Context-Aware Acquisition Module using Vehicle to Roadside Unit Unicast Communication for Intelligent Transport System in Edge Computing

Intelligent Transportation Systems (ITS) are evolving with the integration of edge computing and the Internet of Vehicles (IoV), enabling responsive, data-driven traffic management. This paper proposes a Context-Aware Acquisition Module (CAAM) to enhance real-time vehicular decision-making using unicast communication between vehicles and Roadside Units (RSUs). Unlike conventional broadcast approaches, CAAM selectively acquires and transmits critical contextual information such as vehicle speed, acceleration, and CO 2 emissions, thereby reducing communication overhead and improving response accuracy. The system architecture incorporates RSUs, edge servers, and On-Board Units (OBUs), with RSUs periodically transmitting Wave Short Messages (WSMs) using the IEEE 802.11p WAVE protocol. CAAM dynamically filters context data and enables targeted, low-latency control actions in complex urban environments. Simulations are conducted using OMNeT++ and VEINS, integrated with SUMO for realistic vehicular mobility and communication modeling. Performance is evaluated using key metrics, including vehicle speed, acceleration profiles, CO 2 emissions, communication busy time, and packet delivery ratio (PDR). In a representative scenario, a vehicle responded to an RSU-issued deceleration command, reducing its speed from 13.89 m/s to 1.38 m/s. CAAM achieved the lowest end-to-end delay (0.48 s) and highest PDR (100%), outperforming Adaptive V2R, Shortest Distance, and Random Selection strategies, supporting its alignment with emerging 5G-V2X standards.