Distributed parameter estimation for monitoring diffusion phenomena using physical models

Rossi, Lorenzo; Krishnamachari, Bhaskar; Kuo, Chin-Hwa

doi:10.1109/sahcn.2004.1381948

Cited by 55 publications

(44 citation statements)

References 12 publications

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“…The noisy input function is given by s(z, t) = 50. Approximating the solution p(z, t) by means ofp(z, t) = Ψ T (z)α(t), the partial differential equation (6) can be spatially decomposed leading to the finite-dimensional state-space form (7). The state vector x k can be derived from temporal discretization of the weighting factors α(t) of the approximate solution, as shown in Sec.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…That means, by given sensor measurements, it is desirable to find the corresponding model parameters. By this means, the discrete-time samples measured by the individual sensor nodes are incorporated in the physical model in order to improve its accuracy in terms of estimated model parameters [6]. For sensor network applications, the parameter identification becomes even more essential due to the harsh and unknown environment, unpredictable variations of the phenomenon, and possibly unknown sensor locations.…”

Section: Introductionmentioning

confidence: 99%

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Parameter identification and reconstruction for distributed phenomena based on hybrid density filter

Sawo

Huber

Hanebeck

2007

2007 10th International Conference on Information Fusion

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Abstract-This paper addresses the problem of model-based reconstruction and parameter identification of distributed phenomena characterized by partial differential equations. The novelty of the proposed method is the systematic approach and the integrated treatment of uncertainties, which naturally occur in the physical system and arise from noisy measurements. The main challenge of accurate reconstruction is that model parameters, i.e., diffusion coefficients, of the physical model are not known in advance and usually need to be identified. Generally, the problem of parameter identification leads to a nonlinear estimation problem. Hence, a novel efficient recursive procedure is employed. Unlike other estimators, the so-called Hybrid Density Filter not only assures accurate estimation results for nonlinear systems, but also offers an efficient processing. By this means it is possible to reconstruct and identify distributed phenomena monitored by autonomous wireless sensor networks. The performance of the proposed estimation method is demonstrated by means of simulations.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Parameter identification and reconstruction for distributed phenomena based on hybrid density filter

Sawo

Huber

Hanebeck

2007

2007 10th International Conference on Information Fusion

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“…1) Spatial and Temporal Discretization: The simplest method for the spatial and temporal discretization of distributed phenomena is the finite-difference method [7], [8]. In order to solve the partial differential equation (1), the derivatives need to be approximated with finite differences according to…”

Section: Problem Formulationmentioning

confidence: 99%

“…Then, by distributing local information to a global processing node, the phenomenon can be coöperatively reconstructed in an intelligent and autonomous manner [5], [6], [7]. In such scenarios, the sensor network can be exploited as a huge information field collecting data from its surrounding and then providing useful information both to mobile agents and to humans.…”

mentioning

confidence: 99%

Sensor node localization methods based on local observations of distributed natural phenomena

Sawo

Henderson

Sikorski

et al. 2008

2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems

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Abstract-This paper addresses the model-based localization of sensor networks based on local observations of a distributed phenomenon. For the localization process, we propose the rigorous exploitation of strong mathematical models of distributed phenomena. By unobtrusively exploiting background phenomena, the individual sensor nodes can be localized by only observing its local surrounding without the necessity of heavy infrastructure. In this paper, we introduce two novel approaches: (a) the polynomial system localization method (PSL-method) and (b) the simultaneous reconstruction and localization method (SRL-method). The first approach (PSLmethod) is based on restating the mathematical model of the distributed phenomenon in terms of a polynomial system. These equations depend on both the state of the phenomenon and the node locations. Solving the system of polynomials for each individual sensor node directly leads to the desired locations. The second approach (SRL-method) basically regards the localization problem as a simultaneous state and parameter estimation problem with the node locations as parameters. By this means, the distributed phenomenon is reconstructed and the individual nodes are localized in a simultaneous fashion. In addition, within this framework the uncertainties in the mathematical model and the measurements are considered. The performance of the two different localization approaches is demonstrated by means of simulation results.

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“…There appear to be two primary uses for overhearing: in in-network processing, with applications such as TinyDB [9] or monitoring diffusion phenomena [11], and in network protocols such as MINTRoute that use it to collect statistics about network performance. In the former case, overheard messages are used to improve performance but are not necessary for correctness; in the latter case, as in MINTRoute, overhearing is necessary for acceptable network performance.…”

Section: The Idle Listening Vs Snooping Trade-offmentioning

confidence: 99%

A Measurement-Based Analysis of the Interaction Between Network Layers in TinyOS

Malesci¹,

Madden²

2006

Lecture Notes in Computer Science

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Abstract.There have been a number of recent proposals for link and network-layer protocols in the sensor networking literature, each of which claims to be superior to other approaches. However, a proposal for a networking protocol at a given layer in the stack is typically evaluated in the context of a single set of carefully selected protocols at other layers. Because of the limited data available about interactions between different protocols at various layers of the stack, it is difficult for developers of sensor network applications to select from amongst the range of alternative sensor networking protocols. This paper evaluates the interaction between several protocols at the MAC and network layers measuring their performance in terms of end-to-end throughput and loss on a large testbed. We identify some common sources of poor performance; based on this experience, we propose a set of design principles for the designers of future interfaces.

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Distributed parameter estimation for monitoring diffusion phenomena using physical models

Cited by 55 publications

References 12 publications

Parameter identification and reconstruction for distributed phenomena based on hybrid density filter

Parameter identification and reconstruction for distributed phenomena based on hybrid density filter

Sensor node localization methods based on local observations of distributed natural phenomena

A Measurement-Based Analysis of the Interaction Between Network Layers in TinyOS

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