scholarly journals Modern approach to design a distributed and scalable platform architecture for smart cities complex events data collection

2020 ◽  
Vol 170 ◽  
pp. 43-50 ◽  
Author(s):  
Wadii Basmi ◽  
Azedine Boulmakoul ◽  
Lamia Karim ◽  
Ahmed Lbath
Keyword(s):  
Data Collection ◽  
Smart Cities ◽  
Modern Approach ◽  
Platform Architecture ◽  
Complex Events ◽  
Events Data

Community-Centered Urban Sensing

2019 ◽  
Vol 8 (4) ◽  
pp. 1-16 ◽  
Author(s):  
Andrew Mondschein ◽  
Zihao Zhang ◽  
Mona El Khafif
Keyword(s):  
Data Collection ◽  
Smart Cities ◽  
Urban Systems ◽  
University Of Virginia ◽  
Planning And Design ◽  
Data Collection And Analysis ◽  
Urban Planning And Design ◽  
Urban Sensing ◽  
The University ◽  
Urban Data

The authors examine the problem of integrating urban sensing into engaged planning. The authors ask whether enhanced urban data and analysis can enhance resident engagement in planning and design, rather than hinder it, even when current urban planning and design practices are dysfunctional. The authors assess the outcomes of a planning and design effort in Charlottesville, Virginia, USA. Community-Centered Urban Sensing is a participatory urban sensing initiative developed by urban planners and designers, architects, landscape architects, and technologists at the University of Virginia to address the need for actionable information on the urban environment through community-engaged urban data collection and analysis. These findings address how technological urbanism moves from data to action, as well as its potential for marginalization. Finally, the authors discuss a conceptualization of smart and engaged planning that accounts for urban dysfunction. The smart cities paradigm should encompass modes and methods that function even when local urban systems are dysfunctional.

scholarly journals Integrated Security Home Automation Based on IOT

2018 ◽  
Vol 7 (2.32) ◽  
pp. 348
Author(s):  
Prof. V.Srikanth ◽  
H Suchetha ◽  
K Pravllika
Keyword(s):  
Data Collection ◽  
Internet Of Things ◽  
Digital Signature ◽  
Temperature Sensor ◽  
Smart Cities ◽  
Home Automation ◽  
Biometric System ◽  
Collection Data ◽  
Level Sensor ◽  
Sensor Temperature

Internet of Things (IoT)is the network of home appliances, vehicles and physical devices, which enables the objects to connect and exchange of the data. By using these components IoT supports to develop numerous services in various domains, such as smart cities and smart homes.These components can interact with other components by enabling security such asproxies, data collection, data sharing and other activities in the context of service providence. Until now various research works have studied on these securityissues ofIoT by validating their claim.In this study we are developing a framework, which provides security to the home as well as to operate the appliances in the home using smart technology.For entering into the house using a biometric system, which uses authentication and digital signature to access.We have used biometric system to overcome problems we are facing. In this project there are different sensors such as water level sensor, light sensor, temperature sensor, gas sensor for operation of different appliances in the house.  

Training competences in smart cities: an online program for higher education students

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Joaquín Adiego ◽  
Natalia Martín-Cruz
Keyword(s):  
Higher Education ◽  
Training Program ◽  
Smart Cities ◽  
Online Training ◽  
Interdisciplinary Teams ◽  
Project Based Learning ◽  
Modern Approach ◽  
Content Type ◽  
Teaching Learning ◽  
Major Adjustment

Purpose This paper aims to explain the development of an online training curriculum to enable students to acquire the transversal competences needed to work on smart cities projects. In this curriculum, a modern approach to the teaching-learning process was applied, suitable for the interdisciplinary and multinational learning challenges that smart cities impose, but within the framework of a university-industry European partnership. Design/methodology/approach To develop the curriculum, the competences needed for smart cities, common to all disciplines and fields, had to be researched. In addition, real smart cities projects also had to be selected for work following a project-based learning methodology. For both, this study applied the Delphi method, selecting the most relevant ones based on the data obtained by performing a multi-criteria decision analysis. Findings The procedure followed for the identification of transversal competences in a field, the design of an innovative online training program and the results of the first edition of the program are discussed. Research limitations/implications The processes that were developed, both to detect the most relevant transversal competences and to design the online training program, could be extrapolated to other areas. Moreover, it is very likely that the competences detected in this work could also be extrapolated, for the most part, to interdisciplinary teams. Originality/value To date, there is no European initiative addressing the challenges of smart cities that requires a major adjustment in higher education, in the relationship between universities and all the mechanisms of lifelong learning with the industry related to smart cities. This work is a pioneer in this regard.

scholarly journals Energy Efficient IoT Data Collection in Smart Cities Exploiting D2D Communications

Sensors ◽  
2016 ◽  
Vol 16 (6) ◽  
pp. 836 ◽  
Author(s):  
Antonino Orsino ◽  
Giuseppe Araniti ◽  
Leonardo Militano ◽  
Jesus Alonso-Zarate ◽  
Antonella Molinaro ◽  
...  
Keyword(s):  
Data Collection ◽  
Energy Efficient ◽  
Smart Cities ◽  
D2d Communications

scholarly journals Logistic Regression in Rare Events Data

2001 ◽  
Vol 9 (2) ◽  
pp. 137-163 ◽  
Author(s):  
Gary King ◽  
Langche Zeng
Keyword(s):  
Logistic Regression ◽  
Data Collection ◽  
Rare Events ◽  
Explanatory Variables ◽  
Relative Risks ◽  
Efficient Sampling ◽  
Dependent Variables ◽  
Data Collections ◽  
Events Data ◽  
Collection Strategies

We study rare events data, binary dependent variables with dozens to thousands of times fewer ones (events, such as wars, vetoes, cases of political activism, or epidemiological infections) than zeros (“nonevents”). In many literatures, these variables have proven difficult to explain and predict, a problem that seems to have at least two sources. First, popular statistical procedures, such as logistic regression, can sharply underestimate the probability of rare events. We recommend corrections that outperform existing methods and change the estimates of absolute and relative risks by as much as some estimated effects reported in the literature. Second, commonly used data collection strategies are grossly inefficient for rare events data. The fear of collecting data with too few events has led to data collections with huge numbers of observations but relatively few, and poorly measured, explanatory variables, such as in international conflict data with more than a quarter-million dyads, only a few of which are at war. As it turns out, more efficient sampling designs exist for making valid inferences, such as sampling all available events (e.g., wars) and a tiny fraction of nonevents (peace). This enables scholars to save as much as 99% of their (nonfixed) data collection costs or to collect much more meaningful explanatory variables. We provide methods that link these two results, enabling both types of corrections to work simultaneously, and software that implements the methods developed.

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