Echo State Network-Enabled Intelligent Smart Sensor Design for Creating a Robotic Nervous System

Abstract

Introduction: The echo states that networks in the Internet of Things (IoT) are currently being implemented in the widest sense. Echo state networks are fast and efficient recurrent neural networks. This consists of an input layer, a reservoir with many sparsely connected neurons, and an output layer. Issues: In the existing wireless sensor networks, strong mobility may disrupt an existing link between two communicating nodes. There is an inconvenience in data communication, and then it searches for a new node to build a better connection. Methods: To overcome these issues, the recently introduced echo state network (ESN) model opened the way to an extremely efficient approach for designing neural networks for temporal data. The study focuses on the ESN-enabled Intelligent Smart Sensor Design (IS2D) for creating the robotic nervous system with a smart healthcare Digital Nervous System (DNS) using the techniques of IoT, DNS, and Smart Sensor Design and Strain Sensor Fabrication (SSF). Results: Experimental results demonstrate the training set testing against the IS2D, the confusion matrix for ESN outcome, the real-time healthcare monitoring for the DNS, the IS2D sensor accuracy, and the DNS intensity calculation. Discussion: The performance analysis of the proposed model in realistic environments attests to the benefits of energy-centric metrics such as energy consumption, network lifetime, delay, and throughput. Finally, we discuss the challenges and opportunities by summarizing the study and proposing possible future works. The training set testing against the IS2D is based on time count, and the voltage result is estimated. The first portion of the data set should be 11.46% at the initial level. Further, this will increase from 1% to 5%, from 6% to 10%, and from 16% to 28% at the consecutive data set. The confusion matrix for ESN outcome is based on accuracy 28.45% higher than the existing strategies. In this part, the initial accuracy is 8.45% while accessing the initial stage. This value should increase with consecutive data sets from 18.45% to 28.45%.