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Sensors 2018, 18(5), 1593; https://doi.org/10.3390/s18051593

SOBER-MCS: Sociability-Oriented and Battery Efficient Recruitment for Mobile Crowd-Sensing

1,†
and
2,†,*
1
Department of Electrical and Computer Engineering, Clarkson University, Potsdam, NY 13699, USA
2
School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Received: 29 March 2018 / Revised: 5 May 2018 / Accepted: 8 May 2018 / Published: 17 May 2018
(This article belongs to the Special Issue Realization of Large-Scale Mobile Crowd Sensing Experiments)
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Abstract

The Internet of Things (IoT) concept is aiming at being an integral part of the next generation networking services by introducing pervasiveness and ubiquitous interconnectivity of uniquely-identifiable objects. The massive availability of personalized smart devices such as smartphones and wearables enable their penetration into the IoT ecosystem with their built-in sensors, particularly in Mobile Crowd-Sensing (MCS) campaigns. The MCS systems achieve the objectives of the large-scale non-dedicated sensing concept in the IoT if a sufficient number of participants are engaged to the collaborative data acquisition process. Therefore, user recruitment is a key challenge in MCS, which requires effective incentivization of cooperative, truthful and trustworthy users. A grand concern for the participants is the battery drain on the mobile devices. It is a known fact that battery drain in a smartphone is a function of the user activity, which can be modeled under various contexts. With this in mind, we propose a new social activity-aware recruitment policy, namely Sociability-Oriented and Battery-Efficient Recruitment for Mobile Crowd-Sensing (SOBER-MCS). SOBER-MCS uses sociability and the residual power of the participant smartphones as two primary criteria in the selection of participating devices. The former is an indicator of the participant willingness toward sensing campaigns, whereas the latter is used to prioritize personal use over crowd-sensing under critical battery levels. We use sociability profiles that were obtained in our previous work and use those values to simulate the sociability behavior of a large pool of participants in an MCS environment. Through simulations, we show that SOBER-MCS is able to introduce battery savings up to 18.5% while improving user and platform utilities by 12% and 20%, respectively. View Full-Text
Keywords: mobile crowd-sensing; behaviometrics; energy efficiency; user profiling; smart cities mobile crowd-sensing; behaviometrics; energy efficiency; user profiling; smart cities
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Anjomshoa, F.; Kantarci, B. SOBER-MCS: Sociability-Oriented and Battery Efficient Recruitment for Mobile Crowd-Sensing. Sensors 2018, 18, 1593.

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