A novel framework for optimizing the edge network node for wearable devices

Abstract

The Multi-access edge computing (MEC) server would provide context-aware capabilities. When edge computing uses high-quality computing performance to supplement edge applications with vast IoT-based data services, substantial constraints are placed on the collaboration of edge nodes. Conversely to cloud computing, situational circumstances in the edge network are more complicated. In this paper, we provide a novel Edge Network (EDN) optimization (EDN-Opt) to boost the efficiency of edge computing jobs. In particular, we initially specify the parameters for cooperative assessment through the Internet of Things (IoT). Furthermore, the effectiveness of the proposed architecture is shown using real datasets collected from elderly individuals and various activity trackers. A comprehensive study on QoC intended with EDN is used to assess collaboration effectiveness. The cooperative optimization method developed provides improved efficiency To assess the effectiveness of EDN optimization, the discrepancy between the proposed equivalent and the real equivalent is examined. Investigation in this sector analyses several practical cases. The Spearman rank correlation factor is +1 or −1 when a perfect monotonic association is attained with no identifying data. The examination of this article demonstrates that trials show that our proposed edge cooperation optimization technique can quickly assess the EDN and then provide information on the collaborative relationship's replacement occurrences that can help the EDN's design.