Network Energy Optimization of IOTs in Wireless Sensor Networks Using Capsule Neural Network Learning Model

Abstract

In this paper, an effective network energy optimization is carried out for the internet of things (IoTs) sensor nodes in respect of the wireless sensor networks. A capsule neural network architectural model is proposed here to achieve better performance by minimizing the network energy overhead for the wireless sensor network (WSN) aided internet of things. In WSN models, each sensor nodes gets communicated in a diversified manner for transferring the information from the cloud IoT to the virtual modules. Basically, the process of clustering in sensor networks aids in improving the network quality by controlling the energy consumption rate and improving the rate of data accuracy. Optimization of internet of things in wireless sensor networks that aims in managing the energy and accuracy rate involves highly complex clustering algorithms. Due to which, this research paper is intended to develop a new capsule neural network based learning model that takes care in maintaining the network energy in an optimal level thereby maintaining a better throughput and accuracy with network overhead been taken care off. Capsule neural network (CNN) architecture is a plausible neural model and is proven to be effective for routing and for optimization operations wherein the capsule’s activation is calculated at the time of forward pass. The main contribution of this paper is to model a novel neural network architectural model for improving the sensor network performance and as well to carry out optimization of network overhead that is present between the cloud storage space and the wireless sensor network model. Also, the designed neural network architecture aims to optimize the network energy utilization by selecting the optimal nodes in the wireless sensor environment. Simulation results attained establishes the reliability and effectiveness of the proposed CNN learning for energy optimization of IOTs in sensor networks in comparison with that of the existing methods from literature.