Article

The Tenet architecture for tiered sensor networks

Authors:

Omprakash Gnawali,

Jeongyeup Paek,

Ramesh Govindan,

Ben Greenstein,

Deborah Estrin,

Eddie KohlerAuthors Info & Claims

SenSys '06: Proceedings of the 4th international conference on Embedded networked sensor systems

Pages 153 - 166

https://doi.org/10.1145/1182807.1182823

Published: 31 October 2006 Publication History

Abstract

Most sensor network research and software design has been guided by an architectural principle that permits multi-node data fusion on small-form-factor, resource-poor nodes, or motes. We argue that this principle leads to fragile and unmanageable systems and explore an alternative. The Tenet architecture is motivated by the observation that future large-scale sensor network deployments will be tiered, consisting of motes in the lower tier and masters, relatively unconstrained 32-bit platform nodes, in the upper tier. Masters provide increased network capacity. Tenet constrains multi-node fusion to the master tier while allowing motes to process locally-generated sensor data. This simplifies application development and allows mote-tier software to be reused. Applications running on masters task motes by composing task descriptions from a novel tasklet library. Our Tenet implementation also contains a robust and scalable networking subsystem for disseminating tasks and reliably delivering responses. We show that a Tenet pursuit-evasion application exhibits performance comparable to a mote-native implementation while being considerably more compact.

References

[1]

The MIT Roofnet Project. http://pdos.csail.mit.edu/roofnet/.

[2]

A. Arora et al. ExScal: Elements of an extreme scale wireless sensor network. In Proc. 11th IEEE International Conference on Real-Time and Embedded Computing Systems and Applications (RTCSA '05), August 2005.

Digital Library

[3]

R.A. Brooks. Solving the find-path problem by good representation of free space. IEEE Transactions on Systems, Man and Cybernetics, 13(3):190--197, Mar./Apr. 1983.

[4]

N. Bulusu, J. Heidemann, and D. Estrin. GPS-less low cost outdoor localization for very small devices. IEEE Personal Communications, 7(5):28--34, Oct. 2000.

[5]

D. Culler et al. Towards a sensor network architecture: Lowering the waistline. In Proc. 10th Hot Topics in Operating Systems Symposium (HotOS-X), pages 139--144, June 2005.

Digital Library

[6]

D. Estrin, R. Govindan, J. Heidemann, and S. Kumar. Next century challenges: Scalable coordination in sensor networks. In Proc. 5th Annual International Conference on Mobile Computing and Networking (MobiCom '99), pages 263--270, Aug. 1999.

Digital Library

[7]

D. Gay et al. The nesC language: A holistic approach to networked embedded systems. In Proc. ACM SIGPLAN 2003 Conference on Programming Language Design and Implementation (PLDI), pages 1--11, June 2003.

Digital Library

[8]

B. Greenstein, E. Kohler, and D. Estrin. A sensor network application construction kit (SNACK). In Proc. 2nd ACM Conference on Embedded Networked Sensor Systems (SenSys 2004), pages 69--80, Nov. 2004.

Digital Library

[9]

B. Greenstein et al. Capturing high-frequency phenomena using a bandwidth-limited sensor network. In Proc. 4th ACM Conference on Embedded Networked Sensor Systems (SenSys '06), Nov. 2006. To appear.

Digital Library

[10]

R. Guy et al. Experiences with the Extensible Sensing System ESS. Technical Report 61, CENS, Mar. 29 2006.

[11]

C.-C. Han, R. Kumar, R. Shea, E. Kohler, and M. Srivastava. A dynamic operating system for sensor nodes. In Proc. 3rd International Conference on Mobile Systems, Applications, and Services (MobiSys '05), June 2005.

Digital Library

[12]

J. Hill et al. System architecture directions for network sensors. In Proc. 9th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS-IX), pages 93--104, Nov. 2000.

Digital Library

[13]

J. Hui and D. Culler. The dynamic behavior of a data dissemination algorithm at scale. In Proc. 2nd ACM Conference on Embedded Networked Sensor Systems (SenSys 2004), Nov. 2004.

Digital Library

[14]

C. Intanagonwiwat, R. Govindan, and D. Estrin. Directed diffusion: A scalable and robust communication paradigm for sensor networks. In Proc. 6th Annual International Conference on Mobile Computing and Networking (MobiCom 2000), pages 56--67, Aug. 2000.

Digital Library

[15]

V.A. Kottapalli et al. Two-tiered wireless sensor network architecture for structural health monitoring. In S.-C. Liu, editor, Proc. SPIE 5057-Smart Structures and Materials 2003: Smart Systems and Non-destructive Evaluation for Civil Infrastructures, Aug. 2003.

[16]

B.J. Kuipers and Y.-T. Byun. A robust qualitative method for spatial learning in unknown environments. In Proc. 7th National Conference on Artificial Intelligence (AAAI-88), July 1988.

[17]

P. Levis and D. Culler. Maté: A tiny virtual machine for sensor networks. In Proc. 10th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS-X), pages 85--95, Oct. 2002.

Digital Library

[18]

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. 1st Symposium on Networked Systems Design and Implementation (NSDI '04), Mar. 2004.

Digital Library

[19]

P. Levis, D. Gay, and D. Culler. Active sensor networks. In Proc. 2nd Symposium on Networked Systems Design & Implementation (NSDI '05), May 2005.

Digital Library

[20]

B. Liu, Z. Liu, and D. Towsley. On the capacity of hybrid wireless networks. In Proc. 22nd Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Infocom 2003), Mar. 2003.

[21]

J. Liu and F. Zhao. Towards semantic services for sensor-rich information systems. In Proc. 2nd International Conference on Broadband Networks (BROADNETS 2005), pages 44--51, Oct. 2005.

[22]

J. Liu, E. Cheong, and F. Zhao. Semantics-based optimization across uncoordinated tasks in networked embedded systems. In Proc. 5th ACM Conference on Embedded Software (EMSOFT 2005), Sept. 2005.

Digital Library

[23]

S. Madden, M.J. Franklin, J.M. Hellerstein, and W. Hong. TAG: A Tiny AGgregation service for ad-hoc sensor networks. In Proc. 5th Symposium on Operating Systems Design and Implementation (OSDI '02), Dec. 2002.

Digital Library

[24]

V.P. Mhatre, C. Rosenberg, D. Kofman, R. Mazumdar, and N. Shroff. A minimum cost heterogeneous sensor network with a lifetime constraint. IEEE Transactions on Mobile Computing, 4(1):4--15, Jan./Feb. 2005.

Digital Library

[25]

J. Paek, K. Chintalapudi, J. Cafferey, R. Govindan, and S. Masri. A wireless sensor network for structural health monitoring: Performance and experience. In Proc. 2nd IEEE Workshop on Embedded Networked Sensors (EmNetS-II), May 2005.

Digital Library

[26]

J. Polastre et al. A unifying link abstraction for wireless sensor networks. In Proc. 3rd ACMConference on Embedded Networked Sensor Systems (SenSys '05), pages 76--89, Nov. 2005.

Digital Library

[27]

J. Polastre, R. Szewczyk, and D. Culler. Telos: Enabling ultra-low power wireless research. In Proc. 4th International Symposium on Information Processing in Sensor Networks (IPSN '05), SPOTS track, Apr. 2005.

Digital Library

[28]

S. Ratnasamy et al. GHT: A geographic hash table for data-centric storage. In Proc. 1st ACM International Workshop on Wireless Sensor Networks and Applications (WSNA), pages 78--87, Sept. 2002.

Digital Library

[29]

S. Rhee, D. Seetharam, and S. Liu. Techniques for minimizing power consumption in low data-rate wireless sensor networks. In Proc. IEEE Wireless Communications and Networking Conference (WCNC 2004), Mar. 2004.

[30]

J.H. Saltzer, D.P. Reed, and D.D. Clark. End-to-end arguments in system design. ACM Transactions on Computer Systems, 2(4):277, Nov. 1984.

Digital Library

[31]

C. Sharp et al. Design and implementation of a sensor network system for vehicle tracking and autonomous interception. In Proc. 2nd European Workshop on Wireless Sensor Networks (EWSN), Jan. 2005.

[32]

F. Stann and J. Heidemann. RMST: Reliable data transport in sensor networks. In Proc. 1st IEEE Workshop on Sensor Network Protocols and Applications (SNPA), May 2003.

[33]

T. Stathopoulos, J. Heidemann, and D. Estrin. A remote code update mechanism for wireless sensor networks. Technical Report 30, CENS, Nov. 26 2003.

[34]

T. Stathopoulos, L. Girod, J. Heidemann, and D. Estrin. Mote herding for tiered wireless sensor networks. Technical Report 58, CENS, Dec. 7 2005.

[35]

R. Szewczyk, A. Mainwaring, J. Polastre, J. Anderson, and D. Culler. An analysis of a large scale habitat monitoring application. In Proc. 2nd ACM Conference on Embedded Networked Sensor Systems (SenSys 2004), pages 214--226, Nov. 2004.

Digital Library

[36]

D.L. Tennenhouse and D.J. Wetherall. Towards an active network architecture. Computer Communication Review, 26(2):5--17, Apr. 1996.

Digital Library

[37]

C.Y. Wan, A.T. Campbell, and L. Krishnamurthy. PSFQ: A reliable transport protocol for wireless sensor networks. In Proc. 1st ACM International Workshop on Wireless Sensor Networks and Applications (WSNA), Sept. 2002.

Digital Library

[38]

A. Woo and D. Culler. Evaluation of efficient link reliability estimators for low-power wireless networks. Technical Report CSD-03-1270, University of California, Berkeley, Apr. 2003.

[39]

N. Xu et al. A wireless sensor network for structural monitoring. In Proc. 2nd ACM Conference on Embedded Networked Sensor Systems (SenSys 2004), Nov. 2004.

Digital Library

[40]

M. Yarvis et al. Exploiting heterogeneity in sensor networks. In Proc. 24th Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Infocom 2005), Mar. 2005.

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Published In

cover image ACM Conferences

SenSys '06: Proceedings of the 4th international conference on Embedded networked sensor systems

October 2006

444 pages

ISBN:1595933433

DOI:10.1145/1182807

General Chair:
Andrew Campbell
Dartmouth University
,
Program Chairs:
Philippe Bonnet
University of Copenhagen (DIKU), Denmark
,
John Heidemann
University of Southern California/ISI, USA

Copyright © 2006 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 31 October 2006

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