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Title: Path Finding for Maximum Value of Information in Multi-Modal Underwater Wireless Sensor Networks

Abstract

Here, we consider underwater multi-modal wireless sensor networks (UWSNs) suitable for applications on submarine surveillance and monitoring, where nodes offload data to a mobile autonomous underwater vehicle (AUV) via optical technology, and coordinate using acoustic communication. Sensed data are associated with a value, decaying in time. In this scenario, we address the problem of finding the path of the AUV so that the Value of Information (VoI) of the data delivered to a sink on the surface is maximized. We define a Greedy and Adaptive AUV Path-finding (GAAP) heuristic that drives the AUV to collect data from nodes depending on the VoI of their data. For benchmarking the performance of AUV path-finding heuristics, we define an integer linear programming (ILP) formulation that accurately models the considered scenario, deriving a path that drives the AUV to collect and deliver data with the maximum VoI. In our experiments GAAP consistently delivers more than 80 percent of the theoretical maximum VoI determined by the ILP model. We also compare the performance of GAAP with that of other strategies for driving the AUV among sensing nodes, namely, random paths, TSP-based paths and a “lawn mower”-like strategy. Our results show that GAAP always outperforms everymore » other heuristic in terms of delivered VoI, also obtaining higher energy efficiency.« less

Authors:
 [1];  [1];  [2];  [3];  [4];  [4]
  1. Univ. di Roma "La Sapienza", Roma (Italy)
  2. Northeastern Univ., Boston, MA (United States)
  3. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  4. Univ. of Central Florida, Orlando, FL (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1444093
Report Number(s):
SAND-2018-5635J
Journal ID: ISSN 1536-1233; 663438
Grant/Contract Number:  
AC04-94AL85000
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
IEEE Transactions on Mobile Computing
Additional Journal Information:
Journal Volume: 17; Journal Issue: 2; Journal ID: ISSN 1536-1233
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
47 OTHER INSTRUMENTATION; underwater networking; value of information; autonomous underwater vehicle; multi-modal communications

Citation Formats

Gjanci, Petrika, Petrioli, Chiara, Basagni, Stefano, Phillips, Cynthia A., Boloni, Ladislau, and Turgut, Damla. Path Finding for Maximum Value of Information in Multi-Modal Underwater Wireless Sensor Networks. United States: N. p., 2017. Web. doi:10.1109/TMC.2017.2706689.
Gjanci, Petrika, Petrioli, Chiara, Basagni, Stefano, Phillips, Cynthia A., Boloni, Ladislau, & Turgut, Damla. Path Finding for Maximum Value of Information in Multi-Modal Underwater Wireless Sensor Networks. United States. https://doi.org/10.1109/TMC.2017.2706689
Gjanci, Petrika, Petrioli, Chiara, Basagni, Stefano, Phillips, Cynthia A., Boloni, Ladislau, and Turgut, Damla. 2017. "Path Finding for Maximum Value of Information in Multi-Modal Underwater Wireless Sensor Networks". United States. https://doi.org/10.1109/TMC.2017.2706689. https://www.osti.gov/servlets/purl/1444093.
@article{osti_1444093,
title = {Path Finding for Maximum Value of Information in Multi-Modal Underwater Wireless Sensor Networks},
author = {Gjanci, Petrika and Petrioli, Chiara and Basagni, Stefano and Phillips, Cynthia A. and Boloni, Ladislau and Turgut, Damla},
abstractNote = {Here, we consider underwater multi-modal wireless sensor networks (UWSNs) suitable for applications on submarine surveillance and monitoring, where nodes offload data to a mobile autonomous underwater vehicle (AUV) via optical technology, and coordinate using acoustic communication. Sensed data are associated with a value, decaying in time. In this scenario, we address the problem of finding the path of the AUV so that the Value of Information (VoI) of the data delivered to a sink on the surface is maximized. We define a Greedy and Adaptive AUV Path-finding (GAAP) heuristic that drives the AUV to collect data from nodes depending on the VoI of their data. For benchmarking the performance of AUV path-finding heuristics, we define an integer linear programming (ILP) formulation that accurately models the considered scenario, deriving a path that drives the AUV to collect and deliver data with the maximum VoI. In our experiments GAAP consistently delivers more than 80 percent of the theoretical maximum VoI determined by the ILP model. We also compare the performance of GAAP with that of other strategies for driving the AUV among sensing nodes, namely, random paths, TSP-based paths and a “lawn mower”-like strategy. Our results show that GAAP always outperforms every other heuristic in terms of delivered VoI, also obtaining higher energy efficiency.},
doi = {10.1109/TMC.2017.2706689},
url = {https://www.osti.gov/biblio/1444093}, journal = {IEEE Transactions on Mobile Computing},
issn = {1536-1233},
number = 2,
volume = 17,
place = {United States},
year = {Fri May 19 00:00:00 EDT 2017},
month = {Fri May 19 00:00:00 EDT 2017}
}

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Works referencing / citing this record:

Data Gathering from a Multimodal Dense Underwater Acoustic Sensor Network Deployed in Shallow Fresh Water Scenarios
journal, November 2019


Reinforcement Learning-Based Data Forwarding in Underwater Wireless Sensor Networks with Passive Mobility
journal, January 2019


Data gathering from a multimodal dense underwater acoustic sensor network deployed in shallow fresh water scenarios
text, January 2019