Smart agriculture is one of the most diverse research. In addition, the quantity of data to be stored and the choice of the most efficient algorithms to process are significant elements in this field. The storage of c...
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Smart agriculture is one of the most diverse research. In addition, the quantity of data to be stored and the choice of the most efficient algorithms to process are significant elements in this field. The storage of collecting data from Internet of Things (IoT), existing on distributed, local databases and open data need a particular infrastructure to federate all these data to make complex treatments. The storage of this wide range of data that comes at high frequency and variable throughput is particularly difficult. In this paper, we propose the use of distributed databases and high-performance computing architecture in order to exploit multiple re-configurable computing and application specific processing such as CPUs, GPUs, TPUs and FPGAs efficiently. This exploitation allows an accurate training for an application to machine learning, deep learning and unsupervised modeling algorithms. The last ones are used for training supervised algorithms on images when it labels a set of images and unsupervised algorithms on IoT data which are unlabeled with variable qualities. The processing of data is based on Hadoop 3.1 MapReduce to achieve parallel processing and use containerization technologies to distribute treatments on Multi GPU, MIC and FPGA. This architecture allows efficient treatments of data coming from several sources with a cloud high-performance heterogeneous architecture. The proposed 4 layers infrastructure can also implement FPGA and MIC which are now natively supported by recent version of Hadoop. Moreover, with the advent of new technologies like Intel® Movidius TM ; it is now possible to deploy CNN at the Fog level in the IoT network and to make inference with the cloud and therefore limit significantly the network traffic that result in reducing the move of large amounts of data to the cloud.
Smartphones, particularly iPhones, can be relevant instruments for researchers in animal behavior because they are readily available on the planet, contain many sensors and require no hardware development. They are eq...
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Smartphones, particularly iPhones, can be relevant instruments for researchers in animal behavior because they are readily available on the planet, contain many sensors and require no hardware development. They are equipped with high performance inertial measurement units (IMU) and absolute positioning systems analyzing users’ movements, but they can easily be diverted to analyze likewise the behaviors of domestic animals such as cattle. The study of animal behavior using smartphones requires the storage of many high frequency variables from a large number of individuals and their processing through various relevant variables combinations for modeling and decision-making. Transferring, storing, treating and sharing such an amount of data is a big challenge. In this paper, a lambda cloud architecture and a scientific sharing platform used to archive and process high-frequency data are proposed. An application to the study of cattle behavior on pasture on the basis of the data recorded with the IMU of iPhones 4S is exemplified. The package comes also with a web interface to encode the actual behavior observed on videos and to synchronize observations with the sensor signals. Finally, the use of fog computing on the iPhone reduced by 42% on average the size of the raw data by eliminating redundancies.
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