Internet Technology Letters最新文献

Traditional ethnic sports shape the Chinese nation's solid national spirit, and mobile health development has been extended to various fields. In this study, we empower mobile health to traditional ethnic sports. Sensors used for collecting health data are worn on athletes and communicated with sink nodes through the network to provide better training guidance for traditional ethnic sports athletes through data analysis. However, the devices used to collect health data may come from many companies, and aggregating the data inevitably involves data security. As a new basic artificial intelligence technology, federated learning can use the health data of athletes to train the data analysis model in the case of original data localization, to solve the security and privacy problems in health data sharing to a certain extent. To this end, a differentially private-dynamic federated learning framework for dynamic aggregation weights under an untrusted central server is proposed, which sets a dynamic model aggregation weight, and this method directly learns federated learning from the data of different participants. The learning model aggregates the weights so that it is suitable for non-independent data environments. Experimental results show that the proposed framework not only provides local privacy guarantees, but also achieves higher accuracy and improves the security of mobile health data of traditional ethnic sports athletes in federated learning.

This paper investigates the performance of cloud radio access networks (C-RAN) in different power control scenarios in full-duplex (FD) cellular networks. Results reveal that FD C-RAN performance drastically reduces with uplink (UL) power control. We proposed a power control scheme in downlink (DL), which uplifts the UL user rate, but at the cost of DL rate. With UL and DL power control, the average rate saturates after a threshold number of base stations (BS) coordination and has little impact on coordinating additional BSs. Besides, the state-of-the-art self-interference suppression (SI) of 130 dB provides almost equal performance compared to the perfect SI cancelation. Hence co-channel interference management becomes the bottleneck for FD system design. We used the Matern cluster process (MCP) to model the network.

The internet of things (IoT) is advancing human lives by providing various intelligent services. The advancement of cutting-edge technologies necessitates an increase in the need for cyber security. In this light, an extensive research has been conducted to address IoT security concerns. However, several security challenges must be addressed before IoT can be utilized to its full potential in real-time. Motivated by this fact, this study presents a novel integration approach of Natural Language Processing (NLP) with IoT systems to enhance its security. It investigates the current state of NLP and its applications. The key contribution of this paper is the development of a system for identifying malicious behaviors in an IoT environment using n-gram and word-embedding techniques. Several security challenges and possible future directions to enhance IoT security using NLP are also addressed in this study.

The difference in farming culture is the basis for the exchange, reference, and integration of agricultural culture. We propose a method based on data fusion and extraction method for analyzing differences in farming culture between China and Japan in this paper. Specifically, we designed a farming culture difference analysis model based on deep learning technology and improved algorithm. We combined deep neural network and particle swarm algorithm to design and improve the analysis model of farming culture difference. Firstly, we introduce in detail the designed analysis model based on deep learning, namely BP neural network. Secondly, we adopt the particle swarm algorithm (PSO) to improve and upgrade the defects of the BP neural network model. The experimental and comparative analysis of the results shows the characteristics of China's vast land and abundant resources and the characteristics of Japan, an island country with limited cultivated land.

Smart community construction is an integral part of smart city construction, and smart community management requires huge amounts of data as support. Currently, the data generated by some smart communities is scattered, and this data needs further analysis to realize value. This paper primarily studies data classification and parameter optimization. First, a novel K-means clustering group support vector machines (SVM) method is proposed for data classification. For the parameter optimization problem of SVMs, evolutionary computation is used to seek the optimal solution through iterative evolution in a population composed of some feasible solutions. Then, the improved gray wolf optimization (iGWO) algorithm is used to optimize parameters and select features of SVM. Finally, to alleviate the situation that the minority samples are easily misjudged as noise samples due to the redundant features in the initial data, an oversampling method based on the iGWO and synthetic minority oversampling technique (SMOTE) is proposed, called iGWO–SMOTE–SVM. The experimental results demonstrate that the suggested approach on the six UCI datasets has acceptable accuracy, F1, and G-Mean, which can well serve the construction of smart communities.

The Internet of Things is an essential component in the growth of an ecosystem that enables quick and precise judgments to be made for communication on the battleground. The usage of the battlefield of things (BoT) is, however, subject to several restrictions for a variety of reasons. There is a potential for instances of replay, data manipulation, breaches of privacy, and other similar occurrences. As a direct result of this, the implementation of a security mechanism to protect the communication that occurs within BoT has turned into an absolute requirement. To this aim, we propose a blockchain-based solution that is both safe and private for use in communications inside the BoT ecosystem. In addition, research is conducted on the benefits of integrating blockchain technology and cybersecurity into BoT application implementations. This work elaborates on the importance of integrating cybersecurity and blockchain-based tools, techniques and methodologies for BoT.