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Context-aware recruitment scheme for opportunistic mobile crowdsensing

conference contribution
posted on 2015-01-01, 00:00 authored by Ali Hassani, P D Haghighi, P P Jayaraman
The ubiquity of mobile devices coupled with the advances in Internet of Things (IoT) technologies has led to the development of large-scale applications that can collect information about people and their environments in real-time. Such applications are referred to as Mobile Crowdsensing (MCS). In MCS, tasks are allocated to participants (mobile devices) by a remote server according to the application requirements. The key challenge is reducing the energy consumption of the participating mobile devices. One of the effective approaches to reduce energy consumption of MCS applications is to improve efficiency of task allocation. An efficient task allocation approach can optimize several aspects of MCS applications such as task coverage (minimum number of participants required for a MCS task), data quality, and sensing costs. In this paper, we propose a novel Context-Aware Task Allocation (CATA) approach that aims to allocate sensing tasks to the best participant set while improving energy efficiency in MCS applications. Another important feature of the proposed CATA approach is that it preserves the privacy of participants' by only disclosing the less sensitive data to the server. The proposed approach employs local and global task allocation methods to enable two levels of data sharing and privacy. We describe the series of experiments that were conducted to validate our proposed approach in terms of coverage and efficiency.



IEEE Computer Society. Conference (21st : 2015 : Melbourne, Vic.)


IEEE Computer Society Conference


266 - 273


Institute of Electrical and Electronics Engineers


Melbourne, Vic.

Place of publication

Piscataway, N.J.

Start date


End date








Publication classification

E1.1 Full written paper - refereed

Copyright notice

2015, IEEE



Title of proceedings

ICPADS 2015 : Proceedings of the 2015 IEEE 21st International Conference on Parallel and Distributed Systems