Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Real-time data management on a wireless sensor network.
AU - Roadknight, Chris
AU - Parrott, Laura
AU - Boyd, Nathan
AU - Marshall, Ian W.
N1 - This paper reports the progress made on instantiating and experimentally testing self-management in the SECOAS project (DTI, led by Marshall). SECOAS was the first sensor network project to recognize and address network management as a key issue for users. The solutions developed here have formed the basis of the successor projects Prosen (EPSRC WINES, led by Marshall), DIAS (EPSRC WINES), Tramsnod (EPSRC) and Neptune (EPSRC strategic partnership with ABB Yorkshire Water and United Utilities), and also of collaboration with two groups in Australia (funded by a Gledden fellowship and an ARC network). RAE_import_type : Journal article RAE_uoa_type : Computer Science and Informatics
PY - 2005/4/1
Y1 - 2005/4/1
N2 - A multi-layered algorithm is proposed that provides a scalable and adaptive method for handling data on a wireless sensor network. Statistical tests, local feedback, and global genetic style material exchange ensure limited resources such as battery and bandwidth which are used efficiently by manipulating data at the source and important features in the time series are not lost when compression needs to be made. The approach leads to a more 'hands off' implementation which is demonstrated by a real world oceanographic deployment of the system.
AB - A multi-layered algorithm is proposed that provides a scalable and adaptive method for handling data on a wireless sensor network. Statistical tests, local feedback, and global genetic style material exchange ensure limited resources such as battery and bandwidth which are used efficiently by manipulating data at the source and important features in the time series are not lost when compression needs to be made. The approach leads to a more 'hands off' implementation which is demonstrated by a real world oceanographic deployment of the system.
KW - AI
KW - sensor networks
KW - oceanography
U2 - 10.1080/15501320590966468
DO - 10.1080/15501320590966468
M3 - Journal article
VL - 1
SP - 215
EP - 225
JO - International Journal of Distributed Sensor Networks
JF - International Journal of Distributed Sensor Networks
SN - 1550-1329
IS - 2
ER -