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Journal ArticleDOI

Incentives for Mobile Crowd Sensing: A Survey

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TLDR
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.
Abstract
Recent years have witnessed the fast proliferation of mobile devices (e.g., smartphones and wearable devices) in people's lives. In addition, these devices possess powerful computation and communication capabilities and are equipped with various built-in functional sensors. The large quantity and advanced functionalities of mobile devices have created a new interface between human beings and environments. Many mobile crowd sensing applications have thus been designed which recruit normal users to contribute their resources for sensing tasks. To guarantee good performance of such applications, it's essential to recruit sufficient participants. Thus, how to effectively and efficiently motivate normal users draws growing attention in the research community. This paper surveys diverse strategies that are proposed in the literature to provide incentives for stimulating users to participate in mobile crowd sensing applications. The incentives are divided into three categories: entertainment, service, and money. Entertainment means that sensing tasks are turned into playable games to attract participants. Incentives of service exchanging are inspired by the principle of mutual benefits. Monetary incentives give participants payments for their contributions. We describe literature works of each type comprehensively and summarize them in a compact form. Further challenges and promising future directions concerning incentive mechanism design are also discussed.

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Citations
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A Survey of Wearable Devices and Challenges

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A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities

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Privacy-Preserved Data Sharing Towards Multiple Parties in Industrial IoTs

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Blockchain-Enabled Data Collection and Sharing for Industrial IoT With Deep Reinforcement Learning

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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.
References
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Journal ArticleDOI

A survey of mobile phone sensing

TL;DR: This article surveys existing mobile phone sensing algorithms, applications, and systems, and discusses the emerging sensing paradigms, and formulates an architectural framework for discussing a number of the open issues and challenges emerging in the new area ofMobile phone sensing research.
Journal ArticleDOI

Mobile crowdsensing: current state and future challenges

TL;DR: The need for a unified architecture for mobile crowdsensing is argued and the requirements it must satisfy are envisioned.
Journal ArticleDOI

Sold!: auction methods for multirobot coordination

TL;DR: The primary contribution of the paper is to show empirically that distributed negotiation mechanisms such as MURDOCH are viable and effective for coordinating physical multirobot systems.
Proceedings ArticleDOI

Crowdsourcing to smartphones: incentive mechanism design for mobile phone sensing

TL;DR: This work designs an auction-based incentive mechanism for mobile phone sensing that is computationally efficient, individually rational, profitable, and truthful, and shows how to compute the unique Stackelberg Equilibrium, at which the utility of the platform is maximized.
Journal ArticleDOI

The budgeted maximum coverage problem

TL;DR: This work proposes a (1−1/ e ) -approximation algorithm for the budgeted maximum coverage problem and argues that this approximation factor is the best possible, unless NP ⫅ DTIME (n O ( log log n) ) .
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