Adaptive fuzzy convolutional neural network for medical image classification

Abstract

Due to resource constraints and the diverse nature of the devices involved, energy efficiency and scalability enhancement are important challenges in the Internet of Things (IoT) ecosystem. It is difficult to manage the edge resources in a consistent way that promotes cooperation and sharing of resources across the devices because of the heterogeneity of the Internet of Things devices and the dynamic nature of the surroundings in which edge computing takes place. In this research, we offer Intelligent techniques for resource optimization for Internet of Things devices. This is a full-stack system architecture to support across heterogeneous Internet of Things devices that have limited resources. The paradigm that is being suggested is made up of several edge servers, and Internet of Things (IoT) devices have the qualities of being heterogeneity-compatible, high performing, and intelligently adaptable. In order to do this, a clustered environment is generated in heterogeneous Internet of Things devices, and a routing method called Search and Rescue Optimization is used to set up connections between the CH nodes. After that, the edge nodes that are closest to the source of the data are chosen for transmission. Overall, what was suggested Multi-Edge-IoT accomplishes superior efficiency in terms of consumption of energy, latency, communication overhead, and packet loss rate than existing approaches to attaining energy efficiency in the Internet of Things.