Machine learning-based optimal data retrieval and resource allocation scheme for edge mesh coupled information-centric IoT networks and disability support systems

Abstract

Cloud-centric computing, due to its lack of mobility and increased latency, is not suitable for addressing unprecedented challenges within an Internet of Things (IoT) network, especially in the context of disability support systems. However, recent advancements in edge computing provided an alternative to cloud servers by deploying the data processing tasks at the edge level, increasing both the efficiency and throughput of the IoT networks. This paper introduces a novel architecture, dubbed ICN-EdgeMesh, that fuses information-centric networking (ICN) with edge mesh computing to provide optimal data access within an IoT network. Furthermore, we employ Support Vector Machines (SVM) classification models to establish the edge-to-things continuum by allocating the optimal node to each IoT device within the network for retrieving the requested data. Moreover, we evaluate the performance of ICN-EdgeMesh against multiple key factors, where it achieved a high data rate (of 9.1 to 10 Mbps) along with ultra-low latency. In addition, the trained SVM model within the proposed scheme achieved 98.1% accuracy, with a true positive rate of 95.3% and a true negative rate of 98.8%, reflecting the optimal network node allocation for efficient data transmission.