Green Energy Efficient Routing with Deep Learning Based Anomaly Detection for Internet of Things (IoT) Communications

E. Laxmi Lydia, A. Arokiaraj Jovith, A. Francis Saviour Devaraj, Changho Seo and Gyanendra Prasad Joshi
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E. Laxmi Lydia: Department of Computer Science and Engineering, Vignan’s Institute of Information Technology (Autonomous), Visakhapatnam 530049, Andhra Pradesh, India
A. Arokiaraj Jovith: Department of Information Technology, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur 603203, Tamil Nadu, India
A. Francis Saviour Devaraj: Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnankoil 626128, Tamil Nadu, India
Changho Seo: Department of Convergence Science, Kongju National University, Gongju 32588, Korea
Gyanendra Prasad Joshi: Department of Computer Science and Engineering, Sejong University, Seoul 05006, Korea

Mathematics, 2021, vol. 9, issue 5, 1-18

Abstract: Presently, a green Internet of Things (IoT) based energy aware network plays a significant part in the sensing technology. The development of IoT has a major impact on several application areas such as healthcare, smart city, transportation, etc. The exponential rise in the sensor nodes might result in enhanced energy dissipation. So, the minimization of environmental impact in green media networks is a challenging issue for both researchers and business people. Energy efficiency and security remain crucial in the design of IoT applications. This paper presents a new green energy-efficient routing with DL based anomaly detection (GEER-DLAD) technique for IoT applications. The presented model enables IoT devices to utilize energy effectively in such a way as to increase the network span. The GEER-DLAD technique performs error lossy compression (ELC) technique to lessen the quantity of data communication over the network. In addition, the moth flame swarm optimization (MSO) algorithm is applied for the optimal selection of routes in the network. Besides, DLAD process takes place via the recurrent neural network-long short term memory (RNN-LSTM) model to detect anomalies in the IoT communication networks. A detailed experimental validation process is carried out and the results ensured the betterment of the GEER-DLAD model in terms of energy efficiency and detection performance.

Keywords: Internet of Things; deep learning; anomaly detection; energy efficiency; routing (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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