Open Access

ARTICLE

An Incentive Mechanism Model for Crowdsensing with Distributed Storage in Smart Cities

by Jiaxing Wang, Lanlan Rui, Yang Yang^*, Zhipeng Gao, Xuesong Qiu

State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China

* Corresponding Author: Yang Yang. Email:

Computers, Materials & Continua 2023, 76(2), 2355-2384. https://doi.org/10.32604/cmc.2023.034993

Received 03 August 2022; Accepted 10 May 2023; Issue published 30 August 2023

Abstract

Crowdsensing, as a data collection method that uses the mobile sensing ability of many users to help the public collect and extract useful information, has received extensive attention in data collection. Since crowdsensing relies on user equipment to consume resources to obtain information, and the quality and distribution of user equipment are uneven, crowdsensing has problems such as low participation enthusiasm of participants and low quality of collected data, which affects the widespread use of crowdsensing. This paper proposes to apply the blockchain to crowdsensing and solve the above challenges by utilizing the characteristics of the blockchain, such as immutability and openness. An architecture for constructing a crowd-sensing incentive mechanism under distributed incentives is proposed. A multi-attribute auction algorithm and a k-nearest neighbor-based sensing data quality determination algorithm are proposed to support the architecture. Participating users upload data, determine data quality according to the algorithm, update user reputation, and realize the selection of perceived data. The process of screening data and updating reputation value is realized by smart contracts, which ensures that the information cannot be tampered with, thereby encouraging more users to participate. Results of the simulation show that using two algorithms can well reflect data quality and screen out malicious data. With the help of blockchain performance, the architecture and algorithm can achieve decentralized storage and tamper-proof information, which helps to motivate more users to participate in perception tasks and improve data quality.

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Keywords

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