many challenges and to cope with these challenges, different standards and projects have been proposed so far. Accordingly, Artificial Intelligence (AI) has been utilized as a new paradigm for the design and optimization of mobile networks with a high level of intelligence. The objective of this work is to identify and discuss the challenges of mobile networks, alongside IoT and AI, to characterize smart human mobility and to discuss some workable solutions to these challenges. Finally, based on this discussion, we propose paths for future smart human mobility researches.


2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Simon Elias Bibri ◽  
John Krogstie

AbstractThe IoT and big data technologies have become essential to the functioning of both smart cities and sustainable cities, and thus, urban operational functioning and planning are becoming highly responsive to a form of data-driven urbanism. This offers the prospect of building models of smart sustainable cities functioning in real time from routinely sensed data. This in turn allows to monitor, understand, analyze, and plan such cities to improve their energy efficiency and environmental health in real time thanks to new urban intelligence functions as an advanced form of decision support. However, prior studies tend to deal largely with data-driven technologies and solutions in the realm of smart cities, mostly in relation to economic and social aspects, leaving important questions involving the underlying substantive and synergistic effects on environmental sustainability barely explored to date. These issues also apply to sustainable cities, especially eco-cities. Therefore, this paper investigates the potential and role of data-driven smart solutions in improving and advancing environmental sustainability in the context of smart cities as well as sustainable cities, under what can be labeled “environmentally data-driven smart sustainable cities.” To illuminate this emerging urban phenomenon, a descriptive/illustrative case study is adopted as a qualitative research methodology§ to examine and compare Stockholm and Barcelona as the ecologically and technologically leading cities in Europe respectively. The results show that smart grids, smart meters, smart buildings, smart environmental monitoring, and smart urban metabolism are the main data-driven smart solutions applied for improving and advancing environmental sustainability in both eco-cities and smart cities. There is a clear synergy between such solutions in terms of their interaction or cooperation to produce combined effects greater than the sum of their separate effects—with respect to the environment. This involves energy efficiency improvement, environmental pollution reduction, renewable energy adoption, and real-time feedback on energy flows, with high temporal and spatial resolutions. Stockholm takes the lead over Barcelona as regards the best practices for environmental sustainability given its long history of environmental work, strong environmental policy, progressive environmental performance, high environmental standards, and ambitious goals. It also has, like Barcelona, a high level of the implementation of applied data-driven technology solutions in the areas of energy and environment. However, the two cities differ in the nature of such implementation. We conclude that city governments do not have a unified agenda as a form of strategic planning, and data-driven decisions are unique to each city, so are environmental challenges. Big data are the answer, but each city sets its own questions based on what characterize it in terms of visions, policies, strategies, pathways, and priorities.


Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 720 ◽  
Author(s):  
Gonçalo Marques ◽  
Nuno Miranda ◽  
Akash Kumar Bhoi ◽  
Begonya Garcia-Zapirain ◽  
Sofiane Hamrioui ◽  
...  

This paper presents a real-time air quality monitoring system based on Internet of Things. Air quality is particularly relevant for enhanced living environments and well-being. The Environmental Protection Agency and the World Health Organization have acknowledged the material impact of air quality on public health and defined standards and policies to regulate and improve air quality. However, there is a significant need for cost-effective methods to monitor and control air quality which provide modularity, scalability, portability, easy installation and configuration features, and mobile computing technologies integration. The proposed method allows the measuring and mapping of air quality levels considering the spatial-temporal information. This system incorporates a cyber-physical system for data collection and mobile computing software for data consulting. Moreover, this method provides a cost-effective and efficient solution for air quality supervision and can be installed in vehicles to monitor air quality while travelling. The results obtained confirm the implementation of the system and present a relevant contribution to enhanced living environments in smart cities. This supervision solution provides real-time identification of unhealthy behaviours and supports the planning of possible interventions to increase air quality.


2020 ◽  
Vol 1 (1) ◽  
pp. 7-13
Author(s):  
Bayu Prastyo ◽  
Faiz Syaikhoni Aziz ◽  
Wahyu Pribadi ◽  
A.N. Afandi

Internet use in Banyumas Regency is now increasingly diverse according to the demands of the needs. The development of communication technology raises various aspects that also develop. For example, the use of the internet for a traffic light control system so that it can be adjusted according to the settings and can be monitored in real time. In the development of communication technology, the term Internet of Things (IoT) emerged as the concept of extending the benefits of internet communication systems to give impulses to other systems. In other words, IoT is used as a communication for remote control and monitoring by utilizing an internet connection. The Internet of Things in the era is now being developed to create an intelligent system for the purposes of controlling various public needs until the concept of the smart city emerges. Basically, smart cities utilize internet connections for many purposes such as controlling CCTV, traffic lights, controlling arm robots in the industry and storing data in hospitals. If the system is carried out directly from the device to the central server, there will be a very long queue of data while the system created requires speed and accuracy of time so that a system is needed that allows sufficient data control and processing to be carried out on network edge users. Then fog Computing is used with the hope that the smart city system can work with small latency values ​​so that the system is more real-time in sending or receiving data.


2020 ◽  
Vol 1 (2) ◽  
pp. 6-13
Author(s):  
Bayu Prastyo ◽  
Faiz Syaikhoni Aziz ◽  
Wahyu Pribadi ◽  
A.N. Afandi

Internet use in Banyumas Regency is now increasingly diverse according to the demands of the needs. The development of communication technology raises various aspects that also develop. For example, the use of the internet for a traffic light control system so that it can be adjusted according to the settings and can be monitored in real time. In the development of communication technology, the term Internet of Things (IoT) emerged as the concept of extending the benefits of internet communication systems to give impulses to other systems. In other words, IoT is used as a communication for remote control and monitoring by utilizing an internet connection. The Internet of Things in the era is now being developed to create an intelligent system for the purposes of controlling various public needs until the concept of the smart city emerges. Basically, smart cities utilize internet connections for many purposes such as controlling CCTV, traffic lights, controlling arm robots in the industry and storing data in hospitals. If the system is carried out directly from the device to the central server, there will be a very long queue of data while the system created requires speed and accuracy of time so that a system is needed that allows sufficient data control and processing to be carried out on network edge users. Then fog Computing is used with the hope that the smart city system can work with small latency values ​​so that the system is more real-time in sending or receiving data


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