Next Article in Journal
A Multi-Hop Energy Neutral Clustering Algorithm for Maximizing Network Information Gathering in Energy Harvesting Wireless Sensor Networks
Next Article in Special Issue
AUV Underwater Positioning Algorithm Based on Interactive Assistance of SINS and LBL
Previous Article in Journal
A Novel Method of Multi-Information Acquisition for Electromagnetic Flow Meters
Previous Article in Special Issue
Subjective Quality Assessment of Underwater Video for Scientific Applications
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(1), 28; doi:10.3390/s16010028

Mission Planning and Decision Support for Underwater Glider Networks: A Sampling on-Demand Approach

Research Department, NATO STO Centre for Maritime Research and Experimentation (CMRE), Viale San Bartolomeo 400, 19126 La Spezia, Italy
Dipartimento di Ingegneria dell’Informazione, University of Pisa, Largo Lucio Lazzarino 1, 56122 Pisa, Italy
Author to whom correspondence should be addressed.
Academic Editor: Jaime Lloret Mauri
Received: 9 November 2015 / Revised: 18 December 2015 / Accepted: 21 December 2015 / Published: 26 December 2015
(This article belongs to the Special Issue Underwater Sensor Nodes and Underwater Sensor Networks 2016)
View Full-Text   |   Download PDF [1361 KB, uploaded 28 December 2015]   |  


This paper describes an optimal sampling approach to support glider fleet operators and marine scientists during the complex task of planning the missions of fleets of underwater gliders. Optimal sampling, which has gained considerable attention in the last decade, consists in planning the paths of gliders to minimize a specific criterion pertinent to the phenomenon under investigation. Different criteria (e.g., A, G, or E optimality), used in geosciences to obtain an optimum design, lead to different sampling strategies. In particular, the A criterion produces paths for the gliders that minimize the overall level of uncertainty over the area of interest. However, there are commonly operative situations in which the marine scientists may prefer not to minimize the overall uncertainty of a certain area, but instead they may be interested in achieving an acceptable uncertainty sufficient for the scientific or operational needs of the mission. We propose and discuss here an approach named sampling on-demand that explicitly addresses this need. In our approach the user provides an objective map, setting both the amount and the geographic distribution of the uncertainty to be achieved after assimilating the information gathered by the fleet. A novel optimality criterion, called A η , is proposed and the resulting minimization problem is solved by using a Simulated Annealing based optimizer that takes into account the constraints imposed by the glider navigation features, the desired geometry of the paths and the problems of reachability caused by ocean currents. This planning strategy has been implemented in a Matlab toolbox called SoDDS (Sampling on-Demand and Decision Support). The tool is able to automatically download the ocean fields data from MyOcean repository and also provides graphical user interfaces to ease the input process of mission parameters and targets. The results obtained by running SoDDS on three different scenarios are provided and show that SoDDS, which is currently used at NATO STO Centre for Maritime Research and Experimentation (CMRE), can represent a step forward towards a systematic mission planning of glider fleets, dramatically reducing the efforts of glider operators. View Full-Text
Keywords: glider networks; sensor networks; sampling on-demand; optimal sampling; MyOcean forecasts provider; data assimilation glider networks; sensor networks; sampling on-demand; optimal sampling; MyOcean forecasts provider; data assimilation

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Ferri, G.; Cococcioni, M.; Alvarez, A. Mission Planning and Decision Support for Underwater Glider Networks: A Sampling on-Demand Approach. Sensors 2016, 16, 28.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top