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Now showing items 11-20 of 34
Energy-Aware Distributed Edge ML for mHealth Applications with Strict Latency Requirements
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Institute of Electrical and Electronics Engineers Inc.
, 2021 , Article)
Edge machine learning (Edge ML) is expected to serve as a key enabler for real-time mobile health (mHealth) applications. However, its reliability is governed by the limited energy and computing resources of user equipment ...
TIDCS: A Dynamic Intrusion Detection and Classification System Based Feature Selection
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Institute of Electrical and Electronics Engineers Inc.
, 2020 , Article)
Machine learning techniques are becoming mainstream in intrusion detection systems as they allow real-time response and have the ability to learn and adapt. By using a comprehensive dataset with multiple attack types, a ...
RF-based drone detection and identification using deep learning approaches: An initiative towards a large open source drone database
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Elsevier B.V.
, 2019 , Article)
The omnipresence of unmanned aerial vehicles, or drones, among civilians can lead to technical, security, and public safety issues that need to be addressed, regulated and prevented. Security agencies are in continuous ...
The P-ART framework for placement of virtual network services in a multi-cloud environment
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Elsevier B.V.
, 2019 , Article)
Carriers network services are distributed, dynamic, and investment intensive. Deploying them as virtual network services (VNS) brings the promise of low-cost agile deployments, which reduce time to market new services. If ...
Fault and performance management in multi-cloud virtual network services using AI: A tutorial and a case study
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Elsevier B.V.
, 2019 , Article)
Carriers find Network Function Virtualization (NFV) and multi-cloud computing a potent combination for deploying their network services. The resulting virtual network services (VNS) offer great flexibility and cost advantages ...
Detecting anomalies within smart buildings using do-it-yourself internet of things
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Springer
, 2022 , Article)
Detecting anomalies at the time of happening is vital in environments like buildings and homes to identify potential cyber-attacks. This paper discussed the various mechanisms to detect anomalies as soon as they occur. We ...
Machine learning aided load balance routing scheme considering queue utilization
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Institute of Electrical and Electronics Engineers Inc.
, 2019 , Article)
Due to the rapid development of network techniques, packet-switched systems experience high-speed growth of traffic, which imposes a heavy and unbalanced burden on the routers. Hence, efficient routing schemes are required ...
An optimal uplink traffic offloading algorithm via opportunistic communications based on machine learning
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Springer
, 2020 , Article)
Opportunistic communications as an efficient traffic offloading method can be used to offload uplink traffic of cellular networks to Wi-Fi networks. However, because of its contact pattern (contact frequency and contact ...
An adaptive network coding scheme for multipath transmission in cellular-based vehicular networks
(
MDPI AG
, 2020 , Article)
With the emergence of vehicular Internet-of-Things (IoT) applications, it is a significant challenge for vehicular IoT systems to obtain higher throughput in vehicle-to-cloud multipath transmission. Network Coding (NC) has ...
IoT malicious traffic identification using wrapper-based feature selection mechanisms
(
Elsevier Ltd
, 2020 , Article)
Machine Learning (ML) plays very significant role in the Internet of Things (IoT) cybersecurity for malicious and intrusion traffic identification. In other words, ML algorithms are widely applied for IoT traffic identification ...