Efficient code distribution in wireless sensor networks

Abstract

The need to reprogramme a wireless sensor network may arise from changing application requirements, bug fixes, or during the application development cycle. Once deployed, it will be impractical at best to reach each individual node. Thus, a scheme is required to wirelessly reprogramme the nodes. We present an energy-efficient code distribution scheme to wirelessly update the code running in a sensor network. Energy is saved by distributing only the changes to the currently running code. The new code image is built using an edit script of commands that are easy to process by the nodes. A small change to the programme code can cause many changes to the binary code because the addresses of functions and data change. A naive approach to building the edit script string would result in a large script. We describe a number of optimisations and present experimental results showing that these significantly reduce the edit script size.

Sluice: Secure Dissemination of Code Updates in Sensor Networks

“…In [21], the authors propose a diff-like 3 scheme to reduce the amount of data that needs to be transmitted. Instead of disseminating an entire program image, the authors propose creating an edit script by comparing the current data object with the pending data object.…”

“…While the preparation mechanism is well developed, [21] describes only a simple pointto-point distribution mechanism.…”

“…However, even small source-code changes can cause large shifts in binary data that span multiple pages. While [21] is very effective at reducing the amount of data to be transferred, it takes advantage of instruction-set details to build the edit script, and so, is architecture dependent. A platform independent approach based on the rsync algorithm is presented in [8].…”

“…This approach effectively handles code shifts and small binary changes, but presents a negligible speedup with large code changes. Both [21] and [8] assume nodes to be in a consistent state, and can only update nodes that are a single version behind the pending data object.…”

An energy benchmark for software updates on wireless sensor nodes

“…As with all WSN mechanisms, reducing and optimizing energy use is a critical concern. Identified requirements include low overheads, and resource awareness -especially to minimize flash rewriting [10]; minimizing the impact on sensor network lifetime, and limiting the use of memory resources [21]; minimizing processing, limiting communication to save energy and only interrupting the application for a short period while updating the code [3,16]; operating within the hardware constraints of different platforms [12].…”