Results and recommendations from the comparison exercise of sensor embedded processing practices

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D1.6 Results and recommendations from the comparison exercise of sensor embedded processing practices
ENVRIplus logo.jpg
Project ENVRIplus
Deliverable nr D1.6
Submission date 2019-06-16
Type Report

PDF | Zenodo

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Small, generally low-cost sensors, that are deployed in unsupervised networks (or remote locations such as the ocean) are becoming more and more important across RIs and across domain. These kinds of sensors generally come equipped with data/signal processing capabilities that are generally stored in a microcontroller unit accompanying the sensing unit itself. This deliverable aims to sum up what are the main criticalities, issues and guidelines when applying these kinds of sensors in the field. Different applications and different sensors are examined in the deliverable ranging from low cost air pollution wireless sensor networks up to oceanic automated profilers. The result of such a comparison exercise is the highlighting of two main criticalities for which recommendations are provided:

  1. Calibration of the sensors
  2. Management of communications between the remote sensor and the user

Many sensors with embedded capabilities, especially low-cost ones, output data that must be carefully treated to have an effective value for the user. The deliverable shows, therefore, what are the best venues and methodologies to analyze these kinds of data and what are the pitfalls in the calibration procedures.

Many of the described sensors are often deployed in remote or unsupervised location and therefore it is of utmost importance to correctly approach the networking and communication capabilities to embed on the sensing platform. Depending on the amount of data produced and the type of sensor, this deliverable offers specific guidelines to manage this aspect.

Overall D1.6 reports the experience of CNR/ANAEE and partners (IFREMER, PLOCAN, CNRS, University of Bremen, CEA) developed within ENVRIPLUS with sensor embedded processing practices and gives a reference guide for any RI that wants to introduce this practice into its field measurements.

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