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Abstract
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.
The authors would like to acknowledge the technical input of Antonio Gonzalez (UCL) for his advice and contribution to the programming of the simulation environment.
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Notes
The authors would like to acknowledge the technical input of Antonio Gonzalez (UCL) for his advice and contribution to the programming of the simulation environment.
A running measure of variance is kept and if recent variance is higher than long term variance the QoS value is increased and vice versa.
2 This is a fairly modest compression, it is safe to say that as compression requirements increase the importance of more intelligent compression algorithms detailed in this paper also increases.