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28 | 09 | 2020
10.14489/vkit.2020.09.pp.046-051

DOI: 10.14489/vkit.2020.09.pp.046-051

Хайтбаев А. Ф., Эшмурадов А. М.
ПОВЫШЕНИЕ ЭНЕРГОЭФФЕКТИВНОСТИ БЕСПРОВОДНЫХ СЕНСОРНЫХ СЕТЕЙ С ПОМОЩЬЮ НЕЙРОСЕТЕВЫХ ТЕХНОЛОГИЙ
(с. 46-51)

Аннотация. Показана возможность повышения энергоэффективности беспроводных сенсорных сетей за счет более эффективного управления. Представленная модель беспроводной сенсорной сети учитывает подзарядку от окружающей среды. Проверена возможность организации распределенных вычислений в беспроводных сенсорных сетях путем использования имитационного моделирования с помощью нейронных сетей.

Ключевые слова:  сенсорные сети; моделирование; энергоэффективность; нейронные сети; протоколы для сенсорных сетей.

 

Khaytbaev A. F., Eshmuradov A. M.
APPLICATIONS OF NEURAL NETWORK TECHNOLOGIES IN WIRELESS SENSOR NETWORKS
(pp. 46-51)

Abstract. The purpose of the article is to study the possibilities of improving the efficiency of the sensory network management technique, using the neural network method. The presented model of the wireless sensor network takes into account the charging of the environment. The article also tests the hypothesis of the possibility of organizing distributed computing in wireless sensor networks. To achieve this goal, a number of tasks are allocated: review and analysis of existing methods for managing BSS nodes; definition of simulation model components and their properties of neural networks and their features; testing the results of using the developed method. The article explores the major historical insights of the application of the neural network technologies in wireless sensor networks in the following practical fields: engineering, farming, utility communication networks, manufacturing, emergency notification services, oil and gas wells, forest fires prevention equipment systems, etc. The relevant applications for the continuous monitoring of security and safety measures are critically analyzed in the context of the relevancy of specific decisions to be implemented within the system architecture. The study is focused on the modernization of methods of control and management for the wireless sensor networks considering the environmental factors to be allocated using senor systems for data maintenance, including the information on temperature, humidity, motion, radiation, etc. The article contains the relevant and adequate comparative analysis of the updated versions of node control protocols, the components of the simulation model, and the control method based on neural networks to be identified and tested within the practical organizational settings.

Keywords: Sensor networks; Simulation; Energy efficiency; Neural networks; Protocols for sensor networks.

Рус

А. Ф. Хайтбаев, А. М. Эшмурадов (Ташкентский университет информационных технологий имени Мухаммада ал-Хоразмий, Ташкент, Республика Узбекистан) E-mail: Этот e-mail адрес защищен от спам-ботов, для его просмотра у Вас должен быть включен Javascript  

Eng

A. F. Khaytbaev, A. M. Eshmuradov (Tashkent University of Information Technologies named after Mukhammad al-Khorazmy, Tashkent, Republic of Uzbekistan) E-mail: Этот e-mail адрес защищен от спам-ботов, для его просмотра у Вас должен быть включен Javascript  

Рус

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2. Tampalini F., Cassinis R. Fuzzy Logic Controller Based on XML Formatted Files for Behaviour-based Mobile Robots // Technical Report, DEA-Unibs. 2005.
3. Soto A. R., Capdevila C. A., Fernandez E. C. Fuzzy Systems and Neural Networks XML Schemas for Soft Computing // Mathware Soft Comput. 2003. No. 2–3. P. 43 – 56.
4. Boisseau S., Despesse G. Energy Harvesting, Wireless Sensor Networks & Opportunities for Industrial Applications [Электронный ресурс]. URL: http://www.embedded. com/design/smart-energy-design/4237022/
5. Adu-Manu K., Adam N., Tapparello C., Ayatollahi H. Energy-Harvesting Wireless Sensor Networks (EH-WSNs): A Review // ACM Transactions on Sensor Network. 2018. No. 14(2). P. 1 – 50.
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Eng

1. Karpov A. S. (2017). Comparison of neural network training methods for image recognition problem. Vestnik sovremennyh issledovaniy, 9(6–1), pp. 122–123. [in Russian language]
2. Tampalini F., Cassinis R. (2005). Fuzzy Logic Controller Based on XML Formatted Files for Behaviour-based Mobile Robots. Technical Report, DEA-Unibs.
3. Soto A. R., Capdevila C. A., Fernandez E. C. (2003). Fuzzy Systems and Neural Networks XML Schemas for Soft Computing. Mathware and Soft Computing, (2–3), pp. 43 – 56.
4. Boisseau S., Despesse G. Energy Harvesting, Wireless Sensor Networks & Opportunities for Industrial Applications. Available at: http://www.embedded.com/ design/smart-energy-design/4237022/
5. Adu-Manu K., Adam N., Tapparello C., Ayatollahi H. (2018) Energy-Harvesting Wireless Sensor Networks (EH-WSNs): A Review // ACM Transactions on Sensor Network. No. 14(2), pp. 1 – 50.
6. Szulczyński B. et al. (2018). Determination of Odour Interactions in Gaseous Mixtures Using Electronic Nose Methods with Artificial Neural Networks. Sensors, Vol. 18, (2). DOI: 10.3390/ s18020519
7. Goonatilake R., Bachnak R., Herath S. (2011). Statistical Quality Control Approaches To Network Intrusion Detection. International Journal of Network Security & Its Applications (IJNSA), Vol. 3, (6), pp. 115 – 124.
8. Gowtham C., Veeramallu B. (2013). Bayesian Classifier Approach For An Effective Outlier Intrusion Detection System In Mobile Adhoc Networks. International Journal Of Engineering And Computer Science, Vol. 2, (3), pp. 642 – 649.

Рус

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