Quality of Experience Aware Service Selection Model to Empower Edge Computing in IoT

Quality of experience‐aware service selection can significantly remove well‐known scalability issues of an Internet of Things (IoT) architecture. In traditional IoT architecture, several heterogeneous data streams from connected nodes are transmitted through gateways to the remote mobile cloud servers. The entire procedure is time‐ and energy‐consuming if the target dataset is comparatively small and uninterrupted. Also, using this conventional technique, the reliability grade drops significantly to meet additional security‐related quality of service (QoS) requirements compared to the service cost. We propose a quality of experience‐aware task rescheduling model using edge modules that offer territory‐based three‐layered edge IoT data analysis and service selection. The observation module at the application layer takes a near‐optimal remark upon each usage metric having distinct QoS components. Meanwhile, the QoS manager at the network layer handles network traffic due to the load associated with heterogeneous service needs. The precision of the knowledge is assured to the service manager through the sensing layer with few adaptability characteristics towards assorted service requests. The proposed three‐layered energy‐efficient model helps minimize data delivery time with minimal cost and optimized quality assurance for service‐based IoT infrastructures like smart agriculture, patient monitoring, and student monitoring.