Autonomous and distributed recruitment and data collection framework for opportunistic sensing

Authors:
Güliz Seray Tuncay;Giacomo Benincasa;Ahmed Helmy
Affiliations:
Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, USA;Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, USA;Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, USA
Venue:
ACM SIGMOBILE Mobile Computing and Communications Review
Year:
2013

Citing 2
Cited 1

Urban sensing systems: opportunistic or participatory?

Proceedings of the 9th workshop on Mobile computing systems and applications
Recruitment framework for participatory sensing data collections

Pervasive'10 Proceedings of the 8th international conference on Pervasive Computing

Participant recruitment and data collection framework for opportunistic sensing: a comparative analysis

Proceedings of the 8th ACM MobiCom workshop on Challenged networks

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Abstract

People-centric sensing is a novel approach that exploits the sensing capabilities offered by smartphones and the mobility of users to sense large scale areas without requiring the deployment of sensors in-situ. Given the ubiquitous nature of smartphones, people-centric sensing is a viable and efficient solution for crowdsourcing data. In this work, we propose a fully distributed, opportunistic sensing framework that involves two main components which both work in an ad hoc fashion: Recruitment and Data Collection. We analyzed the feasibility of our distributed approach for both components through preliminary simulations. The results show that our recruitment method is able to select 66% of the nodes that are appropriate for the sensing activity and 88% of the messages sent by these selected nodes reach the sink by using our data collection method.