Ph.D. Defense: Hussain Albarakati

Doctoral Dissertation Defense

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: Tuesday, November 19, 2019 9:45-10:45 am

Location: HBL Instruction 2119A (formerly Video Theater 2 )

Abstract:

Underwater acoustic sensor networks (UWASNs) have been introduced as a new technology to extract data for underwater real-time applications such as seismic monitoring, undersea monitoring and control, oil well inspection, military applications, and disaster prevention. This new technology adds more networking capabilities and enables real-time reporting. However, it is restricted to data sensing, forwarding and data transmission. The data collected could be voluminous and processing the data could be a big challenge. One possibility is to send all the data to a computer on the surface and let this compute do the analysis on the data. However, this could be problematics in many ways: 1) the time for transmitting the data could be very large; 2) Many applications require real-time processing and we may not be able to satisfy real-time constraints; and 3) the energy consumed could be huge.  In this dissertation, we proposed a set of underwater embedded system (UWES) architectures that use a central computer under water in addition to sensors, communication units, and one or more gateways at the water surface. 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. However, some applications may be unable to satisfy the real-time constraints due to the large volumes of data. Therefore, we propose an architecture that can be used for real- and non-real-time applications to obtain a high-performance computing system with sensor node topologies, a data-collection approach, and information extraction approach to meet real-time constraints. Therefore, we developed heuristic algorithms and deployment-based sensor topologies to enhance data-gathering in the lowest layer of architecture. Then, we developed data reduction algorithms for big data of sensitive underwater applications. There are many ways to perform data reduction. Any data reduction technique that closely preserves information is appropriate. For example, one could any rules mining algorithm (such as the apriori algorithm) for data reduction. After finding all the association rules in the data, we could just transmit the rules as the reduced data. This algorithm can deal with limited information and hence meets real-time constraints while reducing propagation delays Furthermore, we focused on the development of high-speed communication topologies between central computer and surface gateways. Therefore, we proposed two unique topologies: a two-dimensional (2D) and three-dimensional (3D), where both incorporate multiple surface gateways by use of geometric distribution characteristics. 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.