scholarly journals A Machine-learning-enabled Context-driven Control Mechanism for Software-defined Smart Home Networks

2019 ◽  
Vol 31 (6) ◽  
pp. 2103
Author(s):  
Ru Huang ◽  
Xiaoli Chu ◽  
Jie Zhang ◽  
Yu Hen Hu ◽  
Huaicheng Yan
Keyword(s):  
Machine Learning ◽  
Smart Home ◽  
Control Mechanism ◽  
Home Networks

scholarly journals Hardening Machine Learning Denial of Service (DoS) Defences Against Adversarial Attacks in IoT Smart Home Networks

2021 ◽  
pp. 102352
Author(s):  
Eirini Anthi ◽  
Lowri Williams ◽  
Amir Javed ◽  
Pete Burnap
Keyword(s):  
Machine Learning ◽  
Smart Home ◽  
Denial Of Service ◽  
Home Networks

A Machine Learning Approach for Blockchain-Based Smart Home Networks Security

IEEE Network ◽  
2020 ◽  
pp. 1-7
Author(s):  
Muhammad Adnan Khan ◽  
Sagheer Abbas ◽  
Abdur Rehman ◽  
Yousaf Saeed ◽  
Asim Zeb ◽  
...  
Keyword(s):  
Machine Learning ◽  
Smart Home ◽  
Learning Approach ◽  
Home Networks ◽  
Machine Learning Approach

A flexible authentication scheme for smart home networks using app interactions and machine learning

2020 ◽  
Vol 39 (5) ◽  
pp. 6009-6020
Author(s):  
Yosef Ashibani ◽  
Qusay H. Mahmoud
Keyword(s):  
Machine Learning ◽  
Smart Home ◽  
User Authentication ◽  
Authentication Scheme ◽  
Home Networks ◽  
Flexible Machine ◽  
Continuous Authentication ◽  
Continuous User ◽  
Access Patterns ◽  
Application Access

Smartphones have now become ubiquitous for accessing and controlling home appliances in smart homes, a popular application of the Internet of Things. User authentication on smartphones is mostly achieved at initial access. However, without applying a continuous authentication process, the network will be susceptible to unauthorized users. This issue emphasizes the importance of offering a continuous authentication scheme to identify the current user of the device. This can be achieved by extracting information during smartphone usage, including application access patterns. In this paper, we present a flexible machine learning user authentication scheme for smart home networks based on smartphone usage. Considering that users may run their smartphone applications differently during different day time intervals as well as different days of the week, new features are extracted by considering this information. The scheme is evaluated on a real-world dataset for continuous user authentication. The results show that the presented scheme authenticates users with high accuracy.

scholarly journals Internet of Things-Based Intelligent Smart Home Control System

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Olutosin Taiwo ◽  
Absalom E. Ezugwu
Keyword(s):  
Machine Learning ◽  
Mobile Application ◽  
Smart Home ◽  
Human Life ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Automation System ◽  
Home Automation ◽  
Area Of Interest ◽  
Home Automation System

The smart home is now an established area of interest and research that contributes to comfort in modern homes. With the Internet being an essential part of broad communication in modern life, IoT has allowed homes to go beyond building to interactive abodes. In many spheres of human life, the IoT has grown exponentially, including monitoring ecological factors, controlling the home and its appliances, and storing data generated by devices in the house in the cloud. Smart home includes multiple components, technologies, and devices that generate valuable data for predicting home and environment activities. This work presents the design and development of a ubiquitous, cloud-based intelligent home automation system. The system controls, monitors, and oversees the security of a home and its environment via an Android mobile application. One module controls and monitors electrical appliances and environmental factors, while another module oversees the home’s security by detecting motion and capturing images. Our work uses a camera to capture images of objects triggered by their motion being detected. To avoid false alarms, we used the concept of machine learning to differentiate between images of regular home occupants and those of an intruder. The support vector machine algorithm is proposed in this study to classify the features of the image captured and determine if it is that of a regular home occupant or an intruder before sending an alarm to the user. The design of the mobile application allows a graphical display of the activities in the house. Our work proves that machine learning algorithms can improve home automation system functionality and enhance home security. The work’s prototype was implemented using an ESP8266 board, an ESP32-CAM board, a 5 V four-channel relay module, and sensors.

scholarly journals Development of Voice-Controlled Smart Surgical Bed

2020 ◽  
Author(s):  
Jeong Hun Kim ◽  
Nicholas Theodore ◽  
Rajiv Iyer ◽  
Amir Manbachi ◽  
Richard Um
Keyword(s):  
Smart Home ◽  
Feasible Solution ◽  
Control Mechanism ◽  
Operation Time ◽  
Medical Personnel ◽  
Operating Costs ◽  
Medical Settings ◽  
Voice Input ◽  
Operative Complications ◽  
Future Work

Abstract Wasted time in the operating room results in higher operating costs and greater post-operative complications. One effective way to reduce operation time is automating basic processes that occur during surgery. Given the rise of smart-home devices, implementation of virtual assistants became a feasible solution in many medical settings. With a consumer smart-home device and off-the-shelf components, we engineered a voice-controlled smart surgical bed that adjusts the bed configuration via a voice input. The resulting device is expected to optimize human resources and reduce surgical site infection by eliminating the need of a traditional touch control mechanism. Future work is needed to develop its proprietary hardware and software, and continuous collaboration with medical personnel to bring this device into market.

Machine learning in smart home control systems - Algorithms and new opportunities

2017 ◽  
Author(s):  
Ivan A. Berg ◽  
Oleg E. Khorev ◽  
Arina I. Matvevnina ◽  
Alexey V. Prisjazhnyj
Keyword(s):  
Machine Learning ◽  
Control Systems ◽  
Smart Home

Temporal-Sound based User Interface for Smart Home

2021 ◽  
Author(s):  
Kido Tani ◽  
Nobuyuki Umezu
Keyword(s):  
Machine Learning ◽  
User Interface ◽  
Smart Home ◽  
Classification Learning ◽  
Activity Classification ◽  
Smart Watch ◽  
Acceleration Data ◽  
Physical Controls ◽  
The Difference

We propose a gesture-based interface to control a smart home. Our system replaces existing physical controls with our temporal sound commands using accelerometer. In our preliminary experiments, we recorded the sounds generated by six different gestures (knocking the desk, mouse clicking, and clapping) and converted them into spectrogram images. Classification learning was performed on these images using a CNN. Due to the difference between the microphones used, the classification results are not successful for most of the data. We then recorded acceleration values, instead of sounds, using a smart watch. 5 types of motions were performed in our experiments to execute activity classification on these acceleration data using a machine learning library named Core ML provided by Apple Inc.. These results still have much room to be improved.

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