Internet of Things (IoT) technologies represent the future challenges of computing and communications. They can also be useful to improve traditional farming practices worldwide. Since the areas where agricultural land is located in remote places, there is a need for new technologies. These technologies must be suitable and reliable for communication over long distances and, at the same time, consume little energy. In particular, one of these relatively new technologies is the LoRa communication protocol, which uses long waves to work over long distances. This is extremely useful in agriculture, where the communicating areas are broad fields of crops and greenhouses. This study developed a greenhouse monitoring system based on LoRa technology, designed to work over long distances. The edge computing paradigms with a machine learning mechanism are proposed to analyze and control the state of the greenhouse, and in particular, to reduce the mount of data transmitted to the server.

Original languageEnglish
Title of host publicationDistributed Computer and Communication Networks - 23rd International Conference, DCCN 2020, Revised Selected Papers
EditorsVladimir M. Vishnevskiy, Dmitry V. Kozyrev, Konstantin E. Samouylov, Dmitry V. Kozyrev
PublisherSpringer Nature
Pages113-125
Number of pages13
ISBN (Print)9783030664701
DOIs
StatePublished - 2020
Event23rd International Conference on Distributed Computer and Communication Networks, DCCN 2020 - Moscow, Russian Federation
Duration: 14 Sep 202018 Sep 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12563 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference23rd International Conference on Distributed Computer and Communication Networks, DCCN 2020
Country/TerritoryRussian Federation
CityMoscow
Period14/09/2018/09/20

    Research areas

  • Edge computing, LoRa, Machine learning, Precision agriculture

    Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

ID: 87324492