EasiLIR: Lightweight Incremental Reprogramming for Sensor Networks

Energy-efficient wireless reprogramming is key issues for long-lived sensor network. Most of wireless reprogramming approaches focus on the energy efficiency of the data transmission phase. However, the program rebuilding phase on target node is possibly as another significant part of the total reprogramming energy consumption, due to the high energy overhead of reading or writing operation on the energy-hungry nonvolatile memory. In this paper, we propose an energy-efficient reprogramming system—EasiLIR. The core of EasiLIR is to avoid r/w operations on nonvolatile memory as much as possible in two fold. Firstly, we design an in situ modification which creates a modified program equivalent to new one without rebuilding program. However, at the cost of no rebuilding program, the redundant binary codes existing in the modified program may break the program time constraint. Therefore, we also design a lightweight segmented rebuilding to directly create the new image in memory. Experiment results show that EasiLIR reduces the r/w operations on nonvolatile memory by approximately 88% and 81% compared to Deluge and R2, and its average reprogramming overhead is about 64.7% of R2. 1. Introduction Wireless sensor network (WSN) systems may be deployed in inaccessible areas and implement a long-term monitoring task. When the software functionality is changed, wireless reprogramming techniques provide an efficient solution without node redeployment. However, the high-energy overhead prevents the wireless reprogramming from applying in sensor networks. Thus, energy efficiency of wireless reprogramming is essential requirement for the constrained resource sensor node. The reprogramming energy overhead can be coarsely divided into two parts: the communication overhead for wireless transmission or receiving the updating data and the rebuilding overhead for generating new image and storing it. Prior efforts on reprogramming mainly focus on the former by minimizing the transferred data, such as the incremental reprogramming: Zephyr and Hermes [1, 2] improve the similarity between the old and new versions application by fixing the variable and function addresses; Li et al., and so forth [3], design an update-conscious complier to create the new image according to the old ones for reducing the differences; or Hu et al., and so forth [4], propose a minimal transferred data algorithm by matching the same binary between different versions as much as possible. On the other hand, most of reprogramming approaches give less consideration to the latter. The traditional view is