Prediction of energy consumption for LoRa based wireless sensors network

Madiyar Nurgaliyev , Ahmet Saymbetov , Yevhen Yashchyshyn , Nurzhigit Kuttybay , Didar Tukymbekov


This paper shows a method for predicting the lifetime of a wireless sensor network based on the LoRa Ra-01 wireless modules. To develop a prediction model of the energy consumption, wireless sensor modules were assembled and it was obtained experimental data using LabView development environment. There were performed experiments to get battery discharge curve. Experimental data of power consumption depending on the packet length were obtained in transmission mode. Using experimental data, we obtained dependencies of system lifetime on sleep mode duration and packet length. The paper also considered a probabilistic approach to predict the system lifetime depending on the probability of data transmission during the day. The lifetime prediction model is based on Markov’s chains. The results obtained in this work can be used to predict lifetime of sensor networks more accurately.
Author Madiyar Nurgaliyev - [Al Farabi Kazakh National University]
Madiyar Nurgaliyev,,
, Ahmet Saymbetov - [Al Farabi Kazakh National University]
Ahmet Saymbetov,,
, Yevhen Yashchyshyn (FEIT / IRMT)
Yevhen Yashchyshyn,,
- The Institute of Radioelectronics and Multimedia Technology
, Nurzhigit Kuttybay - [Al Farabi Kazakh National University]
Nurzhigit Kuttybay,,
, Didar Tukymbekov - [Al Farabi Kazakh National University]
Didar Tukymbekov,,
Journal seriesWireless Networks, ISSN 1022-0038, e-ISSN 1572-8196
Issue year2020
Publication size in sheets0.65
ASJC Classification1705 Computer Networks and Communications; 1710 Information Systems; 2208 Electrical and Electronic Engineering
Languageen angielski
LicenseJournal (articles only); author's original; Other open licence; after publication
Score (nominal)70
Score sourcejournalList
ScoreMinisterial score = 70.0, 21-07-2020, ArticleFromJournal
Publication indicators Scopus Citations = 0; Scopus SNIP (Source Normalised Impact per Paper): 2017 = 1.021; WoS Impact Factor: 2018 = 2.405 (2) - 2018=2.023 (5)
Citation count*1 (2020-08-22)
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* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.
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