Spatial prediction in mobile robotic wireless sensor networks with network constraints

Ranasinghe, R; Kodagoda, S

Spatial prediction in mobile robotic wireless sensor networks with network constraints

Ranasinghe, R

Kodagoda, S

Permalink

Publication Type:: Conference Proceeding
Citation:: 2016 14th International Conference on Control, Automation, Robotics and Vision, ICARCV 2016, 2017
Issue Date:: 2017-01-31

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Accepted ManuscriptAdobe PDF (633.69 kB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Ranasinghe, R https://orcid.org/0000-0002-3785-3723	en_US
dc.contributor.author	Kodagoda, S https://orcid.org/0000-0001-5175-9138	en_US
dc.date.issued	2017-01-31	en_US
dc.identifier.citation	2016 14th International Conference on Control, Automation, Robotics and Vision, ICARCV 2016, 2017	en_US
dc.identifier.isbn	9781509035496	en_US
dc.identifier.uri	http://hdl.handle.net/10453/80268
dc.description.abstract	© 2016 IEEE. In recent years mobile robotic wireless sensor networks have been a popular choice for modelling spatial phenomena. This research is highly demanding and non-trivial due to challenges from both network and robotic aspects. In this paper, we address the spatial modelling of a physical phenomena with the network connectivity constraints while the mobile robots are striving to achieve the minimum modelling mismatch in terms of root mean square error (RMSE). We have resolved it through Gauss markov random field based approach which is a computationally efficient implementation of Gaussian processes. In this strategy, the Mobile Robotic Wireless Sensor Node (MRWSN) are centrally controlled to maintain the connectivity while minimizing the RMSE. Once the number of MRWSNs reach their maximum coverage, a new MRWSN is requested at the most informative location. The experimental results are convincing and they show the effectiveness of the algorithm.	en_US
dc.relation.ispartof	2016 14th International Conference on Control, Automation, Robotics and Vision, ICARCV 2016	en_US
dc.relation.isbasedon	10.1109/ICARCV.2016.7838632	en_US
dc.title	Spatial prediction in mobile robotic wireless sensor networks with network constraints	en_US
dc.type	Conference Proceeding
utslib.for	091303 Autonomous Vehicles	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

© 2016 IEEE. In recent years mobile robotic wireless sensor networks have been a popular choice for modelling spatial phenomena. This research is highly demanding and non-trivial due to challenges from both network and robotic aspects. In this paper, we address the spatial modelling of a physical phenomena with the network connectivity constraints while the mobile robots are striving to achieve the minimum modelling mismatch in terms of root mean square error (RMSE). We have resolved it through Gauss markov random field based approach which is a computationally efficient implementation of Gaussian processes. In this strategy, the Mobile Robotic Wireless Sensor Node (MRWSN) are centrally controlled to maintain the connectivity while minimizing the RMSE. Once the number of MRWSNs reach their maximum coverage, a new MRWSN is requested at the most informative location. The experimental results are convincing and they show the effectiveness of the algorithm.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/80268