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Ph.D. Proposal: Mohammad Alsulami
May 26 @ 10:45 am - 11:45 am EDT
Title: Efficient Computing Systems for Underwater Wireless Sensor Networks.
Ph.D. Student: Mohammad Alsulami
Major Advisor: Dr. Reda Ammar
Associate Advisors: Dr. Sanguthevar Rajasekaran, Dr. Song Han
Date/Time: Wednesday, May 26th, 2021, 10:45AM-11:45AM
Meeting link : https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=m640875a938ed571b8ec7647dbf2454fc
Meeting number: 120 465 3160
Join by phone: +1-415-655-0002 US Toll
Access code: 120 465 3160
Underwater acoustic sensor networks (UASN) 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. Then, the beneficial information is transmitted from processors (computers) to surface gateways. In this research work, a detailed UWSNs architecture proposed to increase the reliability of the planted network. To increase the reliability, we need to deploy more than a single processing machine (computer). We propose a framework to find the ideal number of computers needed as well as their locations. Three different topologies are proposed to fulfill this requirement. Then, we explored two approaches/algorithms that group master nodes in the network into groups and allocate a computer (a server) for each group. In the first algorithm, we cluster master nodes using a bottom-up approach. The process of assigning master nodes to each group is based on the communication range. In the second algorithm, nodes are deployed not only homogeneously but also heterogeneously as well. We added more constraints to make our assumptions closer to real life. Next, we propose a Modified K-Medoids algorithm that helps in identifying the locations of processing machines that we need to deploy. We studied the effectiveness of having such algorithms on end-to-end delay and load balancing among different computers. Simulation was conducted to show the merit of all our work.