Modeling and Worst-Case Dimensioning of Cluster-Tree Wireless Sensor Networks

Time-sensitive wireless sensor network (WSN) applications require finite delay bounds in critical situations. This paper provides a methodology for the modeling and the worst-case dimensioning of cluster-tree WSNs. We provide a fine model of the worst-case cluster-tree topology characterized by its depth, the maximum number of child routers and the maximum number of child nodes for each parent router. Using Network Calculus, we derive "plug-and-play " expressions for the end-to-end delay bounds, buffering and bandwidth requirements as a function of the WSN cluster-tree characteristics and traffic specifications. The cluster-tree topology has been adopted by many cluster-based solutions for WSNs. We demonstrate how to apply our general results for dimensioning IEEE 802.15.4/Zigbee cluster-tree WSNs. We believe that this paper shows the fundamental performance limits of cluster-tree wireless sensor networks by the provision of a simple and effective methodology for the design of such WSNs

[1]  Jerome P. Lynch,et al.  Two-tiered wireless sensor network architecture for structural health monitoring , 2003, SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.

[2]  Luciano Lenzini,et al.  Tight end-to-end per-flow delay bounds in FIFO multiplexing sink-tree networks , 2006, Perform. Evaluation.

[3]  Mohamed F. Younis,et al.  Fault-tolerant clustering of wireless sensor networks , 2003, 2003 IEEE Wireless Communications and Networking, 2003. WCNC 2003..

[4]  Utz Roedig,et al.  Sensor Network Calculus - A Framework for Worst Case Analysis , 2005, DCOSS.

[5]  Utz Roedig,et al.  Sensor Network Calculus with Multiple Sinks , 2006 .

[6]  Yoonmee Doh,et al.  Scheduling support for guaranteed time services in IEEE 802.15.4 low rate WPAN , 2005, 11th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA'05).

[7]  Tarek F. Abdelzaher,et al.  On real-time capacity limits of multihop wireless sensor networks , 2004, 25th IEEE International Real-Time Systems Symposium.

[8]  Luciano Lenzini,et al.  Delay bounds for FIFO aggregates: a case study , 2005, Comput. Commun..

[9]  Eduardo Tovar,et al.  GTS allocation analysis in IEEE 802.15.4 for real-time wireless sensor networks , 2006, Proceedings 20th IEEE International Parallel & Distributed Processing Symposium.

[10]  J. Boudec,et al.  NETWORK CALCULUS Parts II and III A Theory of Deterministic Queuing Systems for the Internet , 2004 .

[11]  A. Koubaa,et al.  Evaluation and improvement of response time bounds for real-time applications under non-pre-emptive Fixed Priority Scheduling , 2004 .

[12]  Utz Roedig,et al.  Worst case dimensioning of wireless sensor networks under uncertain topologies , 2005 .

[13]  Eduardo Tovar,et al.  IEEE 802.15.4: a Federating Communication Protocol for Time-Sensitive Wireless Sensor Networks , 2006 .

[14]  Lui Sha,et al.  Real-time communication and coordination in embedded sensor networks , 2003, Proc. IEEE.

[15]  Wendi Heinzelman,et al.  Energy-efficient communication protocol for wireless microsensor networks , 2000, Proceedings of the 33rd Annual Hawaii International Conference on System Sciences.

[16]  Eduardo Tovar,et al.  i-GAME: an implicit GTS allocation mechanism in IEEE 802.15.4 for time-sensitive wireless sensor networks , 2006, 18th Euromicro Conference on Real-Time Systems (ECRTS'06).

[17]  Jean-Yves Le Boudec,et al.  Network Calculus: A Theory of Deterministic Queuing Systems for the Internet , 2001 .

[18]  C. Chien,et al.  Low power TDMA in large wireless sensor networks , 2001, 2001 MILCOM Proceedings Communications for Network-Centric Operations: Creating the Information Force (Cat. No.01CH37277).