Incentive Mechanisms for Participatory Sensing: Survey and Research Challenges

Participatory sensing is a powerful paradigm which takes advantage of smartphones to collect and analyze data beyond the scale of what was previously possible. Given that participatory sensing systems rely completely on the users' willingness to submit up-to-date and accurate information, it is paramount to effectively incentivize users' active and reliable participation. In this paper, we survey existing literature on incentive mechanisms for participatory sensing systems. In particular, we present a taxonomy of existing incentive mechanisms for participatory sensing systems, which are subsequently discussed in depth by comparing and contrasting different approaches. Finally, we discuss an agenda of open research challenges in incentivizing users in participatory sensing.Comment: Updated version, 4/25/201

Location reliability and gamification mechanisms for mobile crowd sensing

People-centric sensing with smart phones can be used for large scale sensing of the physical world by leveraging the sensors on the phones. This new type of sensing can be a scalable and cost-effective alternative to deploying static wireless sensor networks for dense sensing coverage across large areas. However, mobile people-centric sensing has two main issues: 1) Data reliability in sensed data and 2) Incentives for participants. To study these issues, this dissertation designs and develops McSense, a mobile crowd sensing system which provides monetary and social incentives to users. This dissertation proposes and evaluates two protocols for location reliability as a step toward achieving data reliability in sensed data, namely, ILR (Improving Location Reliability) and LINK (Location authentication through Immediate Neighbors Knowledge). ILR is a scheme which improves the location reliability of mobile crowd sensed data with minimal human efforts based on location validation using photo tasks and expanding the trust to nearby data points using periodic Bluetooth scanning. LINK is a location authentication protocol working independent of wireless carriers, in which nearby users help authenticate each other’s location claims using Bluetooth communication. The results of experiments done on Android phones show that the proposed protocols are capable of detecting a significant percentage of the malicious users claiming false location. Furthermore, simulations with the LINK protocol demonstrate that LINK can effectively thwart a number of colluding user attacks. This dissertation also proposes a mobile sensing game which helps collect crowd sensing data by incentivizing smart phone users to play sensing games on their phones. We design and implement a first person shooter sensing game, “Alien vs. Mobile User”, which employs techniques to attract users to unpopular regions. The user study results show that mobile gaming can be a successful alternative to micro-payments for fast and efficient area coverage in crowd sensing. It is observed that the proposed game design succeeds in achieving good player engagement

A Design Guideline For Non-Monetary Incentive Mechanics In Mobile Health Participatory Sensing System

Participatory sensing emphasizes the participation of citizens and community groups in the process of sensing and documenting current events in their local environment using smart phones and mobile devices. Incentive is crucial in participatory sensing data collection to attract participants to join in a participatory campaign, and to engage participants to use the participatory sensing application. The challenge in studies on non-monetary incentives is how this type of incentives should be represented in a participatory sensing system because they are inherent to the participants. This study proposes a design guideline which consists of set of mechanics and features associated with each incentive construct based on Self-determination Theory and Motivation 3.0. The design guideline is presented in a hierarchical system structure to illustrate dynamic operation of incentive mechanics with different components in participatory sensing system. Content analysis is performed on 283 mobile health monitoring application in the market to determine the reliability of the proposed incentive mechanic and features through descriptive analysis and inter-coder reliability analysis. The findings of the descriptive analysis show that a relatively small proportion of the mobile application (15 percent), addressed at least one feature that tapped on each of the incentive mechanics, and almost all apps contain a minimum of one intrinsic incentive mechanic feature. The findings obtained from inter-coder reliability analysis found 56 percent of the proposed incentive mechanic features with low reliability. Furthermore, the finding shows insignificant reliability degree for almost all extrinsic features. This study provides both theoretical, and practical contributions. On the theoretical aspect, this study provides validation on the incentive mechanics and their features that have been proposed in the design guideline. On the practical aspect, the design guideline may aid system developers and service providers to implement the incentive concepts into systems’ features and further help campaign organizers and service providers to focus on the best incentives strategy for improving participants’ performance in the next participant recruitment

Crowd Sensing and Living Lab Outdoor Experimentation Made Easy

Living lab outdoor experimentation using pervasive computing provides new opportunities: higher realism, external validity, and socio-spatio-temporal observations in large scale. However, experimentation “in the wild” is complex and costly. Noise, biases, privacy concerns, compliance with standards of ethical review boards, remote moderation, control of experimental conditions, and equipment perplex the collection of high-quality data for causal inference. This article introduces Smart Agora, a novel open-source software platform for rigorous systematic outdoor experimentation. Without writing a single line of code, highly complex experimental scenarios are visually designed and automatically deployed to smart phones. Novel geolocated survey and sensor data are collected subject of participants verifying desired experimental conditions, for instance, their localization at certain urban spots. This new approach drastically improves the quality and purposefulness of crowd sensing, tailored to conditions that confirm/reject hypotheses. The features that support this innovative functionality and the broad spectrum of its applicability are demonstrated

Mechanisms for improving information quality in smartphone crowdsensing systems

Given its potential for a large variety of real-life applications, smartphone crowdsensing has recently gained tremendous attention from the research community. Smartphone crowdsensing is a paradigm that allows ordinary citizens to participate in large-scale sensing surveys by using user-friendly applications installed in their smartphones. In this way, fine-grained sensing information is obtained from smartphone users without employing fixed and expensive infrastructure, and with negligible maintenance costs. Existing smartphone sensing systems depend completely on the participants\u27 willingness to submit up-to-date and accurate information regarding the events being monitored. Therefore, it becomes paramount to scalably and effectively determine, enforce, and optimize the information quality of the sensing reports submitted by the participants. To this end, mechanisms to improve information quality in smartphone crowdsensing systems were designed in this work. Firstly, the FIRST framework is presented, which is a reputation-based mechanism that leverages the concept of mobile trusted participants to determine and improve the information quality of collected data. Secondly, it is mathematically modeled and studied the problem of maximizing the likelihood of successful execution of sensing tasks when participants having uncertain mobility execute sensing tasks. Two incentive mechanisms based on game and auction theory are then proposed to efficiently and scalably solve such problem. Experimental results demonstrate that the mechanisms developed in this thesis outperform existing state of the art in improving information quality in smartphone crowdsensing systems --Abstract, page iii

Designing an Artifact to Support Incentives for Medication Adherence

This research is motivated by the current trend towards utilization of mobile technology in healthcare interventions. Despite academic and practitioner efforts, lack of medication adherence continues to be a leading indicator of poor health outcomes and increased hospitalizations worldwide. There are several possible incentive systems that remain relatively unexplored in the field of medication adherence. Our analysis of the current academic research and existing medication adherence applications indicates a research gap and an opportunity to create a significant contribution through the design of an application (app) addressing the complex problem of medication adherence. Therefore, we propose the design of an app to positively influence patient behavior through incentives to improve medication adherence. The contribution of this research is a novel design utilizing multiple incentive types to improve medication adherence