Optimal Control by the Queue with Rate and Quality of Service Depending on the Amount of Harvested Energy as a Model of the Node of Wireless Sensor Network

A queueing model with energy harvesting and multi-threshold control by service regimes is analysed. The available service regimes are characterized by the different service rate, requirements to the number of energy units for a request service and the probability of error occurrence during service. Error accounting is vital for adequate modelling wireless networks due to existence of an interference in a transmission thread. The increase of the number of energy units for service of a request implies an opportunity to send a stronger signal what implies the higher transmission rate and a lower probability of error occurrence during transmission. Error occurrence causes the repeated transmission and, therefore, consumption of more energy. Under the fixed parameters of the control strategy, the system dynamics is described by a continuous-time six-dimensional Markov chain. This allows to compute the steady-state distribution of the system states and, then, formulate and solve optimization problems. Numerical results are presented. © Springer Nature Switzerland AG 2019.

Авторы
Dudin A. 1, 2 , Kim C.3 , Dudin S. 1, 2
Язык
Английский
Страницы
165-178
Статус
Опубликовано
Том
11965 LNCS
Год
2019
Организации
  • 1 Department of Applied Mathematics and Computer Science, Belarusian State University, Minsk, 220030, Belarus
  • 2 Peoples’ Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya St, Moscow, 117198, Russian Federation
  • 3 Sangji University, Wonju, Kangwon 26339, South Korea
Ключевые слова
Energy harvesting; Optimization; Queueing system; Reliability; Service rate control
Дата создания
10.02.2020
Дата изменения
25.05.2021
Постоянная ссылка
https://repository.rudn.ru/ru/records/article/record/56422/
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Другие записи

Pham V.D., Hoang T., Kirichek R., Makolkina M., Koucheryavy A.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Том 11965 LNCS. 2019. С. 495-507