论文标题
记忆力吸引的在线压缩CAN总线数据的未来车辆系统
Memory-aware Online Compression of CAN Bus Data for Future Vehicular Systems
论文作者
论文摘要
车辆从其内部传感器中产生大量数据。这些数据不仅对车辆的适当操作有用，而且还使汽车制造商具有优化具有车队车队（例如，卡车，出租车，拖拉机）的单个车辆和公司的能力，以优化其操作以降低燃油成本和计划维修。本文建议算法压缩CAN数据，特别是包装为MDF4文件。特别是，我们提出了轻巧，在线和可配置的压缩算法，允许有限的设备选择分配给它们的RAM和闪光的量。我们表明，我们的建议可以胜过同一RAM足迹的LZW，甚至可以提供可比或更好的性能以在相同的RAM限制下放气。
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.
