Sensor nodes are increasingly used in critical applications. A file storage system that is fast, lightweight, and reliable across device failures is important to safeguard the data that these devices record. A fast and lightweight file system should enable sensed data to be sampled and stored quickly for later transmission, while imposing a small resource footprint. A reliable file system should provide storage integrity in the face of hardware, software, and other failures.

Network simulation is an important tool for testing and evaluating wireless sensor network applications. Parallel simulation strategies improve the scalability of these tools. However, achieving high performance depends on reducing the synchronization overhead among simulation processes. In this paper, we present an optimistic simulation algorithm with support for backtracking and re-execution. The algorithm reduces the number of synchronization cycles to the number of transmissions in the network under test. We implement SnapSim, an extension to the popular Avrora simulator, based on this algorithm. The experimental results show that our prototype system improves the performance of Avrora by 2 to 10 times for typical network-centric sensor network applications, and up to three orders of magnitude for applications that use the radio infrequently. We also implement a distributed version of SnapSim, D-SnapSim, which runs on a cluster. The experimental results show that D-SnapSim further improves the performance of SnapSim by up to 10 times for applications that use the radio frequently.

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.