Memory-aware Online Compression of CAN Bus Data for Future Vehicular Systems

TL;DR

This paper introduces lightweight, online, and configurable algorithms for compressing CAN bus data in vehicles, outperforming traditional methods like LZW and matching or exceeding DEFLATE performance within limited RAM constraints.

Contribution

It presents novel online compression algorithms tailored for vehicle data, enabling limited devices to efficiently compress CAN bus data with configurable resource usage.

Findings

Outperforms LZW in RAM-limited scenarios

Achieves comparable or better performance than DEFLATE

Supports online and configurable compression for vehicle systems

Abstract

Vehicles generate a large amount of data from their internal sensors. This data is not only useful for a vehicle's proper operation, but it provides car manufacturers with the ability to optimize performance of individual vehicles and companies with fleets of vehicles (e.g., trucks, taxis, tractors) to optimize their operations to reduce fuel costs and plan repairs. This paper proposes algorithms to compress CAN bus data, specifically, packaged as MDF4 files. In particular, we propose lightweight, online and configurable compression algorithms that allow limited devices to choose the amount of RAM and Flash allocated to them. We show that our proposals can outperform LZW for the same RAM footprint, and can even deliver comparable or better performance to DEFLATE under the same RAM limitations.