WiFi
This dataset contains seven connectivity traces that have been derived from the cambridge/haggle/imote traceset (v. 2009-05-29). These connectivity traces can be used for network simulations with the Opportunistic Network Environment (ONE) simulator, since they are in accordance with the syntax of the StandardEventsReader format. The Python scripts that generated these connectivity traces are also provided.
last modified : 2016-08-28
nickname : haggle
institution : University of Ioannina
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243 Views
Mobility data collected by LifeMap monitoring system at Yonsei University in Seoul.
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348 Views
Dataset of radio characteristics of 802.15.4 mobile person-to-person communications.
The dataset contains results from a simple yet systematic set of benchmark experiments that offer a number of important insights into the radio characteristics of mobile 802.15.4 person-to-person communications.
date/time of measurement start: 2007-08-12
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258 Views
CenceMe is a sensing system based on standard and sensor-enabled mobile phones. CenceMe uses the output of the phones' sensors and external data (if such is available) to infer human presence and activity information. This dataset contains movements and inferred activities of participants using CenceMe on their mobile phones.
The CenceMeLite traces were collected from 2008-07-28 to 2008-08-11 by students and staff members at Dartmouth College.
last modified 2010-08-30
reason for most recent change the initial version
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212 Views
This article presents the details of the Cardinal RF (CardRF) dataset. CardRF is acquired to foster research in RF- based UAV detection and identification or RF fingerprinting. RF signals were collected from UAV controllers, UAV, Bluetooth, and Wi-Fi devices. Signals are collected at both visual line-of-sight and beyond-line-of-sight. The assumptions and procedure for the data acquisition are presented. A detailed explanation of how the data can be utilized is discussed. CardRF is over 65 GB in storage memory.
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8107 Views
MANET dataset of outdoor experments for comparing differnet routing algorithms.
This dataset contains outdoor runs of MANET (Mobile Ad-hoc network) routing algorithms to compare the performance of four different routing algorithms.
date/time of measurement start: 2003-10-17
date/time of measurement end: 2003-10-17
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451 Views
Dataset used for "A Machine Learning Approach for Wi-Fi RTT Ranging" paper (ION ITM 2019). The dataset includes almost 30,000 Wi-Fi RTT (FTM) raw channel measurements from real-life client and access points, from an office environment. This data can be used for Time of Arrival (ToA), ranging, positioning, navigation and other types of research in Wi-Fi indoor location. The zip file includes a README file, a CSV file with the dataset and several Matlab functions to help the user plot the data and demonstrate how to estimate the range.
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3162 Views
WiFi measurements dataset for WiFi fingerprint indoor localization compiled on the first and ground floors of the Escuela Técnica Superior de Ingeniería Informática, in Seville, Spain. The facility has 24.000 m² approximately, although only accessible areas were compiled.
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1622 Views
RSSI-Dataset
The RSSI-Dataset provides a comprehensive set of Received Signal Strength Indication (RSSI) readings from within two indoor office buildings. Four wireless technologies were used:
- Zigbee (IEEE 802.15.4),
- WiFi (IEEE 802. 11),
- Bluetooth Low Energy (BLE) and
- Long Range Area-Wide Network (LoRaWAN).
For experimentation Arduinos Raspberry Pi, XBees, Gimbal beacons Series 10 and Dragino LoRa Shield were also used.
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1876 Views
Smartphones perform Wifi scans to adapt to the changing wireless environments causes by mobility. From network monitoring perspective, such scans provide a natural stream of network measurements from client's point of view. In order to see whether such measurements can provide new insights in monitoring large scale wireless networks, we collected the Wifi scan results data, together with other Wifi related logs, from the PhoneLab smartphone testbed over 5 months. All data are collected passively from the smartphones.
last modified: 2016-03-09
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333 Views