Optimal incentive-driven design of participatory sensing systems

Abstract

Participatory sensing has emerged as a novel paradigm for data collection and collective knowledge formation about a state or condition of interest, sometimes linked to a geographic area. In this paper, we address the problem of incentive mechanism design for data contributors for participatory sensing applications. The service provider receives service queries in an area from service requesters and initiates an auction for user participation. Upon request, each user reports its perceived cost per unit of amount of participation, which essentially maps to a requested amount of compensation for participation. The participation cost quantifies the dissatisfaction caused to user due to participation. This cost is considered to be private information for each device, as it strongly depends on various factors inherent to it, such as the energy cost for sensing, data processing and transmission to the closest point of wireless access, the residual battery level, the number of concurrent jobs at the device processor, the required bandwidth to transmit data and the related charges of the mobile network operator, or even the user discomfort due to manual effort to submit data. Hence, participants have strong motive to misreport their cost, i.e. declare a higher cost that the actual one, so as to obtain higher payment.We seek a mechanism for user participation level determination and payment allocation which is most viable for the provider, that is, it minimizes the total cost of compensating participants, while delivering a certain quality of experience to service requesters. We cast the problem in the context of optimal reverse auction design, and we show how the different quality of submitted information by participants can be tracked by the service provider and used in the participation level and payment selection procedures. We derive a mechanism that optimally solves the problem above, and at the same time it is individually rational (i.e., it motivates users to participate) and incentive-compatible (i.e. it motivates truthful cost reporting by participants). Finally, a representative participatory sensing case study involving parameter estimation is presented, which exemplifies the incentive mechanism above.

Equilibrium is priceless: selfish task allocation for mobile crowdsourcing network

“…Although the users could leave and join the crowdsourcing network dynamically, the number of participatory users are limited. Thus, suppose that m n, which is typically considered in previous studies [12,20].…”

“…In contrast, hardly any of these solutions focus on the fair task allocation among users, i.e., encouraging more selfish users involved in crowdsourcing practice [11,12].…”

“…Thus, balancing the working load among selfish users is desperately needed [11][12][13]. Indeed, an inevitable stalemate exists: If there is no effective load balancing scheme among participatory users, it is difficult to propose a powerful incentive mechanism design, because no user would like to cooperate under unfair allocations.…”

The Internet of Things – The future or the end of mechatronics

“…15 in which the system is essentially defined by the user through the selection and specification of appropriate components which then use context based information, supported by user feedback, to establish the desired outputs [38][39][40][41][42][43][44][45][46][47][48][49][50][51].…”

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

“…[11][12][13][14][15][16]) have been proposed to address this problem, many of which are designed to suit the crowdsensing context and specific requirements of the underlying sensing initiative. Despite the proliferation of these technical solutions, Restuccia et al [17] draw attention to the fact that it is yet unclear what is the most appropriate incentive to adopt in motivating users to participate in mobile crowdsensing.…”