In order to avoid the negative impacts of ecology, the need in the systems, monitoring the states of the ecology, based on the Internet of Things technology arises. In the work the imitation model of the automated system of environment using the LoRa technology has been presented. The imitation model represents the multifunctional hardware and software complex of technical means for the exchange, collection, processing, storage and transmission of the data, reflecting a wide range of the meteorological parameters. Using the results of the imitation simulation the influence of the ratio of the heights of the antennas of the base stations and measuring posts, where the sensors, the structurization module of the received data and the data transmission module operating according to the LoRa standard are located, has been revealed. The dependence of the packet delivery rate from the measuring stations to the base stations and their quantity have been displayed. The minimum interval before re-sending a packet without collisions has been detected to ensure the verification, the uninterrupted data collection and the transmission, taking into account the use of a minimum of computing resources. This solution has been considered for further integration into the city project «Smart Moscow».
1. Гинко В.И., Тараров А.Г. Система экологического мониторинга в управлении эко-логическим риском // Современные проблемы науки и образования. – 2015. – №3. – С. 6–10.
2. A green small cells deployment in 5G – Switch ON/OFF via IoT networks & energy effi-cient mesh backhauling / I. Allal, B. Mongazon-Cazavet, K. Al Agha et al. // IFIP Networking Conference (IFIP Networking) and Workshops. – 2017. – P. 1–2.
3. Potéreau M., Veyrac Y., Ferre G. Leveraging LoRa spreading factor detection to enhance transmission efficiency // IEEE International Symposium on Circuits and Systems. – 2018. – P. 1–5.
4. Theodore L. Air pollution control equipment. – John Wiley & Sons, Inc. – 2008. – 578 p.
5. Сысоева Т.И., Петкун А.С., Кучин В.В., Челибанов В.П. Результаты десятилетних измерений ряда параметров атмосферы Антарктики на привязных аэростатах // Известия РАН. Сер. Физическая. – 2016. – Т. 80. – № 5. – С. 600–603.
6. Modeling the process of network scaling for LoRaWan basen on NS3 / S. Muratchaev, A. Bakhtin, A. Volkov et al. // IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering. – 2018. – P. 1309–1312.
7. Goursaud C., Gorce J.M. Dedicated networks for IoT: PHY/MAC state of the art and challenges // EAI Endorsed Transactions on Internet of Things. – 2015. – URL:www.meteorf.ru (дата обращения: 24.04.2019).
8. Lavric A., Popa V. Internet of Things and LoRa™ low-power wide-area networks chal-lenges // 9th International Conference on Electronics, Computers and Artificial Intelligence. – 2017. – P. 1–4.
9. Elshabrawy T., Robert J. Analysis of BER and coverage performance of LoRa modula-tion under same spreading factor interference // IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications. – 2018. – P. 1–6.
10. Magrin D. Network level performances of a LoRa system. – Padua, Italy: 2016. – 104 p.
11. Impact of LoRa imperfect orthogonality: analysis of link-level performance / D. Croce, M. Gucciardo, S. Mangione et al. // IEEE Communications Letters. – 2018. – Vol. 22. – Iss. 4. – P. 796–799.
12. Приказ от 21 июня 2004 г. № 95 Федеральной службы по гидрометеорологии и мониторингу окружающей среды «О внедрении на радиолокационной сети Росгидромета «основных технических требований к системе обнаружения опасных атмосферных явле-ний и штормового оповещения на базе метеорологических радиолокаторов». – URL: www.meteorf.ru (дата обращения: 24.04.2019).