Optimal sampling using singular value decomposition of the parameter variance space

Popa, Dan O.; Sanderson, Arthur C.; Hombal, Vadiraj; Komerska, Rick; Mupparapu, Sai S.; Blidberg, R.; Chappel, S.G.

doi:10.1109/iros.2005.1545135

Cited by 14 publications

(2 citation statements)

References 18 publications

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“…In order to produce energy-aware protocols, a number of researchers have focused on intelligent adaptive sensing [Deshpande et al 2005;Willett et al 2004;Jain and Chang 2004;Popa et al 2005]. Such work has demonstrated how a sensor's lifetime can be extended by sensing only during the most informative situations.…”

Section: Adaptive Sensingmentioning

confidence: 99%

A utility-based adaptive sensing and multihop communication protocol for wireless sensor networks

Padhy

Dash

Martinez

et al. 2010

ACM Trans. Sen. Netw.

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This paper reports on the development of a utility-based mechanism for managing sensing and communication in cooperative multi-sensor networks. The specific application on which we illustrate our mechanism is that of GlacsWeb. This is a deployed system that uses battery-powered sensors to collect environmental data related to glaciers which it transmits back to a base station so that it can be made available world-wide to researchers. In this context, we first develop a sensing protocol in which each sensor locally adjusts its sensing rate based on the value of the data it believes it will observe. The sensors employ a Bayesian linear model to decide their sampling rate and exploit the properties of the Kullback-Leibler divergence to place an appropriate value on the data. Then, we detail a communication protocol that finds optimal routes for relaying this data back to the base station based on the cost of communicating it (derived from the opportunity cost of using the battery power for relaying data). Finally, we empirically evaluate our protocol by examining the impact on efficiency of a static network topology, a dynamic network topology, the size of the network, the degree of dynamism of the environment and the mobility of the nodes. In so doing, we demonstrate that the efficiency gains of our new protocol, over the currently implemented method over a 6 month period, are 78%, 133%, 100% and 93% respectively. Furthermore, we show that our system performs at 65%, 70%, 63% and 70% of the theoretical optimal respectively, despite being a distributed protocol that operates with incomplete knowledge of the environment.

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Section: Adaptive Sensingmentioning

confidence: 99%

A utility-based adaptive sensing and multihop communication protocol for wireless sensor networks

Padhy

Dash

Martinez

et al. 2010

ACM Trans. Sen. Netw.

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“…Mobile robots are increasingly employed in the observation and monitoring of complex processes on the ground, in the air, and undersea [3], [7], [8], [11], [13]. An example is the observation and mapping of harmful algal blooms in order to predict occurrence and trends that impact biological processes and human health [2].…”

Section: Introductionmentioning

confidence: 99%

Adaptive multiscale sampling in robotic sensor networks

Hombal

Sanderson

Blidberg

2009

2009 IEEE/RSJ International Conference on Intelligent Robots and Systems

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This work focuses on the observation of environmental phenomena that occur as spatial distributions in two and three dimensions, using sensor-enabled mobile vehicles(ground,air or undersea). Algorithms to guide an adaptive exploration of a given region through systematic choice of sampling locations under the constraints imposed by vehicles are presented. Variation sensitive multiresolution sample distributions are achieved through an iterative variation sensitive estimation of the unknown process.

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Introduction

2012

Remote Sensing and Actuation Using Unmanned Vehicles

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Optimal sampling using singular value decomposition of the parameter variance space

Cited by 14 publications

References 18 publications

A utility-based adaptive sensing and multihop communication protocol for wireless sensor networks

A utility-based adaptive sensing and multihop communication protocol for wireless sensor networks

Adaptive multiscale sampling in robotic sensor networks

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