Abstraction of Heterogeneous IoT Devices for Management of Smart Homes

2019 ◽  
Author(s):  
Emanuel J. Mendes ◽  
Matheus M. Silveira ◽  
Matheus B. Araujo ◽  
Joaquim Celestino ◽  
Rafael L. Gomes
Keyword(s):  
Smart Homes ◽  
Iot Devices

scholarly journals Sovereign: Self-contained Smart Home with Data-centric Network and Security

2021 ◽  
Author(s):  
Zhiyi Zhang ◽  
Tianyuan Yu ◽  
Xinyu Ma ◽  
Yu Guan ◽  
Philipp Moll ◽  
...  
Keyword(s):  
Smart Homes ◽  
End Users ◽  
Security And Privacy ◽  
Preliminary Evaluation ◽  
Complete Control ◽  
Broadcast Media ◽  
Wireless Broadcast ◽  
Development Platform ◽  
Iot Devices ◽  
Rapid Deployment

<p>Recent years have witnessed the rapid deployment of smart homes; most of them are controlled by remote servers in the cloud. Such designs raise security and privacy concerns for end users. In this paper, we describe the design of Sovereign, a home IoT system framework that provides end users complete control of their home IoT systems. Sovereign lets home IoT devices and applications communicate via application-named data and secures data directly. This enables direct, secure, one-to-one and one-to-many device-to-device communication over wireless broadcast media. Sovereign utilizes semantic names to construct usable security solutions. We implement Sovereign as a publish-subscribe-based development platform together with a prototype home IoT controller. Our preliminary evaluation shows that Sovereign provides a systematic, easy-to-use solution to user-controlled, self-contained smart homes running on existing IoT hardware without imposing noticeable overhead.</p>

scholarly journals Subcarrier-Sniffer：CSI-based Subcarrier-Level Spectrum Sensing Algorithm

2019 ◽  
Author(s):  
Zhenjiang Tan ◽  
Zheng Lu ◽  
Hongyu Sun
Keyword(s):  
Spectrum Sensing ◽  
Channel State Information ◽  
Spectrum Sharing ◽  
Smart Homes ◽  
Experimental Results ◽  
Spectrum Utilization ◽  
Channel State ◽  
State Information ◽  
Level Spectrum ◽  
Iot Devices

Abstract: As the massive deployment of the heterogeneous IoT devices in the coexisting environment such as smart homes，Traditional channel-based spectrum sharing algorithms such as CSMA has great limitations to further optimize spectrum utilization. Therefore, exploring more efficient spectrum sensing algorithm becomes hot topic these years. This paper proposes Subcarrier-Sniffer, which utilizes Channel State Information (CSI) to sense the subcarrier-level detailed status of the spectrum. In order to evaluate the performance of Subcarrier-Sniffer, we implemented Subcarrier-Sniffer by USRP B200min, and the experimental results show that when the distance between Subcarrier-Sniffer and the monitored devices is not great than 7 m, the accuracy of subcarrier-level spectrum sensing could achieve 100% in our settings.

scholarly journals Automatic Failure Recovery for Container-Based IoT Edge Applications

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 3047
Author(s):  
Kolade Olorunnife ◽  
Kevin Lee ◽  
Jonathan Kua
Keyword(s):  
Internet Of Things ◽  
Smart Cities ◽  
Smart Homes ◽  
Edge Computing ◽  
Failure Recovery ◽  
Software Failures ◽  
Iot Applications ◽  
Remote Management ◽  
Management Ability ◽  
Iot Devices

Recent years have seen the rapid adoption of Internet of Things (IoT) technologies, where billions of physical devices are interconnected to provide data sensing, computing and actuating capabilities. IoT-based systems have been extensively deployed across various sectors, such as smart homes, smart cities, smart transport, smart logistics and so forth. Newer paradigms such as edge computing are developed to facilitate computation and data intelligence to be performed closer to IoT devices, hence reducing latency for time-sensitive tasks. However, IoT applications are increasingly being deployed in remote and difficult to reach areas for edge computing scenarios. These deployment locations make upgrading application and dealing with software failures difficult. IoT applications are also increasingly being deployed as containers which offer increased remote management ability but are more complex to configure. This paper proposes an approach for effectively managing, updating and re-configuring container-based IoT software as efficiently, scalably and reliably as possible with minimal downtime upon the detection of software failures. The approach is evaluated using docker container-based IoT application deployments in an edge computing scenario.

Applications of IOT in Smart Cities and Smart Environment

2019 ◽  
pp. 7-17 ◽  
Author(s):  
Keerthivasan G ◽  
Aishwarya G ◽  
Jawahar G ◽  
Muthukumar C
Keyword(s):  
Internet Of Things ◽  
Smart Cities ◽  
Smart Homes ◽  
The Internet ◽  
Large Network ◽  
Smart Environment ◽  
Transfer Data ◽  
Ecological Resources ◽  
The World ◽  
Iot Devices

Internet of things is one of the emerging technologies in the world. Through which we can generate a large network among the tiny devices to communicate with each other to develop environmental and ecological resources. Most of the smart technology devices are designed by IoT network of devices. By connecting these devices that help to interact with each other and to collect and transfer data over the internet. The IoT devices working speed and their performance have improved by introducing a device called a sensor. The idea of IoT devices with sensors that sense the data and make smart decisions in the environment. This paper makes it clear about the benefits of IoT devices over technology in the modern environment. The sensors in IoT devices are connected to Wi-Fi, Bluetooth and RFID etc. to collect useful data. By connecting devices over the network, the world will become smart and thus it evolves the smart environment including smart homes, smart buildings and smart cities. It is believed that about 30 billion people in the world will use at least one IoT technology devices by the year 2020. To maintain our environment safe and secure the IoT devices play a major role in several enabling technologies. This paper is to present the applications of IoT in smart cities and the environment and a brief explanation about their uses.

Device-Free Occupant Activity Sensing Using WiFi-Enabled IoT Devices for Smart Homes

2018 ◽  
Vol 5 (5) ◽  
pp. 3991-4002 ◽  
Author(s):  
Jianfei Yang ◽  
Han Zou ◽  
Hao Jiang ◽  
Lihua Xie
Keyword(s):  
Smart Homes ◽  
Iot Devices ◽  
Device Free ◽  
Activity Sensing

scholarly journals Sovereign: Self-contained Smart Home with Data-centric Network and Security

2021 ◽  
Author(s):  
Zhiyi Zhang ◽  
Tianyuan Yu ◽  
Xinyu Ma ◽  
Yu Guan ◽  
Philipp Moll ◽  
...  
Keyword(s):  
Smart Homes ◽  
End Users ◽  
Security And Privacy ◽  
Preliminary Evaluation ◽  
Complete Control ◽  
Broadcast Media ◽  
Wireless Broadcast ◽  
Development Platform ◽  
Iot Devices ◽  
Rapid Deployment

<p>Recent years have witnessed the rapid deployment of smart homes; most of them are controlled by remote servers in the cloud. Such designs raise security and privacy concerns for end users. In this paper, we describe the design of Sovereign, a home IoT system framework that provides end users complete control of their home IoT systems. Sovereign lets home IoT devices and applications communicate via application-named data and secures data directly. This enables direct, secure, one-to-one and one-to-many device-to-device communication over wireless broadcast media. Sovereign utilizes semantic names to construct usable security solutions. We implement Sovereign as a publish-subscribe-based development platform together with a prototype home IoT controller. Our preliminary evaluation shows that Sovereign provides a systematic, easy-to-use solution to user-controlled, self-contained smart homes running on existing IoT hardware without imposing noticeable overhead.</p>

Wireless Charging of IoT Devices in Smart Homes Using Retrodirective WPT

2021 ◽  
Author(s):  
Zahra Katbay ◽  
Dimitrios Sounas ◽  
Mohammed Ismail
Keyword(s):  
Smart Homes ◽  
Wireless Charging ◽  
Iot Devices

scholarly journals Internet of Things (IoT)-Enabled Elderly Fall Verification, Exploiting Temporal Inference Models in Smart Homes

2020 ◽  
Vol 17 (2) ◽  
pp. 408 ◽  
Author(s):  
Grigorios Kyriakopoulos ◽  
Stamatios Ntanos ◽  
Theodoros Anagnostopoulos ◽  
Nikolaos Tsotsolas ◽  
Ioannis Salmon ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Synthetic Data ◽  
Smart Homes ◽  
The Elderly ◽  
Classification Methods ◽  
Inference Models ◽  
Fall Incident ◽  
Temporal Inference ◽  
Iot Devices ◽  
Serious Injuries

Everyday life of the elderly and impaired population living in smart homes is challenging because of possible accidents that may occur due to daily activities. In such activities, persons often lean over (to reach something) and, if they not cautious, are prone to falling. To identify fall incidents, which could stochastically cause serious injuries or even death, we propose specific temporal inference models; namely, CM-I and CM-II. These models can infer a fall incident based on classification methods by exploiting wearable Internet of Things (IoT) altimeter sensors adopted by seniors. We analyzed real and synthetic data of fall and lean over incidents to test the proposed models. The results are promising for incorporating such inference models to assist healthcare for fall verification of seniors in smart homes. Specifically, the CM-II model achieved a prediction accuracy of 0.98, which is the highest accuracy when compared to other models in the literature under the McNemar’s test criterion. These models could be incorporated in wearable IoT devices to provide early warning and prediction of fall incidents to clinical doctors.

scholarly journals LT10 A LIGHTWEIGHT PROPOSED ENCRYPTION ALGORITHM FOR IOT

2018 ◽  
Vol 44 (1) ◽  
pp. 1-5
Author(s):  
Hiba Taresh
Keyword(s):  
Smart Homes ◽  
Encryption Algorithm ◽  
Block Length ◽  
Network Information ◽  
Iot Devices ◽  
Key Length ◽  
Lightweight Encryption

In this paper, algorithm (LT10) which is originally consist of four kasumi elements is proposed as a lightweight encryption algorithm, the proposed algorithm take into account that the IOT devices have a limit computation abilities and the sensitivity of smart homes and IOT network information that need to be exchanged the key length is 128 bit and the block length is 128 bit

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

Processes ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1593
Author(s):  
Zeinab Shahbazi ◽  
Yung-Cheol Byun ◽  
Ho-Young Kwak
Keyword(s):  
Reinforcement Learning ◽  
Smart Home ◽  
Smart Homes ◽  
Smart Devices ◽  
Home Gateway ◽  
Home Based ◽  
Information And Communication ◽  
Smart Home Gateway ◽  
Iot Devices ◽  
The Right

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.

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