In the last few years, combining multiple algorithms to improve the performance of machine learning models has been a common practice. However, its application to stress detection still needs to be explored. This pape...
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In the context of an increasingly severe cybersecurity landscape and the growing complexity of offensive and defen-sive techniques,Zero Trust Networks(ZTN)have emerged as a widely recognized *** Trust not only address...
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In the context of an increasingly severe cybersecurity landscape and the growing complexity of offensive and defen-sive techniques,Zero Trust Networks(ZTN)have emerged as a widely recognized *** Trust not only addresses the shortcomings of traditional perimeter security models but also consistently follows the fundamental principle of“never trust,always verify.”Initially proposed by John Cortez in 2010 and subsequently promoted by Google,the Zero Trust model has become a key approach to addressing the ever-growing security threats in complex network *** paper systematically compares the current mainstream cybersecurity models,thoroughly explores the advantages and limitations of the Zero Trust model,and provides an in-depth review of its components and key ***,it analyzes the latest research achievements in the application of Zero Trust technology across various fields,including network security,6G networks,the Internet of Things(IoT),and cloud computing,in the context of specific use *** paper also discusses the innovative contributions of the Zero Trust model in these fields,the challenges it faces,and proposes corresponding solutions and future research directions.
Multi-focus image fusion is a technique that combines multiple out-of-focus images to enhance the overall image quality. It has gained significant attention in recent years, thanks to the advancements in deep learning...
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Mobile edge computing (MEC) considerably enhances the capabilities and performance of connected autonomous vehicles (CAVs) by deploying edge servers (ESs) on roadside units (RSUs) near CAVs, thereby ensuring low-laten...
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Multiple patterning lithography (MPL) has been introduced in the integrated circuits manufacturing industry to enhance feature density as the technology node advances. A crucial step of MPL is assigning layout feature...
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software developers and maintainers frequently conduct software refactorings to improve software quality. Identifying the conducted software refactorings may significantly facilitate the comprehension of software evol...
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Dialogue policy trains an agent to select dialogue actions frequently implemented via deep reinforcement learning (DRL). The model-based reinforcement methods built a world model to generate simulated data to alleviat...
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The demand for high-quality datasets is rapidly increasing across sectors such as healthcare, finance, and cybersecurity, yet challenges like data scarcity and privacy concerns persist. To address this, we introduce a...
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Consolidating the Internet of Things (IoT) and software Defined Networks (SDN) has been a great concern among researchers. In IoT, Wireless Sensor Network (WSN) is important communication component. Due to the large v...
Consolidating the Internet of Things (IoT) and software Defined Networks (SDN) has been a great concern among researchers. In IoT, Wireless Sensor Network (WSN) is important communication component. Due to the large volume of data generated in IoT and the limitations of WSN, load distribution in these networks is a serious challenge. The Base Station (BS) in these networks may experience a lot of delay due to high processing. Also, data of applications in these networks must inform to users with the lowest delay. Therefore, in addition to load distribution, reducing response time is also one of the factors that must be considered. Therefore, load distribution among BS’s seems to be crucial Considering several BSs and their relationships with the network nodes and preventing them from overloading through load distribution may solve the problem. This can be implemented by applying the common nodes that belong to several BSs. That is, to reduce the load of the overloaded BS, the common node in the cluster corresponding to BS and the other BSs is selected to send common node load to the other BSs. The common node is selected through the load-balancing node-finder algorithm. Load transfer is done through the forwarding node, which is specified in the proposed routing process. The Colored Petri Nets (CPN) s are applied to implement the proposed method. Here, queue length, residual energy, nodes, BSs, and delay time are simulated in three scenarios. The results show that most of the nodes are applied in the proposed algorithm to implement load balancing between the nodes and BSs. The results show the proposed SDN-based algorithm reduces the residual energy to 18%, the queue length to 9.5%, and the delay to 35%.
The utilization of Data-Driven Machine Learning (DDML) models in the healthcare sector poses unique challenges due to the crucial nature of clinical decision-making and its impact on patient outcomes. A primary concer...
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