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.

本文针对无线传感器网络，对物联网（IoT）传感器节点进行了有效的网络能量优化。本文提出了一种胶囊神经网络架构模型，以通过最小化无线传感器网络（WSN）辅助物联网的网络能量开销来获得更好的性能。在WSN模型中，每个传感器节点以多种方式进行通信，以将信息从云IoT传输到虚拟模块。基本上，传感器网络中的群集过程通过控制能耗率和提高数据准确率来帮助改善网络质量。旨在管理能量和准确率的无线传感器网络中的物联网优化涉及高度复杂的聚类算法。因此，本研究论文旨在开发一种新的基于胶囊神经网络的学习模型，该模型将网络能量保持在最佳水平，从而在不考虑网络开销的情况下保持更好的吞吐量和准确性。胶囊神经网络（CNN）体系结构是一种合理的神经模型，并被证明对路由选择和优化操作有效，其中胶囊的激活是在向前通过时进行计算的。本文的主要贡献是对新型神经网络架构模型进行建模，以改善传感器网络性能，并进行云存储空间和无线传感器网络模型之间存在的网络开销的优化。也，设计的神经网络体系结构旨在通过选择无线传感器环境中的最佳节点来优化网络能量利用。仿真结果与文献中的现有方法相比，建立了所提出的CNN学习用于传感器网络中IOT能量优化的可靠性和有效性。