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Intelligent Congestion Control Mechanism for IoT-Enabled Wireless Sensor Networks Using Hybrid Aggregation and

Shiv H Sutar¹, Y Bevish Jinila², Kailas Patil³

¹School of Computing, Sathyabama Institute of Science & Technology (Deemed to be University); Department of Computer Engineering and Technology, MIT World Peace University; shiv.sutar@mitwpu.edu.in.

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Summary

This summary is machine-generated.

This study introduces an intelligent congestion control protocol for IoT-enabled wireless sensor networks (WSNs). The hybrid approach enhances packet delivery and network lifetime by combining adaptive scheduling and data aggregation.

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Area of Science:

Computer Science
Network Engineering
Artificial Intelligence

Background:

Congestion in IoT-enabled wireless sensor networks (WSNs) significantly degrades performance, particularly under bursty traffic.
Existing protocols struggle to efficiently manage network load, impacting packet delivery, latency, and energy consumption.
There is a need for intelligent congestion control mechanisms that are energy-efficient, scalable, and Quality of Service (QoS)-aware.

Purpose of the Study:

To develop and evaluate an intelligent congestion control protocol for IoT-enabled WSNs.
To combine hybrid data aggregation, adaptive scheduling, and a neuro-fuzzy decision engine for efficient network load handling.
To provide a reproducible framework for exploring advanced congestion control strategies.

Main Methods:

Simulated network topologies with varying node densities and traffic patterns using NS-2.35.
Implemented a hybrid aggregation mechanism combining packets based on time and count with priority labels.
Utilized an adaptive scheduling approach with dual priority queues managed by weighted round robin.
Developed a neuro-fuzzy controller evaluating buffer occupancy, link quality, channel utilization, residual energy, and traffic priority to regulate network parameters.

Main Results:

The proposed protocol demonstrated superior performance compared to baseline schemes in simulations.
Key performance metrics including packet delivery ratio, end-to-end latency, throughput, and energy consumption were improved.
The protocol effectively managed network load, leading to enhanced network lifetime and energy efficiency.

Conclusions:

The intelligent congestion control protocol offers a viable solution for improving the performance of IoT-enabled WSNs.
The hybrid approach combining aggregation, scheduling, and neuro-fuzzy control is effective in handling complex traffic conditions.
This work provides a reproducible framework for future research in energy-efficient, scalable, and QoS-aware WSNs.