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Mitigating congestion in wireless sensor networks
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Source Conference On Embedded Networked Sensor Systems archive
Proceedings of the 2nd international conference on Embedded networked sensor systems table of contents
Baltimore, MD, USA
SESSION: Congestion table of contents
Pages: 134 - 147  
Year of Publication: 2004
ISBN:1-58113-879-2
Authors
Bret Hull  MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA
Kyle Jamieson  MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA
Hari Balakrishnan  MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA
Sponsors
SIGARCH: ACM Special Interest Group on Computer Architecture
SIGBED: ACM Special Interest Group on Embedded Systems
ACM: Association for Computing Machinery
SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing
SIGCOMM: ACM Special Interest Group on Data Communication
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): 28,   Downloads (12 Months): 229,   Citation Count: 50
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ABSTRACT

Network congestion occurs when offered traffic load exceeds available capacity at any point in a network. In wireless sensor networks, congestion causes overall channel quality to degrade and loss rates to rise, leads to buffer drops and increased delays (as in wired networks), and tends to be grossly unfair toward nodes whose data has to traverse a larger number of radio hops.

Congestion control in wired networks is usually done using end-to-end and network-layer mechanisms acting in concert. However, this approach does not solve the problem in wireless networks because concurrent radio transmissions on different "links" interact with and affect each other, and because radio channel quality shows high variability over multiple time-scales. We examine three techniques that span different layers of the traditional protocol stack: hop-by-hop flow control, rate limiting source traffic when transit traffic is present, and a prioritized medium access control (MAC) protocol. We implement these techniques and present experimental results from a 55-node in-building wireless sensor network. We demonstrate that the combination of these techniques, Fusion, can improve network efficiency by a factor of three under realistic workloads.


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  50

Collaborative Colleagues:
Bret Hull: colleagues
Kyle Jamieson: colleagues
Hari Balakrishnan: colleagues