Adaptive Virtual Clustering Methods for Dynamic IoT Edge Systems

Ever increasing numbers of Internet of Things (IoT) devices have created a more fragmented edge environment. The majority of traditional clustering mechanisms are not competent to resource elasticity over multiple IoT systems and hence, handling load distribution effectively underutilizes the resources. To address the increasing complexities and constraints of modern IoT systems, adaptive mechanisms are required to implement virtual clustering, which can dynamically optimize performance and resource utilization by responding to real-time network changes and device heterogeneity.The article proposes the design and performance evaluation of adaptive virtual clustering methods tailored to the requirements of emerging IoT edge systems with dynamic environments. The goal is to optimize resource allocation, balance load, and enhance the overall system performance by deploying virtual clusters with the potential of adapting the best to changing network loads as well as device heterogeneity.We developed a multistep approach that combined state-of-the-art clustering algorithms, the K-means and agglomerative clustering, to our insight of Amdahl's law using meta learning strategies. There are 4 architecture layers in our system: physical device layer, cluster management layer, coordination layer and virtual group. This paper revisits KPIs such as Silhouette Coefficient, Davies-Bouldin Index and Calinski-Harabasz Index under different network loads and performance of the device being considered.The proposed methods achieved better performance than existing clustering algorithms, particularly in high network traffic scenarios. In competitive testing, adaptive virtual clusters with up to 10% better in total performance over traditional clusters at full network load through smart node allocation and leveraging of the virtual memory for better load balancing.Adaptive virtual clustering appears to be a promising fit for the challenges posed by dynamic IoT edge environments. This limber approach yields improved in network efficiency, load distribution and overall network performance. Future work should focus on optimizing these adaptive clustering approaches for extreme network conditions, such as high device churn and congested IoT environments. Additionally, the integration of digital twin technologies and real-time adaptive machine learning models could further improve system responsiveness and performance in highly dynamic settings.