Agent-Based Simulation to Measure the Effectiveness of Citizen Sensing Applications—The Case of Missing Children

Citizen sensing applications need to have a number of users defined that ensures their effectiveness. This is not a straightforward task because neither the relationship between the size of the userbase or its effectiveness is easily quantified, nor is it clear which threshold for the number of users would make the application ‘effective’. This paper presents an approach for estimating the number of users needed for location-based crowdsourcing applications to work successfully, depending on the use case, the circumstances, and the criteria of success. It circumvents various issues, ethical or practical, in performing real-world controlled experiments and tackles this challenge by developing an agent-based modelling and simulation framework. This framework is tested on a specific scenario, that of missing children and the search for them. The search is performed with the contribution of citizens being made aware of the disappearance through a mobile application. The result produces an easily reconfigurable testbed for the effectiveness of citizen sensing mobile applications, allowing the study of the marginal utility of new users of the application. The resulting framework aims to be the digital twin of a real urban scenario, and it has been designed to be easily adapted and support decisions on the feasibility, evaluation, and targeting of the deployment of spatial crowdsourcing applications.

Finding trafficked children through crowdsourcing: a usability evaluation

PurposeThe purpose of this paper is to investigate how perceived usability and user characteristics influence the intention to use a crowdsourcing application for finding potentially trafficked children. As part of this effort, the authors also attempt to uncover the usability concerns surrounding the use of this application.Design/methodology/approachThe authors first describe Zhongxun, which is the application used in the present paper. Next, they conducted a survey eliciting usability perceptions of Zhongxun. A total of 287 participants were recruited for the survey which used constructs adapted from the Computer System Usability Questionnaire as well as various demographic variables. Hierarchical regression analysis was used to ascertain factors influencing intention to use Zhongxun. Participants' qualitative feedback was also analyzed to derive themes pertaining to areas of improvement.FindingsThe results showed that system usefulness was the factor that most positively influenced intention to use Zhongxun, followed by information quality and interface quality. Interestingly, a higher level of education was negatively associated with intention to use the application. Qualitative feedback suggested various ways of improving Zhongxun's functionality. Participants recommended the incorporation of gamification mechanisms as a new feature of the application. Cultivating awareness of Zhongxun was also suggested as a means to attract new users.Practical implicationsThe work can help inform the design of crowdsourcing applications for finding missing and potentially trafficked children, as well as similar systems. Implications include the need for simplicity of design, communication strategies to attract new and retain existing users, and instilling confidence in the quality of crowdsourced contributions.Originality/valuePrior research in evaluating the usability of crowdsourcing applications has been performed but not in the context of finding missing and potentially trafficked children. The task of finding such children is markedly different from previous usage contexts and could impact perceptions of usability and usefulness. Hence, the present study attempts to plug this research gap.

A Dynamic Privacy Protection Mechanism for Spatiotemporal Crowdsourcing

In spatiotemporal crowdsourcing applications, sensing data uploaded by participants usually contain spatiotemporal sensitive data. If application servers publish the unprocessed sensing data directly, it is easy to expose the privacy of participants. In addition, application servers usually adopt the static publishing mechanism, which is easy to produce problems such as poor timeliness and large information loss for spatiotemporal crowdsourcing applications. Therefore, this paper proposes a spatiotemporal privacy protection (STPP) method based on dynamic clustering methods to solve the privacy protection problem for crowd participants in spatiotemporal crowdsourcing systems. Firstly, the working principles of a dynamic privacy protection mechanism are introduced. Then, based on k-anonymity and l-diversity, the spatiotemporal sensitive data are anonymized. In addition, this paper designs the dynamic k-anonymity algorithm based on the previous anonymous results. Through extensive performance evaluation on real-world data, compared with existing methods, the proposed STPP algorithm could effectively solve the problem of poor timeliness and improve the privacy protection level while reducing the information loss of sensing data.