# Blockchain and Machine Learning Inspired Secure Smart Home Communication Network
###### Abstract
The first architecture is the IoT layer, in which the users use multiple types of devices and communicate via the application layer. The application layer provides a user Interface (UI) to the user in order to submit and process any request generated by the devices for communication access. The application layer may contain home care, hospitals, city services, data, and marketplaces. This communication generates a bulk amount of data that is aggregated by the blockchain layer. This layer generates the ledger of the communication details and generates a lot of information regarding the transaction that has been made. The transactions are made through Smart Contracts that are established and evaluated in the blockchain layer [13; 14; 15]. Additionally, the layer creates data in terms of Quality of Service (QoS) parameters, such as Throughput and Bit Error Rate (BER). The blockchain layer is further connected to the Cloud Layer, which stores and processes the data that are generated by the blockchain layer. The cloud layer incorporates the class generator that divides all the data into three segments, namely \"SMART T\", \"MOD T\", and \"AVO-T\", where T represents transactions and MOD and AVO stand for \"moderate\" and \"avoidance\". In the application architecture, the system incorporates a swarm-based data selection algorithm to remove data redundancy, which is further fed to the neural engine for systematic learning. The cloud layer assists the blockchain layer in the decision-making for the transaction. In our article, Random Forest is abbreviated as RF; Naive Bayes is abbreviated as NB; and the proposed result is abbreviated as P. The contributions of the paper are as follows. 1. The proposed algorithm outperforms the Fused Real-Time Sequential Deep Extreme Learning Machine (RTS-DELM) system, the data fusion technique, and artificial intelligence Internet of Things technology in providing electronic information engineering and analysis optimization schemes in terms of the computation complexity, false authentication rate, and qualitative parameters with a lower average computation complexity; in addition, it ensures a secure, efficient smart home communication network to enhance the lifestyle of human beings. 2. Application of a secure blockchain layer for user authentication and the generation of a ledger for the communication network. 3. Application of a cloud-based data evaluation layer to segregate and process data to generate a rank model based on three broad categories of transactions. 4. Utilization and enhancement of an SI-based algorithm for the preciseness of the training in the cloud layer. 5. Utilization of a neural engine for the training and classification of the categories that help the blockchain layer with improvisation in the decision-making of the blockchain layer. The rest of the paper is organized in the following manner. Section 2 illustrates the related work that has been performed in the enhancement of the smart home-based application architecture. Section 3 incorporates the proposed work that supports machine learning (ML), blockchain, and swarm-based application architecture for the enhancement of the smart home network. Section 4 contains the experimental results and discussion, and finally, the paper is concluded in Section 5.