Providing Long-Term Participation Incentive in Participatory Sensing

Providing an adequate long-term participation incentive is important for a participatory sensing system to maintain enough number of active users (sensors), so as to collect a sufficient number of data samples and support a desired level of service quality. In this work, we consider the sensor selection problem in a general time-dependent and location-aware participatory sensing system, taking the long-term user participation incentive into explicit consideration. We study the problem systematically under different information scenarios, regarding both future information and current information (realization). In particular, we propose a Lyapunov-based VCG auction policy for the on-line sensor selection, which converges asymptotically to the optimal off-line benchmark performance, even with no future information and under (current) information asymmetry. Extensive numerical results show that our proposed policy outperforms the state-of-art policies in the literature, in terms of both user participation (e.g., reducing the user dropping probability by 25% to 90%) and social performance (e.g., increasing the social welfare by 15% to 80%).Comment: This manuscript serves as the online technical report of the article published in IEEE International Conference on Computer Communications (INFOCOM), 201

Mechanisms for improving information quality in smartphone crowdsensing systems

Given its potential for a large variety of real-life applications, smartphone crowdsensing has recently gained tremendous attention from the research community. Smartphone crowdsensing is a paradigm that allows ordinary citizens to participate in large-scale sensing surveys by using user-friendly applications installed in their smartphones. In this way, fine-grained sensing information is obtained from smartphone users without employing fixed and expensive infrastructure, and with negligible maintenance costs. Existing smartphone sensing systems depend completely on the participants\u27 willingness to submit up-to-date and accurate information regarding the events being monitored. Therefore, it becomes paramount to scalably and effectively determine, enforce, and optimize the information quality of the sensing reports submitted by the participants. To this end, mechanisms to improve information quality in smartphone crowdsensing systems were designed in this work. Firstly, the FIRST framework is presented, which is a reputation-based mechanism that leverages the concept of mobile trusted participants to determine and improve the information quality of collected data. Secondly, it is mathematically modeled and studied the problem of maximizing the likelihood of successful execution of sensing tasks when participants having uncertain mobility execute sensing tasks. Two incentive mechanisms based on game and auction theory are then proposed to efficiently and scalably solve such problem. Experimental results demonstrate that the mechanisms developed in this thesis outperform existing state of the art in improving information quality in smartphone crowdsensing systems --Abstract, page iii

CENTURION: Incentivizing Multi-Requester Mobile Crowd Sensing

The recent proliferation of increasingly capable mobile devices has given rise to mobile crowd sensing (MCS) systems that outsource the collection of sensory data to a crowd of participating workers that carry various mobile devices. Aware of the paramount importance of effectively incentivizing participation in such systems, the research community has proposed a wide variety of incentive mechanisms. However, different from most of these existing mechanisms which assume the existence of only one data requester, we consider MCS systems with multiple data requesters, which are actually more common in practice. Specifically, our incentive mechanism is based on double auction, and is able to stimulate the participation of both data requesters and workers. In real practice, the incentive mechanism is typically not an isolated module, but interacts with the data aggregation mechanism that aggregates workers' data. For this reason, we propose CENTURION, a novel integrated framework for multi-requester MCS systems, consisting of the aforementioned incentive and data aggregation mechanism. CENTURION's incentive mechanism satisfies truthfulness, individual rationality, computational efficiency, as well as guaranteeing non-negative social welfare, and its data aggregation mechanism generates highly accurate aggregated results. The desirable properties of CENTURION are validated through both theoretical analysis and extensive simulations