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Information-Based Hierarchical Planning for a Mobile Sensing Network in Environmental Mapping

Research output: Contribution to journalJournal articlepeer-review

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  • T. Li
  • K. Tong
  • M. Xia
  • B. Li
  • C.W. De Silva
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Article number8844270
<mark>Journal publication date</mark>1/06/2020
<mark>Journal</mark>IEEE Systems Journal
Issue number2
Volume14
Number of pages12
Pages (from-to)1692-1703
Publication StatusPublished
Early online date18/09/19
<mark>Original language</mark>English

Abstract

This article investigates the problem of information-based sampling design and path planning for a mobile sensing network to predict scalar fields of monitored environments. A hierarchical framework with a built-in Gaussian Markov random field model is proposed to provide adaptive sampling for efficient field reconstruction. In the proposed framework, a nonmyopic planner is operated at a sink to navigate the mobile sensing agents in the field to the sites that are most informative. Meanwhile, a myopic planner is carried out on board each agent. A tradeoff between computationally intensive global optimization and efficient local greedy search is incorporated into the system. The mobile sensing agents can be scheduled online through an anytime algorithm to visit and observe the high-information sites. Experiments on both synthetic and real-world datasets are used to demonstrate the feasibility and efficiency of the proposed planner in model exploitation and adaptive sampling for environmental field mapping.

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©2019 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.