Resource scheduling in edge computing IoT networks using hybrid deep learning algorithm
Вантажиться...
Дата
2022
Науковий керівник
Назва журналу
Номер ISSN
Назва тому
Видавець
КПІ ім. Ігоря Сікорського
Анотація
The proliferation of the Internet of Things (IoT) and wireless sensor networks
enhances data communication. The demand for data communication rapidly
increases, which calls the emerging edge computing paradigm. Edge computing
plays a major role in IoT networks and provides computing resources close to the
users. Moving the services from the cloud to users increases the communication,
storage, and network features of the users. However, massive IoT networks require a
large spectrum of resources for their computations. In order to attain this, resource
scheduling algorithms are employed in edge computing. Statistical and machine
learning-based resource scheduling algorithms have evolved in the past decade, but
the performance can be improved if resource requirements are analyzed further. A
deep learning-based resource scheduling in edge computing IoT networks is presented
in this research work using deep bidirectional recurrent neural network
(BRNN) and convolutional neural network algorithms. Before scheduling, the IoT
users are categorized into clusters using a spectral clustering algorithm. The proposed
model simulation analysis verifies the performance in terms of delay, response
time, execution time, and resource utilization. Existing resource scheduling
algorithms like a genetic algorithm (GA), Improved Particle Swarm Optimization
(IPSO), and LSTM-based models are compared with the proposed model to validate
the superior performances.
Опис
Ключові слова
edge computing, cloud computing, Internet of Things (IoT), resource scheduling, deep learning, периферійні обчислення, хмарні обчислення, інтернет речей (IoT), планування ресурсів, глибоке навчання
Бібліографічний опис
Vijayasekaran, G. Resource scheduling in edge computing IoT networks using hybrid deep learning algorithm / G. Vijayasekaran, M. Duraipandian // Системні дослідження та інформаційні технології : міжнародний науково-технічний журнал. – 2022. – № 3. – С. 86-101. – Бібліогр.: 25 назв.