- Title
- Wifi-based localisation datasets for No-GPS open areas using smart bins
- Creator
- Nassar, Mohamed; Hasan, Mahmud; Khan, Md; Sultana, Mirza; Hasan, Md; Luxford, Len; Cole, Peter; Oatley, Giles; Koutsakis, Polychronis
- Date
- 2020
- Type
- Text; Journal article; Data article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/174679
- Identifier
- vital:14902
- Identifier
-
https://doi.org/10.1016/j.comnet.2020.107422
- Identifier
- ISBN:1389-1286
- Abstract
- In recent years, Wifi-based localisation systems have gained significant interest because of the lack of Global Positioning System (GPS) signal in indoor and certain open areas. Over the past decade, many datasets have been introduced to enable researchers to compare different localisation techniques. Existing datasets, however, have failed to cover open areas such as parks in cases where GPS is still unavailable, and there is a lack of Wifi access points. Also, the existing datasets only focus on getting Wifi fingerprint collected and labelled by users. To the best of our knowledge, no dataset provides Received Signal Strengths (RSS) collected by Wireless Access Points (APs). In this work, we offer two datasets publicly. The first is the Fingerprint dataset in which four users generated 16,032 accurate and consistently labelled WiFi fingerprints for all available Reference Points (RPs) in a central and busy area of Murdoch University, known as Bush Court. The second is the APs dataset that includes 2,450,865 auto-generated records received from 1000 users' devices, including the four users, associated with Wifi signal strengths. To overcome the Wifi coverage problem for the Bush Court, we attached our previously designed Wireless Sensor Nodes (WSNs) to existing garbage bins, enabling them to provide real-time environmental sensing and act as soft APs that sense MAC addresses and Wifi signals from surrounding devices.
- Publisher
- Elsevier
- Relation
- Computer Networks Vol. 180, no. (2020), p. 1-5
- Rights
- Metadata is freely available under a CCO license
- Rights
- Copyright © 2020 Elsevier B.V. All rights reserved.
- Subject
- 08 Information and Computing Sciences; 09 Engineering; 10 Technology; Wifi datasets; Fingerprinting; Smart bins; Smart street furniture; Open area localisation
- Reviewed
- Funder
- The work of the first author as a doctoral candidate is jointly supported by: a) Murdoch University and Global Smart Cities (www.ystop.com.au), b) the Science Industry PhD Fellowship Program of the Department of Jobs, Tourism, Science and Innovation, Government of Western Australia.
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