Abstract
The selfishness and randomness of users in the mobile crowd sensing network could cause
them unwilling to participate in sensing activities and lead to lower completion rates of sensing tasks.
In order to deal with these problems, this paper proposes a novel incentive mechanism based on a new
auction model for mobile crowd sensing, which consists of two consecutive stages. In the first stage,
a novel Incentive Method based on Reverse Auction for Location-aware sensing (IMRAL) is proposed to
maximize user utility. By introducing a task-centric method to determine the winning bids, it can provide
higher user utility and higher task coverage ratio. To ensure the truthfulness of IMRAL, we design a
unique payment determination algorithm based on critical payment for the incentive platform. In the second
stage, we propose a user-interaction incentive model (UIBIM) to cover the situation that a user may
drop out of the sensing activity. This new incentive model includes a dynamic double auction framework
prompting users’ interaction and a user matching algorithm based on a bipartite graph. The proposed new
mechanism achieves the goal of improving task completion rates without increasing the cost of the incentive
platform. The simulation results show that comparing with other solutions, such as a truthful auction for
location-aware collaborative sensing in mobile crowdsourcing and incentive mechanism for crowdsourcing
in the single-requester single-bid-model, IMRAL can achieve better performance in terms of average user
utility and tasks coverage ratio, and the UIBIM can significantly improve task completion rates.
| Original language | American English |
|---|---|
| Journal | IEEE Access |
| Volume | 7 |
| DOIs | |
| State | Published - Jan 1 2019 |
Disciplines
- Computer Sciences
- Digital Communications and Networking