The development of information and communication technology in terms of sensor technologies cause the Internet of Things (IoT) step toward smart homes for prevalent sensing and management of resources. The gateway connections contain various IoT devices in smart homes representing the security based on the centralized structure. To address the security purposes in this system, the blockchain framework is considered a smart home gateway to overcome the possible attacks and apply Deep Reinforcement Learning (DRL). The proposed blockchain-based smart home approach carefully evaluated the reliability and security in terms of accessibility, privacy, and integrity. To overcome traditional centralized architecture, blockchain is employed in the data store and exchange blocks. The data integrity inside and outside of the smart home cause the ability of network members to authenticate. The presented network implemented in the Ethereum blockchain, and the measurements are in terms of security, response time, and accuracy. The experimental results show that the proposed solution contains a better outperform than recent existing works. DRL is a learning-based algorithm which has the most effective aspects of the proposed approach to improve the performance of system based on the right values and combining with blockchain in terms of security of smart home based on the smart devices to overcome sharing and hacking the privacy. We have compared our proposed system with the other state-of-the-art and test this system in two types of datasets as NSL-KDD and KDD-CUP-99. DRL with an accuracy of 96.9% performs higher and has a stronger output compared with Artificial Neural Networks with an accuracy of 80.05% in the second stage, which contains 16% differences in terms of improving the accuracy of smart homes.

Smart Home Gateway Based on Integration of Deep Reinforcement Learning and Blockchain Framework

The high demand use of Ultra-Wideband (UWB) in wireless body area network (WBAN) based medical applications opens a way for many types of research in the current decade. The continuous health monitoring of the patients during normal daily activities is the primary concentration of the WBAN system. Many studies and analyses are taken into and performance is evaluated. The major sections which are discussed in this paper are UWB technologies, on-off body communication; WBAN ideas are discussed to overcome several research drawbacks.

A Review on UWB Antenna Sensor for Wireless Body Area Networks

The importance of preserving the environment from waste and its pollution lies in many matters such as preserving people health, enhancing the aesthetic character of cites, attracting tourists, and protecting society from environmental disasters. The environmental wastes are the main dilemmas in our daily life and in the world at large. With the existence of modern technology, development and the field of the internet, many solutions have been undertaken to get rid these dilemmas. In this paper, a smart waste system based on internet of things (IoT) technique has been proposed using ESP-32 Wi-Fi microcontroller. This system can be adopted to avoid the accumulation of waste in the streets that distort the face of civilization, also to reduce the burden of workers and limit the workforce. The system is based on a multiple sensors in the garbage baskets, as they measure the waste level by using ultrasonic sensor, the moisture percent and temperature degree using DHT-22 sensor. The sensors data are processed by ESP32 microcontroller and displayed to both LCD screen using I2C protocol and mobile application using IoT cloud. System baskets automatically open their covers when the person approaches with a distance less or equal to 30 cm to throw garbage. Any approval waste basket is automatically discharged through an underground dump system using conveyor belt if the basket is full by 80% garbage and/or the basket moisture reaches to 40%.

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An internet of things based smart waste system

Smart homes incorporate several devices that automate tasks and make our lives easy. These devices can be useful for many things, like security access, lighting, temperature, etc. Using the Internet of Things (IoT) platform, smart homes essentially let homeowners control appliances and devices remotely. Due to their self-learning skills, smart homes can learn homeowners' schedules and adapt accordingly to make adjustments. Since convenience and cost savings is necessary in such an environment, and there are multiple devices involved, there is a need to analyze power consumption in smart homes. Moreover, increased energy consumption leads to an increase in carbon footprint, elevates the risk of climate, and leads to increased demand in supply. Hence, monitoring energy consumption is crucial. In this paper, we perform an overall analysis of energy consumption in smart homes by deploying machine learning models. We rely on machine learning techniques, like Decision Trees (DT), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and k-Nearest Neighbor (KNN) for predicting the power consumption of multiple datasets. We also propose a DT-RF-XGBoost-based Ensemble Model for analyzing the consumption and comparing it with the baseline algorithms. The evaluation parameters used in the study are Mean Square Error (MSE), R-squared (R 2, ), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE), respectively. The study has been performed on multiple datasets and our study shows that the proposed DT-RF-XG-based Ensemble Model outperforms all the other baseline algorithms for multiple datasets with R 2 around 0.99. 

A machine-learning ensemble model for predicting energy consumption in smart homes

The use of the stand-alone PV systems for electric power generation is important to meet the needs of electric power, especially in countries that have major problems in the generation and continuity of electricity such as Iraq. The aim of this research is to design and simulate a stand-alone PV system installed on the roof of the parking garage in the College of Engineering at Al-Nahrain University for the purpose of using the electrical energy generated by this system to meet the energy demand of the daily light for the parking garage and engineering laboratories buildings beside it. The main factor in the design was the total area of the roof of the parking garage. The Pvsyst6 program is used to simulate the suggested PV system and to determine the effect of partial shading which caused by the trees surrounding the garage on the available generated power of the system. A comparison was made between four types of solar panels to see the suitability of these types to the weather conditions in Iraq, in addition to the impact of the shadows surrounding the garage. These types of solar panels were (CIS SL2-145, CdTe F-4112-3, Poly JAP6-60-260, and Mono JAM6-60-260), CdTe type showed better performance compared to other types in terms of available energy generated because it has small losses due to temperature and shadows. It was found that the best tilt angle of the solar panels in the case of shadows is 19o to the south. Finally, the financial analysis was made to see the economic feasibility of this PV system and it is found that the PV system that uses CdTe panels has the smallest kWh unit price (0.236 $/kWh) as compared with the other three types.

https://ijpeds.iaescore.com/index.php/IJPEDS/article/download/20496/13148

Design and performance analysis of stand-alone PV system at Al-Nahrain University, Baghdad, Iraq

The developments of the internet of things (IoT) technologies fascinated the universe and provided great opportunities to introduce these innovations in smart house networks. Smart home automation is highly required these days. Smart home automation is a collection of electronic devices connected to monitor and control in the market home appliance remotely. However, it is still needed to design a friendly and reliable system since the system mainly depends on the devices used and the environment of the network. NETPI and BLYNK are IoT frameworks used for hardware-agnostic with smartphones, websites, private clouds, system security, data mining, and deep learning. The results confirmed that NETPI provides flexibility to deal with several NODEMCU controllers in a single control framework. The proposed system shows its applicability in monitoring and controlling home appliances remotely.

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Haneen Sameer Mhmood

Papers

IoT based implemented comparison analysis of two well-known network platforms for smart home automation