Mobile crowdsensing incentives under participation uncertainty
| Autor: | Iordanis Koutsopoulos, Panagiota Micholia, Merkourios Karaliopoulos |
|---|---|
| Jazyk: | angličtina |
| Předmět: |
Operations research
Iterative method media_common.quotation_subject Ubiquitous and mobile computing theory concepts and paradigms 05 social sciences 050801 communication & media studies 020206 networking & telecommunications 02 engineering and technology Solver Ubiquitous and mobile computing Task (project management) 0508 media and communications Incentive Crowds Human-centered computing Convex optimization Incentives 0202 electrical engineering electronic engineering information engineering Economics Mobile Crowdsourcing Quality (business) Set (psychology) Simulation media_common |
| Zdroj: | Proceedings of the 3rd ACM Workshop on Mobile Sensing, Computing and Communication-MSCC '16 MSCC@MobiHoc Proceedings of the 3rd ACM Workshop on Mobile Sensing, Computing and Communication-MSCC 16 |
| DOI: | 10.1145/2940353.2940357 |
| Popis: | Mobile crowdsensing applications rely on crowds of contributors who are willing to carry out certain tasks of interest. This willingness varies widely across users and tasks and (monetary) incentives are often engaged to strengthen it or make up for its total absence. These incentives need to carefully account for the highly non-homogeneous response of users to external motivation. We propose a framework that explicitly accounts for the implicit uncertainty about the eventual participation and contributions of users. In the framework, the impact of incentives on the user choice to contribute or not is modeled probabilistically. First, we formulate a convex optimization problem for incentive allocation with the goal of achieving maximum expected quality while taking into account task budget limitations and constraints related to the physical locations of users and tasks. We then propose an iterative algorithm to alleviate the complexity of the original problem with two basic steps: an allocation step that applies incentive allocation to a set of available contributors; and a refinement step that revokes portions of the allocated incentives. We study the performance characteristics of our algorithm by comparing it to a default solver for convex optimization problems. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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