Age-optimal information gathering in linear underwater networks: A deep reinforcement learning approach

Ahmed A. Al-Habob, Octavia A. Dobre, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we consider an underwater linear network, where an autonomous underwater vehicle (AUV) gathers data from a set of underwater devices. The AUV monitors a set of physical processes, where the status of each process can be sensed by one or more devices and each device is capable of sensing one or more processes. The AUV needs to maintain freshness of its information status about the monitored processes. To quantify the freshness of the information at the AUV, we consider the concept of the age of information (AoI), which represents the amount of time elapsed since the most recently delivered update information was generated. A framework is proposed to optimize the AUV’s linear movement trajectory and scheduling of process status updates with the objective of minimizing the normalized weighted sum of the average AoI of the monitored physical processes. The formulated optimization problem is a non-convex mixed integer problem, which cannot be solved by the standard optimization techniques. We develop a solution approach based on the technique of deep reinforcement learning (DRL). Specifically, we leverage an actor-critic DRL approach to find the optimum locations and stopping time of the data gathering points. Simulation results illustrate that the proposed framework maintains robustness under different scenarios and provides better performance when compared with baseline and K-means clustering approaches.

Original languageEnglish (US)
Pages (from-to)13129-13138
Number of pages10
JournalIEEE Transactions on Vehicular Technology
Volume70
Issue number12
DOIs
StatePublished - Dec 1 2021

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Aerospace Engineering
  • Electrical and Electronic Engineering
  • Applied Mathematics

Keywords

  • Age-of-information (AoI)
  • Deep reinforcement learning (DRL)
  • Mobile data gathering centres
  • Underwater linear networks

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