Real-time Edge Analytics for Cyber Physical Systems using Compression Rates

There is a movement in many practical applications of Cyber-Physical Systems to push processing to the edge. This is particularly important were the CPS is carrying out monitoring and control, where the latency between the decision making and control message reception should be minimal. However, CPS are limited by the capabilities of the typically battery powered low resourced devices. In this paper we present a self-adaptive scheme that both reduces the amount of resources required to store high sample rate data at the edge and at the same time carries out initial data analytics. Using out Smart Water datasets, plus a selection from other real world CPS applications, we show that our algorithm reduces computation by 98%; data volumes by 55%; while requiring only 11KB of memory at runtime (including the compression algorithm). In addition we show that our system supports self-tuning and automatic reconfiguration which means that manual tuning is alleviated and the scheme can be both applied to any kind of raw data automatically and is able self-optimize as the nature of the incoming data changes over time.