scholarly journals Contact Strategy for VDTN Data Collection in Smart Cities

Author(s):  
Ngurah Indra ER ◽  
Kamal Deep SINGH ◽  
Jean-Marie BONNIN
Keyword(s):  
2019 ◽  
Vol 8 (4) ◽  
pp. 1-16 ◽  
Author(s):  
Andrew Mondschein ◽  
Zihao Zhang ◽  
Mona El Khafif

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.


2018 ◽  
Vol 7 (2.32) ◽  
pp. 348
Author(s):  
Prof. V.Srikanth ◽  
H Suchetha ◽  
K Pravllika

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.  


Sensors ◽  
2016 ◽  
Vol 16 (6) ◽  
pp. 836 ◽  
Author(s):  
Antonino Orsino ◽  
Giuseppe Araniti ◽  
Leonardo Militano ◽  
Jesus Alonso-Zarate ◽  
Antonella Molinaro ◽  
...  

Sensors ◽  
2017 ◽  
Vol 17 (4) ◽  
pp. 888 ◽  
Author(s):  
Yixuan Xu ◽  
Xi Chen ◽  
Anfeng Liu ◽  
Chunhua Hu

2021 ◽  
Vol 1 (1) ◽  
pp. 1-32
Author(s):  
Sage Cammers-Goodwin ◽  
Naomi Van Stralen

“Transparency” is continually set as a core value for cities as they digitalize. Global initiatives and regulations claim that transparency will be key to making smart cities ethical. Unfortunately, how exactly to achieve a transparent city is quite opaque. Current regulations often only mandate that information be made accessible in the case of personal data collection. While such standards might encourage anonymization techniques, they do not enforce that publicly collected data be made publicly visible or an issue of public concern. This paper covers three main needs for data transparency in public space. The first, why data visibility is important, sets the stage for why transparency cannot solely be based on personal as opposed to anonymous data collection as well as what counts as making data transparent. The second concern, how to make data visible onsite, addresses the issue of how to create public space that communicates its sensing capabilities without overwhelming the public. The final section, what regulations are necessary for data visibility, argues that for a transparent public space government needs to step in to regulate contextual open data sharing, data registries, signage, and data literacy education.  


Inventions ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 8 ◽  
Author(s):  
Omid Ameri Sianaki ◽  
Ashkan Yousefi ◽  
Azadeh Tabesh ◽  
Mehregan Mahdavi

Dramatic changes in the way we collect and process data has facilitated the emergence of a new era by providing customised services and products precisely based on the needs of clients according to processed big data. It is estimated that the number of connected devices to the internet will pass 35 billion by 2020. Further, there has also been a massive escalation in the amount of data collection tools as Internet of Things devices generate data which has big data characteristics known as five V (volume, velocity, variety, variability and value). This article reviews challenges, opportunities and research trends to address the issues related to the data era in three industries including smart cities, healthcare and transportation. All three of these industries could greatly benefit from machine learning and deep learning techniques on big data collected by the Internet of Things, which is named as the internet of everything to emphasise the role of connected devices for data collection. In the smart grid portion of this paper, the recently developed deep reinforcement learning techniques and their applications in Smart Cities are also presented and reviewed.


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