EasiLIR: Lightweight Incremental Reprogramming for Sensor Networks. (16th January 2014)

• Record Type: Journal Article
• Authors: Qiu, Jiefan; Li, Dong; Shi, Hailong; Cui, Li
• Other Names: Liu Yonghe Academic Editor.
• Is Part Of: International journal of distributed sensor networks. (2014)
• Publication Date: 2014-01-16
• Subjects: Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1155/2014/120597
• Languages: English
• ISSNs: 1550-1329

Abstract

Abstract : 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.