Quality-Aware Incentive Mechanism for Mobile Crowd Sensing

Mobile crowd sensing (MCS) is a novel sensing paradigm which can sense human-centered daily activities and the surrounding environment. The impact of mobility and selfishness of participants on the data reliability cannot be ignored in most mobile crowd sensing systems. To address this issue, we present a universal system model based on the reverse auction framework and formulate the problem as the Multiple Quality Multiple User Selection (MQMUS) problem. The quality-aware incentive mechanism (QAIM) is proposed to meet the quality requirement of data reliability. We demonstrate that the proposed incentive mechanism achieves the properties of computational efficiency, individual rationality, and truthfulness. And meanwhile, we evaluate the performance and validate the theoretical properties of our incentive mechanism through extensive simulation experiments.

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Incentive Mechanism Design in Mobile Crowd Sensing Systems with Budget Restriction and Capacity Limit

2017 26th International Conference on Computer Communication and Networks (ICCCN) ◽

10.1109/icccn.2017.8038379 ◽

2017 ◽

Cited By ~ 4

Author(s):

Yu Zhou ◽

Yuan Zhang ◽

Sheng Zhong

Keyword(s):

Mechanism Design ◽

Incentive Mechanism ◽

Mobile Crowd Sensing ◽

Sensing Systems ◽

Crowd Sensing ◽

Capacity Limit ◽

Incentive Mechanism Design ◽

Mobile Crowd ◽

Budget Restriction

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Incentive Mechanism for Privacy-Aware Data Aggregation in Mobile Crowd Sensing Systems

IEEE/ACM Transactions on Networking ◽

10.1109/tnet.2018.2840098 ◽

2018 ◽

Vol 26 (5) ◽

pp. 2019-2032 ◽

Cited By ~ 30

Author(s):

Haiming Jin ◽

Lu Su ◽

Houping Xiao ◽

Klara Nahrstedt

Keyword(s):

Data Aggregation ◽

Incentive Mechanism ◽

Mobile Crowd Sensing ◽

Sensing Systems ◽

Crowd Sensing ◽

Mobile Crowd

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A Context-Aware Multiarmed Bandit Incentive Mechanism for Mobile Crowd Sensing Systems

IEEE Internet of Things Journal ◽

10.1109/jiot.2019.2903197 ◽

2019 ◽

Vol 6 (5) ◽

pp. 7648-7658 ◽

Cited By ~ 6

Author(s):

Yue Wu ◽

Fan Li ◽

Liran Ma ◽

Yadong Xie ◽

Ting Li ◽

...

Keyword(s):

Incentive Mechanism ◽

Context Aware ◽

Mobile Crowd Sensing ◽

Sensing Systems ◽

Crowd Sensing ◽

Mobile Crowd ◽

Multiarmed Bandit

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Mobile Crowd-sensing Applications: Data Redundancies, Challenges, and Solutions

ACM Transactions on Internet Technology ◽

10.1145/3431502 ◽

2022 ◽

Vol 22 (2) ◽

pp. 1-15

Author(s):

Tu N. Nguyen ◽

Sherali Zeadally

Keyword(s):

Mobile Devices ◽

Cyber Physical System ◽

Redundant Information ◽

Mobile Crowd Sensing ◽

Sensing System ◽

Sensing Systems ◽

Sensing Applications ◽

Crowd Sensing ◽

Mobile Crowd ◽

Collection Methods

Conventional data collection methods that use Wireless Sensor Networks (WSNs) suffer from disadvantages such as deployment location limitation, geographical distance, as well as high construction and deployment costs of WSNs. Recently, various efforts have been promoting mobile crowd-sensing (such as a community with people using mobile devices) as a way to collect data based on existing resources. A Mobile Crowd-Sensing System can be considered as a Cyber-Physical System (CPS), because it allows people with mobile devices to collect and supply data to CPSs’ centers. In practical mobile crowd-sensing applications, due to limited budgets for the different expenditure categories in the system, it is necessary to minimize the collection of redundant information to save more resources for the investor. We study the problem of selecting participants in Mobile Crowd-Sensing Systems without redundant information such that the number of users is minimized and the number of records (events) reported by users is maximized, also known as the Participant-Report-Incident Redundant Avoidance (PRIRA) problem. We propose a new approximation algorithm, called the Maximum-Participant-Report Algorithm (MPRA) to solve the PRIRA problem. Through rigorous theoretical analysis and experimentation, we demonstrate that our proposed method performs well within reasonable bounds of computational complexity.

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Mobile Crowd Sensing: Incentive Mechanism Design

10.1007/978-3-030-01024-9 ◽

2019 ◽

Cited By ~ 2

Author(s):

Fen Hou ◽

Yingying Pei ◽

Jingyi Sun

Keyword(s):

Mechanism Design ◽

Incentive Mechanism ◽

Mobile Crowd Sensing ◽

Crowd Sensing ◽

Incentive Mechanism Design ◽

Mobile Crowd

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Staged Incentive and Punishment Mechanism for Mobile Crowd Sensing

Sensors ◽

10.3390/s18072391 ◽

2018 ◽

Vol 18 (7) ◽

pp. 2391 ◽

Cited By ~ 6

Author(s):

Dan Tao ◽

Shan Zhong ◽

Hong Luo

Keyword(s):

Incentive Mechanism ◽

Extensive Study ◽

Mobile Crowd Sensing ◽

Space Correlation ◽

Data Utility ◽

Sensing Data ◽

Crowd Sensing ◽

Time Space ◽

Punishment Mechanism ◽

Mobile Crowd

Having an incentive mechanism is crucial for the recruitment of mobile users to participate in a sensing task and to ensure that participants provide high-quality sensing data. In this paper, we investigate a staged incentive and punishment mechanism for mobile crowd sensing. We first divide the incentive process into two stages: the recruiting stage and the sensing stage. In the recruiting stage, we introduce the payment incentive coefficient and design a Stackelberg-based game method. The participants can be recruited via game interaction. In the sensing stage, we propose a sensing data utility algorithm in the interaction. After the sensing task, the winners can be filtered out using data utility, which is affected by time–space correlation. In particular, the participants’ reputation accumulation can be carried out based on data utility, and a punishment mechanism is presented to reduce the waste of payment costs caused by malicious participants. Finally, we conduct an extensive study of our solution based on realistic data. Extensive experiments show that compared to the existing positive auction incentive mechanism (PAIM) and reverse auction incentive mechanism (RAIM), our proposed staged incentive mechanism (SIM) can effectively extend the incentive behavior from the recruiting stage to the sensing stage. It not only achieves being a real-time incentive in both the recruiting and sensing stages but also improves the utility of sensing data.

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