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Ph.D. Proposal: Hussain Albarakati
February 1, 2019 @ 10:00 am - 11:00 am UTC-5
Title: Efficient Embedded Computing System for Underwater Real-Time Applications
Ph.D. Candidate: Hussain Albarakati
Major Advisor: Dr. Reda A. Ammar
Associate Advisors: Dr. Sanguthevar Rajasekaran, Dr. Song Han
Date/Time: Friday, February 1, 2019 10:00 am
Location: HBL Video Theater 2
Underwater acoustic sensor networks have emerged as a new technology for underwater real-time applications such as oil inspection, seismic monitoring, and disaster prevention. However, this new technology is bound to data sensing, transmission, and forwarding, which makes the transmission of large volumes of data costly in terms of both execution time and power consumption. This has inspired our research activities to develop underwater computing systems with minimum execution time and less power consumption. In this advanced technology, information is extracted under the water using embedded processors via data mining and/or data compression. In this dissertation, we proposed a set of underwater embedded system (UWES) architectures. The architectures are designed to reduce both end-to-end delay and network power consumption (life time of network). The idea is having dynamic architectures configured based on network parameters (data rate, central processing node capabilities, gathering nodes capabilities, and depth of water) for both homogeneous and heterogeneous applications. To satisfy real-time constraints, we designed a new set of real-time underwater embedded system (RTUWES) architectures that can handle various network configurations. For real or non-real time architectures, our aim is to get high performance computing system according sensor nodes deployment and data-gathering, information extraction and the communication topology between central computer and surface gateways. Therefore, we developed heuristic algorithms and deployment-based sensor topologies to enhance data-gathering in the lowest layer of architecture. Then, we developed information extraction algorithms for big data of sensitive underwater applications. Furthermore, we will be studying and proposing an efficient communication topology between central computer and surface gateways. Finally, analytical models are discussed and a case study is presented. We also build up a simulator for practical studies. This simulator is used to verify the results and to evaluate the performance of our proposed architectures.