PS-Sim: A Framework for Scalable Simulation of Participatory Sensing Data
Rajesh P Barnwal, Nirnay Ghosh, Soumya K Ghosh, and Sajal K Das

TL;DR
This paper introduces PS-Sim, a scalable simulation framework that models human participation and event distribution in participatory sensing, aiding validation of PS applications without real-world data.
Contribution
The work presents a novel, empirically validated simulation framework for participatory sensing that accurately replicates real-world participation and event behaviors.
Findings
Synthetic data closely matches real participation patterns
PS-Sim effectively models event occurrence distributions
Validated using real vehicular traffic dataset
Abstract
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…
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