TY - GEN
T1 - Quality of information aware incentive mechanisms for mobile crowd sensing systems
AU - Jin, Haiming
AU - Su, Lu
AU - Chen, Danyang
AU - Nahrstedt, Klara
AU - Xu, Jinhui
N1 - Funding Information:
∗This work was supported in part by the National Science Foundation under award numbers CNS-1329686, 1329737, 1330142, and 1330491. †The first two authors contribute equally to this paper.
Funding Information:
This work was supported in part by the National Science Foundation under award numbers CNS-1329686, 1329737, 1330142, and 1330491.
Publisher Copyright:
Copyright © 2015 ACM.
PY - 2015/6/22
Y1 - 2015/6/22
N2 - Recent years have witnessed the emergence of mobile crowd sensing (MCS) systems, which leverage the public crowd equipped with various mobile devices for large scale sensing tasks. In this paper, we study a critical problem in MCS systems, namely, incentivizing user participation. Different from existing work, we incorporate a crucial metric, called users' quality of information (QoI), into our incentive mechanisms for MCS systems. Due to various factors (e.g., sensor quality, noise, etc.) the quality of the sensory data contributed by individual users varies significantly. Obtaining high quality data with little expense is always the ideal of MCS platforms. Technically, we design incentive mechanisms based on reverse combinatorial auctions. We investigate both the singleminded and multi-minded combinatorial auction models. For the former, we design a truthful, individual rational and computationally efficient mechanism that approximately maximizes the social welfare with a guaranteed approximation ratio. For the latter, we design an iterative descending mechanism that achieves close-tooptimal social welfare while satisfying individual rationality and computational efficiency. Through extensive simulations, we validate our theoretical analysis about the close-to-optimal social welfare and fast running time of our mechanisms.
AB - Recent years have witnessed the emergence of mobile crowd sensing (MCS) systems, which leverage the public crowd equipped with various mobile devices for large scale sensing tasks. In this paper, we study a critical problem in MCS systems, namely, incentivizing user participation. Different from existing work, we incorporate a crucial metric, called users' quality of information (QoI), into our incentive mechanisms for MCS systems. Due to various factors (e.g., sensor quality, noise, etc.) the quality of the sensory data contributed by individual users varies significantly. Obtaining high quality data with little expense is always the ideal of MCS platforms. Technically, we design incentive mechanisms based on reverse combinatorial auctions. We investigate both the singleminded and multi-minded combinatorial auction models. For the former, we design a truthful, individual rational and computationally efficient mechanism that approximately maximizes the social welfare with a guaranteed approximation ratio. For the latter, we design an iterative descending mechanism that achieves close-tooptimal social welfare while satisfying individual rationality and computational efficiency. Through extensive simulations, we validate our theoretical analysis about the close-to-optimal social welfare and fast running time of our mechanisms.
KW - Crowd Sensing
KW - Incentive Mechanism
KW - Quality of Information
UR - http://www.scopus.com/inward/record.url?scp=84990061128&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84990061128&partnerID=8YFLogxK
U2 - 10.1145/2746285.2746310
DO - 10.1145/2746285.2746310
M3 - Conference contribution
AN - SCOPUS:84990061128
T3 - Proceedings of the International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc)
SP - 167
EP - 176
BT - MobiHoc'15 - Proceedings of the 16th ACM International Symposium on Mobile Ad Hoc Networking and Computing
PB - Association for Computing Machinery
T2 - 16th ACM International Symposium on Mobile Ad Hoc Networking and Computing, MobiHoc 2015
Y2 - 22 June 2015 through 25 June 2015
ER -