Analyzing Spatial and Temporal User Behavior in Participatory Sensing

Abstract

The large number of mobile devices and their increasingly powerful computing and sensing capabilities have enabled the participatory sensing concept. Participatory sensing applications are now able to effectively collect a variety of information types with high accuracy. Success, nevertheless, depends largely on the active participation of the users. In this article, we seek to understand spatial and temporal user behaviors in participatory sensing. To do so, we conduct a large-scale deployment of Citizense, a multi-purpose participatory sensing framework, in which 359 participants of demographically different backgrounds were simultaneously exposed to 44 participatory sensing campaigns of various types and contents. This deployment has successfully gathered various types of urban information and at the same time portrayed the participants’ different spatial, temporal and behavioral patterns. From this deployment, we can conclude that (i) the Citizense framework can effectively help participants to design data collecting processes and collect the required data, (ii) data collectors primarily contribute in their free time during the working week; much fewer submissions are done during the weekend, (iii) the decision to respond and complete a particular participatory sensing campaign seems to be correlated to the campaign’s geographical context and/or the recency of the data collectors’ activities, and (iv) data collectors can be divided into two groups according to their behaviors: a smaller group of active data collectors who frequently perform participatory sensing activities and a larger group of regular data collectors who exhibit more intermittent behaviors. These identified user behaviors open avenues to improve the design and operation of future participatory sensing applications.

A Citizen-Sensing-Based Digital Service for the Analysis of On-Site Post-Earthquake Messages

“…In order to avoid complex (and therefore slow) processing of the messages from the citizens, we assume that those who will use our app must know that their messages will be counted as reports of damaged assets or damaged infrastructures. In other words, we operate in the (already mentioned) hypothesis of a friendly world, whose natural frame is participatory sensing [2].…”

“…In the Abstract of [2], Khoi and Casteleyn wrote the following: "Participatory sensing applications are now able to effectively collect a variety of information types with high accuracy. Success, nevertheless, depends largely on the active participation of the users."…”

“…Two case studies carried out by Middleton and Modafferi [1], showed that crisis maps generated from tweets can be compared to the post-event maps delivered by national civil protection authorities. The frame of the present study is the so-called participatory sensing [2], i.e., a subset of mobile crowdsourcing that emphasizes the active and intentional participation of citizens to collect data from the place where they live or work (for such a reason also called citizen sensing).…”

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

“…From left to right (1) An atomic sensing task. (2) The list of available campaigns. (3) The details of a participant's account.…”

“…As a result, the task of detecting mosquitos can be completed much quicker and with higher accuracy by humans. Information on the presence of mosquitos produced by a large number of individuals might be aggregated into a comprehensive real-time map of mosquitos in the city 2 .…”