Date of Completion

4-30-2020

Embargo Period

4-29-2023

Keywords

Underwater acoustic sensor networks (UWASNs), Real-time underwater-embedded system (RTUWES) architectures, information extraction: Apriori algorithm, Multi surface-gateways placement topologies, Data-gathering Algorithms, Deployment-based sensor topologies

Major Advisor

Reda A. Ammar

Associate Advisor

Sanguthevar Rajasekaran

Associate Advisor

Song Han

Field of Study

Computer Science and Engineering

Degree

Doctor of Philosophy

Open Access

Campus Access

Abstract

Underwater acoustic sensor networks 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 includes more networking capabilities and enables real-time reporting, but it is restricted to data sensing, forwarding, and transmission. The data collected could be voluminous, so processing the data could present a great challenge. One possibility is to send all of the data to a computer on the surface that then analyzes the data. However, this solution could be problematic in several ways: 1) considerable time could be needed for data transmission; 2) many applications require real-time processing, and we may not be able to satisfy real-time constraints; and 3) a huge amount of energy could be consumed. In this dissertation, we propose a set of underwater embedded system architectures that use a central computer underwater, in addition to sensors, communication units, and one or more gateways at the water’s surface. The architectures are designed to reduce both end-to-end delay and network power consumption (which affect the network lifetime). The idea is to have dynamic architectures configured on the basis of network parameters (data rate, central processing node capabilities, gathering node 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 architectures that can handle various network configurations. However, we may not be able to satisfy the real-time constraints in some applications due to the large volumes of data. Therefore, we propose an architecture that can be used for real-time and non–real-time applications to obtain a high-performance computing system with sensor node topologies, a data-collection approach, and an information-extraction approach to meet real-time constraints. We therefore developed heuristic algorithms and deployment-based sensor topologies to enhance data-gathering at the lowest layer of the architecture in addition to data-reduction algorithms to handle big data for sensitive underwater applications. Data reduction can be performed in many ways. Any data reduction technique that closely preserves information is appropriate. For example, any rule-mining algorithm (such as the a priori algorithm) could be used for data reduction. After all association rules in the data are determined, we could simply transmit the rules as reduced data. This algorithm can deal with limited information and hence meets real-time constraints while reducing propagation delays. Furthermore, we focus on the development of high-speed communication topologies between the central computer and the surface gateways. We therefore propose two unique topologies: a two-dimensional and three-dimensional that incorporate multiple surface gateways via geometric distribution characteristics. Finally, analytical models are discussed, and a case study is presented. We also built a simulator for practical studies and to verify the results and evaluate the performance of our proposed architectures.

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