A Budget Feasible Incentive Mechanism for Weighted Coverage Maximization in Mobile Crowdsensing

Zheng, Zhenzhe; Wu, Fan; Gao, Xiaofeng; Zhu, Hongzi; Tang, Shaojie; Chen, Guihai

doi:10.1109/tmc.2016.2632721

Cited by 107 publications

(31 citation statements)

References 43 publications

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“…However, MCS also poses several challenges, including efficient task design and allocation, target participant selection, incentive mechanisms, security and privacy concerns, and ensuring the reliability, trustworthiness, and quality of the collected data. Many of these issues have been addressed in prior research [15], [16], [17], [18], [19], [20]. While early MCS studies have focused on a few very common sensor devices, such as GPS traces and accelerometer readings, more recent efforts include many other types of sensor readings, such as physiological data (e.g., heart rate and calorie burn), behavioral data (e.g., sleep habits, battery recharge patterns, and different types of physical activities), social networking data, and systems information, including scans of Wi-Fi, Bluetooth, and cell radios.…”

Section: Related Workmentioning

confidence: 99%

Opportunistic Discovery of Personal Places Using Multi-Source Sensor Data

Vhaduri

Poellabauer

2021

IEEE Trans. Big Data

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Modern smartphones and wearables are able to continuously collect significant amounts of sensor data, where such data can be helpful to study a user's mobility or social interaction patterns, but also to deliver various services based on a user's presence at different places during certain times of the day. Therefore, it is important to accurately identify personal places of interest (POIs), such as a user's workplace or home. Such places are usually determined using segmentation of location traces, but frequent gaps in the data (i.e., missing location readings) can result in a large number of small and incomplete segments that should actually be grouped together into a single large segment. This paper presents a segmentation approach that utilizes a user's personal data obtained from multiple sensor sources and devices such as the battery recharge behavior (measured on smartphones), step counts, and sleep patterns (measured by wearables), to opportunistically fill gaps in the user's location traces. Using the data from a mobile crowd sensing study of more than 450 users over a 2-year period, we show that our approach is able to generate fewer, but more complete segments compared to the state of the art.

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Section: Related Workmentioning

confidence: 99%

Opportunistic Discovery of Personal Places Using Multi-Source Sensor Data

Vhaduri

Poellabauer

2021

IEEE Trans. Big Data

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“…In [18], the authors considered the user-centric model where each user can ask for reserve price, and designed the truthful and scalable auction mechanism for the CSP to achieve revenue maximization. The authors in [19] addressed how to maximize the valuation of the covered interested regions under limited budget for strategy-proof mobile crowdsensing. In [20], the authors proposed a long-term dynamic incentive mechanism to capture the dynamic nature of long-term data quality of participants, where a truthful, quality-aware and budget feasible algorithm is designed for task allocation with polynomial-time computational complexity.…”

Section: Related Workmentioning

confidence: 99%

“…The only difference is that the best-response function update policy in the 4th line of Algorithm 1 is replaced by another update policy obtained from Eq. (19). Since we have validated the existence and uniqueness of the Bayesian Nash equilibrium, the modified Algorithm 1 can achieve the Bayesian Nash equilibrium [49].…”

Section: B Stackelberg Game Equilibrium Analysismentioning

confidence: 99%

A Stackelberg Game Approach Toward Socially-Aware Incentive Mechanisms for Mobile Crowdsensing

Nie

Luo

Xiong

et al. 2019

IEEE Trans. Wireless Commun.

159

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Mobile crowdsensing has shown a great potential to address large-scale data sensing problems by allocating sensing tasks to pervasive mobile users. The mobile users will participate in a crowdsensing platform if they can receive satisfactory reward. In this paper, to effectively and efficiently recruit sufficient number of mobile users, i.e., participants, we investigate an optimal incentive mechanism of a crowdsensing service provider. We apply a two-stage Stackelberg game to analyze the participation level of the mobile users and the optimal incentive mechanism of the crowdsensing service provider using backward induction. In order to motivate the participants, the incentive is designed by taking into account the social network effects from the underlying mobile social domain. For example, in a crowdsensing-based road traffic information sharing application, a user can get a better and accurate traffic report if more users join and share their road information. We derive the analytical expressions for the discriminatory incentive as well as the uniform incentive mechanisms. To fit into practical scenarios, we further formulate a Bayesian Stackelberg game with incomplete information to analyze the interaction between the crowdsensing service provider and mobile users, where the social structure information (the social network effects) is uncertain. The existence and uniqueness of the Bayesian Stackelberg equilibrium is validated by identifying the best response strategies of the mobile users.Numerical results corroborate the fact that the network effects tremendously stimulate higher mobile participation level and greater revenue of the crowdsensing service provider. In addition, the social structure information helps the crowdsensing service provider to achieve greater revenue gain. Index TermsCrowdsensing, social network effects, incentive mechanism, complete and incomplete information, Bayesian game, Stackelberg game, uncertainty, social influence Jiangtian Nie is with ERI@N, Interdisciplinary Graduate School as well as

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“…In [26], a 'double or nothing' incentivecompatible mechanism is proposed to ensure workers behave honesty based on their self-confidence; this protocol is provable to avoid spammers from the crowd, under the assumption that every worker wants to maximize their expected payment. In [40,39,41,42,44,43], Zheng et al, the authors leverage the tools of game theory and mechanism design to analyze the interaction of rational and selfish mobile users, then design efficient incentive mechanisms for four classical and representative applications in mobile Internet: dynamic spectrum redistribution, mobile crowdsensing, data marketplace, and cloud bandwidth management, to stimulate selfish mobile users to cooperate, achieving a win-win situation.…”

Section: Mechanism Design In Crowdsourcingmentioning

confidence: 99%

A Brief Survey of Data Pricing for Machine Learning

Tang¹,

Lv²,

Wu³

2020

8th International Conference on Signal, Image Processing and Pattern Recognition

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Big data and machine learning are poised to revolutionize the field of artificial intelligence and represent a step towards building an intelligent society. Big data is considered to be the key to unlocking the next great waves of growth in productivity, the value of data is realized through machine learning. In this survey, we begin with an introduction to the general field of data pricing and distributed machine learning then progress to the main streams of data pricing and mechanism design methods. Our survey will cover several current areas of research within the field of data pricing, including the incentive mechanism design for federated learning, reinforcement learning, auction, crowdsourcing, and blockchain, especially, focus on reward function for machine learning and payment scheme. In parallel, we highlight the pricing scheme in data transactions, focusing on data evaluation via distributed machine learning. To conclude, we discuss some research challenges and future directions of data pricing for machine learning.

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A Budget Feasible Incentive Mechanism for Weighted Coverage Maximization in Mobile Crowdsensing

Cited by 107 publications

References 43 publications

Opportunistic Discovery of Personal Places Using Multi-Source Sensor Data

Opportunistic Discovery of Personal Places Using Multi-Source Sensor Data

A Stackelberg Game Approach Toward Socially-Aware Incentive Mechanisms for Mobile Crowdsensing

A Brief Survey of Data Pricing for Machine Learning

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