Efficient Congestion Routing in Dynamic Wireless Sensor Networks utilizing Parallel Kruskal's Algorithm and Adaptive Edge Weighting

**Abstract:** This paper presents a novel approach to congestion routing in dynamic wireless sensor networks (WSNs) by leveraging the principles of Minimum Spanning Tree (MST) algorithms, specifically a parallel implementation of Kruskal's Algorithm coupled with adaptive edge weighting based on real-time network congestion metrics. Traditional routing protocols in WSNs often struggle to adapt to fluctuating traffic demands and node failures, leading to congestion and data loss. Our system, termed Parallel Kruskal Congestion Management (PKCM), dynamically constructs and adapts a near-optimal MST for data transmission, proactively mitigating congestion and maximizing throughput. This introduces a significant improvement over existing reactive congestion control mechanisms by allowing for predictive adjustments to data flow based on continuous MST re-computation.