An AUV-Assisted Data Collection Approach for UASNs Based on Hybrid Clustering and Matrix Completion

Underwater Acoustic Sensor Networks (UASNs) have emerged as pivotal contributors to various domains. Never-theless, UASNs grapple with intrinsic challenges, notably propa-gation delays, heightened energy consumption, and inconsistent transmissions, which cumulatively impair the fidelity of under-water communication. Given these challenges, the exigency for a mechanism that assures both energy efficiency and reliable data collection becomes paramount. In this paper, we propose a data acquisition framework with the support of Autonomous Underwater Vehicles (AUVs), utilizing the Hybrid Clustering and Matrix Completion (AHMC) methodology, aiming to enhance the efficiency of data collection in UASNs. Initially, our approach har-nesses a novel hybrid clustering strategy, melding the virtues of the fuzzy c mean (FCM) algorithm with the Firefly Optimization Method (FA) to bolster network performance. The core strategy delineates the formation of energy-optimized clusters through FCM, post which the Elbow method ascertains the optimal K-value. Successively, the FA algorithm becomes instrumental in pinpointing the most suitable cluster head (CH) for each cluster. Furthermore, we introduce an ant colony algorithm tailored for the UASN s, encapsulating factors like energy, transmission latency, and the comprehensive trajectory span of AUV s during their operational phase. This strategic inclusion refines the pheromone update model, seamlessly amalgamating multifaceted elements to chart an optimally efficient AUV pathway. To cul-minate, the intra-cluster data aggregation is honed via a matrix completion methodology. Simulation results attest to the superior performance of our AHMC paradigm, showcasing commendable metrics in energy efficiency, latency, and network lifetime.