Incentive mechanism design via smart contract in blockchain-based edge-assisted crowdsensing

Abstract

Edge-assisted mobile crowdsensing (EMCS) has gained significant attention as a data collection paradigm. However, existing incentive mechanisms in EMCS systems rely on centralized platforms, making them impractical for the decentralized nature of EMCS systems. To address this limitation, we propose CHASER, an incentive mechanism designed for blockchain-based EMCS (BEMCS) systems. In fact, CHASER can attract more participants by satisfying the incentive requirements of budget balance, double-side truthfulness, double-side individual rationality and also high social welfare. Furthermore, the proposed BEMCS system with CHASER in smart contracts guarantees the data confidentiality by utilizing an asymmetric encryption scheme, and the anonymity of participants by applying the zero-knowledge succinct non-interactive argument of knowledge (zk-SNARK). This also restrains the malicious behaviors of participants. Finally, most simulations show that the social welfare of CHASER is increased by approximately 42\mathrm{\%} when compared with the state-of-the-art approaches. Moreover, CHASER achieves a competitive ratio of approximately 0.8 and high task completion rate of over 0.8 in large-scale systems. These findings highlight the robustness and desirable performance of CHASER as an incentive mechanism within the BEMCS system.

Graphical abstract

Keywords

mobile crowdsensing / edge computing / blockchain / smart contract / incentive mechanism

Chenhao Ying received the PhD degree in the Department of Computer Science and Engineering, Shanghai Jiao Tong University, China in 2022. He is currently a research assistant professor in the Department of Computer Science and Engineering, Shanghai Jiao Tong University, China. His current research interests include mobile crowd sensing, blockchain, and mobile computing

Haiming Jin is currently a tenure-track associate professor in the Department of Computer Science and Engineering at Shanghai Jiao Tong University, China. He is interested in addressing unfolding research challenges in the general areas of urban computing, cyber-physical systems, crowd and social sensing systems, network economics and game theory, reinforcement learning, and mobile pervasive and ubiquitous computing

Jie Li received the BE degree in computer science from Zhejiang University, China, the ME degree in electronic engineering and communication systems from China Academy of Posts and Telecommunications, China, and the Dr Eng degree from the University of Electro-Communications, Japan. He is currently a chair professor in Department of Computer Science and Engineering, the director of SJTU Blockchain Research Centre, Shanghai Jiao Tong University, China. His research interests include Big Data and AI, blockchain, network systems, and security. He was a full professor at the Department of Computer Science, University of Tsukuba, Japan. He is the co-chair of IEEE Technical Community on Big Data and the founding Chair of IEEE ComSoc Technical Committee on Big Data and the cochair of IEEE Big Data Community. He serves as an associated editor for many IEEE journals and transactions. He has also served on the program committees for several international conferences

Xueming Si is the director of Frontier Information Technology Research Institute of Zhongyuan University of Technology, China. He is currently the director of the Blockchain Special Committee of the China Computer Federation. His research interests are cryptography, data science, computer architecture, network and information system security, and blockchain

Yuan Luo received the BS degree in applied mathematics and the MS and PhD degrees in probability statistics from Nankai University, China in 1993, 1996, and 1999, respectively. Since 2006, he has been a full professor with the Department of Computer Science and Engineering, Shanghai Jiao Tong University, China. His research interests include coding theory, information theory, and big data analysis

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Acknowledgement

This work was supported in part by the Shanghai Science and Technology Innovation Action Plan (23511100400), and in part by the National Natural Science Foundation of China (Grants Nos. 62372288, and U20A20181), the 2023−2024 Open Project of Key Laboratory Ministry of Industry and Information Technology-Blockchain Technology and Data Security (20242216).

Competing interests

The authors declare that they have no competing interests or financial conflicts to disclose.

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