15,110 research outputs found
Decentralized Clustering and Linking by Networked Agents
We consider the problem of decentralized clustering and estimation over
multi-task networks, where agents infer and track different models of interest.
The agents do not know beforehand which model is generating their own data.
They also do not know which agents in their neighborhood belong to the same
cluster. We propose a decentralized clustering algorithm aimed at identifying
and forming clusters of agents of similar objectives, and at guiding
cooperation to enhance the inference performance. One key feature of the
proposed technique is the integration of the learning and clustering tasks into
a single strategy. We analyze the performance of the procedure and show that
the error probabilities of types I and II decay exponentially to zero with the
step-size parameter. While links between agents following different objectives
are ignored in the clustering process, we nevertheless show how to exploit
these links to relay critical information across the network for enhanced
performance. Simulation results illustrate the performance of the proposed
method in comparison to other useful techniques
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
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