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Home arrow Seminar Abstracts arrow Wassenaar 2008

Wassenaar 2008
Workshop on Algebra, Networks, Signal Processing, and System Theory
Wassenaar, The Netherlands,

Adaptive Networks

June 23, 2008

Distributed networks linking sensors and actuators will form the backbone of future data communication and control networks. Applications will range from sensor networks to precision agriculture, environment monitoring, disaster relief management, smart spaces, target localization, as well as medical applications. In all these cases, the distribution of the nodes in the field yields spatial diversity, which should be exploited alongside the temporal dimension in order to enhance the robustness of the processing tasks and improve the probability of signal and event detection. Distributed processing techniques allow for the efficient extraction of temporal and spatial information from data collected at such distributed nodes by relying on local cooperation and data processing.

This talk describes recent developments in distributed processing over adaptive networks. The presentation covers adaptive algorithms that allow neighboring nodes to communicate with each other. At each node, estimates exchanged with neighboring nodes are fused and promptly fed into the local adaptation rules. In this way, an adaptive network is obtained where the structure as a whole is able to respond in real-time to the temporal and spatial variations in the statistical profile of the data. Different adaptation or learning rules at the nodes, allied with different cooperation protocols, give rise to adaptive networks of various complexities and potential. The ideas are illustrated by considering algorithms of the least-mean-squares type, although more general adaptation rules are also possible including least-squares rules and Kalman-type rules. Both incremental and diffusion collaboration strategies are considered.



 

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