Do Monetary Incentives Influence Users’ Behavior in Participatory Sensing?

Abstract

Participatory sensing combines the powerful sensing capabilities of current mobile devices with the mobility and intelligence of human beings, and as such has to potential to collect various types of information at a high spatial and temporal resolution. Success, however, entirely relies on the willingness and motivation of the users to carry out sensing tasks, and thus it is essential to incentivize the users’ active participation. In this article, we first present an open, generic participatory sensing framework (Citizense) which aims to make participatory sensing more accessible, flexible and transparent. Within the context of this framework we adopt three monetary incentive mechanisms which prioritize the fairness for the users while maintaining their simplicity and portability: fixed micro-payment, variable micro-payment and lottery. This incentive-enabled framework is then deployed on a large scale, real-world case study, where 230 participants were exposed to 44 different sensing campaigns. By randomly distributing incentive mechanisms among participants and a subset of campaigns, we study the behaviors of the overall population as well as the behaviors of different subgroups divided by demographic information with respect to the various incentive mechanisms. As a result of our study, we can conclude that (1) in general, monetary incentives work to improve participation rate; (2) for the overall population, a general descending order in terms of effectiveness of the incentive mechanisms can be established: fixed micro-payment first, then lottery-style payout and finally variable micro-payment. These two conclusions hold for all the demographic subgroups, even though different different internal distances between the incentive mechanisms are observed for different subgroups. Finally, a negative correlation between age and participation rate was found: older participants contribute less compared to their younger peers.

Analyzing Spatial and Temporal User Behavior in Participatory Sensing

“…Together with the 10 campaigns launched by the organizers, the total number of campaigns available during the deployment was 44. Table 2 gives an overview of the first half (22 campaigns), having a substantial number of submissions and data collectors, among them five campaigns that were applied with a monetary incentive to study the data collectors' reactions to monetary incentive (i.e., campaign number 1, 2, 3, 4 and 6) [22]. These 22 campaigns lay the foundation for subsequent discussion and analysis in this article.…”

“…Furthermore, Citizense enables campaign authors to select participants using both their static profile information (i.e., post code, gender) and dynamic profile information (i.e., age, level of experience within the framework). Second, Citizense provides both intrinsic and extrinsic incentives to the participants, to stimulate them to participate and compensate them for the various costs caused by their sensing activities [22]. Third, the framework also makes it possible for campaign authors to create campaigns on their preferred topics with dynamic workflow within these campaigns and collecting various types of data.…”

“…We continuously extend the capabilities of the Citizense framework. Compared with our earlier technical description of the Citizense framework [14], the latest version of Citizense has substantial improvements, some of which were already mentioned in less detail in [22], which focused on exploring the participants' behavior under monetary incentives [22]. For clarity, we list novelties compared to [14] here:…”

“…We then describe a deployment of the Citizense framework in a real-world scenario, in which 359 participants were exposed to 44 different sensing campaigns (Section 4). While, in previous work, we reported on the participants' reactions to monetary incentives in this deployment (7 of the 44 campaigns were applied with monetary incentives) [22], in this article, we give an overall overview of the interaction of participants with the framework (Section 5.1) and its effectiveness (Section 5.2), after which we study the temporal (Sections 5.3 and 5.4) and spatial (Section 5.5) behavior of data collectors in depth. We expect to find spatial and temporal patterns valid for all campaigns, yet, due to the variety of campaigns, we also expect to find different spatial and temporal distributions of submissions, influenced by factors such as the type of user, or the context and content of these campaigns.…”

Studying user behavior through a participatory sensing framework in an urban context

“…The world population is growing at a considerably fast pace. Based on the number given by the United States Census Bureau, the world population is at 7,475 billions 1 (as of today 26 May 2018). According to well-established projections, the world population will reach at least 8.75 billions in 2050 (Lutz et al, 2017) and 11,184 billions in 2100 (United Nations, 2017).…”