Enhancing IoT-Enabled Wireless Sensor Network Performance through Adaptive Congestion Control: Investigation of Hybrid Aggregation and Scheduling Techniques

In the rapidly expanding domain of the Internet of Things (IoT), Wireless Sensor Networks (WSNs) have become indispensable, supporting applications ranging from environmental monitoring to industrial automation. However, as the IoT ecosystem continues to burgeon with an array of devices and applications, the effective management of data transmission and congestion control within these networks presents an escalating challenge. To address this, this paper introduces a ground-breaking Optimal Congestion Control Mechanism tailored explicitly for IoT-enabled Wireless Sensor Networks.  This innovative mechanism incorporates a Hybrid Aggregation and Scheduling technique to tackle the dual hurdles of congestion relief and energy efficiency in WSNs. By seamlessly blending   data aggregation with dynamic scheduling, this approach endeavors to optimize network resources and alleviate congestion-related issues. Data aggregation intelligently consolidates multiple data packets into a single transmission, reducing overhead and maximizing the con- strained bandwidth of wireless channels. Concurrently, dynamic scheduling adapts the transmission schedule in real-time based on network conditions, ensuring the timely delivery of critical data while minimizing congestion. To achieve an optimal configuration, the mechanism employs an intelligent decision-making algorithm that considers factors like data priority, network traffic, and energy constraints. Furthermore, machine learning techniques, notably reinforcement learning, can be leveraged to enhance the algorithm’s adaptability over time. The efficacy of the proposed mechanism undergoes rigorous assessment through simulations and real-world experiments, validating its ability to diminish congestion, enhance data delivery, and prolong the operational life of the network. The outcomes underscore the significant potential of this Optimal Congestion Control Mechanism to elevate the reliability and efficiency of IoT-enabled Wireless Sensor Networks.  By harnessing the combined advantages of data aggregation and dynamic scheduling, the proposed mechanism offers a comprehensive solution for efficiently managing congestion and optimizing network resource utilization.