@inProceedings{
 title = {Quality of Information Aware Incentive Mechanisms for Mobile Crowd Sensing Systems},
 type = {inProceedings},
 year = {2015},
 keywords = {crowd,incentive,quality,sensing},
 pages = {1-10},
 id = {b2098ef0-2ba4-3737-8978-3c1586836f36},
 created = {2018-12-14T01:03:38.331Z},
 file_attached = {false},
 profile_id = {111a25b3-1df9-3944-a3cc-f6efb7b0f058},
 group_id = {c1e9e98b-e33c-3718-9343-9cc7f3e51d80},
 last_modified = {2018-12-14T01:03:38.331Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Mobihoc2015Haiming},
 source_type = {inproceedings},
 private_publication = {false},
 abstract = {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 single-minded 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-to-optimal
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.},
 bibtype = {inProceedings},
 author = {Jin, Haiming and Su, Lu and Chen, Danyang and Nahrstedt, Klara and Xu, Jinhui},
 booktitle = {The 16th ACM Symposium on Mobile Ad Hoc Networking and Computing (ACM MobiHoc 2015)}
}

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