Exploring AIoT Blockchain Transaction Semantic Detection and Incentive Mechanism With Evolutionary Game Toward Web 3.0 Ecosystem

Qinnan Zhang, Zishuai Zhang, Yiran Chen, Misha Xu, Zehui Xiong, Jiequ Ji, Wangjie Qiu, Hongwei Zheng, Jianming Zhu, Jin Dong, Zhiming Zheng

Abstract: In the Web 3.0 ecosystem, blockchain and Artificial Intelligence of Things (AIoT) construct the infrastructure, where blockchain transaction semantic detection (BTSD) aims to enhance blockchain security by identifying illegal transactions through distributed miners executing AI algorithms. However, the computational cost of performing semantic detection discourages miners from participating without adequate incentives. Existing studies focus on algorithmic aspects of BTSD, which generally ignore the critical issue of incentive mechanism. To fill this gap, we propose the first incentive-based BTSD framework in the transaction pool phase, emphasizing how incentives affect the behavior of miners and users. We use evolutionary game theory to model miner-user interactions and define three key scenarios to simulate the impact of reward decay and penalty factors on system dynamics. Our results demonstrate that adjusting these parameters significantly influences the number of miners engaging in semantic detection and users initiating legitimate transactions. Under certain conditions, a well-designed incentive mechanism can lead to an evolutionary stable strategy (ESS), thereby achieving systemic stability. This study introduces a novel incentive mechanism for BTSD during the transaction pool phase and validates its effectiveness through both theoretical insights and numerical solutions to enhance blockchain security.

External IDs:dblp:journals/iotj/ZhangZCXXJQZZDZ25