Participant Service Ability Aware Data Collecting Mechanism for Mobile Crowd Sensing

To collect data efficiently and reliably in Mobile Crowd Sensing (MCS), a Participant Service Ability Aware (PSAA) data collecting mechanism is proposed. First, participants select the best sensing task according to the task complexity and desired reward in the multitasking scenario. Second, the Stackelberg Game model is established based on the mutual choice of participants and platform to maximize their utilities to evaluate the service ability of participants. Finally, participants transmit data to platform directly or indirectly through the best relay and the sensing data from the participants with better service ability is selected to complete sensing tasks accurately and efficiently with the minimum overall reward expense. The numerical results show that the proposed data collection mechanism can maximize the utility of participants and platform, efficiently accomplish sensing tasks and significantly reduce the overall reward expense.

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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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A Cost-Effective Distributed Framework for Data Collection in Cloud-Based Mobile Crowd Sensing Architectures

IEEE Transactions on Sustainable Computing ◽

10.1109/tsusc.2017.2666043 ◽

2017 ◽

Vol 2 (1) ◽

pp. 3-16 ◽

Cited By ~ 40

Author(s):

Andrea Capponi ◽

Claudio Fiandrino ◽

Dzmitry Kliazovich ◽

Pascal Bouvry ◽

Stefano Giordano

Keyword(s):

Data Collection ◽

Cost Effective ◽

Mobile Crowd Sensing ◽

Crowd Sensing ◽

Distributed Framework ◽

Mobile Crowd

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Leveraging Mobile Nodes for Preserving Node Privacy in Mobile Crowd Sensing

Wireless Communications and Mobile Computing ◽

10.1155/2018/9567302 ◽

2018 ◽

Vol 2018 ◽

pp. 1-11 ◽

Cited By ~ 2

Author(s):

Qinghua Chen ◽

Shengbao Zheng ◽

Zhengqiu Weng

Keyword(s):

Mobile Node ◽

Privacy Preserving ◽

Sensitive Information ◽

Node Mobility ◽

Mobile Nodes ◽

Mobile Crowd Sensing ◽

Sensitive Data ◽

Sensing Data ◽

Crowd Sensing ◽

Mobile Crowd

Mobile crowd sensing has been a very important paradigm for collecting sensing data from a large number of mobile nodes dispersed over a wide area. Although it provides a powerful means for sensing data collection, mobile nodes are subject to privacy leakage risks since the sensing data from a mobile node may contain sensitive information about the sensor node such as physical locations. Therefore, it is essential for mobile crowd sensing to have a privacy preserving scheme to protect the privacy of mobile nodes. A number of approaches have been proposed for preserving node privacy in mobile crowd sensing. Many of the existing approaches manipulate the sensing data so that attackers could not obtain the privacy-sensitive data. The main drawback of these approaches is that the manipulated data have a lower utility in real-world applications. In this paper, we propose an approach called P3 to preserve the privacy of the mobile nodes in a mobile crowd sensing system, leveraging node mobility. In essence, a mobile node determines a routing path that consists of a sequence of intermediate mobile nodes and then forwards the sensing data along the routing path. By using asymmetric encryptions, it is ensured that a malicious node is not able to determine the source nodes by tracing back along the path. With our approach, upper-layer applications are able to access the original sensing data from mobile nodes, while the privacy of the mobile node is not compromised. Our theoretical analysis shows that the proposed approach achieves a high level of privacy preserving capability. The simulation results also show that the proposed approach incurs only modest overhead.

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Verifiable Location-Encrypted Spatial Aggregation Computing for Mobile Crowd Sensing

Security and Communication Networks ◽

10.1155/2021/6654539 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Kun Niu ◽

Changgen Peng ◽

Weijie Tan ◽

Zhou Zhou ◽

Yi Xu

Keyword(s):

Privacy Protection ◽

Spatial Information ◽

Homomorphic Encryption ◽

Security Analysis ◽

Spatial Aggregation ◽

Mobile Crowd Sensing ◽

Sensitive Data ◽

Sensing Data ◽

Crowd Sensing ◽

Mobile Crowd

Benefiting from the development of smart urban computing, the mobile crowd sensing (MCS) network has emerged as momentous communication technology to sense and collect data. The users upload data for specific sensing tasks, and the server completes the aggregation analysis and submits to the sensing platform. However, users’ privacy may be disclosed, and aggregate results may be unreliable. Those are challenges in the trust computation and privacy protection, especially for sensitive data aggregation with spatial information. To address these problems, a verifiable location-encrypted spatial aggregation computing (LeSAC) scheme is proposed for MCS privacy protection. In order to solve the spatial domain distributed user ciphertext computing, firstly, we propose an enhanced-distance-based interpolation calculation scheme, which participates in delegate evaluator based on Paillier homomorphic encryption. Then, we use aggregation signature of the sensing data to ensure the integrity and security of the data. In addition, security analysis indicates that the LeSAC can achieve the IND-CPA indistinguishability semantic security. The efficiency analysis and simulation results demonstrate the communication and computation overhead of the LeSAC. Meanwhile, we use the real environment sensing data sets to verify availability of proposed scheme, and the loss of accuracy (global RMSE) is only less than 5%, which can meet the application requirements.

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Repeated Overlapping Coalition Game Model for Mobile Crowd Sensing Mechanism

KSII Transactions on Internet and Information Systems ◽

10.3837/tiis.2017.07.006 ◽

2017 ◽

Vol 11 (7) ◽

Keyword(s):

Game Model ◽

Coalition Game ◽

Mobile Crowd Sensing ◽

Sensing Mechanism ◽

Crowd Sensing ◽

Mobile Crowd

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LCBPA: two-stage task allocation algorithm for high-dimension data collecting in mobile crowd sensing network

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-019-1610-2 ◽

2019 ◽

Vol 2019 (1) ◽

Author(s):

Ning Zhou ◽

Jianhui Zhang ◽

Binqiang Wang ◽

Jia Xiao

Keyword(s):

Data Collection ◽

High Dimension ◽

Task Allocation ◽

High Dimensional Data ◽

High Dimensional ◽

Mobile Crowd Sensing ◽

Allocation Algorithm ◽

Crowd Sensing ◽

High Dimension Data ◽

Mobile Crowd

AbstractMobile crowd sensing (MCS) is a novel emerging paradigm that leverages sensor-equipped smart mobile terminals (e.g., smartphones, tablets, and intelligent wearable devices) to collect information. Compared with traditional data collection methods, such as construct wireless sensor network infrastructures, MCS has advantages of lower data collection costs, easier system maintenance, and better scalability. However, the limited capabilities make a mobile crowd terminal only support limited data types, which may result in a failure of supporting high-dimension data collection tasks. This paper proposed a task allocation algorithm to solve the problem of high-dimensional data collection in mobile crowd sensing network. The low-cost and balance-participating algorithm (LCBPA) aims to reduce the data collection cost and improve the equality of node participation by trading-off between them. The LCBPA performs in two stages: in the first stage, it divides the high-dimensional data into fine-grained and smaller dimensional data, that is, dividing an m-dimension data collection task into k sub-task by K-means, where (k < m). In the second stage, it assigns different nodes with different sensing capability to perform sub-tasks. Simulation results show that the proposed method can improve the task completion ratio, minimizing the cost of data collection.

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