Titlebook: Secure Edge and Fog Computing Enabled AI for IoT and Smart Cities ; Includes selected Pa Ahmed A. Abd El-Latif,Lo’ai Tawalbeh,Brij B. Gupta

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Multilevel Edge Computing System for Autonomous Vehicless resources at the user end in a decentralized manner, enabling faster response times compared to cloud-based processing (Muthanna et al., Information, 12:76, 2021). This allows for real-time analytics and knowledge generation closer to the data source, potentially leveraging resources like laptops,

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UAV-Based Edge Computing System for Smart City Applicationshigh speeds. The implementation of these services using 5G technologies has paved the way for creating smart city concepts. The smart city, enabled by 5G technology and the use of unmanned aerial vehicles (UAVs) as telecommunication nodes, can assist in resolving issues related to environmental and

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有权威

Archipelago

Federated Learning for Linux Malware Detection: An Experimental Studyd. Federated Learning (FL) is considered an approach to overcoming challenges of data sensibility. In this study, we perform experiments with the Federated Learning model on detecting malware in the Linux operating system. We compare Federated Learning with distributed data and traditional Fully Con

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Delay Prediction in M2M Networks Using the Deep Learning Approache (M2M) connections and network traffic generated by M2M devices. On the other hand, inadequate management of network services and potential cybersecurity risks could result from the insufficient analysis of the growing M2M traffic. In this chapter, we apply deep learning based on a long short-term

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Energy-Efficient Beam Shaping in MIMO System Using Machine Learningmethods. The task of clustering subscribers by service level and distance was formulated. The k-means algorithm was chosen as a clustering method for solving the beamforming problem. It is shown that the suggested algorithm may increase the 5G network energy efficiency up to 38%.

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