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Plants sense their environment by producing electrical signals which in essence represent changes in underlying physiological processes. These electrical signals, when monitored, show both stochastic and deterministic dynamics. In this paper, we compute 11 statistical features from the raw non-stationary plant electrical signal time series to classify the stimulus applied (causing the electrical signal). By using different discriminant analysis-based classification techniques, we successfully establish that there is enough information in the raw electrical signal to classify the stimuli. In the process, we also propose two standard features which consistently give good classification results for three types of stimuli-sodium chloride (NaCl), sulfuric acid (H 2 SO 4 ) and ozone (O 3 ). This may facilitate reduction in the complexity involved in computing all the features for online classification of similar external stimuli in future.

The assessment of environmental pollution levels is a complex and expensive task that public administration and often also private entities are willing or forced to take over. Focusing on the assessment of environmental noise pollution in urban areas, we provide qualitative considerations and experimental results to show the feasibility of wireless sensor networks to be used in this context. We present a prototype for the collection and logging of noise pollution data based on the Tmote invent prototyping platform., using which we performed indoor and outdoor noise pollution measurements. We build upon these first experimental results to depict the potentials and limits of currently available wireless sensor networks prototyping platforms to be used as noise pollution sensors. Furthermore, we present tinyLAB, a Matlab-based tool developed in the context of this work, which enables real-time acquisition, processing and visualization of data collected in wireless sensor networks. Copyright 2008 ACM

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Becchetti, L.; Korteweg, P.; Marchetti Spaccamela, A.; Skutella, M.; Stougie, L.; Vitaletti, A. Published: 01/01/2006 Document VersionPublisher's PDF, also known as Version of Record (includes final page, issue and volume numbers) Please check the document version of this publication:• A submitted manuscript is the author's version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication Citation for published version (APA):Becchetti, L., Korteweg, P., Marchetti Spaccamela, A., Skutella, M., Stougie, L., & Vitaletti, A. (2006). Latency constrained aggregation in sensor networks. (SPOR-Report : reports in statistics, probability and operations research; Vol. 200608). Eindhoven: Technische Universiteit Eindhoven. General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Abstract. A sensor network consists of sensing devices which may exchange data through wireless communication. A particular feature of sensor networks is that they are highly energy constrained due to their use of batteries. Data aggregation is a possible way to save energy consumption: nodes may delay data in order to aggregate them into a single packet before forwarding them towards some central node (sink). Since transmission is highly energy consuming, aggregation can contribute to increasing network lifetime. However, many applications impose constraints on the maximum delay of data; this translates into latency constraints for data arriving at the sink. Data aggregation, latency constraints and energy preservation give rise to a wide variety of combinatorial optimization problems.In this paper we study the problem of data aggregation to minimize maximum energy consumption under latency constraints on sensed data delivery. In the problem we study, transmission energy and time depend only on the pair of nodes involved in the transmissi...

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DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal. If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement:

In this paper, system identification approach has been adopted to develop a novel dynamical model for describing the relationship between light as an environmental stimulus and the electrical response as the measured output for a bay leaf (Laurus nobilis) plant. More specifically, the target is to predict the characteristics of the input light stimulus (in terms of on-off timing, duration and intensity) from the measured electrical response-leading to an inverse problem. We explored two major classes of system estimators to develop dynamical models-linear and nonlinear-and their several variants for establishing a forward and also an inverse relationship between the light stimulus and plant electrical response. The best class of models are given by the Nonlinear Hammerstein-Wiener (NLHW) estimator showing good data fitting results over other linear and nonlinear estimators in a statistical sense. Consequently, a few set of models using different functional variants of NLHW has been developed and their accuracy in detecting the on-off timing and intensity of the input light stimulus are compared for 19 independent plant datasets (including 2 additional species viz. Zamioculcas zamiifolia and Cucumis sativus) under similar experimental scenario. © 2014 Elsevier B.V. All rights reserved