Optimal Routing and Scheduling for Cognitive Radio Sensor Networks using Ensemble Multi Probabilistic Optimization and Truncated Energy Flow Classification Model

Optimal Routing and Scheduling for Cognitive Radio Sensor Networks using Ensemble Multi Probabilistic Optimization and Truncated Energy Flow Classification Model
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© 2021 by IJETT Journal
Volume-69 Issue-9
Year of Publication : 2021
Authors : Kumaresh Sheelavant, R. Sumathi, Charan K V
DOI :  10.14445/22315381/IJETT-V69I9P221

How to Cite?

Kumaresh Sheelavant, R. Sumathi, Charan K V, "Optimal Routing and Scheduling for Cognitive Radio Sensor Networks using Ensemble Multi Probabilistic Optimization and Truncated Energy Flow Classification Model," International Journal of Engineering Trends and Technology, vol. 69, no. 9, pp. 168-178, 2021. Crossref, https://doi.org/10.14445/22315381/IJETT-V69I9P221

Abstract
Providing routing to the Cognitive Radio Sensor Network (CRSN) is one of the crucial and demanding issues in recent decades. The routing issues can be listed as data jamming, illegal tracking of Sensor ID, and position detection in the fast-moving of sensors. So, different types of communication protocols and routing algorithms have been developed in the conventional works for ensuring both reliable communication and increased routing. Still, it limits to problems related to high time consumption, complexity, and inefficient routing. In order to avoid these problems, this paper intends to develop a new Ensemble Multi-Probabilistic Optimization (EMPO) – Truncated Energy Flow Classification (TEFC) algorithm for CRSN. Here, the channel selection model is deployed to analyze the parameters of network architecture, which includes the computation cost and sensor information used for the communication service. Also, the channel selection is deployed for providing random licensed parameters and temporary parameters based on the data link that forms the random parameters generation process. There are two stages here; at first, the EMPO technique is implemented to select the most suitable path for enabling the data transmission on the network. Then, a TEFC algorithm is employed to select the original data before it is transmitted to the corresponding destination. The experimental results evaluate the performance of the proposed technique by analyzing various evaluation measures. Also, the results are compared with some of the existing techniques for proving the superiority of the proposed technique.

Keywords
Cognitive radio sensor network, Ensemble Multi-Probabilistic Optimization, Optimal routing, and scheduling, Truncated Energy Flow Classification

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