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

Summary

This protocol introduces a smart congestion control method for IoT-enabled WSN using a hybrid aggregation and scheduling technique complemented by a neuro-fuzzy decision layer. The proposed system enhances packet delivery, delay, throughput, and energy efficiency, thus increasing the network lifetime while ensuring QoS in the case of variable IoT workloads.

Abstract

Congestion in IoT-enabled wireless sensor networks (WSNs) degrades packet delivery, latency, and energy usage, impairing the network, especially under bursty and heterogeneous traffic conditions. This protocol illustrates an intelligent congestion control technique that combines hybrid data aggregation, adaptive scheduling, and a neuro-fuzzy decision engine to efficiently handle network load. The method involves first generating simulation data, creating topologies of different node densities, and setting up traffic patterns using NS-2.35. Packet traces are obtained for each scenario to allow reproducible evaluation. The protocol workflow refers to the combination of two mechanisms: (1) hybrid aggregation, which combines packets in time- and count-based windows while retaining priority labels, and (2) adaptive scheduling, which handles dual priority queues via weighted round robin. A neuro-fuzzy controller always evaluates buffer occupancy, link quality, channel utilization, residual energy, and traffic priority. Taking these inputs, it regulates aggregation depth, queue weights, and transmission decisions by fuzzy inference and neuro-adaptive learning. Performance measurement tasks encompass the calculation of packet delivery ratio, end-to-end latency, throughput, node-level energy consumption, and network lifetime. Statistical analyses are performed across multiple runs to check the reliability of the results. The approach reveals better performance in the simulation compared to the baseline schemes. This protocol offers a reproducible framework for exploring hybrid congestion control methods that enable energy-efficient, scalable, and QoS-aware operation in IoT-enabled WSN environments.

Introduction

The massive growth of the Internet of Things (IoT) has necessitated a deployment of billions of interlinked devices, out of which wireless sensor networks (WSNs) are used to provide real-time monitoring and decision support in most cases^1,2. These networks become the core structures of IoT-enabled systems in radically different fields, such as healthcare monitoring³, smart cities⁴, precision agriculture⁵, industrial automation⁶, and environmental sensing⁷. WSNs are made up of sensor nodes tha....

Protocol

1. Simulation environment setup

1. Install Ubuntu 22.04 LTS on a workstation with at least an Intel i5 processor, 8 GB RAM, and 20 GB storage. Configure the GNU C/C++ compiler and Tcl/Tk libraries to compile and run NS-2.35.
2. Install Python 3.10 along with NumPy, SciPy, Pandas, and Matplotlib for analysis and plotting. Enable the NS-2 energy and queue monitoring modules to capture enqueue, dequeue, drop, delivery, and energy consumption events.
3. Execute each experiment 5x using independent random seeds to ensure reproducibility.
4. Structure the simulation directory into subfolders for configurations, scripts, results....

Results

The PRAM scheme was feasible in a wireless sensor network environment with single-hop communication and limited buffer size. The experiments were carried out with different traffic rates, and the performance of the scheme was compared with the traditional Aloha and a variant of tree-based Aloha (T-Aloha) in terms of throughput, average access delay, and average number of non-empty buffers. The results clearly demonstrated that the proposed scheme achieved superior performance in all the above metrics.

Discussion

The results demonstrate that congestion control has been significantly improved in IoT-enabled wireless sensor networks by the integration of hybrid aggregation, adaptive scheduling, and neuro-fuzzy adaptation. In fact, the new protocol has been seen to achieve better performance than the aggregation-only, scheduling-only, and PCCP baselines in terms of packet delivery, latency, throughput, energy efficiency, and network lifetime across different node densities and traffic conditions. In fact, these gains serve as a conv.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Aqua-Sim Module (optional)	NS2 Extension Repository	–	Used if underwater WSN scenarios are tested
Desktop/Laptop (Intel i5/AMD Ryzen 5, ≥8 GB RAM, ≥20 GB storage)	Commercially available	–	Used to run all simulations
GNU C/C++ Compiler, Tcl/Tk	Ubuntu repository	–	Required for compiling NS-2
MATLAB R2022a (optional)	MathWorks	https://www.mathworks.com	Used for ANFIS/fuzzy modeling if preferred
NS-2.35 Network Simulator	ISI/NS2 Project	https://www.isi.edu/nsnam/ns/	Core simulation environment
Processed results (.csv)	Generated in this study	-	Computed metrics: PDR, delay, throughput, energy, lifetime
Python 3.10 (NumPy, SciPy, Pandas, Matplotlib)	Python Software Foundation	https://www.python.org	Used for analysis and plotting
Python analysis scripts	Generated in this study	-	Scripts to parse traces and generate plots
Raw NS-2 trace files (.tr)	Generated in this study	-	Contain packet-level transmission and energy events
Simulation configuration files (topology, traffic models, random seeds)	Generated in this study	-	Required for replicating experiments
Ubuntu Linux 22.04 LTS	Canonical Ltd.	https://ubuntu.com	Operating system for NS-2