International Journal of Communication Networks and Information Security (IJCNIS)

A Study of Innovative Technologies for Energy-Efficient Enterprise Management of Wireless Heterogeneous Networks in Collaborative Communications

2024-01-16T20:06:54-07:00

Collaborative communication technology has become a popular research area in wireless communications due to its ability to resist varying degrees of channel fading through the collaborative transmission of network nodes. This thesis focuses on energy-efficient collaborative communication systems in increasingly complex environments in heterogeneous wireless networks, with the aim of optimizing energy efficiency and improving user data rates in small areas (e.g., within an enterprise). A brief introduction to the basic technologies of wireless energy-carrying collaborative communication systems is given, summarising relay forwarding strategies, three basic communication models, and energy and information co-transmission reception mechanisms before proposing an ED-OEH relaying protocol at the end of the section that integrates energy classification and opportunity energy harvesting. Immediately afterwards, the heterogeneity of network nodes in terms of computation and storage is pointed out, and a sensor network security protocol based on a hybrid encryption regime is designed. Finally, the problem of intra-enterprise resource allocation and energy efficiency optimization in heterogeneous wireless network scenarios based on deep augmented learning algorithms is investigated. Nature DQN is used as the core algorithm, and the input dimension and loss function in traditional neural networks are improved to reduce the complexity of the algorithm. Experimental results show that the Nature DQN algorithm converges faster than traditional algorithms such as Q-learning, and the energy efficiency ratio can reach up to 300%.

The Role of Wireless Network Technology in Analysis of Audience Satisfaction of Chinese Web Dramas in the Big Data Era

2024-01-18T00:19:46-07:00

The continuous development of Wireless network technology has made web dramas popular on a large scale and made the cause of web dramas popular become the focus of research on mobile communication and modern communication. As an essential component of media effect research, analysis of the Audience Satisfaction plays a significant role in web drama research. However, the original click-through rate measurement method can not effectively solve the problem of analyzing the Audience Satisfaction of web dramas in the era of big data, and the accuracy of cause analysis is low. Therefore, this paper proposes an analysis model based on wireless network technology to analyze the popular Audience Satisfaction of web dramas from the perspective of the uses and gratifications theory. Firstly, wireless network technology is used to summarize the data transmission rate of web dramas, and judgment is made according to the popular methods, and reasons for data characteristics, and irrelevant popular data of web dramas is discarded. Then, the results are analyzed according to the data transmission rate and data form of the web drama and compared with the click-through rate measurement method to find out the reasons for the possibility of existence. After simulation test and analysis, Wireless network technology can improve the accuracy of judging the Audience Satisfaction of web dramas, with an accuracy rate of 90.3%, judge the reasons for different types of web drama content and forms, and calculate the cause analysis time, and find that this method can meet the cause analysis of web dramas Multifaceted needs.

Technology-Enabled Medical IoT System for Drug Management

2024-01-30T01:48:42-07:00

This study introduces an innovative framework for the storage and administration of pharmaceuticals, which effectively tackles the pressing requirements of maintaining optimal temperature and humidity conditions, monitoring medicine inventory, and processing real-time data in healthcare establishments. By utilizing a comprehensive network of Internet of Things (IoT) sensors strategically positioned within pharmaceutical storage facilities, our technology effectively guarantees the preservation and security of stored drugs. The study conducted in our research demonstrates that low temperature fluctuation effectively protects medicinal substances, hence reducing potential dangers to patients. The real-time inventory management system effectively optimizes medicine control by following expiry criteria and minimizing wasted spending. Furthermore, our study emphasizes the importance of cloud response latency, as the average data transfer time is a rapid 100 milliseconds. The expeditious integration of crucial data enables prompt notifications and alerts, hence augmenting the quality and safety of pharmaceutical products.

Numerical Protection Analysis of Shen Xiu Intangible Cultural Heritage Based on Software Definition Technology

2024-02-20T18:07:57-07:00

The role of numerical protection analysis in the digital protection of Shen Xiu's intangible culture has changed the numerical value of Shen Xiu's intangible culture and made the digital protection of Shen Xiu's intangible culture a hot spot. However, in the process of numerical protection of intangible culture in Shen Xiu, there are some problems, such as poor digital collection effect and small amount of digital processing data. The main reason is that the traditional way of oral transmission and heart-to-heart transmission limits the development of digital numerical protection of intangible culture in Shen Xiu. Therefore, this paper proposes a digital protection method for Shen Xiu's intangible culture based on software-defined technology and plans the characteristics of Shen Xiu's intangible culture with different values. First of all, the data of Shen Xiu intangible culture in Shen Xiu are collected by software-defined technology, and the data of different values are summarized by software-defined technology, and the numerical division of Shen Xiu intangible culture is carried out according to the characteristics of Suzhou embroidery, leaving common characteristics. Then, according to the software-defined technology, the protective communication of numerical protection is carried out to promote the integration of the characteristics of Shen Xiu's intangible culture. The results of numerical protection analysis show that software-defined technology can improve the numerical extraction level of Shen Xiu intangible culture, promote the development of digital numerical protection of Shen Xiu intangible culture by using software-defined technology, and meet the requirements of numerical protection of Shen Xiu intangible culture.

Deep Belief Neural Network Framework for an Effective Scalp Detection System Through Optimization

2024-03-27T02:32:18-06:00

In an era where technology rapidly enhances various sectors, medical services have greatly benefited, particularly in tackling the prevalent issue of hair loss, which affects individuals' self-esteem and social interactions. Acknowledging the need for advanced hair and scalp care, this paper introduces a cost-effective, tech-driven solution for diagnosing scalp conditions. Utilizing the power of deep learning, we present the Grey Wolf-based Enhanced Deep Belief Neural (GW-EDBN) method, a novel approach trained on a vast array of internet-derived scalp images. This technique focuses on accurately identifying key symptoms like dandruff, oily scalp, folliculitis, and hair loss. Through initial data cleansing with Adaptive Gradient Filtering (AGF) and subsequent feature extraction methods, the GW-EDBN isolates critical indicators of scalp health. By incorporating these features into its Enhanced Deep Belief Network (EDBN) and applying Grey Wolf Optimization (GWO), the system achieves unprecedented precision in diagnosing scalp ailments. This model not only surpasses existing alternatives in accuracy but also offers a more affordable option for individuals seeking hair and scalp analysis, backed by experimental validation across several performance metrics including precision, recall, and execution time. This advancement signifies a leap forward in accessible, high-accuracy medical diagnostics for hair and scalp health, potentially revolutionizing personal care practices.

Design of an Integrated Model for Security Establishment in Iot-Enabled Software Defined Networks

2024-04-19T01:34:41-06:00

Robust network designs are provided by software-defined networks (SDNs) for Internet of Things (IoT) applications, both present and future. At the same time, because of their programmability and global network perspective, SDNs are a desirable target for cyber threats. Among its primary drawbacks is the susceptibility of standard SDN architectures to Distributed Denial of Service (DDoS) flooding attacks. DDoS flooding assaults often result in a complete failure or service outage by rendering SDN controllers useless with respect to their underlying infrastructure. This study looks at popular machine learning (ML) methods for classifying and detecting DDoS flooding attacks on SDNs. Restricted Boltzmann Machine with Restricted Whales’ Optimizer (RBM-RWO) is the classifier integrated optimizer and other machine learning techniques examined. In this case study, experimental data (jitter, throughput, and reaction time measurements) from a realistic SDN architecture appropriate for typical midsized enterprise-wide networks are used to construct classification models that effectively detect and describe DDoS flooding assaults. Attackers using DDoS floods used low orbit ion cannons (LOIC), user datagram protocol (UDP), transmission control protocol (TCP), and hypertext transfer protocol (HTTP). Despite the high effectiveness of all the ML techniques examined in identifying and categorizing DDoS flooding assaults, When it came to training time is 17.5 ms, prediction speed is 7e-3 observations/s, prediction accuracy of 98%, and overall performance, RBM-RWO performed the best.

Smart Garden Management System Based on the combination of Internet of Things and Geographic Information System Technology

2024-04-22T19:38:14-06:00

The Internet of Things (IoT) technology is inevitably forging ahead in a number of areas all among them being the agricultural sector and in particular the affected environment. This paper utilizes the farm's IoT where through an effective analysis, the smart garden is designed and managed. It blends field research, project implementations and theoretical analysis to improve the case implementation. It can be considered as more rigorous and practical. The study intends, first and foremost, to look at agricultural IoT based technologies and how IoT changes the way farmers work and agricultural landscapes look. Different theories have been used in the implementation of these concepts which include agriculture, tourism, and landscape design. The technical side will integrate the Internet of Things technology, the adaptation theory of agricultural industrialization, the formation of the theory of ecotourism, the application of tourism psychological theory in tourism discipline, the ecology theory of landscape and aesthetics theory, and landscape gardening planning and design theory. A comprehensive examination of the smart park has been conducted, including relevant theories and the application of planning and design principles. This systematic research aims to provide scientific direction for the agricultural aspects of the smart park. In order to offer scientific direction for the planning and design of agricultural Internet of Things (IoT) in smart gardens, this study presents a theoretical framework for the planning and design of smart gardens that incorporate agricultural IoT. The framework is comprehensively explained, encompassing its concept, distinguishing features, relevant theories, and guidance approaches.

A Constructive Model for Cyber-Attack Prediction Using Efficient Weighted Bi-Directional Learning Approaches

2024-04-23T01:22:31-06:00

Anomaly detection algorithms based on machine and deep learning are currently the most promising techniques for identifying cyber-attacks. However, hostile attacks lower forecast accuracy which is made against these techniques. The resilience of anomaly detection has been measured using a variety of methods in the literature. They neglect to consider the fact that a little disruption in an anomalous sample caused by an assault like a denial of service might cause it to become a genuinely normal sample, but a huge perturbation can transform an anomalous sample into a truly normal sample without affecting the whole system. Even so, it can lead to it being wrongly classified as normal. The approach for determining an anomaly detection model's resilience in industrial contexts is presented in this work. To detect abnormalities brought on by various cyber-attacks; this work used the method of a Support Vector Machine (SVM) for feature extraction and weight analysis. In this case, a unique deep learning-based Bi-LSTM (Bi-directional Long Short Term Memory) only requires a disruption of 60% with 99.6% accuracy of the original sample to create adversarial samples as opposed to the model, which requires a disruption of the entire original sample.

Landscape Planning and Public Space Optimization of Grand Canal Cultural Park based on Computer-aided Design

2024-01-25T23:34:55-07:00

Collaborative As the rate of urbanisation is increasing at a very fast pace, there is a huge demand for Landscape planning with proper public space optimisation. China, a country which has witnessed rapid growth in population and economics, has become a target of urbanisation. The country is known for its aesthetic appeal in the Grand Canal cultural park, which has been a primary factor in the country's development since ancient times. However, landscape planning with efficient utilization of available public space in the region using contemporary computing technologies is the need of the hour. This work focuses on deploying Computer-Aided Design in landscape planning using the Artisan plugin, specifically meant for environment planning. The special tools available in this plugin help landscape planning architects to accurately study the characteristics of the landscape, like terrain, water bodies, planar regions, etc. Also, this work proposed a four-phased model that aids the development process of landscape planning activity by including micro-level factors that directly interact with the environment. In future, this model could be extended to include AR, VR, AI and ML technologies.

Blockchain Framework for Digital Learning and Information and Communications Technology

2024-04-18T02:12:06-06:00

At present, the economic ties between countries worldwide are getting closer and closer. In a world where the internet industry is developing rapidly, Digital learning and ICT applications in blockchain have gradually matured. This paper takes digital learning and ICT blockchain application in e-commerce as the main research object, The rapid development of e-commerce has been promoted through the extensive application of digital learning and information and communication technology blockchain in e-commerce. Digital learning and information and communication technology solve the problems of e-commerce payment with encryption characteristics and security and openness in blockchain; At the same time, the information can be traced and cannot be tampered with to solve the quality problem of e-commerce goods. In a real sense to promote the sustainable development of the field of e-commerce, this study provides new ideas and guidance for the blockchain framework of e-learning and ICT in e-commerce.