Participation-Aware Incentive for Active Crowd Sensing

Abstract

An increasing popularity of smartphones affects interaction behaviors between people. The ubiquity of smartphones with embedded sensors can enable attractive sensing applications for collecting and reporting data in participatory sensing environment. However, time and monetary cost hinder people from providing sensing data using their smartphones. Smartphone users will probably not participate in sensing tasks without incentive in the real world. Therefore, we propose SenseUtil which provides participation-aware incentive for crowd sensing environment. The main objectives of SenseUtil are increasing the number of sensings while keeping moderate payment. SenseUtil applies the concept of microeconomics, where demand and supply decide the value of sensed data. Several factors including sensing frequency, nearby sensing points and users' preference, affect the incentive which dynamically changes over time. We implemented a simulation program to study the impact of participationaware incentive mechanism in terms of sensing frequency, paid reward and traveled distance. The results demonstrate that people actively participate in sensing tasks with little additional cost in term of traveled distance while decreasing payments moderately.

Deep Learning for Mobile Crowdsourcing Techniques, Methods, and Challenges: A Survey

“…In order to solve crowdworkers' incentive issues in crowdsourcing applications and motivate them to join the MCS process (i.e., incentives), epvilojanapong et al is proposed in Ref. [33], which is a participation-aware incentive framework and utilizes the microeconomics concept to depict the performances collected by sensors. e authors in Ref.…”

“…Major contributions Wang et al [31] Presenting a framework with privacy awareness and incentive measures Wang et al [32] A general model to compose of multiple functional modules epvilojanapong et al [33] A participation-aware incentive framework Rahman et al [34] Presenting a location-aware MCS solution Zhang et al [35] A motivating model to enhance the workers' profits Wang et al [36] A framework that selects the candidate workers statically and selects winners dynamically after bidding To et al [37] A location privacy mechanism based on differential privacy and geocasting Wu et al [38] A framework to address the problem of resource constraints Islam et al [39] Addressing the issues including incentives, privacy protection, and resource constrains been utilized in various real settings such as smart transportation and environment monitoring.…”

Adopting incentive mechanisms for large-scale participation in mobile crowdsensing: from literature review to a conceptual framework

“…In static incentive, the price for a task is estimated in advance and stays the same whereas in dynamic incentive, the price changes based on the minimum amount of money a participant is willing to accept to do the task [31]. In addition to participants' preference, incentives for mobile crowdsensing can also be designed to dynamically change depending on factors such as the time of day, number of available participants, location and type of data captured as demonstrated in a participation-aware incentive mechanism known as SenseUtil [32,33]. Similarly, Biswas et al [34] proposed PISCES, a closed-loop incentive framework that considers the sensing demand and changing availability and reporting behaviour of participants in order to compute rewards that dynamically converge to a minimum value after several trials.…”