Serial Number: 17
Year: 2010
Kind of Traffic: Real
Publicly Available: Yes
Count of Records:
Features Count:
No. of citations: 175
Attack Type: N/A
Download Links: https://home.uncg.edu/cmp/downloads/lwsndr.html
Abstract: Sutaharan and colleagues contended that existing synthetic datasets in Wireless Sensor Networks (WSNs) lacked proper labelling. To address this gap, they implemented a sensor-data network with both multi-hop and single-hop configurations. From this network the data generated is labelled and categorized to various categories of irregularities. The dataset contains readings recorded from actual temperature-humidity sensors utilizing Crossbow TelosB motes. These measurements were gathered at five-second intervals over a span of six hours. Controlled anomalous situations were deliberately induced. Irregularities within sensor networks can appear at varying levels, encompassing individual readings or attributes related to traffic of network, the entirety of data regarding neighbouring nodes, and a collection of sensor nodes within the network. The labelled Wireless Sensor Network Dataset for Anomaly Detection and Recognition (LWSNDR) is now available.