Event logging is an important technique for networked embedded systems like wireless sensor networks. It can greatly help developers to understand complex system behaviors and diagnose program bugs. Existing logging facilities do not well satisfy three practical requirements: flexibility , efficiency , and high synchronization accuracy . To simultaneously satisfy these requirements, we present Dylog, a dynamic logging facility for networked embedded systems. Dylog employs several techniques. First, Dylog uses binary instrumentation for dynamically inserting or removing logging statements, enabling flexible and interactive debugging at runtime. Second, Dylog incorporates an efficient storage system and log collection protocol for recording and transferring the logging messages. Third, Dylog employs a lightweight data-driven approach for reconstructing the synchronized time of the logging messages. Dylog uses MAC-layer timestamping and drift compensation to achieve high synchronization accuracy . We implement Dylog on the TinyOS 2.1.1/TelosB platform. Results show the following: (1) Dylog incurs a small overhead. Indirections in Dylog incur an additional execution overhead of less than 1%. Dylog reduces the logging storage size by approximately 50% compared with the standard TinyOS radio printf library. Dylog reduces the patch size by more than 90%, compared with incremental reprogramming. (2) Dylog reduces the synchronization overhead by 78% in terms of transmission cost, compared with a traditional time synchronization protocol, FTSP, and it can achieve a high time synchronization accuracy of 5.4μs. (3) Dylog can help diagnose system problems effectively at the source-code level for three real-world scenarios.

We present Dylog, a dynamic logging facility for networked embedded systems. Dylog employs several techniques to enable lightweight and interactive logging. First, Dylog uses binary instrumentation for dynamically inserting or removing logging statements, enabling interactive debugging at the runtime. Second, Dylog incorporates an efficient storage system and log collection protocol for recording and transferring the logging messages. In particular, Dylog significantly reduces the communication cost by storing string identifiers and restoring them back to corresponding strings at the PC. Third, Dylog employs MAC layer timestamping and a linear clock model for reconstructing the synchronized time of the logging messages with a very high precision. We implement and evaluate Dylog on TinyOS 2.1.1/TelosB. Results show that Dylog incurs a reasonable overhead. Dylog can help gain great visibility into the system behaviors, and diagnose performance issues at the source code level.

We present a holistic reprogramming system called R3. R3 has two salient features. First, the binary differencing algorithm within R3 (R3diff) ensures an optimal result in terms of the delta size under a configurable cost measure. Second, the similarity preserving method within R3 (R3sim) optimizes the binary code format for achieving a large similarity with a small metadata overhead. Overall, R3 achieves the smallest delta size compared to other incremental approaches such as Rsync [11], RMTD [9], Zephyr/Hermes [17], [18], and R2 [2], e.g., 50%–99% reduction compared to Stream and about 20%–40% reduction compared to R2. R3's implementation on TelosB/TinyOS is lightweight and efficient. We release our code at http://code.google.com/p/r3-dongw.

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 effects of both function shifts and data shifts. R2 adopts a content-aware differencing algorithm to generate small delta files for efficient dissemination. Besides, it makes efficient use of memory and does not degrade program quality. We implement R2 based on TinyOS 2.1 and demonstrate its advantages through detailed analysis of TinyOS examples. We also present case studies on the software programs of a large-scale sensor system GreenOrbs. Results show that R2 reduces the dissemination cost by approximately 65 percent compared to state-of-the-art network reprogramming approach Deluge, and reduces the dissemination cost by approximately 20 percent compared to Zephyr and Hermes the latest works on incremental reprogramming.

We present ℛ2, an incremental ℛeprogramming approach using Relocatable code, to improve program similarity for efficient incremental reprogramming in networked embedded systems. ℛ2achieves a higher degree of similarity than existing approaches by mitigating the effects of both function shifts and data shifts. ℛ2makes 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 ℛ2reduces 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.