scholarly journals The Internet of Things as a Deep Neural Network

2020 ◽  
Vol 58 (9) ◽  
pp. 20-25
Author(s):  
Rong Du ◽  
Sindri Magnusson ◽  
Carlo Fischione
Keyword(s):  
Neural Network ◽  
Internet Of Things ◽  
Deep Neural Network ◽  
The Internet ◽  
The Internet Of Things

scholarly journals Remote Patient Monitoring and classifying Using the Internet of Things Platform Combined with Cloud Computing

2021 ◽  
Author(s):  
Somayeh Iranpak ◽  
Asadollah Shahbahrami ◽  
Hassan Shakeri
Keyword(s):  
Neural Network ◽  
Cloud Computing ◽  
Internet Of Things ◽  
Deep Neural Network ◽  
Sensor Data ◽  
The Internet ◽  
Sensitive Information ◽  
Iot Platform ◽  
Remote Patient ◽  
The Internet Of Things

Abstract Patients' health and providing an optimal and appropriate solution have recently been considered by many researchers. With the advent of technologies such as cloud computing and 5G network, information can be exchanged faster and more securely. Using cloud computing in this process can significantly improve the monitoring of certain patients. Therefore, providing a favorable method in the medical industry and computer science to monitor the status of patients using connected sensors is very important. Thus, due to its optimal efficiency, speed, and accuracy of data processing and classification, the use of cloud computing to process the data collected from remote patient sensors and the Internet of Things (IoT) platform has been suggested. In this paper, a prioritization system is used to prioritize sensitive information in IoT, and in cloud computing, LSTM deep neural network is used to classify and monitor patients' condition remotely, which can be considered as an important innovative aspect of this paper. Sensor data in the IoT platform is sent to the cloud with the help of the 5th generation Internet. The core of cloud computing uses the LSTM deep neural network algorithm. Through simulating the proposed method and comparing the obtained results with other methods, it was observed that the accuracy of the proposed method has been improved significantly compared to other methods.

scholarly journals Remote patient monitoring and classifying using the internet of things platform combined with cloud computing

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Somayeh Iranpak ◽  
Asadollah Shahbahrami ◽  
Hassan Shakeri
Keyword(s):  
Neural Network ◽  
Cloud Computing ◽  
Internet Of Things ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Sensor Data ◽  
The Internet ◽  
Iot Platform ◽  
Remote Patient ◽  
The Internet Of Things

AbstractMany researchers have recently considered patients’ health and provided an optimal and appropriate solution. With the advent of technologies such as cloud computing, Internet of Things and 5G, information can be exchanged faster and more securely. The Internet of things (IoT) offers many opportunities in the field of e-health. This technology can improve health services and lead to various innovations in this regard. Using cloud computing and IoT in this process can significantly improve the monitoring of patients. Therefore, it is important to provide a useful method in the medical industry and computer science to monitor the status of patients using connected sensors. Thus, due to its optimal efficiency, speed, and accuracy of data processing and classification, the use of cloud computing to process the data collected from remote patient sensors and IoT platform has been suggested. In this paper, a prioritization system is used to prioritize sensitive information in IoT, and in cloud computing, LSTM deep neural network is applied to classify and monitor patients’ condition remotely, which can be considered as an important innovative aspect of this paper. Sensor data in the IoT platform is sent to the cloud with the help of the 5th generation Internet. The core of cloud computing uses the LSTM (long short-term memory) deep neural network algorithm. By simulating the proposed method and comparing the obtained results with other methods, it is observed that the accuracy of the proposed method is 97.13%, which has been improved by 10.41% in average over the other methods.

scholarly journals Security-Aware Routing Protocol Based on Artificial Neural Network Algorithm and 6LoWPAN in the Internet of Things

2022 ◽  
Vol 2022 ◽  
pp. 1-8
Author(s):  
Jiangdong Lu ◽  
Dongfang Li ◽  
Penglong Wang ◽  
Fen Zheng ◽  
Meng Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Internet Of Things ◽  
Routing Protocols ◽  
Intelligent Systems ◽  
Smart Devices ◽  
The Internet ◽  
Routing Mechanism ◽  
Artificial Neural ◽  
The Internet Of Things

Today, with increasing information technology such as the Internet of Things (IoT) in human life, interconnection and routing protocols need to find optimal solution for safe data transformation with various smart devices. Therefore, it is necessary to provide an enhanced solution to address routing issues with respect to new interconnection methodologies such as the 6LoWPAN protocol. The artificial neural network (ANN) is based on the structure of intelligent systems as a branch of machine interference, has shown magnificent results in previous studies to optimize security-aware routing protocols. In addition, IoT devices generate large amounts of data with variety and accuracy. Therefore, higher performance and better data handling can be achieved when this technology incorporates data for sending and receiving nodes in the environment. Therefore, this study presents a security-aware routing mechanism for IoT technologies. In addition, a comparative analysis of the relationship between previous approaches discusses with quality of service (QoS) factors such as throughput and accuracy for improving routing mechanism. Experimental results show that the use of time-division multiple access (TDMA) method to schedule the sending and receiving of data and the use of the 6LoWPAN protocol when routing the sending and receiving of data can carry out attacks with high accuracy.

Security Model of Internet of Things Based on Binary Wavelet and Sparse Neural Network

2019 ◽  
Vol 10 (1) ◽  
pp. 1-17
Author(s):  
Zhihui Wang ◽  
Jingjing Yang ◽  
Benzhen Guo ◽  
Xiao Zhang
Keyword(s):  
Neural Network ◽  
Internet Of Things ◽  
Network Architecture ◽  
Safety Management ◽  
The Internet ◽  
Security Model ◽  
The Novel ◽  
Simulated Environment ◽  
Proposed Model ◽  
The Internet Of Things

At present, the internet of things has no standard system architecture. According to the requirements of universal sensing, reliable transmission, intelligent processing and the realization of human, human and the material, real-time communication between objects and things, the internet needs the open, hierarchical, extensible network architecture as the framework. The sensation equipment safe examination platform supports the platform through the open style scene examination to measure the equipment and provides the movement simulated environment, including each kind of movement and network environment and safety management center, turning on application gateway supports. It examines the knowledge library. Under this inspiration, this article proposes the novel security model based on the sparse neural network and wavelet analysis. The experiment indicates that the proposed model performs better compared with the other state-of-the-art algorithms.

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