A stochastic time-domain model for burst data aggregation in IEEE 802.15.4 wireless sensor networks

Haghighi, Mohammad Sayad, Xiang, Yang, Varadharajan, Vijay and Quinn, Barry 2015, A stochastic time-domain model for burst data aggregation in IEEE 802.15.4 wireless sensor networks, IEEE transactions on computers, vol. 64, no. 3, pp. 627-639, doi: 10.1109/TC.2013.2296773.

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Title A stochastic time-domain model for burst data aggregation in IEEE 802.15.4 wireless sensor networks
Author(s) Haghighi, Mohammad Sayad
Xiang, YangORCID iD for Xiang, Yang orcid.org/0000-0001-5252-0831
Varadharajan, Vijay
Quinn, Barry
Journal name IEEE transactions on computers
Volume number 64
Issue number 3
Start page 627
End page 639
Total pages 13
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2015-03
ISSN 0018-9340
Keyword(s) Science & Technology
Computer Science, Hardware & Architecture
Engineering, Electrical & Electronic
Computer Science
Sensor networks
burst traffic
batch traffic arrival
IEEE 802.15.4
medium access control (MAC)
IEEE-802.15.4 MAC
Summary In many network applications, the nature of traffic is of burst type. Often, the transient response of network to such traffics is the result of a series of interdependant events whose occurrence prediction is not a trivial task. The previous efforts in IEEE 802.15.4 networks often followed top-down approaches to model those sequences of events, i.e., through making top-view models of the whole network, they tried to track the transient response of network to burst packet arrivals. The problem with such approaches was that they were unable to give station-level views of network response and were usually complex. In this paper, we propose a non-stationary analytical model for the IEEE 802.15.4 slotted CSMA/CA medium access control (MAC) protocol under burst traffic arrival assumption and without the optional acknowledgements. We develop a station-level stochastic time-domain method from which the network-level metrics are extracted. Our bottom-up approach makes finding station-level details such as delay, collision and failure distributions possible. Moreover, network-level metrics like the average packet loss or transmission success rate can be extracted from the model. Compared to the previous models, our model is proven to be of lower memory and computational complexity order and also supports contention window sizes of greater than one. We have carried out extensive and comparative simulations to show the high accuracy of our model.
Language eng
DOI 10.1109/TC.2013.2296773
Field of Research 080501 Distributed and Grid Systems
0803 Computer Software
0805 Distributed Computing
1006 Computer Hardware
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30077753

Document type: Journal Article
Collections: School of Information Technology
2018 ERA Submission
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