R2: Incremental Reprogramming Using Relocatable Code in Networked Embedded Systems

Dong, Wei; Chen, Chun; Bu, Jiajun; Huang, Chao; Zhao, Zhiwei

doi:10.1109/tc.2012.161

Cited by 33 publications

(26 citation statements)

References 29 publications

(55 reference statements)

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“…Hu et al [4] propose the same block-matching algorithm RTMD to reuse the code blocks in the old image as much as possible. The R2 [12] combines the merits both of Zephyr and RTMD. Relative to Zephyr, R2 leverages the relocatable position-independent code to keep the references of functions and variables not being changed between different version.…”

Section: Related Workmentioning

confidence: 99%

“…These updating cases will be detailed in Section 6.1. Figure 1(b) shows the ratio of the size of the updating script to the size of the new image in Step 3 which is written into the program flash using a traditional reprogramming [12]. On average, more than 1265 bytes are transmitted in the five cases.…”

Section: Inmentioning

confidence: 99%

“…On the other side, we expand formula (1): case REPLACE: (5) overwrite codes located in StAddr with the new one; (6) case MOVE: (7) move the common block form OldAddr into NewAddr; (8) case DELETE: (9) write "jmp $ + size" into StAddr; (10) case INSERT: (11) store the function in RAM with IFpt; (12) write "call # IFpt" into InsAddr; …”

Section: Lightweight Segmentedmentioning

confidence: 99%

See 2 more Smart Citations

EasiLIR: Lightweight Incremental Reprogramming for Sensor Networks

Qiu

Shi

et al. 2014

International Journal of Distributed Sensor Networks

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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 systemEasiLIR. 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.

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Section: Related Workmentioning

confidence: 99%

Section: Inmentioning

confidence: 99%

Section: Lightweight Segmentedmentioning

confidence: 99%

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EasiLIR: Lightweight Incremental Reprogramming for Sensor Networks

Qiu

Shi

et al. 2014

International Journal of Distributed Sensor Networks

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“…WENS have been widely deployed recently to support Internet-of-things [3,4,5,6]. Bulk data dissemination is used to distribute a large data object reliably from a sink node to all network nodes in WENS, becoming an essential building module for a variety of WENS systems, e.g., remote software management [7], security patches [8], reprogramming [9,10] and video distribution [11].…”

Section: Introductionmentioning

confidence: 99%

Highly-Efficient Bulk Data Transfer for Structured Dissemination in Wireless Embedded Network Systems

Zhao

Wang

Min

et al. 2017

Journal of Systems Architecture

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Recent years have witnessed the remarkable development of wireless embedded network systems (WENS) such as cyber-physical systems and sensor networks. Reliable bulk data dissemination is an important building module in WENS, supporting various applications, e.g., remote software update, video distribution. The existing studies often construct network structures to enable time-slotted multi-hop pipelining for data dissemination. However, the adopted transmission mechanism was originally designed for structureless protocols, and thus posing significant challenges on efficient structured data dissemination. In this paper, we investigate the problem of structured bulk data dissemination. Specifically, we propose reliable out-of-order transmission and bursty encoding mechanisms to transmit packets as many as possible in each transmission slot. As a consequence, the resulting transmission protocol (ULTRA) can fully utilize each transmission slot and propagate data in the network as fast as possible. The performance results obtained from both testbed and simulation experiments demonstrate that, compared to the state-of-the-art protocols, ULTRA can greatly enhance the dissemination performance by reducing the dissemination delay by 34.8%.

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“…The performance of systematic, modular and virtual machine based reprogramming is poor because those approaches need to transmit large size patch and demand high processing time and capabilities to be provided by the node's CPU, it is intolerable for the energy-limited nodes. Differential approaches [3][4][5] generate a patch by comparing the new code with the old version at the base-station. The salient feature of this approach is that the base-station only transmits the differential patch.…”

Section: Introductionmentioning

confidence: 99%

Differential Reprogramming Based on Constructive Interference for Wireless Sensor Network

Sun

2016

j comput theor nanosci

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Abstract:To improve the performance of reprogramming in wireless sensor network, we present a novel reprogramming structure and constructive interference-based dissemination protocol (CIDP) to transmit the patch through out the network fast and reliability. CIDP disseminates the patch, which is divided into several packets, to the network exploiting constructive interference. We evaluate our implementation of CIDP using simulation under different number of nodes. Our results show that CIDP disseminates the patch less than 4 milliseconds. In general, the probability of a node receives the complete patch as high as 99.99%.

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R2: Incremental Reprogramming Using Relocatable Code in Networked Embedded Systems

Cited by 33 publications

References 29 publications

EasiLIR: Lightweight Incremental Reprogramming for Sensor Networks

EasiLIR: Lightweight Incremental Reprogramming for Sensor Networks

Highly-Efficient Bulk Data Transfer for Structured Dissemination in Wireless Embedded Network Systems

Differential Reprogramming Based on Constructive Interference for Wireless Sensor Network

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