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  • Dataset that contains simultaneous GPS traces collected at 1 Hz from a team of firefighters during a forest fire exercise. The traces were generated by Android phones placed in each of four firefighters and a generic GPS device placed in the firetruck.,Changelog: the initial version,
    Data Types:
    • Dataset
  • Ten dataset files containing 4G\/5G MAC, RRC and PDCP statistics and monitoring data, grouped into two versions of 5 datasets each: raw statistics and processed monitoring data. Raw datasets are recorded using ElasticMon v0.1, a prototype version of a monitoring framework extension of the FlexRAN 5G programmable platform for Software-Defined Radio Access Networks. For details, see here: http:\/\/mosaic-5g.io\/flexran\/. Scenarios & setup: Raw datasets are recorded for one eNB and a single mobile User Equipment (UE) in five different mobility scenarios by following different motions and distance patterns relative to the eNB . All raw data have been recorded without including Tx power amplification on the RF frontend (0 dBm transmit power), which implies an approximately 10m maximum range of coverage. Future versions of the datasets will refer to multiple UEs monitoring and an eNB with Tx power amplification. How to use: The contributed raw datasets can be processed and used for training intelligent 4G\/5G models. The processed datasets that are also contributed here follow a proposed stepwise paradigm procedure. Therefore, you are advised to customize and adapt the proposed processing steps to match your own needs.,Changelog: the initial version,
    Data Types:
    • Dataset
  • The dataset includes packet captures collected from controlled experiments with various devices. The dataset captures active scanning behavior of the devices. Name of each folder represents the name of the cause of active scanning. For details please refer to our papers - Learning to Rescue WiFi Networks from Unnecessary Active Scans, WoWMoM 2019.,Changelog: the initial version,
    Data Types:
    • Dataset
  • The dataset contains records of authenticated user associations to the wireless network of the KTH Royal Institute of Technology in Stockholm. The dataset also includes scan results and mapping information of Wi-Fi networks, collected by means of war-walking at the university's two largest campuses.,Changelog: the initial version,
    Data Types:
    • Dataset
  • The BLEBeacon dataset is a collection of Bluetooth Low Energy (BLE) advertisement packets\/traces generated from BLE beacons carried by people following their daily routine inside a university building for a whole month. A network of Raspberry Pi 3 (RPi)-based edge devices were deployed inside a multi-floor facility continuously gathering BLE advertisement packets and storing them in a cloud-based environment. The focus is on presenting a real-life realization of a location-aware sensing infrastructure, that can provide insights for smart sensing platforms, crowd-based applications, building management, and user-localization frameworks.,Changelog: the initial version,
    Data Types:
    • Dataset
  • Dataset of traces of IEEE 802.11b\/g, IEEE 802.15.4 and Bluetooth packet transmissions with varying SNRs in the baseband. Additionally, different frequency offsets were added in the baseband to reflect different channels of the wireless technologies.,Changelog: the initial version,
    Data Types:
    • Dataset
  • Real-time position data reported by buses, updated every minute, from the city of Rio de Janeiro, Brazil. The file is CSV, containing the date, time(24h format), bus ID, bus line, latitude, longitude and speed of more than 12,000 buses.,Changelog: the initial version,
    Data Types:
    • Dataset
  • Real-time position data reported by buses, updated every minute, from the city of Rio de Janeiro, Brazil. The file is CSV, containing the date, time(24h format), bus ID, bus line, latitude, longitude and speed of more than 12,000 buses.,Changelog: the initial version,
    Data Types:
    • Dataset
  • This dataset comprises experiments carried out with the open-source middleware NSense (fomerly named as USense), available at https://github.com/COPELABS-SITI/NSense. The data has been collected based on four sensors: bluetooth; Wi-Fi; microphone; accelerometer. NSense then relies on four different pipelines to compute aspects such as relative distance (Wi-Fi); social strength (based on bluetooth contact duration); sound activity level; motion. We set up experiments making use of Samsung Galaxy S3 devices. For each experiment, there is the following set of data files: - SocialProximity.dat has three columns: Timestamp, DeviceName, Encounter Duration, Average Encounter Duration, Social Strength (Per hour) and Social Strength(Per minute) towards DeviceName - DistanceOutput.dat has three columns: Timestamp, DeviceName, and Distance towards DeviceName - Microphone.dat has two columns: Timestamp, and Soundlevel(QUIET, NORMAL, ALERT and NOISY) - PhysicalActivity.dat has two columns: Timestamp, and Activity as STATIONARY, WALKING and RUNNING There are two tracesets. A first traceset has been collected relying on a first NSense version in 2015. Then, a second traceset has been collected in 2016, with a refined version of NSense. In all tracesets, devices have been carried around by people that share the same affiliation during their individual daily routines (24 hour periods).,Changelog: a second traceset was collected in 2016, with a refined version of NSense.,
    Data Types:
    • Dataset
  • A complete collection of all management and control frames (including Radiotap headers) observed at our research lab from 28 January to 8 Febuary 2016. This dataset was used to calculate the "stability" and "variability" of Probe Request IEs (see our paper for more details on these metrics).,Changelog: the initial version,
    Data Types:
    • Dataset