CORTEXA
← Browse
crossrefJournal of Intelligent Decision Making and Information Science2026-06-30Cited by 0

A Novel Congestion Avoidance Technique to Optimize the QoS Parameters in Wireless Sensor Network Using Semi-Superwised Machine Learning

Shiv H. Sutar

Congestion control in Wireless Sensor Network is a significant area which has to be addressed for smooth and seamless packet transfer from source to the destination node. Existing literatures have deployed various congestion control schemes to reduce the congestion in network., which are based on congestion and their severity, the remedial mechanisms are applied to overcome the problem of congestion. The congestion can be caused because of Channel occupancy, high reporting rate and quick buffer occupancy. Various techniques have been proposed and implemented to overcome the congestion problem which manages channel occupancy, reporting rate and buffer occupancy. Hence it motivates to propose some alternative technique which avoids the congestion itself. In this paper, congestion avoidance technique is proposed, as it causes severe problem in the network performance such as packet loss, wastage of processing power, inefficient resource utilization, and other Quality of Service parameters. Here the proposed technique named Congestion Avoidance Technique (CAT) a buffer management technique. Every node is attached with input and output buffer which has limited capacity. This technique efficiently manages the buffers of sensing nodes by continuously monitoring the buffer occupancy. CAT always ensures that the buffer contents are always in between allowable congestion window that is between PTM and LTM marking of the buffer. Here proposed technique avoids the congestion in WSN and ultimately increase the QoS parameters of the network. CAT is implemented using experimental scenario in NS2 and the results are analyzed which shows that the buffer is always occupied between congestion windowongestion control in Wireless Sensor Network is a significant area which has to be addressed for smooth and seamless packet transfer from source to the destination node. Existing literatures have deployed various congestion control schemes to reduce the congestion in network., which are based on congestion and their severity, the remedial mechanisms are applied to overcome the problem of congestion. The congestion can be caused because of Channel occupancy, high reporting rate and quick buffer occupancy. Various techniques have been proposed and implemented to overcome the congestion problem which manages channel occupancy, reporting rate and buffer occupancy. Hence it motivates to propose some alternative technique which avoids the congestion itself. In this paper, congestion avoidance technique is proposed, as it causes severe problem in the network performance such as packet loss, wastage of processing power, inefficient resource utilization, and other Quality of Service parameters. Here the proposed technique named Congestion Avoidance Technique (CAT) a buffer management technique. Every node is attached with input and output buffer which has limited capacity. This technique efficiently manages the buffers of sensing nodes by continuously monitoring the buffer occupancy. CAT always ensures that the buffer contents are always in between allowable congestion window that is between PTM and LTM marking of the buffer. Here proposed technique avoids the congestion in WSN and ultimately increase the QoS parameters of the network. CAT is implemented using experimental scenario in NS2 and the results are analyzed which shows that the buffer is always occupied between congestion window.

View free PDFSource page

Related papers

crossrefJournal of Intelligent Decision Making and Information Science2026-07-14

Plant Leaf Disease Detection Using Machine Learning and Deep Learning: A Review and Experimental Study

Yuvraj Narayan Gholap

India’s economy is primarily based on agriculture. Agriculture has significant contribution in nation’s GDP. Food security and employment significantly influenced by agriculture. However factors like uncertain weather conditions, poor quality of seeds and plant diseases impact on…

View free PDFSource page
crossrefJournal of Intelligent Decision Making and Information Science2026-07-23

Early Prediction of Neurological Disorders using Automatic Deep Feature Extraction and Machine Learning

Audil Hussain

The focus of recent research has been on using advanced computer-aided diagnostic (CAD) techniques and a variety of modalities to identify neurological disorders. Important and possibly deadly conditions, neurological diseases such as Alzheimer's disease (AD), stroke, epilepsy, P…

View free PDFSource page
crossrefJournal of Intelligent Decision Making and Information Science2026-07-23

A Comprehensive Framework For Automated Tur Dal Variety Classification Using Computer Vision And Machine Learning

Veena B.Mindolli

Tur dal, renowned as one of the most popular pulses globally, encompasses a wide range of varieties with significant variations in texture, colour, and other attributes. Accurately identifying tur dal varieties, is essential to satisfy consumer demands and uphold consumer rights…

View free PDFSource page
crossrefJournal of Intelligent Decision Making and Information Science2026-07-23

A Hybrid Spectral–Spatial Deep Learning Framework with Harris Hawk Optimization and Support Vector Machine for Accurate Arecanut Plantation Mapping Using Sentinel-2 Imagery

Sumithra C. V, Manjula T. R.

Spectral similarity with other perennial vegetation and heterogeneous agricultural landscapes still make accurate identification of arecanut plantations from medium resolution satellite imagery a challenge. In this paper, a Hybrid Spectral–Spatial DeepLabV3+ with Harris Hawk Opti…

View free PDFSource page
crossrefJournal of Intelligent Decision Making and Information Science2026-07-14

Multi-Class Millet Classification Using A Fusion Deep Learning Convolutional Neural Network

Dewendra Onkar Bharambe, Pushpalata Ganesh Aher

Reliable identification of millet cultivars is essential for maintaining grain quality, supporting seed authentication, and improving automation in post-harvest processing. Despite recent advances in computer vision, accurate classification of millet varieties remains challenging…

View free PDFSource page
crossrefJournal of Intelligent Decision Making and Information Science2026-07-23

Machine Learning–Augmented Hybrid Risk Management and Deep Uncertainty Quantification in Nepalese Management Systems: Fractional Stochasticity, Wasserstein Robustness, and Rough–Path Neural Filtering

Suresh Kumar Sahani

Risk management in Nepal has never been a matter of applying textbook formulas to Himalayan data. The country’s management systems—spanning hydropower consortia in Gandaki, microfinance networks in the Terai, tourism supply chains in Solu-Khumbu, and federal bureaucracies still f…

View free PDFSource page