# Blockchain and Machine Learning Inspired Secure Smart Home Communication Network
## 2 Related Work  
This literature review provides an overview of recent studies on smart home technologies. While conducting the literature review, the authors investigated the concept of peer-to-peer energy trading among smart homes. Peer-to-peer energy represents the communication among the attached devices with routing topology [2]. The next review was on a blockchain-based smart home network security model based on a decentralized network that used fused machine learning. The author's aim of fusing network security with machine learning was to reuse the calculations and the distributions that were made in the communication between smart devices. The target was to reduce the computation delay and provide security via a cloud platform [3]. The author proposed a data security model for smart homes that used blockchain technology via blockchain interpretation; the security of the overall system was enhanced, as it kept a record of communication and every linked communication. The model introduced an encrypted communication index termed the hash value. The hash was maintained in an open-source ledger for the virtualization of the entire concept; in this, the difficulty target could be used to control the working phase of machines for new block generation [4]. The machine learning intelligence approach was evaluated on the basis of energy consumption and was proposed by the author to reduce the expenses of smart homes [16]. It aims to find the resources that are used the least as well as those that are highly used. The authors relied on the training and classification mechanism to provide an understanding of the system [11]. While conducting the review, the author introduced smart home networks developed on the basis of the occupancy detection model using interoperable building automation technologies. The model implemented propagational neural networks for the recognition of occupancies via a detection mechanism. The cloud was utilized for data centralization and monitoring purposes. It is often observed that security is one of the major concerns in blockchain-based IoT frameworks and architecture, along with power and performance efficacy. The communication aspect and the channel modeling through which the data are transferred from one end to another are also an area of discussion in the same context. The efficiency aspect was the main focus of these papers, as they proposed different optimization algorithms to reduce energy consumption and improve the efficiency of smart homes and IoT devices [12]. The author proposed an optimization algorithm based on the bat algorithm with inertia weight to optimize energy consumption and improve the comfort level of smart homes. The proposed algorithm used a feedback mechanism to adjust the parameters of the algorithm and adapt to changes in the environment. The paper mentioned the security measures taken to protect the smart home system from unauthorized access [13]. The author conducted a study on a bio-inspired algorithm called the NBA algorithm, which can optimize energy consumption in wireless sensor networks (WSN) for IoT applications. The algorithm proposed by the author was based on the behavior of honeybees and used a pheromone trail mechanism to guide the search for optimal solutions. The paper mentioned the security measures taken to protect the WSN from attacks, such as jamming and eavesdropping, but it did not discuss them in detail. The paper also proposed a communication protocol based on the message queuing telemetry transport (MQTT) protocol to improve the efficiency of IoT devices [14]. Therefore, the author proposed task management for IoT, and this mechanism was based on predictive optimization to improve the IoT energy efficiency and scalability in smart residential buildings. The proposed mechanism used a machine learning algorithm to predict the energy consumption of IoT devices and schedule their tasks accordingly to reduce energy consumption. The paper briefly mentioned the security measures taken to protect IoT devices from attacks.