15 research outputs found
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
PS-Sim: A Framework for Scalable Simulation of Participatory Sensing Data
Emergence of smartphone and the participatory sensing (PS) paradigm have
paved the way for a new variant of pervasive computing. In PS, human user
performs sensing tasks and generates notifications, typically in lieu of
incentives. These notifications are real-time, large-volume, and multi-modal,
which are eventually fused by the PS platform to generate a summary. One major
limitation with PS is the sparsity of notifications owing to lack of active
participation, thus inhibiting large scale real-life experiments for the
research community. On the flip side, research community always needs ground
truth to validate the efficacy of the proposed models and algorithms. Most of
the PS applications involve human mobility and report generation following
sensing of any event of interest in the adjacent environment. This work is an
attempt to study and empirically model human participation behavior and event
occurrence distributions through development of a location-sensitive data
simulation framework, called PS-Sim. From extensive experiments it has been
observed that the synthetic data generated by PS-Sim replicates real
participation and event occurrence behaviors in PS applications, which may be
considered for validation purpose in absence of the groundtruth. As a
proof-of-concept, we have used real-life dataset from a vehicular traffic
management application to train the models in PS-Sim and cross-validated the
simulated data with other parts of the same dataset.Comment: Published and Appeared in Proceedings of IEEE International
Conference on Smart Computing (SMARTCOMP-2018
Interval Tree-Based Task Scheduling Method for Mobile Crowd Sensing Systems
Nowadays there is an increasing demand to provide a real-time environmental information. So, the growing number of mobile devices carried by users establish a new and fastgrowing sensing paradigm to satisfy this need, which is called Mobile Crowd Sensing (MCS). The MCS uses different sensing abilities to acquire local knowledge through enhanced mobile devices. In MCS, it is very important to collect high-quality sensory data that satisfies the needs of all assigned tasks and the task organizers with a minimum cost for the participants. One of the most important factors which affect the MCS cost is how to schedule different sensing tasks which must be assigned to a smartphone with the objective of minimizing sensing energy consumption while ensuring high-quality sensory data. In this paper, the problem of task scheduling the which have mutual sensor is formulated and a scheduling method to minimize the energy consumption by reducing the sensor utilization is proposed. The proposed method will incentive the users to participate in multiple tasks at the same time, which minimizes the total cost of the performed tasks and increases his rewards. The experimental results by using synthetic and real data show that the proposed scheduling method can minimize the energy consumption and preserve the task requirements compared to existing algorithms