Hybrid Network Assisted Dynamic Worker Recruitment Algorithm

Abstract

With the wide application of mobile electronic devices such as smartphones, the emerging mobile crowd sensing (MCS) has gradually become an effective way of real-time sensing and information collection. In the MCS system, worker recruitment is a core and common research issue. The cold start problem limits the application of traditional MCS worker recruitment methods. Introducing social relationships can solve the cold start problem to some extent. Therefore, this paper borrows the idea of influence propagation on social networks and proposes a worker recruitment algorithm based on hybrid network mixing social network and communication network. The core idea is that first select seed workers according to the recruitment probability by using the communication network, then initiate the task spread on social networks and communication networks at the same time in a greedy way. The goal of worker recruitment is to maximize task spatial coverage. When calculating the probability of recruitment, this paper considers various factors such as worker's ability, sojourn time and worker's movement to improve the accuracy of recruitment probability. The experimental results on real datasets show that compared with the existing algorithms, the algorithm in this paper can guarantee the time constraint of the task and have better performance in terms of spatial coverage and running time.