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Taming the underlying challenges of reliable multihop routing in sensor networks

Woo, Alec, Tong, Terence, Culler, David·2003·Proceedings of the 1st international conference on Embedded networked sensor systems

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.

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Metadata

Authors
Woo, A., Tong, T., & Culler, D.
Venue
Proceedings of the 1st international conference on Embedded networked sensor systems
Publisher
ACM
Published
2003-11-05
Pages
14-27
OpenAlex
W2169458211
Cited by
1,512
References
0
FWCI
51.54
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Taming the underlying challenges of reliable multihop routing in sensor networks. https://app.cochat.ai/papers/10.1145/958491.958494
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Woo, A., Tong, T., & Culler, D. (2003). Taming the underlying challenges of reliable multihop routing in sensor networks. Proceedings of the 1st international conference on Embedded networked sensor systems. https://doi.org/10.1145/958491.958494

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.