Efficient reprogramming of sensor networks using incremental updates and data compression

Reprogramming is an important issue in wireless sensor networks. It enables users to extend or correct functionality of a sensor network after deployment, at a low cost. In this paper, we investigate two problems: improving the energy efficiency and improving the delay of reprogramming. As enabling technologies we use data compression and incremental updates. We analyze different algorithms for both approaches, as well as their combination, when applied to resource-constrained devices. All algorithms are ported to the Contiki embedded operating system, and profiled for different types of reprogramming. Our results show that there is a clear trade-off between performance and resource requirements. Either VCDIFF, or the combination of Lempel-Ziv-77 or FastLZ compression algorithms with BSDIFF for delta encoding, have the best overall performance compared to other compression algorithms.