Abstract
We present ℛ2, an incremental ℛeprogramming approach using Relocatable code, to improve program similarity for efficient incremental reprogramming in networked embedded systems. ℛ2 achieves a higher degree of similarity than existing approaches by mitigating the effects of both function shifts and data shifts. ℛ2 makes efficient use of memory and does not degrade program quality. It adopts an optimized differencing algorithm to generate small delta files for efficient dissemination. We demonstrate ℛ2's advantages through detailed analysis of TinyOS examples. We also present case studies on the software programs of a large-scale and long-term sensor system—GreenOrbs. Results show that ℛ2 reduces the dissemination cost by approximately 65% compared to Deluge—state-of-the-art network reprogramming approach, and reduces the dissemination cost by approximately 20% compared to Zephyr and Hermes—the latest works on incremental reprogramming.
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