An efficient code update solution for wireless sensor network reprogramming

We present an incremental code update strategy used to efficiently reprogram wireless sensor nodes. We adapt a linear space and quadratic time algorithm (Hirschberg's algorithm) for computing maximal common subsequences to build an edit map specifying an edit sequence, required to transform the code running in a sensor network to a new code image. We then present a heuristic-based optimization strategy for efficient edit script encoding to reduce th.e edit map size. Finally, we present experimental results to demonstrate the reduction in data size to reprogram a network using this mechanism. The approach achieves reductions of 99.987% for simple changes, and between 86.95% and 94.58% for more complex changes, compared to full image transmissions - leading to significantly lower energy costs for wireless sensor network reprogramming. We compare the results with reductions achieved by other incremental update strategies described in prior work.