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Handbook on Array Processing and Sensor Networks by K. J. Ray Liu, Simon Haykin

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images CHAPTER 22

Distributed Adaptive Learning Mechanisms

Ali H. Sayed and Federico S. Cattivelli

Electrical Engineering Department, University of California, Los Angeles, California

22.1 INTRODUCTION

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 [1–5]. 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. For example, each node in the network could collect noisy observations related to a certain parameter of interest. The nodes would then interact with their neighboring nodes, as dictated by the network topology, in order to estimate the parameter. The objective is to arrive at an estimate that is as reliable as the one that would be obtained if each node had access to the information across the entire network. ...

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