A Stochastic Team Formation Approach for Collaborative Mobile Crowdsourcing

• URL: http://arxiv.org/abs/2004.13881v1
• Date: Tue, 28 Apr 2020 22:44:37 GMT
• Title: A Stochastic Team Formation Approach for Collaborative Mobile Crowdsourcing
• Authors: Aymen Hamrouni, Hakim Ghazzai, Turki Alelyani, and Yehia Massoud
• Abstract summary: We develop an algorithm that exploit workers knowledge about their SN neighbors and asks a designated leader to recruit a suitable team. The proposed algorithm is inspired from the optimal stopping strategies and uses the odds-algorithm to compute its output. Experimental results show that, compared to the benchmark exponential optimal solution, the proposed approach reduces time and produces reasonable performance results.
• Score: 1.4209473797379666
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mobile Crowdsourcing (MCS) is the generalized act of outsourcing sensing tasks, traditionally performed by employees or contractors, to a large group of smart-phone users by means of an open call. With the increasing complexity of the crowdsourcing applications, requesters find it essential to harness the power of collaboration among the workers by forming teams of skilled workers satisfying their complex tasks' requirements. This type of MCS is called Collaborative MCS (CMCS). Previous CMCS approaches have mainly focused only on the aspect of team skills maximization. Other team formation studies on social networks (SNs) have only focused on social relationship maximization. In this paper, we present a hybrid approach where requesters are able to hire a team that, not only has the required expertise, but also is socially connected and can accomplish tasks collaboratively. Because team formation in CMCS is proven to be NP-hard, we develop a stochastic algorithm that exploit workers knowledge about their SN neighbors and asks a designated leader to recruit a suitable team. The proposed algorithm is inspired from the optimal stopping strategies and uses the odds-algorithm to compute its output. Experimental results show that, compared to the benchmark exponential optimal solution, the proposed approach reduces computation time and produces reasonable performance results.

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