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Dr. Carlos Cambra
GICAP Research Group, University of Burgos, Burgos, Spain

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0 Machine Learning
0 Precision Agriculture
0 IoT
0 Drones
0 Time-series forecasting

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Conference paper
Published: 04 November 2020 in Transactions on Petri Nets and Other Models of Concurrency XV
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It is widely claimed that a major challenge in Robotics is to get reliable systems while both response and down times are minimized. In keeping with this idea, present paper proposes the application of a Hybrid Artificial Intelligence System (HAIS) to preprocess data with the aim of improving the detection of performance anomalies. One of the main problems when analyzing real-life data is the presence of missing values. It is usually solved by removing incomplete data, what causes a loss of information that may be critical in some domains. As an alternative, present paper proposes the application of regression models to impute those missing values. Prediction is optimized by generating personalized models on previously clustered data. Experiments are run on a public and up-to-date dataset that contains information about anomalies affecting the component-based software of a robot. The obtained results validate the proposed HAIS, as it successfully imputes missing values from the different features in the original dataset.

ACS Style

Ángel Arroyo; Nuño Basurto; Carlos Cambra; Álvaro Herrero. Clustering and Regression to Impute Missing Values of Robot Performance. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 86 -94.

AMA Style

Ángel Arroyo, Nuño Basurto, Carlos Cambra, Álvaro Herrero. Clustering and Regression to Impute Missing Values of Robot Performance. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():86-94.

Chicago/Turabian Style

Ángel Arroyo; Nuño Basurto; Carlos Cambra; Álvaro Herrero. 2020. "Clustering and Regression to Impute Missing Values of Robot Performance." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 86-94.

Conference paper
Published: 27 October 2020 in Transactions on Petri Nets and Other Models of Concurrency XV
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The detection of anomalies (affecting hardware or software) is an open challenge for cyber-physical systems in general and robots in particular. Physical anomalies related to the hardware components of such systems have been widely researched. However, scant attention has been devoted so far to study the anomalies affecting the software components. In order to bridge this gap, the present paper proposes the application of different classifiers to a robot performance dataset for the first time. The applied supervised models are targeted at detecting synthetically-induced software anomalies, having a detrimental impact on the performance of a collaborative robot. Obtained results demonstrate that the applied Machine Learning models can successfully address the target problem, with acceptable detection rates.

ACS Style

Héctor Quintián; Esteban Jove; José Luis Calvo-Rolle; Nuño Basurto; Carlos Cambra; Álvaro Herrero; Emilio Corchado. Detecting Performance Anomalies in the Multi-component Software a Collaborative Robot. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 533 -540.

AMA Style

Héctor Quintián, Esteban Jove, José Luis Calvo-Rolle, Nuño Basurto, Carlos Cambra, Álvaro Herrero, Emilio Corchado. Detecting Performance Anomalies in the Multi-component Software a Collaborative Robot. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():533-540.

Chicago/Turabian Style

Héctor Quintián; Esteban Jove; José Luis Calvo-Rolle; Nuño Basurto; Carlos Cambra; Álvaro Herrero; Emilio Corchado. 2020. "Detecting Performance Anomalies in the Multi-component Software a Collaborative Robot." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 533-540.

Conference paper
Published: 29 August 2020 in Advances in Intelligent Systems and Computing
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Reducing water consumption is an important target required for a sustainable farming. In order to do that, the actual water needs of different crops must be known and irrigation scheduling must be adjusted to satisfy them. This is a complex task as the phenology of plants and its water demand vary with soil properties and weather conditions. To address such problem, present paper proposes the application of time-series neural networks in order to predict the soil water content in a potato field crop, in which a soil humidity probe was installed. More precisely, Non-linear Input-Output, Non-linear Autoregressive and Non-linear Autoregressive with Exogenous Input models are applied. They are benchmarked, together with different interpolation methods in order to find the best combination for accurately predicting water needs. Promising results have been obtained, supporting the proposed models and their viability when predicting the real humidity level in the soil.

ACS Style

Mercedes Yartu; Carlos Cambra; Milagros Navarro; Carlos Rad; Ángel Arroyo; Álvaro Herrero. Neural Models to Predict Irrigation Needs of a Potato Plantation. Advances in Intelligent Systems and Computing 2020, 600 -613.

AMA Style

Mercedes Yartu, Carlos Cambra, Milagros Navarro, Carlos Rad, Ángel Arroyo, Álvaro Herrero. Neural Models to Predict Irrigation Needs of a Potato Plantation. Advances in Intelligent Systems and Computing. 2020; ():600-613.

Chicago/Turabian Style

Mercedes Yartu; Carlos Cambra; Milagros Navarro; Carlos Rad; Ángel Arroyo; Álvaro Herrero. 2020. "Neural Models to Predict Irrigation Needs of a Potato Plantation." Advances in Intelligent Systems and Computing , no. : 600-613.

Conference paper
Published: 29 August 2020 in Advances in Intelligent Systems and Computing
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Anomaly detection has been a challenging topic for decades and it still is open to new contributions nowadays. More specifically, the detection of anomalies (not only hardware ones but also those affecting the software) suffers from many problems when monitoring cyber-physical systems. One such usual problem is the much fewer data samples of anomalies than those available for the normal functioning of systems. This class-imbalance problem is addressed in the present paper and a novel strategy for oversampling the minority class is applied to an open dataset containing information about the performance of a component-based robot. The proposed strategy mainly consists on selecting the instances to be oversampled according to different criteria instead of randomly oversampling. Obtained results demonstrate that the proposed strategy improves predictive performance, especially when the SVM (Support Vector Machine) is used as classifier.

ACS Style

Nuño Basurto; Michał Woźniak; Carlos Cambra; Álvaro Herrero. Advanced Oversampling for Improved Detection of Software Anomalies in a Robot. Advances in Intelligent Systems and Computing 2020, 3 -12.

AMA Style

Nuño Basurto, Michał Woźniak, Carlos Cambra, Álvaro Herrero. Advanced Oversampling for Improved Detection of Software Anomalies in a Robot. Advances in Intelligent Systems and Computing. 2020; ():3-12.

Chicago/Turabian Style

Nuño Basurto; Michał Woźniak; Carlos Cambra; Álvaro Herrero. 2020. "Advanced Oversampling for Improved Detection of Software Anomalies in a Robot." Advances in Intelligent Systems and Computing , no. : 3-12.

Conference paper
Published: 15 August 2020 in Advances in Intelligent Systems and Computing
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As in many other fields, mobility of people is a fact in higher education. Although attention has been devoted to this topic, new challenges emerge at the present time. Globalization is an increasing reality and investigating the recent trends associated with it is required also in the educational context. In keeping with this idea, the present paper analyzes student mobility in both European and Non-European contexts under the frame of the Computer Science Bachelor at the University of Burgos. The findings are useful for students and lecturers together with university managers and policy makers in order to improve student mobility programs.

ACS Style

Ángel Arroyo; Secil Bayraktar; Carlos Cambra; Daniel Urda; Álvaro Herrero. Trends and Patterns of International Student Mobility: The Case of Bachelor’s Degrees in Computer Science at the University of Burgos. Advances in Intelligent Systems and Computing 2020, 142 -153.

AMA Style

Ángel Arroyo, Secil Bayraktar, Carlos Cambra, Daniel Urda, Álvaro Herrero. Trends and Patterns of International Student Mobility: The Case of Bachelor’s Degrees in Computer Science at the University of Burgos. Advances in Intelligent Systems and Computing. 2020; ():142-153.

Chicago/Turabian Style

Ángel Arroyo; Secil Bayraktar; Carlos Cambra; Daniel Urda; Álvaro Herrero. 2020. "Trends and Patterns of International Student Mobility: The Case of Bachelor’s Degrees in Computer Science at the University of Burgos." Advances in Intelligent Systems and Computing , no. : 142-153.

Journal article
Published: 18 July 2020 in Computers & Electrical Engineering
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Intelligent robots are foreseen as a technology that would be soon present in most public and private environments. In order to increase the trust of humans, robotic systems must be reliable while both response and down times are minimized. In keeping with this idea, present paper proposes the application of machine learning (regression models more precisely) to preprocess data in order to improve the detection of failures. Such failures deeply affect the performance of the software components embedded in human-interacting robots. To address one of the most common problems of real-life datasets (missing values), some traditional (such as linear regression) as well as innovative (decision tree and neural network) models are applied. The aim is to impute missing values with minimum error in order to improve the quality of data and consequently maximize the failure-detection rate. Experiments are run on a public and up-to-date dataset and the obtained results support the viability of the proposed models.

ACS Style

Nuño Basurto; Ángel Arroyo; Carlos Cambra; Álvaro Herrero. Imputation of Missing Values Affecting the Software Performance of Component-based Robots. Computers & Electrical Engineering 2020, 87, 106766 .

AMA Style

Nuño Basurto, Ángel Arroyo, Carlos Cambra, Álvaro Herrero. Imputation of Missing Values Affecting the Software Performance of Component-based Robots. Computers & Electrical Engineering. 2020; 87 ():106766.

Chicago/Turabian Style

Nuño Basurto; Ángel Arroyo; Carlos Cambra; Álvaro Herrero. 2020. "Imputation of Missing Values Affecting the Software Performance of Component-based Robots." Computers & Electrical Engineering 87, no. : 106766.

Journal article
Published: 01 July 2020 in Neurocomputing
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ACS Style

Nuño Basurto; Carlos Cambra; Álvaro Herrero. Improving the detection of robot anomalies by handling data irregularities. Neurocomputing 2020, 1 .

AMA Style

Nuño Basurto, Carlos Cambra, Álvaro Herrero. Improving the detection of robot anomalies by handling data irregularities. Neurocomputing. 2020; ():1.

Chicago/Turabian Style

Nuño Basurto; Carlos Cambra; Álvaro Herrero. 2020. "Improving the detection of robot anomalies by handling data irregularities." Neurocomputing , no. : 1.

Journal article
Published: 09 December 2019 in Logic Journal of the IGPL
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This study presents the application of self-organizing maps to air-quality data in order to analyze episodes of high pollution in Madrid (Spain’s capital city). The goal of this work is to explore the dataset and then compare several scenarios with similar atmospheric conditions (periods of high Nitrogen dioxide concentration): some of them when no actions were taken and some when traffic restrictions were imposed. The levels of main pollutants, recorded at these stations for eleven days at four different times from 2015 to 2018, are analyzed in order to determine the effectiveness of the anti-pollution measures. The visualization of trajectories on the self-organizing map let us clearly see the evolution of pollution levels and consequently evaluate the effectiveness of the taken measures, after and during the protocol activation time.

ACS Style

Ángel Arroyo; Carlos Cambra; Álvaro Herrero; Verónica Tricio; Emilio Corchado. Self-Organizing Maps to Validate Anti-Pollution Policies. Logic Journal of the IGPL 2019, 28, 596 -614.

AMA Style

Ángel Arroyo, Carlos Cambra, Álvaro Herrero, Verónica Tricio, Emilio Corchado. Self-Organizing Maps to Validate Anti-Pollution Policies. Logic Journal of the IGPL. 2019; 28 (4):596-614.

Chicago/Turabian Style

Ángel Arroyo; Carlos Cambra; Álvaro Herrero; Verónica Tricio; Emilio Corchado. 2019. "Self-Organizing Maps to Validate Anti-Pollution Policies." Logic Journal of the IGPL 28, no. 4: 596-614.

Research article
Published: 02 June 2019 in Complexity
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Present research proposes the application of unsupervised and supervised machine-learning techniques to characterize Android malware families. More precisely, a novel unsupervised neural-projection method for dimensionality-reduction, namely, Beta Hebbian Learning (BHL), is applied to visually analyze such malware. Additionally, well-known supervised Decision Trees (DTs) are also applied for the first time in order to improve characterization of such families and compare the original features that are identified as the most important ones. The proposed techniques are validated when facing real-life Android malware data by means of the well-known and publicly available Malgenome dataset. Obtained results support the proposed approach, confirming the validity of BHL and DTs to gain deep knowledge on Android malware.

ACS Style

Rafael Vega Vega; Héctor Quintián; Carlos Cambra; Nuño Basurto; Álvaro Herrero; José Luis Calvo-Rolle. Delving into Android Malware Families with a Novel Neural Projection Method. Complexity 2019, 2019, 1 -10.

AMA Style

Rafael Vega Vega, Héctor Quintián, Carlos Cambra, Nuño Basurto, Álvaro Herrero, José Luis Calvo-Rolle. Delving into Android Malware Families with a Novel Neural Projection Method. Complexity. 2019; 2019 ():1-10.

Chicago/Turabian Style

Rafael Vega Vega; Héctor Quintián; Carlos Cambra; Nuño Basurto; Álvaro Herrero; José Luis Calvo-Rolle. 2019. "Delving into Android Malware Families with a Novel Neural Projection Method." Complexity 2019, no. : 1-10.

Journal article
Published: 27 April 2019 in Agronomy
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New technologies have the potential to transform agriculture and to reduce environmental impact through a green revolution. Internet of Things (IoT)-based application development platforms have the potential to run farm management tools capable of monitoring real-time events when integrated into interactive innovation models for fertirrigation. Their capabilities must extend to flexible reconfiguration of programmed actions. IoT platforms require complex smart decision-making systems based on data-analysis and data mining of big data sets. In this paper, the advantages are demonstrated of a powerful tool that applies real-time decisions from data such as variable rate irrigation, and selected parameters from field and weather conditions. The field parameters, the index vegetation (estimated using aerial images), and the irrigation events, such as flow level, pressure level, and wind speed, are periodically sampled. Data is processed in a decision-making system based on learning prediction rules in conjunction with the Drools rule engine. The multimedia platform can be remotely controlled, and offers a smart farming open data network with shared restriction levels for information exchange oriented to farmers, the fertilizer provider, and agricultural technicians that should provide the farmer with added value in the form of better decision making or more efficient exploitation operations and management.

ACS Style

Carlos Cambra Baseca; Sandra Sendra; Jaime Lloret; Jesus Tomas. A Smart Decision System for Digital Farming. Agronomy 2019, 9, 216 .

AMA Style

Carlos Cambra Baseca, Sandra Sendra, Jaime Lloret, Jesus Tomas. A Smart Decision System for Digital Farming. Agronomy. 2019; 9 (5):216.

Chicago/Turabian Style

Carlos Cambra Baseca; Sandra Sendra; Jaime Lloret; Jesus Tomas. 2019. "A Smart Decision System for Digital Farming." Agronomy 9, no. 5: 216.

Journal article
Published: 25 April 2018 in Sensors
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Improving the sustainability in agriculture is nowadays an important challenge. The automation of irrigation processes via low-cost sensors can to spread technological advances in a sector very influenced by economical costs. This article presents an auto-calibrated pH sensor able to detect and adjust the imbalances in the pH levels of the nutrient solution used in hydroponic agriculture. The sensor is composed by a pH probe and a set of micropumps that sequentially pour the different liquid solutions to maintain the sensor calibration and the water samples from the channels that contain the nutrient solution. To implement our architecture, we use an auto-calibrated pH sensor connected to a wireless node. Several nodes compose our wireless sensor networks (WSN) to control our greenhouse. The sensors periodically measure the pH level of each hydroponic support and send the information to a data base (DB) which stores and analyzes the data to warn farmers about the measures. The data can then be accessed through a user-friendly, web-based interface that can be accessed through the Internet by using desktop or mobile devices. This paper also shows the design and test bench for both the auto-calibrated pH sensor and the wireless network to check their correct operation.

ACS Style

Carlos Cambra; Sandra Sendra; Jaime Lloret; Raquel Lacuesta. Smart System for Bicarbonate Control in Irrigation for Hydroponic Precision Farming. Sensors 2018, 18, 1333 .

AMA Style

Carlos Cambra, Sandra Sendra, Jaime Lloret, Raquel Lacuesta. Smart System for Bicarbonate Control in Irrigation for Hydroponic Precision Farming. Sensors. 2018; 18 (5):1333.

Chicago/Turabian Style

Carlos Cambra; Sandra Sendra; Jaime Lloret; Raquel Lacuesta. 2018. "Smart System for Bicarbonate Control in Irrigation for Hydroponic Precision Farming." Sensors 18, no. 5: 1333.

Conference paper
Published: 27 September 2017 in Proceedings XIII Jornadas de Ingenieria Telematica - JITEL2017
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Debido a los cambios medioambientales, aumento demográfico o incluso la propia volatilidad de precios en los mercados, el sector agrícola necesita actualmente mejorar el manejo de los recursos agrarios. Las nuevas tecnologías son clave para mejorar la sostenibilidad en el sector agrícola y producir alimentos con calidad alimentaria contrastada. En los últimos años España está sufriendo etapas de escasez de agua y zonas semiáridas dedicadas a la horticultura sufren estos efectos, por lo que los agricultores se ven obligados a trasladar sus cultivos a zonas más húmedas que en muchas ocasiones, presentan condiciones climatológicas menos adecuadas. Gracias a la tecnología podemos monitorizar y crear entornos con condiciones ambientales idóneas mediante el uso de invernaderos, que nos permiten la producción de alimentos controlando todo tipo de parámetros ambientales, nutricionales y de la propia planta. En este artículo presentamos el desarrollo de una red de sensores móviles orientada a monitorizar los patrones de necesidades de las plantas y tomar decisiones inteligentes según la captación de datos ambientales obtenida. La red está compuesta por nodos sensores comunicados con tranceptores de radio distribuidos en una red mallada, que podría ser fácilmente dapatada a cualquier tipo de uso a petición del profesional. Esta red ha sido probada en un entorno de agricultura hidropónica. Finalmente el paper muestra los resultados obtenidos en cuanto a tráfico generado, lo que nos permitirá en un futuro, hacer la red escalable..

ACS Style

Jose Miguel Jimenez; Carlos Cambra; Sandra Sendra; Jaime Lloret. Red de Sensores Inalámbricos de Bajo Consumo Energético en Agricultura Hidropónica. Proceedings XIII Jornadas de Ingenieria Telematica - JITEL2017 2017, 55 -62.

AMA Style

Jose Miguel Jimenez, Carlos Cambra, Sandra Sendra, Jaime Lloret. Red de Sensores Inalámbricos de Bajo Consumo Energético en Agricultura Hidropónica. Proceedings XIII Jornadas de Ingenieria Telematica - JITEL2017. 2017; ():55-62.

Chicago/Turabian Style

Jose Miguel Jimenez; Carlos Cambra; Sandra Sendra; Jaime Lloret. 2017. "Red de Sensores Inalámbricos de Bajo Consumo Energético en Agricultura Hidropónica." Proceedings XIII Jornadas de Ingenieria Telematica - JITEL2017 , no. : 55-62.

Conference paper
Published: 01 September 2017 in 2017 10th IFIP Wireless and Mobile Networking Conference (WMNC)
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Wireless Sensor Networks (WSNs), Internet of things (IoT) and cloud data are nowadays being greatly used in our society. This challenge requires a new and smart wireless network topology for communicating devices. Problems like scalability and manageability are important issues when there are large amounts of devices. This paper presents the design of a smart IoT communication system for detecting rodents. The system is based on low energy consumption and low cost RF communication microcontrollers which costs around 1-2 $. A large number of devices connected in real time are deployed to monitor rodent pests mainly in food industries and agricultural activities. The movements of rodents and the trapping estimation for periodical dates (weekly, monthly or yearly) on each company or selected area can be remotely monitored by accessing to the multimedia platform through mobile phone or via computer. Finally, we have measured the bandwidth consumed for different configurations as well as the detection of rodents using a Passive Infrared (PIR) sensor. Our goal is the expansion of micro RF transceivers with a cost less than 5 $ on industries and ultra-low power wireless communications.

ACS Style

Carlos Cambra; Sandra Sendra; Laura Garcia; Jaime Lloret. Low cost wireless sensor network for rodents detection. 2017 10th IFIP Wireless and Mobile Networking Conference (WMNC) 2017, 1 -7.

AMA Style

Carlos Cambra, Sandra Sendra, Laura Garcia, Jaime Lloret. Low cost wireless sensor network for rodents detection. 2017 10th IFIP Wireless and Mobile Networking Conference (WMNC). 2017; ():1-7.

Chicago/Turabian Style

Carlos Cambra; Sandra Sendra; Laura Garcia; Jaime Lloret. 2017. "Low cost wireless sensor network for rodents detection." 2017 10th IFIP Wireless and Mobile Networking Conference (WMNC) , no. : 1-7.

Conference paper
Published: 01 May 2017 in 2017 IEEE International Conference on Communications (ICC)
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Wireless Sensor Networks (WSNs), Internet of things (IoT) and aerial mapping are nowadays being used very much in agriculture. The challenge of joining those technologies requires a new and smart wireless network topology for devices communication. Problems like scalability and manageability are important challenges when there are many devices. This paper presents the design of a smart IoT communication system manager used as a low cost irrigation controller. The proposal is a powerful irrigation tool that uses real time data such as the variable rate irrigation and some parameters taken from the field. The field parameters, the index vegetation (estimated using aerial images) and the irrigation events, such as flow level, pressure level or wind speed, are periodically sampled. Data is processed in a smart cloud service based on the Drools Guvnor (a Business Rules Manager). The developed multimedia platform can be controlled remotely by a mobile phone. Finally, we measured the bandwidth consumed when the system is sending different kinds of commands and data.

ACS Style

Carlos Cambra; Sandra Sendra; Jaime Lloret; Laura Garcia. An IoT service-oriented system for agriculture monitoring. 2017 IEEE International Conference on Communications (ICC) 2017, 1 -6.

AMA Style

Carlos Cambra, Sandra Sendra, Jaime Lloret, Laura Garcia. An IoT service-oriented system for agriculture monitoring. 2017 IEEE International Conference on Communications (ICC). 2017; ():1-6.

Chicago/Turabian Style

Carlos Cambra; Sandra Sendra; Jaime Lloret; Laura Garcia. 2017. "An IoT service-oriented system for agriculture monitoring." 2017 IEEE International Conference on Communications (ICC) , no. : 1-6.

Journal article
Published: 13 January 2016 in Network Protocols and Algorithms
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Intelligent Unmanned Aerial Vehicles (UAVs) on emergency rescue is widely used to detect injured mountaineers in inaccessible areas. These systems should satisfied several features. On the one hand, it is important to know the georeference of the injured mountaineers. It is also important to have real-time images of the situation and the area where people have been hilly. For this reasons, in this paper, we present the development of a UAV integrated within a wireless ad hoc network and a communication protocol able to transfer between several UAV’s and Smartphones working with Android OS. This paper also shows how to extend the concept of ad hoc network on Smartphones to extend wireless coverage for emergency situations in critical areas without GSM cellular coverage. After developing our system, we have focused our effort on demonstrating the correct operation of our UAV and its network performance when the system is used to track within a zone. Experimental results show the big potential of this kind of networks working on hostile terrain such as big mountains, ravines and river canyons without GSM signal communication.

ACS Style

Carlos Cambra; Sandra Sendra; Jaime Lloret; Lorena Parra. Ad hoc Network for Emergency Rescue System based on Unmanned Aerial Vehicles. Network Protocols and Algorithms 2016, 7, 72 -89.

AMA Style

Carlos Cambra, Sandra Sendra, Jaime Lloret, Lorena Parra. Ad hoc Network for Emergency Rescue System based on Unmanned Aerial Vehicles. Network Protocols and Algorithms. 2016; 7 (4):72-89.

Chicago/Turabian Style

Carlos Cambra; Sandra Sendra; Jaime Lloret; Lorena Parra. 2016. "Ad hoc Network for Emergency Rescue System based on Unmanned Aerial Vehicles." Network Protocols and Algorithms 7, no. 4: 72-89.

Book chapter
Published: 10 February 2015 in Transactions on Petri Nets and Other Models of Concurrency XV
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Recent advances in technology applied to agriculture have made possible the Precision Agriculture (PA). It has been widely demonstrated that precision agriculture provides higher productivity with lower costs. The goal of this paper is to show the deployment of a real-time precision sprayer which uses video sensing captured by lightweight UAVs (unmanned aerial vehicles) forming ad hoc network. It is based on a geo-reference system that takes into account weeds inside of a mapped area. The ad hoc network includes devices such as AR Drones, a laptop and a sprayer in a tractor. The experiment was carried out in a corn field with different locations selected to represent the diverse densities of weeds that can be found in the field. The deployed system allows saving high percentage of herbicide, reducing the cost spent in fertilizers and increasing the quality of the product.

ACS Style

Carlos Cambra; Juan R. Díaz; Jaime Lloret. Deployment and Performance Study of an Ad Hoc Network Protocol for Intelligent Video Sensing in Precision Agriculture. Transactions on Petri Nets and Other Models of Concurrency XV 2015, 8629, 165 -175.

AMA Style

Carlos Cambra, Juan R. Díaz, Jaime Lloret. Deployment and Performance Study of an Ad Hoc Network Protocol for Intelligent Video Sensing in Precision Agriculture. Transactions on Petri Nets and Other Models of Concurrency XV. 2015; 8629 ():165-175.

Chicago/Turabian Style

Carlos Cambra; Juan R. Díaz; Jaime Lloret. 2015. "Deployment and Performance Study of an Ad Hoc Network Protocol for Intelligent Video Sensing in Precision Agriculture." Transactions on Petri Nets and Other Models of Concurrency XV 8629, no. : 165-175.