scholarly journals A Secure and Scalable Smart Home Gateway to Bridge Technology Fragmentation

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3587
Author(s):  
Ezequiel Simeoni ◽  
Eugenio Gaeta ◽  
Rebeca I. García-Betances ◽  
Dave Raggett ◽  
Alejandro M. Medrano-Gil ◽  
...  
Keyword(s):  
Smart Home ◽  
Home Automation ◽  
Dynamic Configuration ◽  
Home Gateway ◽  
Security Authentication ◽  
Living Lab ◽  
Automation Systems ◽  
Authentication And Authorization ◽  
Iot Devices ◽  
Bridge Technology

Internet of Things (IoT) technologies are already playing an important role in our daily activities as we use them and rely on them to increase our abilities, connectivity, productivity and quality of life. However, there are still obstacles to achieving a unique interface able to transfer full control to users given the diversity of protocols, properties and specifications in the varied IoT ecosystem. Particularly for the case of home automation systems, there is a high degree of fragmentation that limits interoperability, increasing the complexity and costs of developments and holding back their real potential of positively impacting users. In this article, we propose implementing W3C’s Web of Things Standard supported by home automation ontologies, such as SAREF and UniversAAL, to deploy the Living Lab Gateway that allows users to consume all IoT devices from a smart home, including those physically wired and using KNX® technology. This work, developed under the framework of the EC funded Plan4Act project, includes relevant features such as security, authentication and authorization provision, dynamic configuration and injection of devices, and devices abstraction and mapping into ontologies. Its deployment is explained in two scenarios to show the achieved technology’s degree of integration, the code simplicity for developers and the system’s scalability: one consisted of external hardware interfacing with the smart home, and the other of the injection of a new sensing device. A test was executed providing metrics that indicate that the Living Lab Gateway is competitive in terms of response performance.

scholarly journals A Zigbee and Sip-Based Smart Home System Design and Implementation

2017 ◽  
Vol 13 (01) ◽  
pp. 42
Author(s):  
Luo Jian
Keyword(s):  
Smart Home ◽  
Environmental Parameters ◽  
Cloud Service ◽  
Remote Access ◽  
Probability Density Distribution ◽  
Home Automation ◽  
Home Gateway ◽  
Service Unit ◽  
Address Translation ◽  
Set Up

This investigation presents a Zigbee and Sip based smart home system(ZSS) that automates home-service operations and remote access. The ZSS home system comprises five subsystems,namely home automation network,home IP netework,home gateway,remote access unit, and cloud service unit. The home automation network is a wireless sensor network , responsible to sample sensors data and control actuators by Zigbee instructions. Security system adopts IP network technology to  transmit videos from IP cameras to remote units. A home gateway middleware is designed to extract atomic service provied by a single device, then customize service rules which can meet variable user demands.Peer to peer transparent data channels between remote mobile phones and home devices are set up by Sip framework to overcome connection issues due to network address translation and dymaic public IP address. A novel  data stream algorithm named GCOKDE is exploited to get probability density distribution of environmental parameters which can be uesd for the condition value of service rule.At the end,this study constructes a prototype to reveal the procedure of realization.

scholarly journals Finger Vein Biometric based Secure Access Control in Smart Home Automation

2019 ◽  
Vol 8 (6) ◽  
pp. 851-855
Keyword(s):  
Smart Home ◽  
Interconnection Network ◽  
Svm Classifier ◽  
Automation System ◽  
Home Automation ◽  
Finger Vein ◽  
Authentication Mechanism ◽  
Automation Systems ◽  
Home Automation System ◽  
And Control

Smart home automation has become popular with the advent of IoT technology. Smart home automation systems suffer from a number of security issues due to the vulnerabilities that exist in the different devices and the interconnection network. Providing user authentication for smart homes is an important security requirement for preventing intruders from attacking a smart home automation system. Biometric based authentication systems have been used in many applications since they provide high security than the smart cards and password based authentication systems. Finger vein recognition is a biometric authentication technique that applies pattern recognition on the images of human finger vein present beneath the skin's surface. The advantage of using finger vein authentication is that, it is difficult to forge and also provides high accuracy as the external deformities like rashes, cracks and rough epidermis do not have an impact on the matching and recognition process. This paper deals with the implementation of a secure smart home automation system that uses finger vein biometric for the authentication mechanism. The algorithm used for authentication uses K Means Segmentation and canny edge detection for feature extraction. SVM classifier is used for the matching process. The authentication system is then incorporated into the smart home automation system that can be used to monitor and control the devices connected to it. The proposed approach shows better performance than the existing methods used in literature for authentication, monitoring and control of smart home automation systems

scholarly journals A Secure Internet of Things Smart Home Network: Design and Configuration

2021 ◽  
Vol 11 (14) ◽  
pp. 6280
Author(s):  
Jinsuk Baek ◽  
Munene W. Kanampiu ◽  
Cheonshik Kim
Keyword(s):  
Network Design ◽  
Smart Home ◽  
Home Network ◽  
Secure Communications ◽  
Home Networks ◽  
Ip Address ◽  
Home Gateway ◽  
Extended Access ◽  
Significant Performance ◽  
Iot Devices

Many home IoT devices are joining IoT networks by gaining access to some home gateway that configures smart, multimedia, and home networks. To enable secure IoT-based home networking services, (1) an IoT network should be effectively designed and configured with a IoT server, (2) a messaging protocol is required to exchange information between the IoT server and IoT devices, and (3) the home gateway should monitor all safety aspects in both inbound and outbound traffic of the home network. However, not all home network users put in consideration the need for an adequate security posture. Instead, many users still rely on the minimum home network security by setting an easiest-to-guess password to restrict unauthorized access to their home gateway. In this paper, we propose a network design and configuration that enables secure IoT services with MQTT messaging protocol for home networks. With the proposed network design, a home network is interconnected to external networks through a home gateway. To separate the IoT-subnet from other parts of home network, the home gateway subdivides a home network into an inside-subnet and an IoT-subnet with a private IP address using subnet masking. The IoT server, located in the IoT-subnet can be implemented with either a general HTTP server or a security server that acts as an MQTT broker. The secure communications among network entities are governed by a home gateway operating a well-configured extended access control. The effectiveness of the proposed design and configuration is verified through a simulation by showing that it does not impose any significant performance degradation for reinforced security. We expect the proposed configuration to help facilitate interconnection among heterogeneous network entities.

scholarly journals Big Data and Personalisation for Non-Intrusive Smart Home Automation

2021 ◽  
Vol 5 (1) ◽  
pp. 6
Author(s):  
Suriya Priya R. Asaithambi ◽  
Sitalakshmi Venkatraman ◽  
Ramanathan Venkatraman
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Smart Home ◽  
Big Data Analytics ◽  
Cost Effective ◽  
Smart Devices ◽  
Home Automation ◽  
Open Standard ◽  
Iot Devices ◽  
And Control

With the advent of the Internet of Things (IoT), many different smart home technologies are commercially available. However, the adoption of such technologies is slow as many of them are not cost-effective and focus on specific functions such as energy efficiency. Recently, IoT devices and sensors have been designed to enhance the quality of personal life by having the capability to generate continuous data streams that can be used to monitor and make inferences by the user. While smart home devices connect to the home Wi-Fi network, there are still compatibility issues between devices from different manufacturers. Smart devices get even smarter when they can communicate with and control each other. The information collected by one device can be shared with others for achieving an enhanced automation of their operations. This paper proposes a non-intrusive approach of integrating and collecting data from open standard IoT devices for personalised smart home automation using big data analytics and machine learning. We demonstrate the implementation of our proposed novel technology instantiation approach for achieving non-intrusive IoT based big data analytics with a use case of a smart home environment. We employ open-source frameworks such as Apache Spark, Apache NiFi and FB-Prophet along with popular vendor tech-stacks such as Azure and DataBricks.

scholarly journals Enhanced Intelligent Smart Home Control and Security System Based on Deep Learning Model

2022 ◽  
Vol 2022 ◽  
pp. 1-22
Author(s):  
Olutosin Taiwo ◽  
Absalom E. Ezugwu ◽  
Olaide N. Oyelade ◽  
Mubarak S. Almutairi
Keyword(s):  
Deep Learning ◽  
Smart Home ◽  
Learning Model ◽  
The Internet ◽  
Automation System ◽  
Home Automation ◽  
Home Security ◽  
Automation Systems ◽  
Home Automation System ◽  
Deep Learning Model

Security of lives and properties is highly important for enhanced quality living. Smart home automation and its application have received much progress towards convenience, comfort, safety, and home security. With the advances in technology and the Internet of Things (IoT), the home environment has witnessed an improved remote control of appliances, monitoring, and home security over the internet. Several home automation systems have been developed to monitor movements in the home and report to the user. Existing home automation systems detect motion and have surveillance for home security. However, the logical aspect of averting unnecessary or fake notifications is still a major area of challenge. Intelligent response and monitoring make smart home automation efficient. This work presents an intelligent home automation system for controlling home appliances, monitoring environmental factors, and detecting movement in the home and its surroundings. A deep learning model is proposed for motion recognition and classification based on the detected movement patterns. Using a deep learning model, an algorithm is developed to enhance the smart home automation system for intruder detection and forestall the occurrence of false alarms. A human detected by the surveillance camera is classified as an intruder or home occupant based on his walking pattern. The proposed method’s prototype was implemented using an ESP32 camera for surveillance, a PIR motion sensor, an ESP8266 development board, a 5 V four-channel relay module, and a DHT11 temperature and humidity sensor. The environmental conditions measured were evaluated using a mathematical model for the response time to effectively show the accuracy of the DHT sensor for weather monitoring and future prediction. An experimental analysis of human motion patterns was performed using the CNN model to evaluate the classification for the detection of humans. The CNN classification model gave an accuracy of 99.8%.

Sign in / Sign up

Export Citation Format

Share Document