Mobile Crowdsensing from a Selfish Routing Perspective

Buwaya, Julia; Rolim, José D. P.

doi:10.1109/ipdpsw.2017.136

Cited by 3 publications

(2 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the context of the development of the VIVO platform, we launched an experiment to create a public online map of noise levels within the San Francisco Bay area using data recorded by the smartphones of private citizens 4 . The special focus of the experiment was to test load balancing and task allocation algorithms in mobile crowd-sensing applications [43], [44]. Volunteers were asked to submit non-public information about their availabilities and to download our NoiseBay app to collect anonymous noise levels.…”

Section: Noisebaymentioning

confidence: 99%

VIVO: A secure, privacy-preserving, and real-time crowd-sensing framework for the Internet of Things

Luceri

Cardoso

Papandrea

et al. 2018

Pervasive and Mobile Computing

Self Cite

View full text Add to dashboard Cite

Smartphones are a key enabling technology in the Internet of Things (IoT) for gathering crowd-sensed data. However, collecting crowd-sensed data for research is not simple. Issues related to device heterogeneity, security, and privacy have prevented the rise of crowd-sensing platforms for scientific data collection. For this reason, we implemented VIVO, an open framework for gathering crowdsensed Big Data for IoT services, where security and privacy are managed within the framework. VIVO introduces the enrolled crowd-sensing model, which allows the deployment of multiple simultaneous experiments on the mobile phones of volunteers. The collected data can be accessed both at the end of the experiment, as in traditional testbeds, as well as in real-time, as required by many Big Data applications. We present here the VIVO architecture, highlighting its advantages over existing solutions, and four relevant real-world applications running on top of VIVO.

show abstract

Section: Noisebaymentioning

confidence: 99%

VIVO: A secure, privacy-preserving, and real-time crowd-sensing framework for the Internet of Things

Luceri

Cardoso

Papandrea

et al. 2018

Pervasive and Mobile Computing

Self Cite

View full text Add to dashboard Cite

show abstract

“…CCS-TA, a good task allocation framework has been intended by them, combining the state-of-the-art compressive sensing, Bayesian interface, and various mechanisms for learning to select a smallest number of sensing cells in each cycle while calculating the missing values of remaining cells, at the same time assuring that we get the maximum accuracy [7]. An issue of load balancing in MCS is studied by Julia Buwaya [8] in her paper. A game-theoretic selfish routing model has been created and has a systematic mechanism for automation of task allocation in MCS system has been developed.…”

Section: Figure 1: Mcs Functioningmentioning

confidence: 99%

An Approach for Site Progress Monitoring Using Mobile Crowd Sensing

Deolikar¹,

Neware²

2018

IJCSE

View full text Add to dashboard Cite

Monitoring of construction progress plays a very significant role in any infrastructural construction. It is an exact and sure manner in which we check the quality, progress and accuracy of our construction activity. In order to get a clear glimpse of our project's progress, we need to monitor the activities well so as to avoid or address the problems before and as they arise. Normally, a project manager has to walk around the site so as to monitor the progress of the work done. In this paper, we introduce a concept which uses Mobile crowd sensing and Image processing in a combining way to monitor the construction sites in Metro environment. The user sends an image of construction site which gets stored on the cloud. The admin who wants to check the progress of site can ask for the image of any area of his interest. Image processing module aims to match the captured image with the referenced one. Finally, we integrate the system to get the final progress done to get some value in percentage. Keywords-Mobile Crowd Sensing, Image Processing, construction management, progress monitoring. I. INTRODUCTION A well-timed progress monitoring is very important for the successful completion of any construction project. Lately, mobile crowd sensing and image processing have been examined as a trending means of automated progress monitoring. Mobile Crowd Sensing is advantageous in recording, retrieval and transfer of wireless data, whereas Image Processing is able to analyze the site images to extract progress information[1]. Mobile crowd sensing is defined as the process of obtaining required information, or asking for contributions from a large group of people. It can be called as a trending sensing technique based on the group of crowd mutually with the sensing capabilities of various sensors such as mobile devices, vehicular sensors etc. Recently, the mobile internet and mobile social networking techniques have been developed so fast, that it has given rise to the increased scope of crowd problem solving systems using mobile devices. The traditional internet crowd sourcing is turning into a new benchmark that is Mobile Crowd Sensing. MCS promotes the increasing number of mobile device users to participate in crowd sensing tasks[2]. There have been introduced many projects related to infrastructural construction which made the use of equipments like digital camera or closed circuit television camera (CCTV) to record the images of construction site[3]. However it is not always so convenient to set up CCTV camera or screens to verify the progress. In this paper, an effort is used where we can use the crowd sensing and image processing combined together in order to monitor the construction operation in Metro Environment.

show abstract