Comparative Analysis and Implementation of data streaming methodologies for Vehicle Navigation Mode for Electric Trucks

In the realm of electric truck logistics, optimizing route planning is essential. This paper presents a key navigation feature for analyzing such optimized routes. Integrating map features into the drive mode improves user experience by offering real-time navigation and dynamic route adjustments based on traffic updates, road closures, and vehicle coordinates. This study compares the performance of Server sent events(SSE), web sockets and API polling methodologies, focusing on metrics such as data transmission efficiency, latency, resource utilization, scalability, and reliability. Our findings demonstrate the advantages and limitations of each method, providing insights into their suitability for real-time route optimization in electric truck logistics. The results highlight the potential of SSE in achieving efficient and timely data updates, contributing to more effective route planning and resource management. Additionally, we discuss how API Polling, Web sockets, and SSE each make sense in different scenarios when creating a navigation system (drive mode), considering factors such as the frequency of updates, network conditions, and system architecture. This research underscores the importance of choosing the right communication protocol and integrating advanced map features to enhance the performance and reliability of logistics systems.