A Short Review on Data Freshness and Privacy Issues of Wireless Sensing Systems: Insights From Mobile Crowdsensing
摘要
Mobile crowdsensing (MCS) presents a powerful paradigm for wireless sensing, offering cost-effectiveness, scalability, and flexibility for data collection. However, inherent vulnerabilities and unpredictable user participation raise significant data freshness and privacy concerns. This paper reviews these issues, highlighting the growing reliance on real-time data and heightened privacy awareness to motivate the need for more secure and efficient MCS systems. We provide a structured review of existing literature on data freshness optimization and privacy preservation, considering Age of Information (AoI) evaluation and optimization, sensing data protection, sensing task protection, and mobile terminal location protection. Furthermore, we offer insights into designing novel schemes that balance data freshness and privacy in complex MCS environments, guiding future research toward practical, robust, and user-centric solutions.