Support Emergency Response in Automatic Identification System Using an Opportunistic Resource Utilization Networks
The automatic identification system, or AIS, transmits a vessel’s position, identity, and speed information so that other vessels are aware of the transmitting vessel and can avoid collisions. However, AIS has multiple limitations, including the quality, availability, and validity of data for non-cooperative vessels. We investigate methods of supporting the availability of AIS operation through an emergency response of cooperative vessels, thereby creating an opportunistic resource utilization network, or Oppnet. An Oppnet invites and integrates heterogeneous devices and systems available in the given environment, wherein the integrated devices/systems become the Oppnet's helpers. Helpers are resources that deliver support, communication, computation, and control to find missing cooperative vessels.
We design and build this Oppnet scenario using the Common Open Research Emulator (CORE), an open-source network emulation tool funded by a U.S. Naval Research Laboratory research project. CORE provides a graphic user interface (GUI) tool for creating and tracking virtual network status and performance as well as a Python API to programmatically access, control, and monitor instances. During the simulation, the Oppnet starts by inviting and integrating heterogeneous nodes (devices or systems) available within its reach, becoming helpers. The Oppnet then assigns tasks to all helpers to detect the missing cooperative vessel, “Nemo.” In each iteration, the simulation runs for a specified time until Oppnet’s helpers find “Nemo.” Otherwise, “Nemo” is lost. Initial results of simulation show that the finding “Nemo” rate increase when Oppnet helpers increase; however, the timespan of finding “Nemo” remains nearly identical due to simulation conditions and parameters.
- Year: 2023
- Category: Artificial Intelligence
- Tag: artificial intelligence, emergency preparedness, system identification, human-machine interfaces, unmanned aerial vehicles, computer simulations, signal detection
- Author: Andrew Greisdorn, Quinn Hirt, Raed Salih, Michael R. Clark, Scott Brookes
- Released: SPIE Defense + Commercial Sensing 2023 - Automatic Target Recognition XXXIII
Featured Riverside Research Author(s)
Michael R. Clark
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Scott Brookes
Dr. Scott Brookes is an associate director at Riverside Research. He has received his PhD from Dartmouth college in 2018 studying secure operating system and hypervisor architectures. His work focuses on security in systems software to include computer architecture, program analysis, and systems software architectures.
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