Distributed Learning in Wireless Sensor Networks

Swami, Ananthram; Zhao, Qing; Hong, Y.-W. Peter; Tong, Lang

doi:10.1002/9780470061794.ch8

Cited by 107 publications

(178 citation statements)

References 10 publications

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“…FROMS (Förster & Murphy, 2007) is our own multicast routing protocol, able to accommodate various cost functions, including number of hops, remaining energy at nodes, latency, etc. Additional routing protocols based on reinforcement learning, together with their properties are discussed in (Di & Joo, 2007;Kulkarni et al, 2009;Predd et al, 2006). Examples of applying reinforcement learning to medium access are available in (Liu & Elahanany, 2006;Pandana & Liu, 2005).…”

Section: Conclusion and Further Readingmentioning

confidence: 99%

Machine Learning across the WSN Layers

Förster¹,

Murphy²

2011

Emerging Communications for Wireless Sensor Networks

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Wireless sensor networks (WSNs) have seen rapid research and industrial development in recent years. Both the costs and size of individual nodes have been constantly decreasing, opening new opportunities for a wide range of applications. Nevertheless, designing software to achieve energy-efficient, robust and flexible data dissemination remains an open problem with many competing solutions. In parallel, researchers have effectively exploited machine learning techniques to achieve efficient solutions in environments with distribution and rapidly fluctuating properties, analogous to WSN domains. Applying machine learning techniques to WSNs inherently has the potential to improve the robustness and flexibility of communications and data processing, while simultaneously optimizing energy expenditure. This chapter concentrates on applications of machine learning at all layers in the WSN network stack. First, it provides a brief background and summary of three of the most commonly used machine learning techniques: reinforcement learning, neural networks and decision trees. Then, it uses example research from the literature to describe current efforts at each level of the stack, and outlines future opportunities.

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Section: Conclusion and Further Readingmentioning

confidence: 99%

Machine Learning across the WSN Layers

Förster¹,

Murphy²

2011

Emerging Communications for Wireless Sensor Networks

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“…Nonparametric methods are often desirable in such situations. Predd et al [29] surveyed nonparametric distributed learning in WSN.…”

Section: Introductionmentioning

confidence: 99%

Nonparametric Copula Density Estimation in Sensor Networks

Chen

2011

2011 Seventh International Conference on Mobile Ad-Hoc and Sensor Networks

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Abstract-Statistical and machine learning is a fundamental task in sensor networks. Real world data almost always exhibit dependence among different features. Copulas are full measures of statistical dependence among random variables. Estimating the underlying copula density function from distributed data is an important aspect of statistical learning in sensor networks. With limited communication capacities or privacy concerns, centralization of the data is often impossible. By only collecting the ranks of the data observed by different sensors, we estimate and evaluate the copula density on an equally spaced grid after binning the standardized ranks at the fusion center. Without assuming any parametric forms of copula densities, we estimate them nonparametrically by maximum penalized likelihood estimation (MPLE) method with a Total Variation (TV) penalty. Linear equality and positivity constraints arise naturally as a consequence of marginal uniform densities of any copulas. Through local quadratic approximation to the likelihood function, the constrained TV-MPLE problem is cast as a sequence of corresponding quadratic optimization problems. A fast gradient based algorithm solves the constrained TV penalized quadratic optimization problem. Numerical experiments show that our algorithm can estimate the underlying copula density accurately.

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“…This approach consists of organizing sensors in a network of inference. The output of some sensors is used as input to other sensors to conduct higher-level classification tasks [14,17].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised learning in body-area networks

Bicocchi

Lasagni

Mamei

et al. 2010

Proceedings of the Fifth International Conference on Body Area Networks

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Pattern recognition is becoming a key application in bodyarea networks. This paper presents a framework promoting unsupervised training for multi-modal, multi-sensor classification systems. Specifically, it enables sensors provided with patter-recognition capabilities to autonomously supervise the learning process of other sensors. The approach is discussed using a case study combining a smart camera and a body-worn accelerometer. The body-worn accelerometer sensor is trained to recognize four user activities pairing accelerometer data with labels coming from the camera. Experimental results illustrate the applicability of the approach in different conditions.

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Distributed Learning in Wireless Sensor Networks

Cited by 107 publications

References 10 publications

Machine Learning across the WSN Layers

Machine Learning across the WSN Layers

Nonparametric Copula Density Estimation in Sensor Networks

Unsupervised learning in body-area networks

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