ACM Home Page
Please provide us with feedback. Feedback
Run-time dynamic linking for reprogramming wireless sensor networks
Full text PdfPdf (240 KB)
Source Conference On Embedded Networked Sensor Systems archive
Proceedings of the 4th international conference on Embedded networked sensor systems table of contents
Boulder, Colorado, USA
SESSION: Operating systems table of contents
Pages: 15 - 28  
Year of Publication: 2006
ISBN:1-59593-343-3
Authors
Adam Dunkels  Swedish Institute of Computer Science
Niclas Finne  Swedish Institute of Computer Science
Joakim Eriksson  Swedish Institute of Computer Science
Thiemo Voigt  Swedish Institute of Computer Science
Sponsors
SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing
SIGCOMM: ACM Special Interest Group on Data Communication
SIGOPS: ACM Special Interest Group on Operating Systems
SIGMETRICS: ACM Special Interest Group on Measurement and Evaluation
ACM: Association for Computing Machinery
SIGBED: ACM Special Interest Group on Embedded Systems
SIGARCH: ACM Special Interest Group on Computer Architecture
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 23,   Downloads (12 Months): 149,   Citation Count: 19
Additional Information:

abstract   references   cited by   index terms   collaborative colleagues  

Tools and Actions: Request Permissions Request Permissions    Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/1182807.1182810
What is a DOI?

ABSTRACT

From experience with wireless sensor networks it has become apparent that dynamic reprogramming of the sensor nodes is a useful feature. The resource constraints in terms of energy, memory, and processing power make sensor network reprogramming a challenging task. Many different mechanisms for reprogramming sensor nodes have been developed ranging from full image replacement to virtual machines.We have implemented an in-situ run-time dynamic linker and loader that use the standard ELF object file format. We show that run-time dynamic linking is an effective method for reprogramming even resource constrained wireless sensor nodes. To evaluate our dynamic linking mechanism we have implemented an application-specific virtual machine and a Java virtual machine and compare the energy cost of the different linking and execution models. We measure the energy consumption and execution time overhead on real hardware to quantify the energy costs for dynamic linkin.Our results suggest that while in general the overhead of a virtual machine is high, a combination of native code and virtual machine code provide good energy efficiency. Dynamic run-time linking can be used to update the native code, even in heterogeneous networks.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

1
 
2
Chipcon AS. CC2420 Datasheet (rev. 1.3), 2005. http://www.chipcon.com/
 
3
TIS Committee. Tool Interface Standard (TIS) Executable and Linking Format (ELF) Specification Version 1.2, May 1995.
4
 
5
A. Dunkels, B. Grönvall, and T. Voigt. Contiki - a lightweight and flexible operating system for tiny networked sensors. In Proceedings of the First IEEE Workshop on Embedded Networked Sensors, Tampa, Florida, USA, November 2004.
6
 
7
 
8
 
9
10
 
11
J.A. Gutierrez, M. Naeve, E. Callaway, M. Bourgeois, V. Mitter, and B. Heile. IEEE 802.15.4: A developing standard for low-power low-cost wireless personal area networks. IEEE Network, 15(5):12--19, September/October 2001.
12
13
14
 
15
J. Jeong and D. Culler. Incremental network programming for wireless sensors. In Proceedings of the First IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks IEEE SECON (2004), October 2004.
 
16
J. Jeong, S. Kim, and A. Broad. Network reprogramming. TinyOS documentation, 2003. Visited 2006-04-06. http://www.tinyos.net/tinyos-1.x/doc/NetworkReprogramming.pdf
 
17
J. Koshy and R. Pandey. Remote incremental linking for energy-efficient reprogramming of sensor networks. In Proceedings of the second European Workshop on Wireless Sensor Networks, 2005.
18
19
 
20
P. Levis, D. Gay, and D Culler. Active sensor networks. In Proc. USENIX/ACM NSDI'05, Boston, MA, USA, May 2005.
 
21
P. Levis, N. Patel, D. Culler, and S. Shenker. Trickle: A self-regulating algorithm for code propagation and maintenance in wireless sensor networks. In Proc. NSDI'04, March 2004.
 
22
J. Lilius and I. Paltor. Deeply embedded python, a virtual machine for embedded systems. Web page. 2006-04-06. http://www.tucs.fi/magazin/output.php?ID=2000.N2.LilDeEmPy
23
24
25
26
 
27
P. José Marrón, M. Gauger, A. Lachenmann, D. Minder, O. Saukh, and K. Rothermel. Flexcup: A flexible and efficient code update mechanism for sensor networks. In European Workshop on Wireless Sensor Networks, 2006.
28
 
29
 
30
31
 
32
RF Monolithics. 868.35 MHz Hybrid Transceiver TR1001, 1999. http://www.rfm.com
 
33
 
34
T. Stathopoulos, J. Heidemann, and D. Estrin. A remote code update mechanism for wireless sensor networks. Technical Report CENS-TR-30, University of California, Los Angeles, Center for Embedded Networked Computing, November 2003.
 
35
M. Welsh and G. Mainland. Programming sensor networks using abstract regions. In Proc. USENIX/ACM NSDI'04, San Francisco, CA,, March 2004.
 
36

CITED BY  19

Collaborative Colleagues:
Adam Dunkels: colleagues
Niclas Finne: colleagues
Joakim Eriksson: colleagues
Thiemo Voigt: colleagues