S2: a Small Delta and Small Memory Differencing Algorithm for Reprogramming Resource-constrained IoT Devices

Abstract

Incremental reprogramming is one of the key features for managing resource-constrained IoT devices. Nevertheless, existing approaches fall flat in RAM and flask usage due to the increasing firmware size of contemporary IoT applications. In this paper, we advocate S2, a differencing algorithm for reprogramming resource-constrained IoT devices. S2 achieves small memory and flash footprints by leveraging a topological sort based in-place reconstruction mechanism and stream reconstruction technique, as well as smaller delta size by a prediction-based encoding. Evaluation shows that S2 uses 33.3% less RAM while reducing at most 42.5% delta size than state-of-the-arts.