Spectrum Occupancy Prediction for Internet of Things via Long Short-Term Memory

2019 ◽  
Author(s):  
Haoyu Li ◽  
Xiaojin Ding ◽  
Yiguang Yang ◽  
Xiaogu Huang ◽  
Genxin Zhang
Keyword(s):  
Internet Of Things ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Spectrum Occupancy ◽  
Long Short Term Memory ◽  
Occupancy Prediction

PrivItem2Vec: A privacy-preserving algorithm for top-N recommendation

2021 ◽  
Vol 17 (12) ◽  
pp. 155014772110612
Author(s):  
Zhengqiang Ge ◽  
Xinyu Liu ◽  
Qiang Li ◽  
Yu Li ◽  
Dong Guo
Keyword(s):  
Time Series ◽  
Internet Of Things ◽  
Privacy Protection ◽  
Short Term Memory ◽  
Short Term ◽  
Recommendation Algorithm ◽  
Data Set ◽  
Term Memory ◽  
Protection Mechanism ◽  
Long Short Term Memory

To significantly protect the user’s privacy and prevent the user’s preference disclosure from leading to malicious entrapment, we present a combination of the recommendation algorithm and the privacy protection mechanism. In this article, we present a privacy recommendation algorithm, PrivItem2Vec, and the concept of the recommended-internet of things, which is a privacy recommendation algorithm, consisting of user’s information, devices, and items. Recommended-internet of things uses bidirectional long short-term memory, based on item2vec, which improves algorithm time series and the recommended accuracy. In addition, we reconstructed the data set in conjunction with the Paillier algorithm. The data on the server are encrypted and embedded, which reduces the readability of the data and ensures the data’s security to a certain extent. Experiments show that our algorithm is superior to other works in terms of recommended accuracy and efficiency.

scholarly journals Intrusion Detection System to Advance Internet of Things Infrastructure-Based Deep Learning Algorithms

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Hasan Alkahtani ◽  
Theyazn H. H. Aldhyani
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Internet Of Things ◽  
Intrusion Detection System ◽  
Short Term Memory ◽  
Detection System ◽  
Learning Algorithms ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Smart grids, advanced information technology, have become the favored intrusion targets due to the Internet of Things (IoT) using sensor devices to collect data from a smart grid environment. These data are sent to the cloud, which is a huge network of super servers that provides different services to different smart infrastructures, such as smart homes and smart buildings. These can provide a large space for attackers to launch destructive cyberattacks. The novelty of this proposed research is the development of a robust framework system for detecting intrusions based on the IoT environment. An IoTID20 dataset attack was employed to develop the proposed system; it is a newly generated dataset from the IoT infrastructure. In this framework, three advanced deep learning algorithms were applied to classify the intrusion: a convolution neural network (CNN), a long short-term memory (LSTM), and a hybrid convolution neural network with the long short-term memory (CNN-LSTM) model. The complexity of the network dataset was dimensionality reduced, and to improve the proposed system, the particle swarm optimization method (PSO) was used to select relevant features from the network dataset. The obtained features were processed using deep learning algorithms. The experimental results showed that the proposed systems achieved accuracy as follows: CNN = 96.60%, LSTM = 99.82%, and CNN-LSTM = 98.80%. The proposed framework attained the desired performance on a new variable dataset, and the system will be implemented in our university IoT environment. The results of comparative predictions between the proposed framework and existing systems showed that the proposed system more efficiently and effectively enhanced the security of the IoT environment from attacks. The experimental results confirmed that the proposed framework based on deep learning algorithms for an intrusion detection system can effectively detect real-world attacks and is capable of enhancing the security of the IoT environment.

scholarly journals Long Short Term Memory Network-based Interference Recognition for Industrial Internet of Things

2020 ◽  
pp. 876-885
Author(s):  
Natthanan Promsuk ◽  
◽  
Attaphongse Taparugssanagorn
Keyword(s):  
Internet Of Things ◽  
Short Term Memory ◽  
Minimum Mean Square Error ◽  
Human Beings ◽  
Short Term ◽  
Term Memory ◽  
Industrial Iot ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Iot Devices

Nowadays, the rapid growth of wireless Internet of things (IoT) devices is one of the significant factors leading smart systems in various sectors, such as healthcare, education, and agriculture. This is, of course, not limited to the industrial sector, where the IoT concept is applied for real time monitoring and control of devices instead of human beings. Co-channel interferences occurs when two or more devices are using the same channel. It causes unnecessary contention as the devices will be forced to defer transmissions until the medium is clear causing a loss of throughput. Adjacent channel interference is even more serious and occurs when the devices are on overlapping channels causing corrupted data, which makes indispensable retransmissions. The more devices are added to an environment, the higher the likelihood of interference problem is. Due to a huge number of IoT devices, the interference issue becomes very serious. In this paper, a long short-term memory network-based interference recognition (LSTM-IR) is proposed. This method is integrated into the industrial IoT (IIoT) network in factory environments to mitigate the effect of interferences. The comparative results are done among three interference suppression techniques (IST) including the traditional minimum mean square error (MMSE) approach, the multi-layer perceptron (MLP), and the proposed LSTM-IR. Since the type of transmitting and receiving data is usually a sequencing data type. Therefore, the proposed method with the input data from a fast Fourier transform (FFT) algorithm provides better performances because it is based on an LSTM which is suitable for the sequences of data. The number of the devices in the factory is obviously the key factor because the smaller number of active devices causes less interferences.

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