ntvНаучно-технический вестник информационных технологий, механики и оптикиScientific and Technical Journal of Information Technologies, Mechanics and Optics2226-14942500-0373Университет ИТМО10.17586/2226-1494-2022-22-2-364-375ntv-172Research ArticleМАТЕМАТИЧЕСКОЕ И КОМПЬЮТЕРНОЕ МОДЕЛИРОВАНИЕMODELING AND SIMULATIONIRDFPR-CMDNN: энергоэффективный и надежный протокол маршрутизации для улучшенной передачи данных в MANETIRDFPR-CMDNN: An energy efficient and reliable routing protocol for improved data transmission in MANEThttps://orcid.org/0000-0002-5363-4387СангитаА.SangeethaA.

Арумугам Сангита — магистр, исследователь; доцент 

 Салем, 636011 

 Элуматур, Модаккуричи, 638104 

Sangeetha Arumugam — M.Phil., Research Scholar;  Assistant Professor 

 Salem, 636011 

 Elumathur, Modakkurichi,  638104 

sangeethakng@gmail.comhttps://orcid.org/0000-0002-3646-067XРаджендранТ.RajendranT.

 Тангавел Раджендран — PhD, доцент 

 Кангеям, 638108 

Rajendran Thangavel — PhD, Assistant Professor 

 Kangeyam, 638108 

rajendran_tm@yahoo.comУниверситет Перияр; Государственный колледж искусств и наукиИндияPeriyar University; Assistant ProfessorIndiaГосударственный колледж искусств и наукиИндияGovernment Arts and Science CollegeIndia202214122024222364375Copyright © Сангита А., Раджендран Т., 20242024Сангита А., Раджендран Т.Sangeetha A., Rajendran T.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://ntv.elpub.ru/jour/article/view/172

Мобильные одноранговые сети (Mobile Ad hoc NETworks, MANET) — бесструктурные автономные беспроводные сети с мобильными узлами, которые динамически устанавливают соединения для передачи данных. Из-за динамических топологических изменений маршруты MANET оказываются несбалансированными и постоянно прерываются. Следовательно, обеспечение эффективной и надежной доставки данных с эффективным использованием сетевых ресурсов является сложной задачей, которую необходимо в MANET учитывать. Предложена многослойная глубокая искусственная нейронная сеть на основе пробит-регрессии с ранжированными решениями (IRDFPR-CMDNN) для эффективной передачи данных и более быстрой доставки данных с минимальной сквозной задержкой. Метод IRDFPR-CMDNN выполняет идентификацию и обслуживание маршрута, а также доставку данных более чем на трех уровнях. Затем мобильные узлы отправляются на входной уровень многослойной глубокой искусственной нейронной сети. На первом скрытом уровне применен алгоритм ранжированных лесов решений с мгновенным стоком для классификации мобильных узлов в зависимости от остаточной энергии и нагрузочной способности. К выбранным мобильным узлам применена пробит-регрессия для поиска во втором скрытом слое ближайших соседних узлов. Поиск выполнен на основе оценки качества канала и уровня принимаемого сигнала для получения пути маршрута. Далее установлено несколько путей маршрутизации от узла-источника к узлу-получателю и произведена передача данных. При отказе канала для маршрутизации выбирается альтернативный маршрут с лучшим качеством канала. В результате осуществлена энергоэффективная передача данных от источника к получателю с высокой скоростью доставки данных и минимальными временными затратами. Выполнена экспериментальная оценка энергопотребления, коэффициента доставки пакетов, скорости отбрасывания пакетов, пропускной способности и сквозной задержки с различным количеством мобильных узлов и пакетов данных. Результаты моделирования показали, что метод IRDFPR-CMDNN эффективно улучшает доставку данных, пропускную способность и минимизирует потребление энергии, уменьшает потери пакетов и задержки по сравнению с обычными методами.

Mobile Ad hoc Networks (MANET) are structure less, autonomous wireless networks with mobile nodes that dynamically establish data transmission connections. Due to dynamic topological change, MANET routes are unbalanced and break repeatedly. Hence, providing efficient and reliable data delivery with effective utilization of network resources is a challenging issue to be considered in MANET. This paper proposes an instant-runoff Ranked Decision Forests Probit Regression-based Connectionist Multilayer Deep Neural Network (IRDFPR-CMDNN) for efficient data transmission and higher data delivery with a minimum end-to-end delay. This IRDFPR-CMDNN method performs route identification, data delivery, and route maintenance with more than three layers. Then the mobile nodes are sent to the input layer of the Connectionist Multilayer Deep Neural Network. In hidden layer 1, the Instant-runoff Ranked Decision Forests algorithm is applied for classifying the mobile nodes depending on the residual energy and load capacity. With selected mobile nodes, the Probit Regression is applied for finding the nearest neighboring nodes in the second hidden layer based on the link quality and received signal strength for route path establishment. Then multiple paths for routing are established from source to destination node and start to perform the data transmission. If link failure occurs during the data transmission, another alternative route with better link quality is selected for routing. In this way, energy-efficient data transmission is performed from source to destination with a higher data delivery rate and minimal time consumption. Experimental evaluation is carried out on energy consumption, packet delivery ratio, packet drop rate, throughput, and end-to-end delay with varying numbers of mobile nodes and data packets. Simulation results show that the IRDFPRCMDNN technique effectively enhances data delivery, throughput and minimizes energy consumption, packet loss rate, delay with respect to conventional methods.

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The authors declare that there are no conflicts of interest present.