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Smart city article details
| Title | Trustworthiness And Comfort-Aware Participant Recruitment For Mobile Crowd-Sensing In Smart Environments |
|---|---|
| ID_Doc | 59111 |
| Authors | Dasari V.S.; Kantarci B.; Simsek M. |
| Year | 2019 |
| Published | Proceedings - IEEE Symposium on Computers and Communications, 2019-June |
| DOI | http://dx.doi.org/10.1109/ISCC47284.2019.8969704 |
| Abstract | Mobile crowd-sensing (MCS) has gained significant momentum in recent years for sensory data acquisition through non-dedicated sensors. Even though most of the participants are assumed to be willing to participate in the sensing campaigns, some smartphone users may be reluctant to grant access to particular sensors in their devices. Besides this, the presence of adversaries in the participant pool makes the participant selection problem further challenging. With these in mind, we introduce Reputation and Comfort Level Aware Participant Selection (RACLAPS)which allows participants to modify their available set of sensors that can be accessed by central platform/task publisher i.e. participants can turn-off sensors according to their comfort. To demonstrate the effect of RA-CLAPS, we utilize Selective and Reputation-aware Recruitment (SRR) in which participants are selective in choosing their task to sense solely to improve income. To enrich the discussion, Non-Selective and Reputation-aware Recruitment (NSR) is also considered in which participants are given no choice regarding selectiveness, sensor configuration. Simulation results show that RA-CLAPS improves average user utility by 7.6% compared to its predecessor, SRR. We also mention that average discomfort per participant is reduced by 6.4% under RA-CLAPS when compared to non-selective and selective recruitment approaches. © 2019 IEEE. |
| Author Keywords | Internet of Things; mobile crowdsensing; reputation-awareness; reverse auction; smart cities; trustworthiness; user comfort |
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