Context-Aware Crowd-Sensing in Opportunistic Mobile Social Networks

In this paper, we study the physical crowd-sensing problem and draw the connection to the vertex cover problem in graph theory. Since finding the optimal solution for minimum vertex cover problem is NP-complete and the well-known approximation algorithms do not perform well with under crowd-sensing scenario, we propose the notions of node observability and coverage utility score and design a new context-aware approximation algorithm to find vertex cover that is tailored for crowd-sensing task. In addition, we design human-centric bootstrapping strategies to make initial assignment of sensing devices in the physical crowd based on social information about the users (e.g., Interests, friendship). Our experiments on real-world data traces show that the proposed approach significantly outperforms the baseline approximation algorithms in terms of sensing coverage.