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.
Highlights
The modern world adopts recent communication technologies to make a complete network coverage system and extended the intelligent object count which is correlated to the deployed system
This paper develops a novel Green Energy Efficient Routing with Deep Learning based Anomaly Detection (GEER-DLAD) technique for Internet of Things (IoT) applications
The GEER-DLAD technique accomplishes the error lossy compression (ELC) model to mitigate the quantity of data over the network
Summary
The modern world adopts recent communication technologies to make a complete network coverage system and extended the intelligent object count which is correlated to the deployed system. Mathematics 2021, 9, 500 prediction and intrusion detection as huge data has been produced by IoT devices activates deep models to understand the workflow when compared with shallow methods. Feature learning with deep neural networks have shown considerable developed in the recent times [5,6]. This paper develops a novel Green Energy Efficient Routing with Deep Learning based Anomaly Detection (GEER-DLAD) technique for IoT applications. The presented model permits the IoT devices to exploit energy efficiency in such a way to rise the network span. The GEER-DLAD technique accomplishes the error lossy compression (ELC) model to mitigate the quantity of data over the network. The integration of the GEER with anomaly detection, compression process, and energy efficient routing process shows the novelty of the work. In order to determine the supremacy of the GEER-DLAD model, a series of simulations were carried out
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