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Taming the underlying challenges of reliable multihop routing in sensor networks
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Source Conference On Embedded Networked Sensor Systems archive
Proceedings of the 1st international conference on Embedded networked sensor systems table of contents
Los Angeles, California, USA
SESSION: Networking experience table of contents
Pages: 14 - 27  
Year of Publication: 2003
ISBN:1-58113-707-9
Authors
Alec Woo  University of California, Berkeley, California
Terence Tong  University of California, Berkeley, California
David Culler  University of California & Intel Corporation, Berkeley, California
Sponsors
SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques
ACM: Association for Computing Machinery
SIGCOMM: ACM Special Interest Group on Data Communication
SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing
SIGMETRICS: ACM Special Interest Group on Measurement and Evaluation
SIGOPS: ACM Special Interest Group on Operating Systems
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 35,   Downloads (12 Months): 384,   Citation Count: 188
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ABSTRACT

The dynamic and lossy nature of wireless communication poses major challenges to reliable, self-organizing multihop networks. These non-ideal characteristics are more problematic with the primitive, low-power radio transceivers found in sensor networks, and raise new issues that routing protocols must address. Link connectivity statistics should be captured dynamically through an efficient yet adaptive link estimator and routing decisions should exploit such connectivity statistics to achieve reliability. Link status and routing information must be maintained in a neighborhood table with constant space regardless of cell density. We study and evaluate link estimator, neighborhood table management, and reliable routing protocol techniques. We focus on a many-to-one, periodic data collection workload. We narrow the design space through evaluations on large-scale, high-level simulations to 50-node, in-depth empirical experiments. The most effective solution uses a simple time averaged EWMA estimator, frequency based table management, and cost-based routing.


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.

 
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CITED BY  188

Collaborative Colleagues:
Alec Woo: colleagues
Terence Tong: colleagues
David Culler: colleagues