Classification of Remote Sensing Image Scenes Using Double Feature Extraction Hybrid Deep Learning Approach
Volume-3 | Issue-2
Light Weight CNN based Robust Image Watermarking Scheme for Security
Volume-3 | Issue-2
Principle of 6G Wireless Networks: Vision, Challenges and Applications
Volume-3 | Issue-4
PROGRESS AND PRECLUSION OF KNEE OSTEOARTHRITIS: A STUDY
Volume-3 | Issue-3
Is Internet becoming a Major Contributor for Global warming - The Online Carbon Footprint
Volume-2 | Issue-4
Augmented Reality in Education
Volume-2 | Issue-4
A Study on Various Task-Work Allocation Algorithms in Swarm Robotics
Volume-2 | Issue-2
IoT based Biotelemetry for Smart Health Care Monitoring System
Volume-2 | Issue-3
Tungsten DiSulphide FBG Sensor for Temperature Monitoring in Float Glass Manufacturing
Volume-2 | Issue-4
GUI based Industrial Monitoring and Control System
Volume-3 | Issue-2
AUTOMATION USING IOT IN GREENHOUSE ENVIRONMENT
Volume-1 | Issue-1
Principle of 6G Wireless Networks: Vision, Challenges and Applications
Volume-3 | Issue-4
Classification of Remote Sensing Image Scenes Using Double Feature Extraction Hybrid Deep Learning Approach
Volume-3 | Issue-2
Light Weight CNN based Robust Image Watermarking Scheme for Security
Volume-3 | Issue-2
VIRTUAL REALITY GAMING TECHNOLOGY FOR MENTAL STIMULATION AND THERAPY
Volume-1 | Issue-1
Design of Digital Image Watermarking Technique with Two Stage Vector Extraction in Transform Domain
Volume-3 | Issue-3
Analysis of Natural Language Processing in the FinTech Models of Mid-21st Century
Volume-4 | Issue-3
PROGRESS AND PRECLUSION OF KNEE OSTEOARTHRITIS: A STUDY
Volume-3 | Issue-3
Image Augmentation based on GAN deep learning approach with Textual Content Descriptors
Volume-3 | Issue-3
Comparative Analysis for Personality Prediction by Digital Footprints in Social Media
Volume-3 | Issue-2
Volume - 4 | Issue - 3 | september 2022
Published
28 September, 2022
Energy efficiency is one of the primary requirements for designing a successful Wireless Sensor Network (WSN) model. The WSN systems are generally made with a group of nodes that are operated with a small size battery device. To improve the energy efficiency of such WSNs several methodologies like clustering approach, mobile node technique and optimal route planning designs were developed. Scheduling method is yet an efficient model that is widely used in WSN applications, that allows the nodes to be operated only for a certain prescribed time. The proposed work utilizes the Self Organizing Maps (SOM) approach for improving the performances of the scheduling algorithms to a certain limit. SOM is a kind of artificial neural network that analyzes the problem based on competitive learning rather than the backpropagation methods. The work compares the proposed algorithm with the traditional Ant Colony and Software Defined Network approaches, wherein the proposed approach has shown an improvement in terms of energy conservation and network lifetime.
KeywordsNetwork lifetime network energy management scheduling approach clustering routing method neural network in WSN
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