LiREP：轻量级传感器节点就地修改增量编程

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"LiREP: 轻量级传感器节点就地修改的增量编程技术" 本文是一篇关于无线传感器网络中轻量级增量编程的研究论文，由浙江工业大学计算机科学与技术学院的邱杰凡、黄亮和钱丽萍共同撰写。在无线传感器网络中，能源效率是确保长期运行的关键因素之一。当前的大多数方法侧重于数据传播过程中的功率效率，但忽略了目标节点上的程序重建阶段所消耗的能量，这也是总重编程能量的一个显著部分。作者提出了名为LiREP（Lightweight Incremental Reprogramming）的新颖方法，旨在解决这一问题。LiREP主要包含两个方面：首先，他们设计了一种就地代码修改方法，这种方法比传统的外部闪存重建程序方式更节能。通过提出三种基本的修改操作（可能包括插入、删除和替换等），并实现了这些操作，能够在内存中生成带有冗余代码的修改后的程序，从而减少了对闪存ROM的读写操作，降低了能源消耗。其次，为了消除这些冗余代码，他们还提出了一种新颖的重建方法。在可用存储空间有限的情况下，这种方法可以在不牺牲系统性能的前提下，有效地清除不再需要的冗余代码，进一步优化了内存使用和能源效率。此外，LiREP可能还包括对重编程协议的改进，以适应这种增量更新策略，确保程序更新的正确性和一致性。它可能考虑了错误检测和纠正机制，以应对在网络中传输更新时可能出现的通信错误。 LiREP是一种面向无线传感器网络的创新性编程解决方案，它通过优化程序修改和重建过程，显著降低了能量消耗，对于延长无线传感器网络的生命周期具有重要意义。这种技术可能适用于各种环境监测、物联网(IoT)应用和其他依赖低功耗传感器节点的场景。通过减少对硬件资源的依赖，LiREP有助于实现更加智能、可持续的无线传感器网络。

LiREP: Lightweight Incremental Reprogramming of

Sensor Nodes Based On In-situ Modification

Jiefan Qiu, Liang Hunag, Liping Qian

College of Computer Science and Technology

Zhejiang University of Technology, Hangzhou, China

{qiujiefan,huangliang,lpqian}zjut.edu.cn

Abstract— Energy-efficient consumption of the reprogramming

approaches is one of key issues for long-term living wireless

sensor networks. Most of the current approaches focus on the

power efficiency during data dissemination. However, the

program rebuilding phase on target node is rarely considered as

another significant part of total reprogramming energy

consumed. We propose a novel reprogramming approach–

LiREP in two-fold: first, we design an in-situ code modification

method which saves more energy compared with traditional way

to rebuild program in external flash. Three basic modifying

operations are proposed and realized. These operations can

reduce the writing/reading operations of flash ROM by

producing a modified program with redundant codes in memory.

Secondly, we also propose a novel rebuilding method to remove

the redundant codes when the available memory is not enough

for the in-situ code modification. This rebuilding method totally

runs in-chip with least the rebuilding overhead. Experiment

results show that LiREP reduces the rebuilding energy overhead

by approximately 84% and 74% compared to Deluge and

Hermes, and further minimizes the reprogramming energy

overhead.

Keywords: Sensor Network; reprogramming; in-situ modification;

in-chip rebuilding;

I. INTRODUCTION

Wireless sensor network (WSN) systems possible deployed

in inaccessible areas and implement a long-term monitoring

task. For software update, wireless reprogramming can provide

an efficient solution without redeployments sensor nodes.

However, the high-energy overhead is the bottleneck of scale

applications of reprogramming. Thus how to reduce the energy

overheads is a key issue of reprogramming approaches.

The energy overhead of reprogramming can includes two

parts: the dissemination overhead for wirelessly

communication among sensor nodes, and the rebuilding

overhead for storing the program image. Most of current

reprogramming approaches focus on reducing the

dissemination overhead, like: researcher [2,3] try to improve

the similar between the different versions by fixing addresses

of variable or function; Li W. and Zhang Y. etc [14] design an

update-conscious complier which generates the new binary-

level codes according to the old ones; or, Hu J. and Xue C.J.

etc [7] propose a code match algorithm to match same codes as

many as possible. These approaches attempt to minimize the

transmitted codes size to save the dissemination overhead.

Actually, the program rebuilding process on embedded

sensor node would consume more energy than the data

dissemination in some cases. For example, our WSN system

for relic protection in Forbidden City [16] has a typical

wireless software update requirement. The sensor nodes are

locked in the showcase for at least 3 months shown in Fig 1.

All the software update must be executed through wireless

manner. In practice, we find that to update a software module

of 350 lines needs to transfer about 1.2 KB data which

consumes 22.64mJ, while rebuilding the program on the node

needs to consume 27.1mJ. In rebuilding process, codes are

assembled with old program to create a new version in the

external flash, and then the new version program about 22kB is

written back to the program flash. The rebuilding energy

overhead is mainly produced by read/write operation of flash

ROM. Table 1 shows the overheads of writing/reading

different memory and the overhead of transmission and

receiving in Telosb sensor node. Per-byte rebuilding overhead

is about 10% of disseminating overhead, but the sum size of

rebuilding data possibly reaches 1-2 orders of magnitude of the

transferring data.

Fig 1. The WSN system for relic protection in Forbidden City

Table 1.

Average energy overhead (uJ) with 1kB: Telosb [10]

Operation

Average

overhea

Operation

Average

overhea

Radio listen 2480 Transmit date 16590

Read external flash 1015 Write external flash 2458

Read program Flash 785 Write program flash 1850

Read RAM 50< Write RAM 126

To minimize the rebuilding overhead, we propose a

lightweight incremental reprogramming approach named

LiREP. In this approach, the in-situ code modification is

proposed instead of rebuilding program. The target node is able

to directly assemble the delta code into old program without

changing the storing address of the old codes. The in-situ

modification does not require participation of external flash,

and its overhead is mainly caused by read/write operation of

program flash and RAM in chip.

As the cost of refraining from rebuilding program in

external flash, in-situ modification produces useless redundant

2016 International Conference on Computing, Networking and Communications, Wireless Ad Hoc and Sensor Networks

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