scholarly journals Explora: Interactive Querying of Multidimensional Data in the Context of Smart Cities

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2737
Author(s):  
Leandro Ordonez-Ante ◽  
Gregory Van Seghbroeck ◽  
Tim Wauters ◽  
Bruno Volckaert ◽  
Filip De Turck
Keyword(s):  
Smart City ◽  
Smart Cities ◽  
Temporal Trends ◽  
Multidimensional Data ◽  
Data Sets ◽  
Distributed Data ◽  
Key Factors ◽  
Distributed Data Processing ◽  
Spatial And Temporal Trends ◽  
Query Latency

Citizen engagement is one of the key factors for smart city initiatives to remain sustainable over time. This in turn entails providing citizens and other relevant stakeholders with the latest data and tools that enable them to derive insights that add value to their day-to-day life. The massive volume of data being constantly produced in these smart city environments makes satisfying this requirement particularly challenging. This paper introduces Explora, a generic framework for serving interactive low-latency requests, typical of visual exploratory applications on spatiotemporal data, which leverages the stream processing for deriving—on ingestion time—synopsis data structures that concisely capture the spatial and temporal trends and dynamics of the sensed variables and serve as compacted data sets to provide fast (approximate) answers to visual queries on smart city data. The experimental evaluation conducted on proof-of-concept implementations of Explora, based on traditional database and distributed data processing setups, accounts for a decrease of up to 2 orders of magnitude in query latency compared to queries running on the base raw data at the expense of less than 10% query accuracy and 30% data footprint. The implementation of the framework on real smart city data along with the obtained experimental results prove the feasibility of the proposed approach.

scholarly journals Key Factors in Big Data Implementation for Smart City: A Systematic Literature Review

2021 ◽  
Vol 006 (01) ◽  
pp. 16-22
Author(s):  
Yulida Safitri
Keyword(s):  
Big Data ◽  
Smart City ◽  
Smart Cities ◽  
Research Paper ◽  
City Government ◽  
Data Availability ◽  
Management Support ◽  
Key Factors ◽  
Privacy And Security ◽  
Technological Infrastructure

Many governments implement smart city concept in their daily operation. As the concept continues to develop, smart cities all over the world are now starting to utilize big data. Learning from their private counterparts that already ahead in harnessing the potential benefits of big data implementation, smart cities begin the transformation of implementing big data. The purpose of this research paper is to: 1) Review the possible opportunities offered by big data implementation in smart city, 2) Review the challenges that smart city need to solve in utilizing big data, 3) Develop a framework that addresses the key factors in successful big data implementation in smart city. This research paper produces a framework that addresses several key factors that smart city government should consider ensuring success when implementing big data based on the proposed model indicators in private sectors. This framework consists of key factors of big data implementation for smart city which are top management support, organizational change, privacy and security, data availability and quality, cost, skillset and knowledge, big data policy, and technological infrastructure. It is important to have an understanding that these key factors correlate each other and are equally important.

Spatial and temporal trends and effects of population size on the frequency of color phenotypes in the wild red fox (Vulpes vulpes)

1996 ◽  
Vol 74 (9) ◽  
pp. 1622-1631 ◽  
Author(s):  
Bradley J. Swanson ◽  
Donald R. Johnson
Keyword(s):  
Vulpes Vulpes ◽  
Red Fox ◽  
Meta Analysis ◽  
Temporal Trends ◽  
Selective Advantage ◽  
Data Sets ◽  
Data Set ◽  
Color Morphs ◽  
In The Wild ◽  
Spatial And Temporal Trends

We analyzed the hypothesized relationships of temporal, spatial, and harvest trends with frequency of red fox (Vulpes vulpes) color morphs in 57 Hudson's Bay Company posts over a 20- to 26-year period, but found none of the strong relationships postulated to exist. A meta-analysis of each data set suggested a weak inverse relationship between latitude and frequency of the red morph. Meta-analysis further indicated a weak positive relationship with time and the frequency of the red phase, although this trend was not due to climate change. No relationship was found between harvest size and color phase, or between a 1-year lagged harvest size and color phase, which evaluated the effects of dispersal. The data sets did not allow conclusive determination of the mechanisms behind the trends, but it is postulated that a slight selective advantage is found for the dark morphs at high latitudes, while the temporal increase in frequency of the red phenotype is probably the result of northward dispersal from southern populations.

scholarly journals Visualizing the Impact of COVID-19 in the Mobility Dynamics - A Dashboard Framework for Decision Support in Smart Cities

2021 ◽  
Author(s):  
Nuno Alpalhão ◽  
Miguel de Castro Neto ◽  
Marcel Motta
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Smart Cities ◽  
Temporal Trends ◽  
Mobility Patterns ◽  
Dynamic Framework ◽  
Visualization Techniques ◽  
Spatial And Temporal Trends ◽  
The Impact

Being mobility one of the biggest challenge’s cities face today, the COVID-19 pandemic reinforced this challenge and caused a deep structural change in the mobility of the multilayered dynamic framework of Smart Cities. The need to supply decision support systems to city authorities is higher than ever. Planning and managing mobility in Smart Cities has become more challenging, as the amount of information available and the pressure to enforce sustainable and secure policies increases, stakeholders require faster and more targeted actions. Dashboards are powerful tools that can be used in this context to provide, in an understandable manner, multidimensional information otherwise unavailable in classically static visualizations, as these tools offer a reliable foundation for decision support systems. This chapter goes through the required visualization techniques used to produce meaningful dashboards, to both showcase spatial and temporal trends in the context of mobility in Smart Cities following the COVID-19 pandemic. A general framework for analyzing mobility patterns is suggested by gathering methods and techniques recently developed in the literature.

scholarly journals Lora Technology based Potholes and Humps Detecstion for Smart City Transportation

2019 ◽  
Vol 8 (6) ◽  
pp. 702-705
Keyword(s):  
Developing Countries ◽  
Smart City ◽  
Intelligent System ◽  
Smart Cities ◽  
Transportation System ◽  
Ultrasonic Sensor ◽  
Complex Task ◽  
Key Factors ◽  
The City

In view of developing smart cities, all the infrastructure of the city should be integrated with intelligent system. Transportation is one of the main constraints to the development of cities. Roads maintenance is one of the key factors for transportation system. In developing countries due to the increased vehicular population the maintenance becoming a complex task. Here we propose a system which detects potholes and humps on roads and send the information to higher authorities using LoRa technology. We designed a system with three modules like user module, gateway module and cloud module. User module includes an ultrasonic sensor and Lora GPS shield which should be deployed on vehicles. Through sensor we can detect the pothole and humps on roads, Lora GPS will capture the location and the information will be sent to the gateway using LoRa communication. Gateway can be anywhere in the range of 15km as LoRa module can send the information throughout this range. This information is uploaded to cloud which will be available for higher authorities to repair and maintain the roads effectively.

Smart neighbourhood

2018 ◽  
Vol 9 (2) ◽  
pp. 210-226 ◽  
Author(s):  
Harish Kumar ◽  
Manoj Kumar Singh ◽  
M.P. Gupta ◽  
Jitendra Madaan
Keyword(s):  
Developing Countries ◽  
Urban Development ◽  
Smart City ◽  
Smart Cities ◽  
Matrix Multiplication ◽  
Working Environment ◽  
Sustainable Urban Development ◽  
Key Factors ◽  
Content Type ◽  
City Planners

Purpose This paper aims to identify the key factors to design efficient, healthy and potentially economical neighbourhood places in the surroundings of smart cities to reduce the urban polarization for the sustainable urban development. Design/methodology/approach A two-stage methodology is followed. First, the key factors for neighbourhood are identified from literature studies. The selected factors are validated by sample t-tests. Second, the total interpretive structural modeling is used to interpret the complexity of relationships among various factors. Further, cross-impact matrix multiplication is applied for classification analysis to find the most driving factors for neighbourhood design. Findings The contribution of this research is to show hierarchical relationships among the various factors to design the neighbourhood places as smart from the perspectives of city planners and decision makers. Research limitations/implications The applicability of the research findings is limited to developing countries mainly where population is large and most of cities have high pressure on its infrastructure to fulfil the citizens’ demands. Practical implications This paper will aid policymakers, city planners and government officials to design a sustainable smart city model in which smart neighbourhood would also be the potential solution to decrease pressure on a city’s critical infrastructure especially in developing countries. Social implications A smart city could be considered as the centre point of smart initiatives to develop a place smart, and it should continue beyond the city boundaries to enhance the facilities, services, resources utilization and working environment in neighbourhood places also. Originality/value The study explores the various literature on neighbourhood planning and then link with smart city development as current need of urban development scenario. The authors propose a hierarchical relation framework to develop the neighbourhood places as smart places to meet the future demand of urbanization in developing countries like India.

Smart Ideas for Smart City Development

2021 ◽  
pp. 83-96
Author(s):  
Andrew Omambia
Keyword(s):  
Big Data ◽  
Natural Environment ◽  
Smart City ◽  
Smart Cities ◽  
Critical Factors ◽  
City Development ◽  
Key Factors ◽  
Integrative Framework ◽  
Road Map ◽  
The World

The concept of smart city is a burgeoning strategy that is fast becoming popular as a strategy that will be able to mitigate the problems emanating from the uncontrolled population growth and urbanization. Academicians have turned their attention to the smart city concept, but an in-depth understanding of the concept is still required. There is a dearth of information on the concept and hence the phenomenon is not well understood. This study, therefore, aims to fill the gap in literature regarding smart cities and propose a framework for grasping the concept further. Based on exploratory studies on the concept of smart cities, this chapter focusses on nine key factors that will form the framework for smart cities and the smart cities initiatives. These nine critical factors include the management, organization governance, technology, people, policy, economy, natural environment, built environment, and the implications of big data on smart cities. These factors provide the basis for the development of an integrative framework that can be employed to examine the manner in which governments around the world, including Kenya, are envisioning smart city initiatives. The framework provides the agendas and directions for smart approaches that can be implemented in cities and a road map for the attainment of smart cities.

scholarly journals Concept of implementing Big data in smart city: Applications, Services, Data Security in accordance with Internet of Things and AI

2019 ◽  
Vol 8 (3) ◽  
pp. 6819-6825
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
San Francisco ◽  
Smart City ◽  
Urban Areas ◽  
Smart Cities ◽  
Point Of View ◽  
Data Sets ◽  
Cloud Applications ◽  
The Internet Of Things

Smart cities are the current buzz phrase between infrastructure developments. With a gradually increasing inflow on populations into cities then a continuously thriving necessity to better deal with resources, countless cities kind of San Francisco, united states, Singapore, Portugal, England is experimenting together with upcoming state-of-the-art technologies after fulfill their cities smarter. Among these current trending technologies is the Internet of Things (IoT), Big Data and Artificial Intelligence (AI) which has revolutionized the way we analyze patterns yet traits between human behaviors. With Big Data, current fragmented and remoted data sets do stand well-acquainted beside an overarching point of view in accordance with provide high quality solutions in accordance with frequent issues up to expectation have an effect on rapidly growing cities today. Here are 5 ways within which Big Data could show fundamental in smart cities about the future. A lot of governments are thinking about adopting the smart city thought between theirs urban areas at that point executing impressive records services up to expectation assist smart city components in accordance with attain the required stage concerning supportability and improve the living norms. Smart cities take advantage of more than one technology in conformity with get better the concert about healthiness, transportation, power, education, and cloud applications lead after greater stages about remedy about their citizens. In addition, it attempts in accordance with pick out the necessities as assist the implementation on substantial data purposes for smart city services. The criticism displays as numerous possibilities are accessible because of making use of big data in smart cities; conversely, so are nevertheless various concerns and disputes in conformity with stay addressed to attain higher utilization about this technology.

scholarly journals Ingenious Techniques for Creation of Smart Cities by Big Data Technology & Urban Modelling Simulation by Matsim

2019 ◽  
Vol 8 (2) ◽  
pp. 1922-1927
Keyword(s):  
Big Data ◽  
Smart City ◽  
Urban Areas ◽  
Large Scale ◽  
Smart Cities ◽  
Distributed Data ◽  
Spatially Distributed ◽  
Urban Modelling ◽  
Different Sources ◽  
Big Data Technology

Ingenious Techniques for creation of Smart Cities by Big Data Technology & Urban modeling simulation by MATSimas the smart cities are on nascent stage in India. The extension of huge information and the advancement of Internet of Things (IoT) innovations have assumed a significant job in the practicality of keen city activities. Enormous information offer the potential for urban areas to get significant bits of knowledge from a lot of information gathered through different sources, and the IoT permits the joining of sensors, radiofrequency recognizable proof, and Bluetooth in reality condition utilizing exceedingly organized administrations. Thus the job of urban reenactment models and their perception are utilized to help territorial arranging offices assess elective transportation ventures, land use guidelines, and natural insurance arrangements. Typical urban simulations provide spatially distributed data about number of inhabitants, land prices, traffic, and other variables for ex- MATSim is an activity-based transport simulation framework designed to simulate large scale scenarios. Such technologies which have been developed in the past few years have proven to be very effective in smart cities of various countries. This project is an attempt to study the feasibility of such modified system, by understanding the implementation of such technologies to improve the existing smart cities and those which are about to become one. This is done by proposing an idea that is by implementing a big data server in the proposed smart city, the data will be collected through smart sensors which will then be sent to server and the mined data will be converted to simplified data for planners, engineers etc. in order to make a economic, self-sustainable & fully automated smart city

scholarly journals Modeling and management of usage-aware distributed datasets for global Smart City Application Ecosystems

2017 ◽  
Vol 3 ◽  
pp. e115 ◽  
Author(s):  
Johannes M. Schleicher ◽  
Michael Vögler ◽  
Christian Inzinger ◽  
Schahram Dustdar
Keyword(s):  
Smart City ◽  
Response Times ◽  
Smart Cities ◽  
Data Access ◽  
Data Sources ◽  
Distributed Data ◽  
Multiple Sources ◽  
Original Application ◽  
Novel Applications

The ever-growing amount of data produced by and in today’s smart cities offers significant potential for novel applications created by city stakeholders as well as third parties. Current smart city application models mostly assume that data is exclusively managed by and bound to its original application and location. We argue that smart city data must not be constrained to such data silos so that future smart city applications can seamlessly access and integrate data from multiple sources across multiple cities. In this paper, we present a methodology and toolset to model available smart city data sources and enable efficient, distributed data access in smart city environments. We introduce a modeling abstraction to describe the structure and relevant properties, such as security and compliance constraints, of smart city data sources along with independently accessible subsets in a technology-agnostic way. Based on this abstraction, we present a middleware toolset for efficient and seamless data access through autonomous relocation of relevant subsets of available data sources to improve Quality of Service for smart city applications based on a configurable mechanism. We evaluate our approach using a case study in the context of a distributed city infrastructure decision support system and show that selective relocation of data subsets can significantly reduce application response times.

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