Priority based hybrid congestion management approach for improving sensor data transmission in IoT network

The Internet of Things connects numerous devices in smart cities, enabling seamless data exchange. However, challenges like limited bandwidth, buffer overflows, and heavy traffic often lead to network congestion. To address this issue, a Priority-Based Hybrid Congestion Management approach is proposed. It optimizes data collection in IoT networks by prioritizing sensor data, dynamically adjusting transmission rates, and applying efficient data compression. It incorporates congestion detection, notification, and mitigation strategies to enhance network efficiency. Simulations carried out using Contiki OS and Cooja demonstrate that the proposed technique outperforms existing approaches, achieving a 20% increase in throughput, reducing energy consumption to 7.6 mJ per packet, improving fairness (0.98), reducing delay (12.6 ms), and improving packet delivery ratio. The findings confirm that the proposed technique effectively minimizes congestion while ensuring reliable data transmission in IoT networks, making it a robust solution for smart city applications.