PS-Sim: A framework for scalable data simulation and incentivization in participatory sensing-based smart city applications

Abstract

The widespread penetration of smartphone has paved the way for a new paradigm of pervasive computing, known as the participatory sensing (PS). In PS, a human user explicitly performs the tasks of sensing and reporting, typically in lieu of incentives. One major limitation with PS is the sparsity of data owing to the lack of active participation, thus inhibiting large scale real-life experiments for the research community. In our preliminary work (Barnwal et al., 2018), we propose a spatio-temporal event occurrence and report generation based data simulation framework called PS-Sim. This paper extends the PS-Sim framework with a novel budget allocation mechanism for incentivizing participants. The allocation mechanism guarantees the presence of a threshold number of active participants and also ensures the reporting of the significant fraction of events for sustainability of PS applications. The simulation environment provided by the PS-Sim framework replicates real participation and event occurrence behaviors, which is expected to enable the domain experts to investigate and assess the requisites (benefits and challenges) of introducing smart city applications. As a part of the evaluation of the budget allocation mechanism, we study its performance under varying effects of reward and participation, and establish its fairness as far as the remuneration of active participants is concerned.