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The impact of data aggregation in wireless sensor networks
Abstract
Sensor networks are distributed event-based systems that differ from traditional communication networks in several ways: sensor networks have severe energy constraints, redundant low-rate data, and many-to-one flows. Data-centric mechanisms that perform in-network aggregation of data are needed in this setting for energy-efficient information flow. In this paper we model data-centric routing and compare its performance with traditional end-to-end routing schemes. We examine the impact of source-destination placement and communication network density on the energy costs and delay associated with data aggregation. We show that data-centric routing offers significant performance gains across a wide range of operational scenarios. We also examine the complexity of optimal data aggregation, showing that although it is an NP-hard problem in general, there exist useful polynomial-time special cases.
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Cited by
Boyd, S., Ghosh, A., Prabhakar, B., & Shah, D. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2006.874516 · Cited by 2,439
Machine learning in wireless sensor networks: Algorithms, strategies, and applications
Alsheikh, M. A., Lin, S., Niyato, D., & Tan, H.-P. (2014) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2014.2320099 · Cited by 965
A Survey of Middleware for Sensor and Network Virtualization
Khalid, Z., Fisal, N., & Rozaini, M. (2014) · Sensors · doi:10.3390/s141224046 · Cited by 36
Metadata
- Authors
- Krishnamachari, L., Estrin, D., & Wicker, S.
- Venue
- Proceedings 22nd International Conference on Distributed Computing Systems Workshops
- Publisher
- IEEE Comput. Soc
- Published
- 2003-06-25
- Pages
- 575-578
- OpenAlex
- W2110173519
- Cited by
- 1,448
- References
- 0
- FWCI
- 45.03
- Type
- conference paper
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
