Browsing by Author "Uribe Chavert, Pedro"

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    Distributing and processing data from the edge. A case study with ultrasound sensor modules
    (Springer, Cham, 2021) Poza Lujan, Jose Luis; Uribe Chavert, Pedro; Sáenz Peñafiel, Juan José; Posadas Yagüe, Juan Luis; Poza Lujan, Jose Luis
    Currently, the proliferation of interconnected smart devices is related to smart urban management. These devices can process sensor data in order to obtain significant information. This information can be provided to the upper layers but also can be used by the devices to take some smart actions. This article shows the change from the classic hierarchical devices and data paradigm to a paradigm based on distributed intelligent devices. The distributed model has been used to create a system architecture with Arduino-based Control Nodes interconnected by means of an I2C-bus. Each module can read the distance to each vehicle, and process this data to provide the vehicle speed and length. A case has experimented where modules share raw data and another case where modules share processed data. Results show that it is possible to reduce processing load up to 22% in the case of sharing processed information instead of raw data.
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    Processing at the edge: a case study with an ultrasound sensor-based embedded smart device
    (2022) Poza Lujan, Jose Luis; Uribe Chavert, Pedro; Sáenz Peñafiel, Juan José; Posadas Yagüe, Juan Luis
    In the current context of the Internet of Things, embedded devices can have some intelligence and distribute both data and processed information. This article presents the paradigm shift from a hierarchical pyramid to an inverted pyramid that is the basis for edge, fog, and cloud-based architectures. To support the new paradigm, the article presents a distributed modular architecture. The devices are made up of essential elements, called control nodes, which can communicate to enhance their functionality without sending raw data to the cloud. To validate the architecture, identical control nodes equipped with a distance sensor have been implemented. Each module can read the distance to each vehicle and process these data to provide the vehicle’s speed and length. In addition, the article describes how connecting two or more CNs, forming an intelligent device, can increase the accuracy of the parameters measured. Results show that it is possible to reduce the processing load up to 22% in the case of sharing processed information instead of raw data. In addition, when the control nodes collaborate at the edge level, the relative error obtained when measuring the speed and length of a vehicle is reduced by one percentage point.