Explorative navigation of mobile sensor networks using sparse Gaussian processes

Oh, Songhwai; Xu, Yunfei; Choi, Jongeun

doi:10.1109/cdc.2010.5717331

Cited by 12 publications

(6 citation statements)

References 14 publications

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“…The highest-scored sampling unit is selected for inclusion into U and removed from D. This greedy selection procedure is iterated until U reaches a predefined size. Among the various criteria introduced earlier, the differential entropy score [24] is reported to perform well [26]; it is a monotonic function of the posterior variance Σ ss|U (6), thus resulting in the greedy selection of a sampling unit s ∈ D \ U with the largest variance in each iteration.…”

Section: A Subset Of Data Approximationmentioning

confidence: 99%

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Gaussian Process Decentralized Data Fusion and Active Sensing for Spatiotemporal Traffic Modeling and Prediction in Mobility-on-Demand Systems

Chen

Low

Yao

et al. 2015

IEEE Trans. Automat. Sci. Eng.

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Abstract-Mobility-on-demand (MoD) systems have recently emerged as a promising paradigm of one-way vehicle sharing for sustainable personal urban mobility in densely populated cities. We assume the capability of a MoD system to be enhanced by deploying robotic shared vehicles that can autonomously cruise the streets to be hailed by users. A key challenge of the MoD system is that of real-time, fine-grained mobility demand and traffic flow sensing and prediction. This paper presents novel Gaussian process (GP) decentralized data fusion and active sensing algorithms for real-time, fine-grained traffic modeling and prediction with a fleet of MoD vehicles. The predictive performance of our decentralized data fusion algorithms are theoretically guaranteed to be equivalent to that of sophisticated centralized sparse GP approximations. We derive consensus filtering variants requiring only local communication between neighboring vehicles. We theoretically guarantee the performance of our decentralized active sensing algorithms. When they are used to gather informative data for mobility demand prediction, they can achieve a dual effect of fleet rebalancing to service mobility demands. Empirical evaluation on real-world datasets shows that our algorithms are significantly more time-efficient and scalable in the size of data and fleet while achieving predictive performance comparable to that of state-of-the-art algorithms.

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Section: A Subset Of Data Approximationmentioning

confidence: 99%

“…The first two equalities expand the first component using the definition of Λ (Theorem 2B), (9), (25), (26), and (27). The last two equalities exploit (9) and (11).…”

Section: Appendix B Proof Of Theorem 2bmentioning

confidence: 99%

Gaussian Process Decentralized Data Fusion and Active Sensing for Spatiotemporal Traffic Modeling and Prediction in Mobility-on-Demand Systems

Chen

Low

Yao

et al. 2015

IEEE Trans. Automat. Sci. Eng.

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“…In (Nguyen et al, 2016), a sampling strategy based on entropy maximization is designed to find the most informative locations for mobile robotic wireless sensor networks (MRWSs) over a spatial field modeled with a Gaussian process. In (Oh et al, 2010), they first use sparse Gaussian processes to reduce the computational cost on each sensor before the environment state is predicted. Then, to define an exploratory rule towards the more informative locations, they use and compare three informative strategies: informative vector machine (IVM), principal feature analysis (PFA), and a mutual information based measurement selection algorithm (MI).…”

Section: Related Work and Contributionmentioning

confidence: 99%

Multi-Agent Learning Framework for Environment Redundancy Identification for Mobile Sensors in an Iot Context

Martínez¹,

Mojica‐Nava²,

Watson³

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> From an IoT point of view, the continuous growth of cheap and versatile sensor technologies has generated a massive data flow in communication networks, which most of the time carries unnecessary or redundant information that requires larger storage centers and more time to process and analyze data. Most of this redundancy is due to fact that network nodes are unable to identify environmental cues showing measurement changes to be considered and instead remain at a static location getting the same data. In this work we propose a multi-agent learning framework based on two theoretical tools. Firstly, we use Gaussian Process Regression (GPR) to make each node capable of getting information from the environment based on its current measurement and the measurements taken by its neighbors. Secondly, we use the rate distortion function to define a boundary where the information coming from the environment is neither redundant nor misunderstood. Finally, we show how the framework is applied in a mobile sensor network in which sensors decide to be more or less exploratory by means of the parameter s of the Blahut-Arimoto algorithm, and how it affects the measurement coverage in a spatial area being sensed.</p>

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“…Regression analysis for Gaussian processes requires growing computational complexity since the size of the covariance matrix increases as the number of observations increases. This problem in the context of the mobile sensor networks has been tackled in different directions [15], [16]. Computational complexity of a full Bayesian prediction algorithm for spatio-temporal Gaussian processes with unknown covariance functions grows in a prohibitively fast rate as the observation number increases due to the MCMC method.…”

Section: Introductionmentioning

confidence: 99%

Bayesian prediction and adaptive sampling algorithms for mobile sensor networks

Choi

Dass

et al. 2011

Proceedings of the 2011 American Control Conference

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Abstract-In this paper, we formulate a full Bayesian approach for spatio-temporal Gaussian process regression under practical conditions such as measurement noise and unknown hyperparmeters (particularly, the bandwidths). Thus, multifactorial effects of observations, measurement noise and prior distributions of hyperparameters are all correctly incorporated in the computed predictive distribution. Using discrete prior probabilities and compactly supported kernels, we provide a way to design sequential Bayesian prediction algorithms that can be computed (without using the Gibbs sampler) in constant time as the number of observations increases. Both centralized and distributed sequential Bayesian prediction algorithms have been proposed for mobile sensor networks. An adaptive sampling strategy for mobile sensors, using the maximum a posteriori (MAP) estimation, has been proposed to minimize the prediction error variances. Simulation results illustrate the effectiveness of the proposed algorithms.

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Explorative navigation of mobile sensor networks using sparse Gaussian processes

Cited by 12 publications

References 14 publications

Gaussian Process Decentralized Data Fusion and Active Sensing for Spatiotemporal Traffic Modeling and Prediction in Mobility-on-Demand Systems

Gaussian Process Decentralized Data Fusion and Active Sensing for Spatiotemporal Traffic Modeling and Prediction in Mobility-on-Demand Systems

Multi-Agent Learning Framework for Environment Redundancy Identification for Mobile Sensors in an Iot Context

Bayesian prediction and adaptive sampling algorithms for mobile sensor networks

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