群智感知网络中数据质量保证方法分析

Analysis of Data Quality Assurance Methods in Mobile Crowdsensing Networks

移动智能终端设备和无线技术的快速发展,使得移动群智感知网络作为一种全新的物联网感知范式得到了广泛关注。然而在实际应用中,移动群智感知系统的大规模应用仍面临一些挑战。感知数据作为移动群智感知网络的重要组成部分,其质量的高低直接影响着移动群智感知系统的服务质量,进而对移动群智感知系统能否大规模普及起着关键作用,因此确保高质量数据是移动群智感知网络研究的重点之一。本文首先从感知数据生命周期的角度出发,对数据感知、数据上传、数据交易阶段中影响感知数据质量的因素进行了详细说明。其次。从激励机制、数据可靠性评估、隐私保护三个方面对现有的感知数据质量保证方法进行了总结;最后,对移动群智感知网络的应用前景以及未来的研究方向进行了总结与展望,以期为移动群智感知数据质量保证方法研究提供一些参考。

The rapid development of mobile intelligent terminal devices and wireless technology has made mobile crowdsensing networks as a new paradigm of IoT attract widespread attention. However, in practical applications, the large-scale application of mobile crowdsensing systems still faces some challenges. As an important component of mobile crowdsensing networks, the quality of crowdsensing data directly affects the service quality of mobile crowdsensing systems, and thus plays a crucial role in the large-scale popularization of mobile crowdsensing systems. Therefore, ensuring high-quality data is one of the focuses of research on mobile crowdsensing networks. This article first provides a detailed explanation of the factors that affect the quality of crowdsensing data during the stages of data perception, data upload, and data trading from the perspective of the crowdsensing data lifecycle. Secondly, summarizing the existing methods for ensuring perceived data quality from three aspects: incentive mechanism, data reliability evaluation, and privacy protection. Finally, a summary and outlook were made on the application prospects and future research directions of mobile crowdsensing networks in order to provide some reference for the research on data quality assurance methods for mobile crowdsensing.