Data collection path planning with spatially correlated measurements using growing self-organizing array

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00326573" target="_blank" >RIV/68407700:21230/19:00326573 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1016/j.asoc.2018.11.005" target="_blank" >https://doi.org/10.1016/j.asoc.2018.11.005</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.asoc.2018.11.005" target="_blank" >10.1016/j.asoc.2018.11.005</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Data collection path planning with spatially correlated measurements using growing self-organizing array

Popis výsledku v původním jazyce

Data collection path planning is a problem to determine a cost-efficient path to read the most valuable data from a given set of sensors. The problem can be formulated as a variant of the combinatorial optimization problems that are called the price-collecting traveling salesman problem or the orienteering problem in a case of the explicitly limited travel budget. In these problems, each location is associated with a reward characterizing the importance of the data from the particular sensor location. The used simplifying assumption is to consider the measurements at particular locations independent, which may be valid, e.g., for very distant locations. However, measurements taken from spatially close locations can be correlated, and data collected from one location may also include information about the nearby locations. Then, the particular importance of the data depends on the currently selected sensors to be visited by the data collection path, and the travel cost can be saved by avoiding visitation of the locations that do not provide added value to the collected data. This is a computationally challenging problem because of mutual dependency on the cost of data collection path and the possibly collected rewards along such a path. A novel solution based on unsupervised learning method called the Growing Self-Organizing Array (GSOA) is proposed to address computational challenges of these problems and provide a solution in tens of milliseconds using conventional computational resources. Moreover, the employed GSOA-based approach allows to exploit capability to retrieve data by wireless communication or remote sensing, and thus further save the travel cost.

Název v anglickém jazyce

Data collection path planning with spatially correlated measurements using growing self-organizing array

Popis výsledku anglicky

Data collection path planning is a problem to determine a cost-efficient path to read the most valuable data from a given set of sensors. The problem can be formulated as a variant of the combinatorial optimization problems that are called the price-collecting traveling salesman problem or the orienteering problem in a case of the explicitly limited travel budget. In these problems, each location is associated with a reward characterizing the importance of the data from the particular sensor location. The used simplifying assumption is to consider the measurements at particular locations independent, which may be valid, e.g., for very distant locations. However, measurements taken from spatially close locations can be correlated, and data collected from one location may also include information about the nearby locations. Then, the particular importance of the data depends on the currently selected sensors to be visited by the data collection path, and the travel cost can be saved by avoiding visitation of the locations that do not provide added value to the collected data. This is a computationally challenging problem because of mutual dependency on the cost of data collection path and the possibly collected rewards along such a path. A novel solution based on unsupervised learning method called the Growing Self-Organizing Array (GSOA) is proposed to address computational challenges of these problems and provide a solution in tens of milliseconds using conventional computational resources. Moreover, the employed GSOA-based approach allows to exploit capability to retrieve data by wireless communication or remote sensing, and thus further save the travel cost.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Applied Soft Computing

ISSN

1568-4946

e-ISSN

1872-9681

Svazek periodika

75

Číslo periodika v rámci svazku

February

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

18

Strana od-do

130-147

Kód UT WoS článku

000454941500010

EID výsledku v databázi Scopus

2-s2.0-85056835349