Fairness and social welfare in incentivizing participatory sensing
Tie Luo,Chen-Khong Tham +1 more
- Vol. 1, pp 425-433
TLDR
This paper links incentive to users' demand for consuming compelling services, as an approach complementary to conventional credit or reputation based approaches, and designs two incentive schemes, Incentive with Demand Fairness (IDF) and Iterative Tank Filling (ITF), for maximizing fairness and social welfare, respectively.Abstract:
Participatory sensing has emerged recently as a promising approach to large-scale data collection. However, without incentives for users to regularly contribute good quality data, this method is unlikely to be viable in the long run. In this paper, we link incentive to users' demand for consuming compelling services, as an approach complementary to conventional credit or reputation based approaches. With this demand-based principle, we design two incentive schemes, Incentive with Demand Fairness (IDF) and Iterative Tank Filling (ITF), for maximizing fairness and social welfare, respectively. Our study shows that the IDF scheme is max-min fair and can score close to 1 on the Jain's fairness index, while the ITF scheme maximizes social welfare and achieves a unique Nash equilibrium which is also Pareto and globally optimal. We adopted a game theoretic approach to derive the optimal service demands. Furthermore, to address practical considerations, we use a stochastic programming technique to handle uncertainty that is often encountered in real life situations.read more
Citations
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Journal ArticleDOI
Incentives for Mobile Crowd Sensing: A Survey
TL;DR: Diverse strategies that are proposed in the literature to provide incentives for stimulating users to participate in mobile crowd sensing applications are surveyed and divided into three categories: entertainment, service, and money.
Proceedings ArticleDOI
Optimal incentive-driven design of participatory sensing systems
TL;DR: This paper addresses the problem of incentive mechanism design for data contributors for participatory sensing applications, and derives a mechanism that optimally solves the problem and is individually rational and incentive-compatible.
Journal ArticleDOI
A Survey of Incentive Techniques for Mobile Crowd Sensing
TL;DR: This work establishes a set of design constraints or minimum requirements that any incentive mechanism for CS must have and contributes a taxonomy of CS incentive mechanisms and shows how current systems fit within this taxonomy.
Journal ArticleDOI
Data Collection and Wireless Communication in Internet of Things (IoT) Using Economic Analysis and Pricing Models: A Survey
TL;DR: This paper reviews numerous applications of the economic and pricing models, known as intelligent rational decision-making methods, to develop adaptive algorithms and protocols for WSNs and considers the use of some pricing models in machine-to-machine (M2M) communication.
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Data Collection and Wireless Communication in Internet of Things (IoT) Using Economic Analysis and Pricing Models: A Survey
TL;DR: In this paper, the authors provide a review on economic analysis and pricing models for data collection and wireless communication in Internet of Things (IoT) and highlight some important open research issues as well as future research directions.
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