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

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.