無線感測網路擁有廣泛的應用，例如野生動物觀察，環境偵測與建築物結構偵測。因為大部分的無線感測網路都布建於較難到達之處，因此我們使用無線網路更新制來更新感測器上之軟體以管理這些應用程式的功能。
在此篇論文之前的研究都把重點放在用最少時間完成更新機制或是用最少耗能更新無線感測網路。我們的論文提出一個新的方向，網路更新機制應考慮各個感測器之剩餘能量作為挑選轉達節點的參考。若網路更新機制利用能量將要耗盡之感測器作為轉達節點，則會使此感測器因能量耗盡而無法使用進而影響未來應用程式之執行。因此我們設計一個可以使無線感測網路中剩餘能量最少的感測器最大化的網路更新機制。
更新機制中最主要的問題來自於訊息衝突與隱藏性衝突的問題，我們提出一個傳送者推舉的機制藉以解決前述問題並且能將最小剩餘能量的感測器最大化。傳送者推舉機制能協調附近的傳送者候選人並且依據其接收者個數以及其本身所剩餘之能量來選擇最適合的感測器廣播資料。我們利用流水線法以加快資料傳遞速度以及強迫閒置的感測器進入睡眠模式以達到節省耗能的目的。
網路更新機制通常會使用差異壓縮法以減少無線感測網路的耗能，而我們的論文同時著重在解決使用差異壓縮法會產生的問題。因為之前的研究忽略了解壓縮腳本所產生的額外負擔，使用差異演算法的網路更新機制有可能比不使用差異演算法更耗電。我們的網路更新通訊協定可以避免此情況的發生並且將使用差異性演算法可能產生的副作用減到最小。

Wireless sensor networks have a wide range of applications, such as wildlife tracking, environmental monitoring and building monitoring.
Because WSNs are usually deployed at the places which are not easily accessible, we use wireless network reprogramming to manage the functions of these applications and update the software which is executed in WSNs.
The former researches focus on minimizing the completion time of network reprogramming or minimizing the energy consumption of WSNs.
This paper proposes the new direction which is that the choice of relay nodes should base on the remnant energy of sensor node during the process of network reprogramming.
If we choose the sensor node which is almost out of energy to be our relay node, it may cause the node to out of energy and this situation may affect the normal execution of network reprogramming.
Hence, we design a new multihop network reprogramming protocol which maximizes the minimal remnant energy of sensor node.
The main problems in the network reprogramming are the issue of message collision and hidden terminal problem.
We propose a sender selection mechanism which not only reduces these problems but also maximizes the minimal remnant energy of sensor nodes.
The sender selection mechanism can coordinate the sender candidates of neighborhood and choose the best sender which is decided by its receivers and remnant energy to broadcast data.
We use pipelining to enable fast data propagation and put the node into sleep mode when sensor node is idle for the purpose of reducing energy consumption.
This research also focuses on solving the problems of using delta compression algorithms which are usually used during the process of network reprogramming for the purpose of minimize energy consumption of WSNs.
Because former researches ignore the overhead of decompressing script, delta compression based network reprogramming may cause worse energy consumption than without using the algorithm.
Our network reprogramming protocol can avoid the occurrence of this situation and minimize the drawback of using delta compression algorithm.
We estimate the energy consumption of using delta compression algorithm during the process of network reprogramming and choose the better way which consumes less energy to transmit data.

[1] R. Balani, C.-C. Han, R. K. Rengaswamy, I. Tsigkogiannis, and M. Srivastava. Multi-
level software recon‾guration for sensor networks. In EMSOFT '06: Proceedings of
the 6th ACM & IEEE International conference on Embedded software, pages 112{
121, New York, NY, USA, 2006. ACM Press.
[2] N. M. D. Trenda‾low and T. Suel. zdelta: An e±cient delta compression tool.
Polytechnic University, 2002. Technical report.
[3] C.-C. Han, R. Kumar, R. Shea, E. Kohler, and M. Srivastave. A dynamic operating
system for sensor nodes. In International Conference On Mobile Systems, Applica-
tions And Services, Seattle, Washington, pages 163{176, 2005.
[4] J. Hill, R. Szewczyk, A. Woo, S. Hollar, D. E. Culler, and K. S. J. Pister. System
architecture directions for networked sensors. In Architectural Support for Program-
ming Languages and Operating Systems, pages 93{104, 2000.
[5] J. W. Hui and D. Culler. The dynamic behavior of a data dissemination protocol for
network programming at scale. In Proceedings of the 2nd international conference
on Embedded networked sensor systems (SenSys 2004), pages 81{94, 2004.
[6] J. Jeong and D. Culler. Incremental network programming for wireless sensors. In
Proceedings of the First IEEE Comminucations Society Conference on Sensor and
Ad Hoc Communications and Networks, 2004.
[7] J. Koshy and R. Paudey. Remote incremental linking for energy-e±cient repro-
gramming of sensor network. In Proceedings of the Second European Workshop on
Wireless Sensor Networks (EWSN 2006), pages 354{365, 2005.
[8] S. S. Kulkarni and L. Wang. Mnp: Multihop network reprogramming service for
sensor networks. In 25th International Conference on Distributed Computing Sys-
tems(ICDCS 2005), pages 7{16, 2005.
[9] P. Levis and D. Culler. Mate: A tiny virtual machine for sensor networks. In
International Conference on Architectural Support for Programming Languages and
Operating Systems, San Jose, CA, USA, Oct. 2002.
[10] P. Levis, N. Lee, M. Welsh, and D. Culler. Tossim: accurate and scalable simulation
of entire tinyos applications. In SenSys '03: Proceedings of the 1st international
conference on Embedded networked sensor systems, pages 126{137, New York, NY,
USA, 2003. ACM Press.
[11] P. Levis, N. Patel, D. Culler, and S. Shenker. Trickle: A self-regulating algorithm
for code propagation and maintenance in wireless sensor networks. In In First Sym-
posium on Network Systems Design and Implementation (NSDI),Mar. 2004., 2004.
[12] P. J. Marr□on, M. Gauger, A. Lachenmann, D. Minder, O. Saukh, and K. Rothermel.
Flexcup: A °exible and e±cient code update mechanism for sensor networks. In
Proceedings of the Third European Workshop on Wireless Sensor Networks (EWSN
2006), pages 212{227, February 2006.
[13] V. Naik, A. Arora, P. Sinha, and H. Zhang. Sprinkler: A reliable and energy e±cient
data dissemination service for wireless embedded devices. In RTSS '05: Proceed-
ings of the 26th IEEE International Real-Time Systems Symposium, pages 277{286,
Washington, DC, USA, 2005. IEEE Computer Society.
[14] M. I.-N. P. U. Reference. http://www.tinyos.net/tinyos-1.x/doc/xnp.pdf.
[15] N. Reijers and K. Langendoen. E±cient code distribution in wireless sensor networks.
In Proceedings of the 2nd ACM international conference on Wireless sensor networks
and applications, pages 60{67, 2003.
[16] V. Shnayder, M. Hempstead, B. rong Chen, G. W. Allen, and M. Welsh. Simu-
lating the power consumption of large-scale sensor network applications. In SenSys
'04: Proceedings of the 2nd international conference on Embedded networked sensor
systems, pages 188{200, New York, NY, USA, 2004. ACM Press.
[17] T. Stathopoulos, J. Heidemann, and D. Estrin. A remote code update mechanism
for wireless sensor networks. Technical Report UCLA CENS Technical Report, 2003.
[18] A. Tridgell. E±cient algorithms for sorting and synchronization. Technical Report
PhD thesis, Australian National University, 1999.
[19] Y.-C. Tseng, S.-Y. Ni, Y.-S. Chen, and J.-P. Sheu. The broadcast storm problem
in a mobile ad hoc network. volume 8, pages 153{167, Hingham, MA, USA, 2002.
Kluwer Academic Publishers.
[20] C. von Platen and J. Eker. Feedback linking: optimizing object code layout for up-
dates. In LCTES '06: Proceedings of the 2006 ACM SIGPLAN/SIGBED conference
on Language, compilers and tool support for embedded systems, pages 2{11, New
York, NY, USA, 2006. ACM Press.
[21] R. Xu, Z. Li, C. Wang, and P. Ni. Impact of data compression on energy con-
sumption of wireless-networked handheld devices. In Proceedings of the 23rd IEEE
International Conference on Distributed Computing Systems (ICDCS'03), May 2003.