ACM Home Page
Please provide us with feedback. Feedback
Model-based validation of QoS properties of biomedical sensor networks
Full text PdfPdf (420 KB)
Source
International Conference On Embedded Software archive
Proceedings of the 8th ACM international conference on Embedded software table of contents
Atlanta, GA, USA
SESSION: Modeling, interfaces, and simulation table of contents
Pages 69-78  
Year of Publication: 2008
ISBN:978-1-60558-468-3
Authors
Simon Tschirner  Uppsala University, Uppsala, Sweden
Liang Xuedong  Rikshospitalet University Hospital and University of Oslo, Oslo, Norway
Wang Yi  Uppsala University, Uppsala, Sweden
Sponsors
ACM: Association for Computing Machinery
SIGBED: ACM Special Interest Group on Embedded Systems
SIGMICRO: ACM Special Interest Group on Microarchitectural Research and Processing
SIGDA: ACM Special Interest Group on Design Automation
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 15,   Downloads (12 Months): 147,   Citation Count: 0
Additional Information:

abstract   references   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/10.1145/1450058.1450069
What is a DOI?

ABSTRACT

A Biomedical Sensor Network (BSN) is a small-size sensor network for medical applications, that may contain tens of sensor nodes. In this paper, we present a formal model for BSNs using timed automata, where the sensor nodes communicate using the Chipcon CC2420 transceiver (developed by Texas Instruments) according to the IEEE 802.15.4 standard. Based on the model, we have used UPPAAL to validate and tune the temporal configuration parameters of a BSN in order to meet desired QoS requirements on network connectivity, packet delivery ratio and end-to-end delay. The network studied allows dynamic reconfigurations of the network topology due to the temporally switching of sensor nodes to power-down mode for energy-saving or their physical movements. Both the simulator and model-checker of UPPAAL are used to analyze the average-case and worst-case behaviors. To enhance the scalability of the tool, we have implemented a (new text-based) version of the UPPAAL simulator optimized for exploring symbolic traces of automata containing large data structures such as matrices. Our experiments show that even though the main feature of the tool is model checking, it is also a promising and competitive tool for efficient simulation and parameter tuning. The simulator scales well; it can easily handle up to 50 nodes in our experiments. The model checker installed on a notebook can also deal with networks with 5 up to 16 nodes within minutes depending on the properties checked; these are BSNs of reasonable size for medical applications. Finally, to study the accuracy of our model and analysis results, we compare simulation results by UPPAAL for two medical scenarios with traditional simulation techniques using OMNeT++, one of the most used simulation tools for wireless sensor networks. The comparison shows that our analysis results coincide with the simulation results by OMNeT++ in most cases although there are some differences caused the simplified wireless channel model in UPPAAL.


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
G. Behrmann, A. David, and K. G. Larsen. A tutorial on uppaal. In M. Bernardo and F. Corradini, editors, Formal Methods for the Design of Real-Time Systems: 4th International School on Formal Methods for the Design of Computer, Communication, and Software Systems, SFM-RT 2004, number 3185 in LNCS, pages 200--236. Springer-Verlag, September 2004.
 
3
J. Bengtsson and W. Yi. Timed Automata: Semantics, Algorithms and Tools. Lecture Notes on Concurrency and Petri Nets, LNCS 3098:87--124, 2004.
 
4
D. Chen and P. K. Varshney. QoS support in wireless sensor networks: A survey. In Proc. of the 2004 International Conference on Wireless Networks (ICWN'04), pages 227--233, Las Vegas, Nevada, USA, June 2004. CSREA Press.
 
5
A. Fehnker, L. F. W. van Hoesel, and A. H. Mader. Modelling and verification of the lmac protocol for wireless sensor networks. Technical Report TR-CTIT-07-09, Centre for Telematics and Information Technology, University of Twente, Enschede, February 2007.
 
6
 
7
Y. Guang-Zhong, editor. Body Sensor Networks. Springer, New York, 2006.
 
8
IEEE Standard 802.15.4. Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low-Rate Wireless Personal Area Networks (LR-WPANs), 2003.
 
9
O. Landsiedel, K. Wehrle, B. Titzer, and J. Palsberg. Enabling detailed modeling and analysis of sensor networks. Praxis der Informationsverarbeitung und Kommunikation, 28(2):101--106, April 2005.
 
10
K. G. Larsen, P. Pettersson, and W. Yi. Uppaal in a Nutshell. Int. Journal on Software Tools for Technology Transfer, 1(1--2):134--152, October 1997.
 
11
X. Liang, B. Østvold, W. Leister, and I. Balasingham. Credo: Modeling and analysis of evolutionary structures for distributed services - user driven requirements, March 2007. Diliverable D6.1, EU IST project, number 33826.
 
12
F. Osterlind, A. Dunkels, J. Eriksson, N. Finneand, and T. Voigt. Cross-level sensor network simulation with COOJA. In Proc. of the 31st IEEE Conference on Local Computer Networks, pages 641--648, Tampa, Florida, USA, November 2006.
13
 
14
H. N. Pham, D. Pediaditakis, and A. Boulis. From simulation to real deployments in WSN and back. In Proc. of the 8th IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM'07), pages 1--6, Helsinki, Finland, June 2007.
 
15
J. Polley, D. Blazakis, J. McGee, D.Rusk, and J. Baras. ATEMU: a fine-grained sensor network simulator. In Proc. of the 1st IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks (SECON'04), pages 145--152, Los Angeles, California, USA, October 2004. IEEE Press.
 
16
Texas Instruments Inc. 2.4 GHz IEEE 802.15.4 / ZigBee-Ready RF Transceiver (Rev. B), CC2420 data sheet, March 2007.
 
17
 
18
S. Tschirner, L. Xuedong, and W. Yi. Model-based validation of qos properties of biomedical sensor networks. Technical report, Department of Information Technology, University of Uppsala, 2008.
 
19
A. Varga. The OMNeT++ discrete event simulation system. In Proc. of the 15th European Simulation Multiconference (ESM'01), pages 319--324, Prague, Czech Republic, June 2001. SCS.

Collaborative Colleagues:
Simon Tschirner: colleagues
Liang Xuedong: colleagues
Wang Yi: colleagues