Name
Abstract | ||
|---|---|---|
Vehicular mobile crowd sensing is a fast-emerging paradigm to collect data about the environment by mounting sensors on vehicles such as taxis. An important problem in vehicular crowd sensing is to design payment mechanisms to incentivize drivers (agents) to collect data, with the overall goal of obtaining the maximum amount of data (across multiple vehicles) for a given budget. Past works on this problem consider a setting where each agent operates in isolation---an assumption which is frequently violated in practice. In this paper, we design an incentive mechanism to incentivize agents who can engage in arbitrary collusions. We then show that in a homogeneous setting, our mechanism is optimal, and can do as well as any mechanism which knows the agentsu0027 preferences a priori. Moreover, if the agents are non-colluding, then our mechanism automatically does as well as any other non-colluding mechanism. We also show that our proposed mechanism has strong (and asymptotically optimal) guarantees for a more general heterogeneous setting. Experiments based on synthesized data and real-world data reveal gains of over 30% attained by our mechanism compared to past literature. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Computer Science and Game Theory | Mathematical optimization,Incentive,Homogeneous,A priori and a posteriori,Taxis,Asymptotically optimal algorithm,Payment,Mathematics |
DocType | Volume | Citations |
Journal | abs/1809.05161 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Susu Xu | 1 | 5 | 3.47 |
| Weiguang Mao | 2 | 4 | 1.07 |
| Yue Cao | 3 | 167 | 11.54 |
| Hae Young Noh | 4 | 106 | 30.54 |
| Nihar B. Shah | 5 | 1202 | 77.17 |