@conference {223, title = {A demo of the PEACH IoT-based frost event prediction system for precision agriculture}, booktitle = {Sensing, Communication, and Networking (SECON), 2016 13th Annual IEEE International Conference on}, year = {2016}, publisher = {IEEE}, organization = {IEEE}, doi = {10.1109/SAHCN.2016.7732963}, url = {https://hal.inria.fr/hal-01311527/document}, author = {Brun-Laguna, Keoma and Diedrichs, Ana Laura and Chaar, Javier Emilio and Dujovne, Diego and Taffernaberry, Juan Carlos and Mercado, Gustavo and Watteyne, Thomas} } @conference {222, title = {(Not so) intuitive results from a smart agriculture low-power wireless mesh deployment}, booktitle = {Proceedings of the Eleventh ACM Workshop on Challenged Networks}, year = {2016}, publisher = {ACM}, organization = {ACM}, address = {New York city}, abstract = {
A 21-node low-power wireless mesh network is deployed in a peach orchard. The network serves as a frost event prediction system. On topof sensor values, devices also report network statistics. In 3 months of operations, the network has produced over 4 million temperature values, and over 350,000 network statistics. This paper presents an in-depth analysis of the statistics, in order to precisely understand the performance of the network. Nodes in the network exhibit an expected lifetime between4 and 16 years, with an end-to-end reliability of 100\%. We show how {\textendash}contrary to popular belief {\textendash} wireless links are symmetric. Thanks to the use of Time Slotted Channel Hopping (TSCH), the network topology is very stable, with less than 5 link changes per day in the entire network.
}, doi = {https://doi.org/10.1145/2979683.2979696}, url = {https://hal.inria.fr/hal-01361333/document}, author = {Brun-Laguna, Keoma and Diedrichs, Ana Laura and Dujovne, Diego and L{\'e}one, R{\'e}my and Vilajosana, Xavier and Watteyne, Thomas} } @proceedings {178, title = {Characterization of LQI behavior in WSN for glacier area in Patagonia Argentina}, journal = {Embedded Systems (SASE/CASE), 2013 Fourth Argentine Symposium and Conference on}, year = {2013}, month = {08/2013}, pages = {1--6}, publisher = {IEEE}, address = {Buenos Aires, Argentina}, abstract = {

One of the most important aspects before installing a Wireless Sensor Network (WSN) is a previous study of connectivity constraints that exist in the area to be covered. This study is critical to the final distribution of the sensors, with an important impact in the life of the network by reducing consumption, and on the robustness by contemplating redundancy of paths and sensors. In this paper, we present a summary of the most important aspects of a preliminary empirical study of the Link Quality Indicator (LQI), on different landscapes in the glaciers area of Patagonia Argentina. The landscapes covered varied in geographical structures with different levels of attenuation and extreme environmental conditions. Through the analysis of the Cumulative Distribution Function (CDF) of the measured LQI values, we can characterize the behavior of four different scenarios and correlate the combined effects of the environmental structure with the distance from the transmitter. The measurements performed were designed for characterizing the links at the physical layer with the purpose of defining models to estimate the Packet Error Rate (PER) for the WSN deployment stage.

}, issn = {978-1-4799-1098-4}, doi = {10.1109/SASE-CASE.2013.6636777}, url = {http://dx.doi.org/10.1109/SASE-CASE.2013.6636777}, author = {Diedrichs, Ana Laura and Robles, Mar{\'\i}a In{\'e}s and Bromberg, Facundo and Mercado, Gustavo and Dujovne, Diego} } @article {224, title = {Peach: Predicting frost events in peach orchards using iot technology}, journal = {EAI Endorsed Transactions on the Internet of Things}, year = {2016}, abstract = {

In 2013, 85\% of the peach production in the Mendoza region (Argentina) was lost because of frost. In a couple of hours, farmers can lose everything. Handling a frost event is possible, but it is hard to predict when it is going to happen. The goal of the PEACH project is to predict frost events by analyzing measurements from sensors deployed around an orchard. This article provides an in-depth description of a complete solution we designed and deployed: the low-power wireless network and the back-end system. The low-power wireless network is composed entirely of commercial o -the-shelf devices. We develop a methodology for deploying the network and present the open-source tools to assist with the deployment and to monitor the network. The deployed low-power wireless mesh network is 100\% reliable, with end-to-end latency below 2 s, and over 3 years of battery lifetime. This article discusses how the technology used is the right one for precision agriculture applications.

}, doi = {http://dx.doi.org/10.4108/eai.1-12-2016.151711}, author = {Watteyne, Thomas and Diedrichs, Ana Laura and Brun-Laguna, Keoma and Chaar, Javier Emilio and Dujovne, Diego and Taffernaberry, Juan Carlos and Mercado, Gustavo} } @article {242, title = {Prediction of frost events using machine learning and IoT sensing devices}, journal = {IEEE Internet of Things Journal}, volume = {5}, year = {2018}, month = {27 August 2018}, pages = { 4589-4597}, type = {Full}, chapter = {4589}, abstract = {

Internet of Things (IoT) in agriculture applications have evolved to solve several relevant problems from producers. Here, we describe a component of an IoT-enabled frost prediction system. We follow current approaches for prediction that use machine learning algorithms trained by past readings of temperature and humidity sensors to predict future temperatures. However, contrary to current approaches, we assume that the surrounding thermodynamical conditions are informative for prediction. For that, a model was developed for each location, including in its training information of sensor readings of all other locations, autonomously selecting the most relevant ones (algorithm dependent). We evaluated our approach by training regression and classification models using several machine learning algorithms, many already proposed in the literature for the frost prediction problem, over data from five meteorological stations spread along the Mendoza Province of Argentina. Given the scarcity of frost events, data was augmented using the synthetic minority oversampling technique (SMOTE). The experimental results show that selecting the most relevant neighbors and training the models with SMOTE reduces the prediction errors of both regression predictors for all five locations, increases the performance of Random Forest classification predictors for four locations while keeping it unchanged for the remaining one, and produces inconclusive results for logistic regression predictor. These results demonstrate the main claim of these works: that thermodynamic information of neighboring locations can be informative for improving both regression and classification predictions, but also are good enough to suggest that the present approach is a valid and useful resource for decision makers and producers.

}, author = {Diedrichs, Ana Laura and Bromberg, Facundo and Dujovne, Diego and Brun-Laguna, Keoma and Watteyne, Thomas} }