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Abstract

In the new era of Ambient Intelligence, wireless sensor networks (WSNs) are seen to bridge the gap between physical world and the Internet, making a large amount of information accessible anywhere, anytime. Over the last few years, WSNs are being developed towards a large number of multimedia streaming applications, e.g. video surveillance, traffic control systems, health monitoring, and industrial process control. WSNs consist of small sensor devices (nodes) that are capable of working unattended, without centralized control, under dynamically changing conditions. However, these devices face important limitations in terms of energy, memory and computational power. The uncontrolled use of limited resources in conjunction with the unpredictable nature of WSNs in terms of traffic load injection, wireless link capacity fluctuations and topology modifications may lead to congestion. Congestion can cause increased packet loss and delay. This paper proposes a bio-inspired congestion control approach for WSNs streaming applications that necessitate controlled performance with graceful degradation. In the proposed approach, congestion in WSNs is prevented (or at least minimized) by regulating the rate of each traffic flow based on the Lotka-Volterra competition model. Performance evaluations reveal that the proposed approach achieves adaptability to changing traffic loads, scalability and fairness among flows, while providing graceful performance degradation as the offered load increases. © 2010 Elsevier B.V. All rights reserved. 33 17 2039 2047

Cited By :20

Original document

The different versions of the original document can be found in:

• http://dx.doi.org/10.1016/j.comcom.2010.07.020

• http://gnosis.library.ucy.ac.cy/handle/7/53562

• https://api.elsevier.com/content/article/PII:S0140366410003488?httpAccept=text/xml,

https://api.elsevier.com/content/article/PII:S0140366410003488?httpAccept=text/plain,

http://dx.doi.org/10.1016/j.comcom.2010.07.020 under the license https://www.elsevier.com/tdm/userlicense/1.0/

• https://www.sciencedirect.com/science/article/pii/S0140366410003488,

https://dblp.uni-trier.de/db/journals/comcom/comcom33.html#AntoniouP10,

http://www.cs.ucy.ac.cy/ResearchLabs/netrl/papers/files/PAntoniouCOMCOM4339_FINAL.pdf,

https://doi.org/10.1016/j.comcom.2010.07.020,

https://academic.microsoft.com/#/detail/2041211694