2026-04-14T05:07:57Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/3951042026-01-24T03:34:23Zcom_2072_1033col_2072_452950

urn:hdl:2117/395104 Dataset for anomaly detection in a production wireless mesh community network Cerdà Alabern, Llorenç Iuhasz, Gabriel Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic Wireless communications systems Machine learning Fault location (Engineering) Fault detection Wireless network dataset Wireless community networks Comunicació sense fil, Sistemes de Aprenentatge automàtic Avaries -- Localització Wireless community networks, WCN, have proliferated around the world. Cheap off-the-shelf WiFi devices have enabled this new network paradigm where users build their own network infrastructure in a do-it-yourself alternative to traditional network operators. The fact that users are responsible for the administration of their own nodes makes the network very dynamic. There are frequent reboots of the networking devices, and users that join and leave the network. In addition, the unplanned deployment of the network makes it very heterogeneous, with both high and low capacity links. Therefore, anomaly detection in such dynamic scenario is challenging. In this paper we provide a dataset gathered from a production WCN. The data was obtained from a central server that collects data from the mesh nodes that build the network. In total, 63 different nodes were encountered during the data collection. The WCN is used daily to access the Internet from 17 subscribers of the local ISP available on the mesh. We have produced a dataset gathering a large set of features related not only to traffic, but other parameters such as CPU and memory. Furthermore, we provide the network topology of each sample in terms of the adjacency matrix, routing table and routing metrics. In the data we provide there is a known unprovoked gateway failure. Therefore, the dataset can be used to investigate the performance of unsupervised machine learning algorithms for fault detection in WCN. To our knowledge, this is the first dataset that allows fault detection to be investigated from a production WCN. This work has received funding through the DiPET CHIST-ERA under grant agreement PCI2019-111850-2; Spanish grant PID2019- 106774RB-C21; Romanian DIPET (62652/15.11.2019) project funded via PN 124/2020; and has been partially supported by the EU research project SERRANO (101017168) and hardware resources courtesy of the Romanian Ministry of Research and Innovation UEFISCDI COCO research project PN III-P4-ID-PCE-2020-0407. Peer Reviewed Postprint (published version) 2023-08 Article https://www.sciencedirect.com/science/article/pii/S2352340923004602 info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PCI2019-111850-2/ES/PROCESAMIENTO DE FLUJO DISTRIBUIDO EN SISTEMAS DE NIEBLA Y BORDE MEDIANTE COMPUTACION TRANSPRECISA/ info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-106774RB-C21/ES/SISTEMAS INFORMATICOS Y DE RED DESCENTRALIZADOS CON RECURSOS DISTRIBUIDOS/ http://creativecommons.org/licenses/by/4.0/ Open Access Attribution 4.0 International Elsevier