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Atta ur Rehman Khan
Researcher at Ajman University of Science and Technology
Publications - 11
Citations - 170
Atta ur Rehman Khan is an academic researcher from Ajman University of Science and Technology. The author has contributed to research in topics: Software-defined radio & Wireless network. The author has an hindex of 3, co-authored 10 publications receiving 46 citations. Previous affiliations of Atta ur Rehman Khan include Sohar University.
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An energy, performance efficient resource consolidation scheme for heterogeneous cloud datacenters
TL;DR: A consolidation algorithm is proposed which favours the most effective migration among VMs, containers and applications, and how migration decisions should be made to save energy without any negative impact on the service performance is investigated.
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Vehicular Ad Hoc Network (VANET) Localization Techniques: A Survey
TL;DR: A summary of the use cases of localization in VANET is provided and various techniques that are proposed in the literature are highlighted, which classify the studies in this area with respect to their methodologies, and discuss their respective advantages and disadvantages.
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Improving security of the Internet of Things via RF fingerprinting based device identification system
Sohail Abbas,Qassim Nasir,Douae Nouichi,Mohamed Abdelsalam,Manar Abu Talib,Omnia Abu Waraga,Atta ur Rehman Khan +6 more
TL;DR: This paper proposes a radio frequency fingerprinting-based device identification technique that uses low-cost software defined radio to capture smartphone emissions at a lower sampling rate, using the proposed preamble threshold-based detection algorithm.
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A fog-based security framework for intelligent traffic light control system
TL;DR: A Fog-based Security Framework for Intelligent Traffic Light Control System that provides security services with realistic assumptions and realistic for real world scenario is proposed.
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DGRU Based Human Activity Recognition Using Channel State Information
TL;DR: The proposed DGRU model for non-obtrusive human activity recognition using Channel State Information achieves promising results with an accuracy of 95–99% for all activities, outperforming the traditional benchmark approaches in the literature that use random forest and more advanced deep learning techniques, such as Long-Short Term Memory (LSTM).