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.
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