ℛ2: Incremental ℛeprogramming using ℛelocatable code in networked embedded systems

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.