DRL-APG: Deep Reinforcement Learning Based Adaptive Policy Generation for Accurate and Secure Data Sharing in VANETs

Tiancong Cao, Xuejiao Liu, Yingjie Xia

Abstract: With the rapid expansion of vehicular ad-hoc networks (VANETs), the dynamic traffic environment raises concerns regarding accurate and secure data sharing. Unauthorized entities may exploit vulnerabilities to access sensitive information within shared data. To address these challenges, we propose DRL-APG, a deep reinforcement learning based adaptive policy generation scheme, to enable smarter security policies that can better handle complex changes in data sharing among vehicles. DRL-APG adopts hybrid modeling to capture environment states and fine-grained policy optimization using an ARIMA (Autoregressive Integrated Moving Average)-based reward mechanism to generate adaptive policies. Extensive simulations demonstrate DRL-APG outperforms existing schemes in different kinds of VANET situations including peak/off-peak hours and varying speed limits. In traffic congestion scenario across three simulation typical road networks, average accident zone speed increases 21.7%, 25.6% and 27.8% respectively after data sharing with the policy generated by our proposed DRL-APG, compared to existing schemes.

External IDs:dblp:journals/tits/CaoLX25