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2024 | Buch

Incentive Mechanism for Mobile Crowdsensing

A Game-theoretic Approach

verfasst von: Youqi Li, Fan Li, Song Yang, Chuan Zhang

Verlag: Springer Nature Singapore

Buchreihe : SpringerBriefs in Computer Science

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Über dieses Buch

Mobile crowdsensing (MCS) is emerging as a novel sensing paradigm in the Internet of Things (IoTs) due to the proliferation of smart devices (e.g., smartphones, wearable devices) in people’s daily lives. These ubiquitous devices provide an opportunity to harness the wisdom of crowds by recruiting mobile users to collectively perform sensing tasks, which largely collect data about a wide range of human activities and the surrounding environment. However, users suffer from resource consumption such as battery, processing power, and storage, which discourages users’ participation. To ensure the participation rate, it is necessary to employ an incentive mechanism to compensate users’ costs such that users are willing to take part in crowdsensing.

This book sheds light on the design of incentive mechanisms for MCS in the context of game theory. Particularly, this book presents several game-theoretic models for MCS in different scenarios. In Chapter 1, the authors present an overview of MCS and state the significance of incentive mechanism for MCS. Then, in Chapter 2, 3, 4, and 5, the authors propose a long-term incentive mechanism, a fair incentive mechanism, a collaborative incentive mechanism, and a coopetition-aware incentive mechanism for MCS, respectively. Finally, Chapter 6 summarizes this book and point out the future directions.

This book is of particular interest to the readers and researchers in the field of IoT research, especially in the interdisciplinary field of network economics and IoT.

Inhaltsverzeichnis

Frontmatter
Chapter 1. A Brief Introduction
Abstract
In this chapter, we first introduce the background regarding Mobile Crowdsensing (MCS) and present an overview of MCS. Then, we specifically state the incentive mechanism design problem for MCS. Finally, we demonstrate the book structure for convenience.
Youqi Li, Fan Li, Song Yang, Chuan Zhang
Chapter 2. Long-Term Incentive Mechanism for Mobile Crowdsensing
Abstract
In this chapter, we propose an incentive mechanism for crowdsensing under the continuous and time-varying scenario using a three-stage Stackelberg game. In such a scenario, different requesters generate sensing tasks with payments to the platform at each time slot. The platform makes pricing decisions to determine rewards for tasks without complete information, and then notifies task-price pairs to online users in Stage I. In Stage II, users select optimal tasks as their interests under certain constraints and report back to the platform. The platform fairly selects users as workers in order to ensure users’ long-term participation in Stage III. We use Lyapunov optimization to address online decision problems for the platform in Stage I and III where there are no prior knowledge and future information available. We propose an FPTAS for users to derive their interests of tasks based on their mobile devices’ computing capabilities in Stage II. Numerical results in simulations validate the significance and superiority of our proposed incentive mechanism.
Youqi Li, Fan Li, Song Yang, Chuan Zhang
Chapter 3. Fair Incentive Mechanism for Mobile Crowdsensing
Abstract
In this chapter, we jointly address practical issues in the incentive mechanism for MCS to fairly incentivize high-quality users’ participation, like (1) the platform has no knowledge about users’ sensing qualities beforehand due to their private information. (2) The platform needs users’ continuous participation in the long run, which results in fairness requirements. (3) It is also crucial to protect users’ privacy due to the potential privacy leakage concerns (e.g., sensing qualities) after completing tasks. Particularly, we propose the three-stage Stackelberg-based incentive mechanism for the platform to recruit participants. In detail, we leverage combinatorial volatile multi-armed bandits (CVMAB) to elicit unknown users’ sensing qualities. We use the drift-plus-penalty (DPP) technique in Lyapunov optimization to handle the fairness requirements. We blur the quality feedback with tunable Laplacian noise such that the incentive mechanism protects locally differential privacy (LDP). Finally, we carry out experiments to evaluate our incentive mechanism. The numerical results show that our incentive mechanism achieves sublinear regret performance to learn unknown quality with fairness and privacy guarantee.
Youqi Li, Fan Li, Song Yang, Chuan Zhang
Chapter 4. Collaborative Incentive Mechanism for Mobile Crowdsensing
Abstract
In this chapter, we propose PTASIM, an incentive mechanism that explores cooperation with POI-tagging App for Mobile Edge Crowdsensing (MEC). PTASIM requests the App to tag some edges to be POI (Points-of-Interest), which further guides App users to perform tasks at that location. We further model the interactions of users, a platform, and an App by a three-stage decision process. The App first determines the POI-tagging price to maximize its payoff. Platform and users subsequently decide how to determine tasks reward and select edges to be tagged, and how to select the best task to perform, respectively. We analyze the optimal solution in those stages. Specifically, we prove greedy algorithm could provide the optimal solution for the platform’s payoff maximization in polynomial time. The numerical results show that: (1) the cooperation with App brings long-term and sufficient participation; the optimal strategies reduce the platform’s tasks cost as well as improve App’s revenues.
Youqi Li, Fan Li, Song Yang, Chuan Zhang
Chapter 5. Coopetition-Aware Incentive Mechanism for Mobile Crowdsensing
Abstract
Most of the existing works on MCS only consider designing incentive mechanisms for a single MCS platform. In this chapter, we study the incentive mechanism in MCS with multiple platforms under two scenarios: competitive platform and cooperative platform. We correspondingly propose new competitive and cooperative mechanisms for each scenario. In the competitive platform scenario, platforms decide their prices on rewards to attract more participants, while the users choose which platform to work for. We model such a competitive platform scenario as a two-stage Stackelberg game. In the cooperative platform scenario, platforms cooperate to share sensing data with each other. We model it as many-to-many bargaining. Moreover, we first prove the NP-hardness of exact bargaining and then propose heuristic bargaining. Finally, numerical results show that (1) platforms in the competitive platform scenario can guarantee their payoff by optimally pricing on rewards and participants can select the best platform to contribute; (2) platforms in the cooperative platform scenario can further improve their payoff by bargaining with other platforms for cooperatively sharing collected sensing data.
Youqi Li, Fan Li, Song Yang, Chuan Zhang
Chapter 6. Summary
Abstract
In this chapter, we summarize this book and discuss the future directions for incentive mechanisms in MCS.
Youqi Li, Fan Li, Song Yang, Chuan Zhang
Metadaten
Titel
Incentive Mechanism for Mobile Crowdsensing
verfasst von
Youqi Li
Fan Li
Song Yang
Chuan Zhang
Copyright-Jahr
2024
Verlag
Springer Nature Singapore
Electronic ISBN
978-981-9969-21-0
Print ISBN
978-981-9969-20-3
DOI
https://doi.org/10.1007/978-981-99-6921-0

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