Blockchain technology, based on decentralized data storage and distributed consensus design, has become a promising solution to address data security risks and provide privacy protection in the Internet-of-Things (IoT...
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With technological advancement, distributed Denial of Service (DDoS) attacks are gaining momentum in IoT networks. DDoS attacks are widely spreading on IoT devices since they have limited resources (memory) and no eff...
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作者：
Kamble, SohanGourshettiwar, Palash
Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha442001 India
Faculty of Engineering and Technology Department of Computer Science & Medical Engineering Maharashtra Wardha442001 India
Cyber, physical, and social (CPS) systems have emerged as the driving force behind today's modern information society, where data is defined as the main capital. Information drives almost every technological advan...
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To manage the IoT resources to process the massive amount of collected data in complex systems, Allocation of tasks to nodes to increase user satisfaction has been noticed by researchers. In allocating tasks, the nece...
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To manage the IoT resources to process the massive amount of collected data in complex systems, Allocation of tasks to nodes to increase user satisfaction has been noticed by researchers. In allocating tasks, the necessity of a powerful auxiliary platform is felt. Cloud and fog networks are two suitable options for performing and completing tasks on the Internet of Things. In the cloud network, due to offloading tasks to remote computing centers, problems such as increasing complete time, increasing traffic in the network, and high energy consumption for the Internet of Things network occur. The fog network solves the problem of completing tasks due to reducing the geographical distance between Internet of Things devices and processor nodes. The devices in the fog network cooperate with the Internet of Things network’s diverse and variable nodes with time computing, storage, and processing resources. In this heterogeneous environment, the non-cooperation and dishonesty of malicious fog nodes through misrepresentation of shared resources and non-completion of tasks can disrupt the performance of the Internet of Things network. As a result, two fundamental challenges in the heterogeneous fog network include providing resources to allocate tasks and recognizing valid fog nodes. This paper focuses on creating a resource provider network to allocate tasks to authentic fog nodes with appropriate resources. When an Internet of Things node faces a shortage of resources and link prediction, it can form a resource supply network with neighboring nodes to solve the two stated challenges. For this purpose, in the first stage, efforts are made to select the most appropriate options among the neighboring nodes based on specific criteria. In the second step, reliable nodes are identified based on a trust mechanism, including resource trust and entity trust. In the last step, tasks are divided among reliable devices with sufficient resources. The evaluation of the proposed method i
Results from an explorative video-based study on the influence of a counterpart’s arm configuration changes on arm configuration changes of test persons during a hand trajectory following task are presented. Test per...
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Federated Learning (FL) is a learning paradigm which constructs machine learning models using decentralized datasets, with the goal of training a globally optimized model while preserving data privacy. In practice, an...
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Training agents that are robust to environmental changes remains a significant challenge in deep reinforcement learning (RL). Unsupervised environment design (UED) has recently emerged to address this issue by generat...
Recent years have witnessed a boom in self-supervised learning (SSL) in various areas including speech processing. Speech based SSL models present promising performance in a range of speech related tasks. However, the...
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Apache Kafka's distributed architecture and message queuing capabilities offer significant improvements in real-time and batch data processing efficiency and reliability. This research aims to optimize Kafka setup...
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Modern Electric Arc Furnaces (EAF) employ ultra-high-power electrical input to minimize the scrap melting time. Increasing the operating voltage increases arc length and enhances the active power, but radiation energy...
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