- 2016-03-09 buffalo/phonelab-wifi
Wifi scan results and connection status collected using the PhoneLab smartphone testbed.
Contributed by Jinghao Shi, Chunming Qiao, Dimitrios Koutsonikolas, Geoffrey Challen.
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.
- 2006-01-31 cambridge/haggle
Traces of Bluetooth sightings by groups of users carrying small devices (iMotes) for a number of days.
Contributed by James Scott, Richard Gass, Jon Crowcroft, Pan Hui, Christophe Diot, Augustin Chaintreau.
This data includes a number of traces of Bluetooth sightings by groups of users carrying small devices (iMotes) for a number of days - in office environments and conference environments.
- 2006-02-01 cambridge/inmotion
Dataset of UDP and TCP transfers between a moving car and an 802.11b access point.
Contributed by Richard Gass, James Scott, Christophe Diot.
Dataset of UDP and TCP transfers between a car traveling at speeds from 5 mph to 75 mph, and an 802.11b access point.
- 2012-05-17 cister/rssi
Channel energy levels from Wi-Fi networks as seen from a 802.15.4 radio.
Contributed by Claro Noda, Shashi Prabh, Mário Alves, Thiemo Voigt, Carlo Alberto Boano.
We use a sensor network composed of TelosB motes deployed in the library building to collect RF energy level samples (RSSI) on all 802.15.4 channels in the 2.4 GHz ISM Band. The building has several collocated Wi-Fi networks in normal operation. These networks produce interference for the 802.15.4 radios. Sensor nodes record RSSI values every 20 us, simultaneously on all channels, for 130 ms and then write the result to the respective files. This process is repeated every 8 seconds for around 4h.
- 2009-04-15 cmu/hotspot
Dataset of all visible APs of 13 hotspot locations in Seattle, WA over one week.
Contributed by Jeffrey Pang.
We measured the performance and application support of all visible APs at 13 hotspot locations around University Avenue, Seattle, WA, near the University of Washington over the course of 1 week.
- 2014-05-27 cmu/supermarket
Round-trip Time-of-flight Measurements from a supermarket
Contributed by Aveek Purohit, Shijia Pan, Kaifei Chen, Zheng Sun, Pei Zhang.
The dataset is meant to aid development and evaluation of indoor location in complex indoor environments using round-trip time-of-flight (RToF) and magnetometer measurements. The dataset provides Round-trip time-of-flight (RToF) and magnetometer measurements at 1589 mobile node locations from 30 deployed stationary anchors in the 26m x 24m New Wing Yuan supermarket in Sunnyvale, CA. The data was collected during working hours over a period of 15 days. The nodes are equipped with chirp spread spectrum Nanotron nanoLoc radios (802.15.4a) and a Honeywell's HMC5843, a 3-axis digital magnetometer. At every location, 20 distinct readings were obtained for RToF signatures as well magnetometer measurements, to capture the variance, giving a total of 31780 readings.
- 2020-05-26 cmu/zigbee-smarthome
Captured Zigbee packets from commercial smart home devices.
Contributed by Dimitrios-Georgios Akestoridis, Madhumitha Harishankar, Michael Weber, Patrick Tague.
This Carnegie Mellon University dataset contains Zigbee packets that were captured using a software-defined radio (USRP N210). More specifically, the contributors used GNU Radio with the gr-ieee802-15-4 and gr-foo modules to receive IEEE 802.15.4 packets and store them in PCAP format. The captured network traffic was generated from ten commercial Zigbee devices that can be found in smart home environments. Eight experiments were conducted that differed in the smart hub that was used and the physical topology of the devices. These experiments took place on the Silicon Valley campus of Carnegie Mellon University in order to analyze the security of Zigbee-enabled smart homes. The experiments lasted about 34.644 hours in total and resulted in the logging of 571,509 valid packets. More details about the procedure that was followed and the setup of each experiment are included in the dataset, along with the encryption keys that were used in order to facilitate packet analysis. DOI for this dataset: 10.15783/c7-nvc6-4q28
- 2007-09-26 cnu/cdma
Tcpdump data set collected from a CDMA 1x EV-DO network in South Korea.
Contributed by Youngseok Lee.
We collected tcpdump data from a CDMA 1x EV-DO network in South Korea that provides high-speed "always on" Internet connectivity in a wide-area mobile environment.
- 2006-11-17 columbia/ecsma
Sensor network dataset for enhancing CSMA MAC protocol.
Contributed by Shane B. Eisenman.
This dataset contains packet transmission traces collected from an experimental wireless sensor network testbed, where E(Enhanced)-CSMA MAC protocol is implemented using TinyOS on Mica2 motes.
- 2011-04-07 columbia/enhants
Dataset of radiant light energy measurements.
Contributed by Maria Gorlatova, Michael Zapas, Enlin Xu, Matthias Bahlke, Ioannis (John) Kymissis, Gil Zussman.
This dataset includes radiant light energy measurements from a study by Columbia University's EnHANTs (Energy Harvesting Active Networked Tags) project.
- 2014-05-13 columbia/kinetic
200 hours of accelerometer information recorded over 25 days from 5 participants.
Contributed by Mina Cong, Kanghwan Kim, Maria Gorlatova, John Sarik, John Kymissis, Gil Zussman.
To help us better understand the properties of various energy sources and their impact on energy harvesting adaptive algorithms, we collected acceleration traces from different participants. The volunteers were asked to perform normal daily routines while comfortably carrying SparkFun Electronics ADXL345 accelerometer boards. For our long-term studies, we collected over 200 hours of acceleration information in 25 days from 5 participants. The data simulates the natural motions that participants' belongings (keys, phone, or wallet) experience on a daily basis.
- 2017-01-27 copelabs/usense
Data concerning social interaction and propinquity based on wireless and bluetooth.
Contributed by S. Firdose, L. Lopes, W. Moreira, R. Sofia, P. Mendes.
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).
- 2018-03-19 coppe-ufrj/RioBuses
Dataset of mobility traces of buses in Rio de Janeiro, Brasil
Contributed by Daniel Dias, Lu&#x00ed;s Henrique Maciel Kosmalski Costa.
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.
- 2012-03-15 ctu/personal
Mobile phone records of Czech Ph.D. student Michal Ficek.
Contributed by Michal Ficek.
This dataset contains 142 days of mobile phone records (aka Call Data Records) and ground-truth movement description of Czech Ph.D. student Michal Ficek, stored by his own mobile terminal in 2010-2011.
- 2009-05-08 cu/antenna
Dataset of signal strength collected from 2.4 GHz directional antenna.
Contributed by Eric W. Anderson, Caleb Phillips.
We collected signal strength data to derive a parametric model for 2.4 GHz directional antennas.
- 2011-10-24 cu/cu_wart
Dataset of RSS measurements collected at the University of Colorado Wide-Area Radio Testbed.
Contributed by Caleb Phillips, Eric W. Anderson.
This data was collected by Caleb Phillips at the University of Colorado (CU). It contains RSS measurements (together with GPS data) collected using the CU Wide Area Radio Testbed (CU-WART), which involves seven 802.11 APs with phased array antennas mounted on university buildings.
- 2012-05-04 cu/lte
LTE Measurements on a 100m (triangular) grid on the University of Colorado Campus.
Contributed by Erik Bergal, Caleb Phillips, Chingpu Wu.
This data was collected at the University of Colorado Boulder. It contains careful point measurements, taken on a 100m equilateral triangular lattice, of the Verizon LTE network.
- 2009-05-28 cu/rssi
Dataset of received signal strength indication (RSSI) collected from within an indoor office building.
Contributed by Kevin Bauer, Eric W. Anderson, Damon McCoy, Dirk Grunwald, Douglas C. Sicker.
This data set provides a comprehensive set of received signal strength indication (RSSI) readings from within an indoor office building from several perspectives throughout the building.
- 2012-06-01 cu/wimax
WiMax measurements made with a portable spectrum analyzer on the University of Colorado campus.
Contributed by Michael Ton, Caleb Phillips.
This data was collected at the University of Colorado Boulder. It contains careful point measurements of the WiMax network serving the University of Colorado campus. Measurements were made with a portable spectrum analyzer and contain high fidelity measurements of CINR, RSS, EVM, RCE, Spectrum Flatness, and Frequency Shift. Several hundred measurements were taken on a 100m equalateral triangular lattice as a "first phase" sample. Additional "second phase" measurements were taken at random and optimized points.
- 2007-02-08 dartmouth/campus
Syslog, SNMP, and tcpdump data for 5 years or more from wireless network at Dartmouth College.
Contributed by David Kotz, Tristan Henderson, Ilya Abyzov, Jihwang Yeo.
This dataset includes syslog, SNMP, and tcpdump data for 5 years or more, for over 450 access points and several thousand users at Dartmouth College.
- 2008-08-13 dartmouth/cenceme
Dataset of sensor data collected by the CenceMe system.
Contributed by Mirco Musolesi, Kristof Fodor, Mattia Piraccini, Antonio Corradi, Andrew Campbell.
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.
- 2006-11-06 dartmouth/outdoor
MANET dataset of outdoor experments for comparing differnet routing algorithms.
Contributed by Robert S. Gray, David Kotz, Calvin Newport, Nikita Dubrovsky, Aaron Fiske, Jason Liu, Christopher Masone, Susan McGrath, Yougu Yuan.
This dataset contains outdoor runs of MANET (Mobile Ad-hoc network) routing algorithms to compare the performance of four different routing algorithms.
- 2006-06-02 dartmouth/wardriving
Location-aware dataset for analyzing the errors in location estimates.
Contributed by Minkyong Kim, David Kotz, Jeffrey J. Fielding.
The authors collected the dataset through war driving, i.e., collecting Wi-Fi beacons by driving or walking around Dartmouth Campus, to understand the effect of using estimated AP locations.
- 2008-01-07 dartmouth/zigbee_radio
Dataset of radio characteristics of 802.15.4 mobile person-to-person communications.
Contributed by Emiliano Miluzzo, Xiao Zheng, Kristof Fodor.
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.
- 2015-03-30 due/packet-delivery
Packet delivery performance (packet loss, delay, throughput, energy consumption) over a 802.15.4 link under vairous stack parameter configurations for more than 6 months.
Contributed by Songwei Fu, Yan Zhang.
The data set includes measurements of the data delivery performance of a WSN link in indoor scenario, where 4 major performance metrics, namely energy, throughput, delay and loss, were measured over 6 months under around 50 thousand parameter configurations of 7 key stack parameters. The meta-data of transmission of nearly 200 million packets are included in the dataset.
- 2009-02-24 epfl/mobility
Dataset of mobility traces of taxi cabs in San Francisco, USA.
Contributed by Michal Piorkowski, Natasa Sarafijanovic-Djukic, Matthias Grossglauser.
This dataset contains mobility traces of taxi cabs in San Francisco, USA. It contains GPS coordinates of approximately 500 taxis collected over 30 days in the San Francisco Bay Area.
- 2019-08-28 eurecom/elasticmon5G2019
4G and 5G RAN monitoring data collected using the ElasticMon 5G monitoring framework over FlexRAN
Contributed by Berkay Koksal, Robert Schmidt, Xenofon Vasilakos, Navid Nikaien.
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 t o 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. Dataset DOI: 10.15783/c7-s58c-qn61 / https://doi.org/10.15783/c7-s58c-qn61 datasets: ftp://ftp.eurecom.fr/incoming/01-RawDatasets.zip and ftp://ftp.eurecom.fr/incoming/02-PreprocessedDatasets.zip
- 2014-06-09 gatech/fingerprinting
Fingerprinting of wireless devices exploiting information leaked due to different device hardware compositions: Inter-Arrival-Time (IAT) of packets from wireless devices
Contributed by A. Selcuk Uluagac.
In these datasets, we present the the inter-arrival time information collected actively and passively from different wireless devices using wire-side observations in a local network. The captures were collected from 30 wireless devices including iPads, iPhones, Kindles, Google-Phones, Netbooks, IP Printers, IP Cameras, etc., from various applications and protocols such as Skype, ICMP, SCP, Iperf. Due to heterogeneity in devices (e.g., deterministic hardware and software configurations), time-variant behavior of network traffic stemming from different devices can be used to create unique, reproducible device and device type signatures and to fingerprint devices and their types as explained in A. Selcuk Uluagac, Sakthi V. Radhakrishnan, Cherita Corbett, Antony Baca, and Raheem A. Beyah, A Passive Technique for Fingerprinting Wireless Devices with Wired-side Observations, Proceedings of the IEEE Conference on Communications and Network Security (CNS), October 2013. Further details are available at http://users.ece.gatech.edu/~selcuk/devFingerprinting.html
- 2006-03-15 gatech/vehicular
Dataset for short range communications between vehicles and between vehicle and roadside station.
Contributed by Richard M. Fujimoto, Randall Guensler, Michael P. Hunter, Hao Wu, Mahesh Palekar, Jaesup Lee, Joonho Ko.
This dataset contains the measurement of the performance of short range communications between vehicles and between vehicle and roadside station.
- 2017-05-11 hasselt/glimps2015
A pcap file containing 122,989 anonymized Probe Requests sent by mobile devices at the Glimps 2015 music festival in Ghent, Belgium.
Contributed by Pieter Robyns, Bram Bonné, Peter Quax, Wim Lamotte.
A collection of 122,989 Probe Request frames captured by 8 monitoring stations at the Glimps music festival in Ghent, Belgium (10 - 12 December 2015). To minimize overhead, each monitoring station individually stored only the transmitter MAC address and Information Elements per unique MAC. The dataset was used to show that the high entropy in Information Elements can be used to deanonymize devices that use MAC address randomization.
- 2008-08-07 hope/amd
RFID tracking data collected from the seventh HOPE (Hackers On Planet Earth) conference held in July 18-20, 2008.
Contributed by aestetix, Christopher Petro.
RFID tracking data was collected from the seventh HOPE (Hackers On Planet Earth) conference which was held in July 18-20, 2008. Conference attendees received RFID badges that uniquely identify and track them across the conference space.
- 2010-07-18 hope/nh_amd
Packets generated by the badges at The Next HOPE (Hackers On Planet Earth) conference held July 16-18, 2010.
Contributed by Travis Goodspeed, Nathaniel Filardo.
RFID tracking data was collected at The Next HOPE (Hackers On Planet Earth) conference that was held July 16-18, 2010. Conference attendees received active RFID badges that uniquely identified and tracked them across the conference space.
- 2003-02-19 ibm/watson
SNMP records for a corporate research center (IBM Watson research center) over several weeks.
Contributed by Magdalena Balazinska, Paul Castro.
This dataset includes SNMP records for a corporate research center (IBM Watson research center) over several weeks
- 2015-03-24 icsi/netalyzr-android
Mobile data collected using the Netalyzr for Android App.
Contributed by Narseo Vallina-Rodriguez, Srikanth Sundaresan, Christian Kreibich, Nicholas Weaver, Vern Paxson.
This dataset was collected by the ICSI Netalyzr app for Android to develop a characterization of how operational decisions, such as network configurations, business models, and relationships between operators introduce diversity in service quality and affect user security and privacy. We delve in detail beyond the radio link and into network configuration and business relationships in six countries. We identify the widespread use of transparent middleboxes such as HTTP and DNS proxies, analyzing how they actively modify user traffic, compromise user privacy, and potentially undermine user security. In addition, we identify network sharing agreements between operators, highlighting the implications of roaming and characterizing the properties of MVNOs, including that a majority are simply rebranded versions of major operators. More broadly, our findings using this data highlight the importance of considering higher-layer relationships when seeking to analyze mobile traffic in a sound fashion.
- 2019-06-05 iiitd/wifiactivescanning
Cause-Specific Episodes of Active Scanning
Contributed by Gursimran Singh, Harish Fulara, Dheryta Jaisinghani, Mukulika Maity, Tanmoy Chakraborty, Vinayak Naik.
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.
- 2015-11-26 iitkgp/apptraffic
Android app traffic (primarily video)
Contributed by Satadal Sengupta, Harshit Gupta, Niloy Ganguly, Bivas Mitra, Pradipta De, Sandip Chakraborty.
Traces from Android apps (primarily video) collected under different values of parameters, such as video length, connection strength and device mobility, for the purpose of mobile video app traffic pattern identification.
- 2015-11-06 ilesansfil/wifidog
Dataset of user session traces collected from Wi-Fi hotspots for six years.
Contributed by Michael Lenczner, Anne G. Hoen.
This data set contains user session traces which were collected from a large number of free Wi-Fi hotspots in Montreal, Quebec, Canada for six years.
- 2016-06-13 init/factory
Channel gain within a factory environment
Contributed by Dimitri Block, Niels Hendrik Fliedner, Uwe Meier.
Measurement of the channel gain for multiple distances within a factory environment
- 2015-07-06 init/robotarm
Time- and frequency-variant 2.4 GHz ISM band channel gain
Contributed by Dimitri Block, Niels Hendrik Fliedner, Daniel Toews, Uwe Meier.
The time- and frequency-variant channel gain is measured in the presence of an industrial cyclic moving robot arm obstacle for four coexisting wireless nodes for the whole license-free 2.4-GHz-ISM band with a time- and frequency-resolution of 110 microseconds and 1 MHz, respectively. Results for two links are given.
- 2006-04-16 intel/home
Connectivity and throughput measurement from home wireless networks.
Contributed by Konstantina Papagiannaki, Mark Yarvis, W. Steven Conner.
Measurements reflect connectivity and UDP/TCP throughput data collected from a grid of six nodes placed within three different houses.
- 2004-12-17 intel/placelab
Location-aware dataset collected using Place Lab software.
Contributed by Anthony LaMarca, Yatin Chawathe, Jeffrey Hightower, Gaetano Borriello.
These traces contain 802.11, GSM and GPS trace data collected using Place Lab software, for 3 different neighborhoods in the Seattle metro area. Total trace duration is approximately 2 hours, with around 55,000 total readings.
- 2015-12-02 istanbul_technical/rssi
GSM 900 MHz measurements from a spectrum analyzer combined with GPS measurements taken in an urban macrocell environment.
Contributed by Metin Vural, Saliha Buyukcorak, Gunes Karabulut Kurt.
Our measurement is performed in an urban macrocell environment in the GSM 900 downlink band based on real-life data. By a spectrum analyzer, 5 individual tracks with 10000 measurement data points are obtained. Channel sounding is carried out at a GSM base station located at a height of 6 meters. At every track point measurements are taken in a stationary fashion with 17ms sweep time on the ground level. GPS information is also saved. the signal bandwidth does not exceed the resolution bandwidth of the spectrum analyzer, hence our measurements only include path loss and shadow fading effects Dataset is used in the article "Lognormal Mixture Shadowing".
- 2007-12-19 isti/rural
Dataset of transmission distance vs. packet loss measurement on a Wi-Fi network in rural areas.
Contributed by Paolo Barsocchi, Gabriele Oligeri, Francesco Potortì.
We conducted a series of measurements for relating transmission distance and packet loss on a Wi-Fi network in rural areas to propose a model that relates distance with packet loss probability.
- 2019-09-16 it/vr2marketbaiaotrial
GPS traces collected from a team of firefighters during a forest fire exercise
Contributed by Ana Aguiar.
Dataset 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. DOI link: http://doi.org/10.15783/c7-hpaw-8b51
- 2012-11-03 jiit/accelerometer
Accelerometer samples collected through Android phones when driven on different vehicles
Contributed by Mohit Jain, Ajeet Pal Singh, Soshant Bali, Sanjit Kaul.
This dataset consists of accelerometer samples collected through Android phones when driven on different vehicles. The dataset consists of 678 Km of drive data, which involved 22 different drivers, 5 different types of vehicles (bus, auto rickshaw, cycle rickshaw, motorcycle, and car) and 4 smartphones.
- 2008-06-04 kaist/wibro
Data set of CBR and VoIP traffic measurements from the WiBro network in Seoul, Korea.
Contributed by Mongnam Han, Youngseok Lee, Sue B. Moon, Keon Jang, Dooyoung Lee.
In order to evaluate QoS of VoIP applications over the WiBro network, we collected the CBR and VoIP traffic from the WiBro network in Seoul, Korea. We conducted experiments in both stationary and mobile scenarios, e.g., on a subway train.
- 2019-07-01 kth/campus
Dataset of wireless network measurements at the KTH campuses, collected during 2014-2015. DOI: https://doi.org/10.15783/c7-5r6x-4b46
Contributed by Ljubica Pajevic, Gunnar Karlsson, Viktoria Fodor.
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.
- 2016-01-05 kth/rss
Radio Signal Strength data from a mobile robot along with odometer in indoor and outdoor environments
Contributed by Ramviyas Parasuraman, Sergio Caccamo, Fredrik Baberg, Petter Ogren.
This dataset contains the RSS (Radio Signal Strength) data collected with a mobile robot in two environments: indoor (KTH) and outdoor (Dortmund). RSSI metric was used to collect the RSS data in terms of dBm. The mobile robot location was recorded using its odometry (dead reckoning).
- 2014-05-05 kth/walkers
Micro-simulation of pedestrian mobility
Contributed by Sylvia Todorova Kouyoumdjieva, Ólafur Ragnar Helgason, Gunnar Karlsson.
These traces are from simulation of walkers in a part of downtown Stockholm for which we vary several parameters. We also provide a trace from a subway station for which the parameters could not be modified. They were used to study mobility models for opportunistic communications.
- 2015-06-16 kyutech/interference
Measurements of HTTP requests over 802.11 in dense wireless classrooms
Contributed by Marat Zhanikeev.
The common wisdom tells one not to attach more than 5-10 wireless devices to an access point. This is a problem in wireless educational classes, one can get between 10 and 50 students in the same room. Such spaces are referred to as dense wireless networks. While there are several methods that can be used to deal with dense wireless environments, installing multiple APs and separating them by channel is arguably the most readily available method today. In 802.11g/n the practical advice is to assign CH1, CH5 and CH9 to the three APs. However, even in such environments, wireless channels can become congested. It was noticed that tablet terminals would often fail to complete HTTP requests or fail getting access to the wireless channel entirely. In order to deal with this reliability problem, educational webapps had to be updated in such a way that they would detect failed requests and repeat them multiple times until a valid reply comes back. This dataset contains measurements which can help model such environments and the related application logic. The dataset has slightly non-trivial metrics which described failed/repeated. The experiment was run over multiple parameters and with on-the-fly changes in the layout. The measurement itself was simple -- a web application (in browser) on user terminal would send a GET request to the web server and would download bulk of a randomly selected size. All the performance metrics were recorded at user terminals and gradually uploaded to the server -- specifically, each new request would also contain the variables containing the results of the previous request, which had negligible effect on size but facilitated efficient log-keeping.
- 2015-06-16 kyutech/throughput
Measurements on real HTTP throughput via several 3G/LTE providers in Japan.
Contributed by Marat Zhanikeev.
While recent LTE provider promise very high rates, end users often complain that HTTP throughput is not sufficient for rich multimedia, even as non-demanding as YouTube. It is interesting that this artifact can be found in both 3G and LTE providers. For example, it is not uncommon for a YouTube video to freeze on a terminal with LTE connection. The main cause of such artifacts is the congestion due to high and further increasing number of subscribers. Congestion can happen at several points inside a given 3G/LTE provider, but it is not important to end user because content is commonly found outside of 3G/LTE providers, guaranteeing that all subscribers have to do through the bottleneck. This dataset contains measurements conducted on 3 (anonymized) 3G/LTE providers in Japan in April-May 2013. The measurement design was very simple -- a webapp running on 3 terminals (that were tweaked not to go to sleep and not used for anything else) would regularly send web requests to a web server placed within a university campus and measure the round-trip time of the request. Bulk size was randomly selected from a number of preset values and was always downloaded (HTTP get request with bulk returning in reply). It was verified that the wired part of the e2e connection was never the bottleneck, implying the throughput was always impaired within a given 3G/LTE provider.
- 2008-04-11 mannheim/compass
Traces of signal strength of 802.11 APs for the COMPASS positioning system.
Contributed by Thomas King, Stephan Kopf, Thomas Haenselmann, Christian Lubberger, Wolfgang Effelsberg.
COMPASS is a positioning system based on 802.11 and digital compasses. We apply an two-stage fingerprinting approach: In the training phase, we sample the signal strength of neighboring access points for selected orientations at each reference point and store the data in a database. During the positioning phase, the orientation of the user is utilized to preselect a subset of the training data and based on this data compute her position.
- 2007-05-23 microsoft/osdi2006
Traces of network activity at OSDI 2006.
Contributed by Ranveer Chandra, Ratul Mahajan, Venkat Padmanabhan, Ming Zhang.
The authors gathered a detailed trace of network activity at OSDI 2006 to enable analysis of the behavior of a wireless LAN that is (presumably) heavily used.
- 2007-09-14 microsoft/vanlan
Dataset of WiFi-based connectivity between basestations and vehicles in urban settings.
Contributed by Ratul Mahajan.
We measured from VanLAN, a modest-size testbed that we have deployed, to analyze the fundamental characteristics of WiFi-based connectivity between basestations and vehicles in urban settings.
- 2005-07-01 mit/reality
Traces of communication, proximity, location, and activity information from 100 subjects at MIT over the course of the 2004-2005 academic year.
Contributed by Nathan Eagle, Alex (Sandy) Pentland.
The authors have captured communication, proximity, location, and activity information from 100 subjects at MIT over the course of the 2004-2005 academic year. This data represents over 350,000 hours (~40 years) of continuous data on human behavior. Such rich data on complex social systems have implications for a variety of fields.
- 2009-07-23 ncsu/mobilitymodels
Human mobility data collected from five different sites.
Contributed by Injong Rhee, Minsu Shin, Seongik Hong, Kyunghan Lee, Seongjoon Kim, Song Chong.
We collected human mobilicty traces from five different sites - two university campuses (NCSU and KAIST), New York City, Disney World (Orlando), and North Carolina state fair.
- 2008-07-08 niit/bit_errors
Data set of 802.15.4 and 802.11b traces for investigating the biterror process of 802.15.4 and 802.11b networks.
Contributed by Adnan Iqbal, Khurram Shahzad, Syed Ali Khayam, Yongju Cho.
We collected 802.15.4 traces at NUST school of Electrical Engineering and Computer Science, Rawalpindi, Pakistan, and collected 802.11b traces at Wireless and Video (WAVES) Lab at Michigan State University (MSU), USA, to investigate biterror process of the 802.15.4 and 802.11 networks.
- 2007-08-20 nist/multihop
Dataset of experiments for the automated deployment of a multihop wireless network.
Contributed by Michael R. Souryal, Johannes Geissbuehler, Kamran Sayrafian-Pour, Andreas Wapf, Julio Perez.
To assess the feasibility of deploying wireless relays in real time, we conducted a series of experiments using 900 MHz TinyOS Crossbow MICA2 Motes (MPR400CB).
- 2007-12-20 nottingham/cattle
Dataset of cattle movement and behavior monitoring collected at the University of Nottingham's Dairy Centre.
Contributed by Bartosz Wietrzyk, Milena Radenkovic.
We performed the field experiments of cattle movement and behavior monitoring at the University of Nottingham's Dairy Centre to collect realistic parameters necessary to develop and evaluate an adequate wireless protocol.
- 2012-06-22 nottingham/mall
Bluetooth contact traces from shop employees of a shopping mall over six days.
Contributed by Adriano Galati, Chris Greenhalgh.
This is a dataset of real-world Bluetooth contact data colected from shop employees of a shopping mall over six days.
- 2009-05-01 novay/cosphere
Network traces on the personal mobile devices of 12 trial participants over a period of one month in the February/March 2007 time frame.
Contributed by Arjan Peddemors.
The CoSphere (Communication Context for Adaptive Mobile Applications) trial gathered network traces on the personal mobile devices of 12 trial participants over a period of approximately one month in the February/March 2007 time frame.
- 2007-09-03 nus/bluetooth
Dataset of Bluetooth contact traces collected in Singapore from end 2005 to early 2006.
Contributed by Anirudh Natarajan, Mehul Motani, Vikram Srinivasan.
This dataset contains Bluetooth contact traces collected in Singapore. 12 contact probes-3 static and 9 mobile-collected data from end 2005 to early 2006. We discovered over 10,000 unique devices and recorded over 350,000 contacts in this duration.
- 2006-08-01 nus/contact
Dataset of contact patterns among students, collected during the Spring semester of 2006 in National University of Singapore.
Contributed by Vikram Srinivasan, Mehul Motani, Wei Tsang Ooi.
The authors obtained the contact patterns among 22341 students, which were inferred from the information on class schedules and class rosters for the Spring semester of 2006 in National University of Singapore.
- 2014-05-09 nyupoly/video
DASH and WebRTC video delivery over GENI WiMAX
Contributed by Fraida Fund, Cong Wang, Yong Liu, Thanasis Korakis, Michael Zink, Shivendra Panwar.
This dataset describes measurements from Dynamic Adaptive Streaming over HTTP (DASH) and WebRTC video services, collected over the GENI WiMAX networks at NYU-Poly and UMass Amherst. These measurements are meant to elucidate the experience of an individual user of these services who is moving at walking speeds through the coverage area of a typical cellular network.
- 2016-08-08 oviedo/asturies-er
This dataset contains mobility and connectivity traces extracted from GPS traces collected from the regional Fire Department of Asturias, Spain.
Contributed by Sergio Cabrero, Roberto Garcia, Xabiel G. García, David Melendi.
This dataset contains mobility and connectivity traces extracted from GPS traces collected from the regional Fire Department of Asturias, Spain. The original data source is one year of GPS traces extracted from a Geographical Information System (GIS). The traces were generated by GPS devices embedded mainly in cars and trucks, but also in a helicopter and a few personal radios. A total of 229 devices reported 19,462,339 locations. A new location is reported with an interval of approximately 30 seconds when the GPS device detects movement. To convert GPS traces into ONE connectivity traces, we have assumed circular communication ranges of 10, 50 and 200 meters. There is a connection between nodes that are closer than the given range. For simplicity, we assume that the position of a device is always the last position reported. Our analysis shows several important findings for the design of network protocols from the physical to the application layer. The networks examined are heterogeneous in the contact duration and the number of nodes contacted (degree centrality). In addition, they are sparse and partitioned, but delay- tolerant routes connecting these partitions exist. Finally, there are patterns in the connection between nodes that can ease the discovery of these routes and the deployment of delay-tolerant services.
- 2019-02-12 owl/interference
Traces of various radio signal standards
Contributed by Malte Schmidt, Dimitri Block, Uwe Meier.
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.
- 2011-10-24 pdx/metrofi
Dataset of coverage and performance-related information of MetroFi, a 802.11x municipal wireless mesh network in Portland, Oregon in 2007.
Contributed by Russell Senior, Caleb Phillips.
This is a dataset of location, signal strength, and performance data of MetroFi, a 802.11x municipal wireless mesh network in Portland, Oregon in 2007. The data was collected by Caleb Phillips and Russell Senior to determine the coverage and performance of the network.
- 2009-07-04 pdx/vwave
Dataset of wireless LAN traffic around Portland, Oregon using a commercial sniffer VWave.
Contributed by Caleb Phillips, Suresh Singh.
We collected six wireless LAN traffic traces around Portland, Oregon using a commercial sniffer VWave which has a nano-second time resolution.
- 2007-02-14 princeton/zebranet
Dataset of animal movement traces collected from real-world ZebraNet deployments.
Contributed by Yong Wang, Pei Zhang, Ting Liu, Chris Sadler, Margaret Martonosi.
The data contained in this data set are movement traces collected from two real-world ZebraNet deployments at Sweetwaters Game Reserve near Nanyuki, Kenya. The first deployment was in January 2004 and the second deployment was during summer of 2005. The data offer detailed animal position information using UTM format.
- 2015-11-20 queensu/crowd_temperature
Outdoor temperature data collected by taxis in Rome, Italy.
Contributed by Mohannad A. Alswailim, Hossam S. Hassanein, Mohammad Zulkernine.
This dataset is to be used in conjunction with the roma/taxi dataset and provides the outdoor temperature of the areas in Rome where the taxis were located (289 taxicabs over 4 days).
- 2003-09-11 rice/ad_hoc_city
Dataset of the movement of the fleet of city buses in Seattle.
Contributed by Jorjeta G. Jetcheva, Yih-Chun Hu, Santashil PalChaudhuri, Amit Kumar Saha, David B. Johnson.
We acquired several weeklong traces of the movement of the fleet of city buses in Seattle, Washington, on their normal routes providing passenger bus service throughout the city.
- 2007-08-01 rice/context
Dataset of context information from cellular and Wi-Fi networks through participants from a major US urban area.
Contributed by Ahmad Rahmati, Lin Zhong.
We gathered field data about cellular and Wi-Fi networks through participants from the Rice community in Houston, Texas, a major US urban area.
- 2010-09-01 rice/midas
Smartphone records of accelerometer and compass readings, wireless network state, and application data usage collected for MiDAS project at Rice University.
Contributed by Ardalan Amiri Sani, Lin Zhong, Ashutosh Sabharwal.
Accelerometer and compass readings along with network usage and application data usage information from 11 smartphone users, each for one week in the field.
- 2014-07-17 roma/taxi
Dataset of mobility traces of taxi cabs in Rome, Italy.
Contributed by Lorenzo Bracciale, Marco Bonola, Pierpaolo Loreti, Giuseppe Bianchi, Raul Amici, Antonello Rabuffi.
This dataset contains mobility traces of taxi cabs in Rome, Italy. It contains GPS coordinates of approximately 320 taxis collected over 30 days.
- 2007-08-09 rutgers/ap_density
Dataset of realistic workload and wired and wireless frame dump traces from experiments in the ORBIT testbed.
Contributed by Mesut Ali Ergin, Marco Gruteser, Kishore Ramachandran.
This dataset includes realistic client arrival patterns and realistic application workloads that are used to evaluate effects of high-density deployments of interfering access points to IEEE 802.11 WLAN performance. Both wired and wireless frame dump traces are provided from the experiments in ORBIT wireless research testbed involving four access points and seventy-five stations.
- 2007-04-20 rutgers/capture
Dataset containing RFMON (wireless monitoring) traces capturing transmitted MAC frames on the ORBIT testbed.
Contributed by Kishore Ramachandran, Marco Gruteser, Ivan Seskar, Sachin Ganu, Jing Deng.
In an experiment involving two senders and one receiver, we placed a sniffer (wireless NIC in monitor mode) close to each of the senders so as to capture all transmitted MAC frames from each sender.
- 2007-04-20 rutgers/noise
Dataset of RSSI measurement on the ORBIT testbed.
Contributed by Sanjit Krishnan Kaul, Ivan Seskar, Marco Gruteser.
We performed experiments wherein noise injection was used as a method for mapping real world wireless network topologies onto the ORBIT testbed. This dataset includes received signal strength indicator (RSSI) for each correctly received frame at receiver nodes for different levels of noise injected on the ORBIT testbed.
- 2013-09-10 sapienza/probe-requests
Wireless probe requests collected in Rome between February and May 2013.
Contributed by Marco V. Barbera, Alessandro Epasto, Alessandro Mei, Sokol Kosta, Vasile C. Perta, Julinda Stefa.
Mobile devices try to automatically switch to WiFi connectivity whenever possible. To facilitate this automatic process, they store the list of the names (SSID) of the networks the user typically connects to and, periodically, these SSIDs are sent in broadcast in the form of Probe Request to search for available networks. The following questions then rise naturally: "What do your smartphone probes say about you?"; "Is it possible to infer meaningful relationships among a group of people just using their smartphones' probes?". To answer all these questions, we organized a campaign of probe collection in Rome (Italy): We targeted a university campus as well as city-wide, national and international events. Our campaign lasted three months, and we managed to collect, using commodity hardware only, ~11 million probes sent by ~160 thousand different devices. The release contains anonymized traces in .pcap format.
- 2011-01-25 snu/bittorrent
Dataset of BitTorrent traffic on Korea Telecom's mobile WiMAX network.
Contributed by Seungbae Kim, Xiaofei Wang, Hyunchul Kim, Taekyoung Kwon, Yanghee Choi.
Dataset of BitTorrent traffic from Korea Telecom's mobile WiMAX network, collected in March 2010.
- 2009-10-19 snu/wow_via_wimax
Tcpdump traces from Korean Mobile WiMAX gaming network.
Contributed by Xiaofei Wang.
These tcpdump traces were captured by Xiaofei Wang at Seoul National University during the study of online gaming via Korean WiBro (Mobile WiMAX) network.
- 2011-05-04 spitz/cellular
Mobile phone records of German politician Malte Spitz.
Contributed by Malte Spitz.
This data set contains 6 months of mobile phone records of German Green party politician Malte Spitz, stored by Deutsche Telekom in 2009-2010.
- 2011-10-12 st_andrews/locshare
Traceset of a privacy study, including encounters, sharing preferences, and accelerometer readings, conducted at University of St Andrews.
Contributed by Iain Parris, Tristan Henderson, Fehmi Ben Abdesslem.
This is the traceset of a privacy study, including encounters, sharing preferences, and accelerometer readings. The study was conducted in St Andrews and London.
- 2011-06-03 st_andrews/sassy
Encounter records of a group of participants carrying sensor motes and their social network data generated from Facebook data.
Contributed by Greg Bigwood, Tristan Henderson, Devan Rehunathan, Martin Bateman, Saleem Bhatti.
This is a dataset of sensor mote encounter records and corresponding social network data of a group of participants at University of St Andrews.
- 2003-10-16 stanford/gates
Traces of the Stanford CS department's wireless network.
Contributed by Diane Tang, Mary Baker.
This dataset contains traces of the Stanford CS department's wireless network.
- 2011-03-23 strath/nodobo
Dataset of mobile phone usage records collected with Nodobo suite at the University of Strathclyde.
Contributed by Alisdair McDiarmid, James Irvine, Stephen Bell, Jamie Banford.
Dataset gathered by Nodobo, a suite of social sensor software for Android phones, during a study of the mobile phone usage at University of Strathclyde.
- 2007-06-30 sunysb/mobisteer
Dataset collected from a moving car equipped with a steerable directional antenna.
Contributed by Vishnu Navda, Anand Prabhu Subramanian, Kannan Dhanasekaran, Andreas Timm-Giel, Samir R. Das.
This data set includes data traces that were collected from a moving car equipped with an electronically steerable directinal antenna. We drove the car in two different environments in Stony Brook University campus - Apartment Complex and Parking lot.
- 2009-02-24 sunysb/multi_channel
Data set consisting of measurements from two different wireless mesh network testbeds (802.11g and 802.11a).
Contributed by Anand Prabhu Subramanian, Samir R. Das, Jing Cao, Chul Sung.
We conduct measurement using two mesh network testbeds in two di&#xFB00;erent frequency bands &#x2013; 802.11g in 2.4GHz band and 802.11a in 5GHz band.
- 2012-06-12 tecnalia/humanet
1 day of Bluetooth connectivity and mobility data.
Contributed by Jose M. Cabero, Virginia Molina, Inigo Urteaga, Fidel Liberal, Jose L. Martin.
Our study analyzes the limitations of Bluetooth-based trace acquisition initiatives carried out until now in terms of granularity and reliability. We then go on to propose an optimal configuration for the acquisition of proximity traces and movement information using a fine-tuned Bluetooth system based on custom HW. With this system and based on such a configuration, we have carried out an intensive human trace acquisition experiment resulting in a proximity and mobility database of more than 5 million traces with a minimum granularity of 5 s.
- 2019-04-29 telefonica/mobilephoneuse
Detailed mobile phone use data (events, sensors, emotion self-reports, traits) of 342 people over the course of 342
Contributed by Martin Pielot.
Detailed logs of mobile phone usage of 342 people over the course of ca. 4 weeks. Contains events from 25 physical and virtual sensors (e.g. app in foreground, received notifications, ambient noise level, semantic location, ...), frequent self reports of emotions (valence and arousal), and for some participants self-reported traits (Big 5 Traits, Boredom Proneness [BPS], Patient Health Questionnaire [PHQ8]). Sensors: acceleration, airplane mode, app in foreground, audio, battery, battery drain, cell tower details, data consumption, ambient light, ambient noise, notifications, notification center access, notification dismissed, notifications cleared, device orientation, phone calls, photos, proximity (screen covered or not), screen events (on, off, unlocked), screen orientation (portrait, landscape), semantic location (home, work, ...), sms events, user activity (sitting, in vehicle, ...), wifi details. Details can be found at https://sites.google.com/view/mobile-phone-use-dataset
- 2012-07-15 thlab/sigcomm2009
Traces of Bluetooth encounters, opportunistic messaging, and social profiles of 76 users of MobiClique application at SIGCOMM 2009.
Contributed by Anna-Kaisa Pietilainen, Christophe Diot.
The dataset contains data collected by an opportunistic mobile social application, MobiClique. The application was used by 76 persons during SIGCOMM 2009 conference in Barcelona, Spain. The data sets include traces of Bluetooth device proximity, opportunistic message creation and dissemination, and the social profiles (friends and interests) of the participants.
- 2006-09-29 tools/analyze/802.11/Wit
A tool to analyze wireless MAC.
Contributed by Ratul Mahajan, Maya Rodrig, John Zahorjan.
Wit is a non-intrusive tool that builds on passive monitoring to analyze the detailed MAC-level behavior of operational wireless networks.
- 2012-10-24 tools/analyze/link/PBProbe
PBProbe - a link capacity estimation tool for network links.
Contributed by Ling-Jyh Chen, Wei-Xian Lee.
PBProbe is a link capacity estimation tool that supports a wide range of links, including high speed links, asymmetric links, and wireless links. The tool is based on the CapProbe algorithm excepts that it uses a &#x201C;packet bulk&#x201D; to adapt the number of packets in each probing according to different network characteristics. As a result, it preserves the simplicity, speed, and accuracy of CapProbe, and compensates for the poor system timer granularity problem that may cause problems on high speed links. Compared to other capacity estimation techniques, PBProbe is ideal for real deployments that require online and timely capacity estimation, and it can facilitate various applications, such as peer-to-peer streaming and file sharing, overlay network structuring, pricing and QoS enhancements, as well as network monitoring.
- 2007-09-14 tools/analyze/location/locana
Locana - a visualization tool for 802.11-based positioning systems.
Contributed by Thomas King, Stephan Kopf, Thomas Butter, Hendrik Lemelson, Thomas Haenselmann, Wolfgang Effelsberg.
Locana is a research tool for 802.11-based positioning systems. Locana visualizes the results computed by Loctrace and Loceva.
- 2007-09-14 tools/analyze/location/loceva
Loceva - an evaluation tool for 802.11-based positioning systems.
Contributed by Thomas King, Stephan Kopf, Thomas Butter, Hendrik Lemelson, Thomas Haenselmann, Wolfgang Effelsberg.
Loceva is an evaluation tool for 802.11-based positioning systems. Loceva uses trace files generated by Loctrace to evaluate different kinds of positioning algorithms. A large number of state-of-the-art positioning algorithms are supported by Loceva. Loceva contains a lot of filters and generators to set up different scenarios and enable emulation.
- 2010-01-13 tools/analyze/pcap/WScout
WScout, lightweight PCAP file visualizer.
Contributed by Thomas Claveirole, Marcelo Dias de Amorim.
WScout provides a PCAP traces visualizer that is able to work with huge traces (&gt;10 GiB). Its goals are speed and low memory requirements. Despite its design being protocol-agnostic, it currently handles only Prism and IEEE 802.11 headers, hence its name.
- 2008-10-05 tools/analyze/pcap/wifidelity
Wifidelity toolkit - Trace statistics and timing.
Contributed by Aaron Schulman, Dave Levin, Neil Spring.
The Wifidelity package consists of two tools to identify the completeness and accuracy of 802.11 packet traces. "tracestats" uses 802.11 sequence numbers to quantify completeness, and the "plotscore" script generates a T-Fi plot: an at-a-glance, heatmap visualization of completeness versus load. "tracetiming" uses AP Beacon intervals to quantify packet timestamp accuracy and "plottiming" produces a line plot of timestamp accuracy.
- 2009-04-15 tools/collect/802.11/Wifi-Scanner
Wi-Fi network scanner/wardriving tool.
Contributed by Ben Greenstein, Jeffrey Pang, Michael Kaminsky.
Wi-Fi network scanner/wardriving tool used in the authors' MobiSys 2009 paper [pang-wifi-reports]. The main difference between Wifi-Scanner and other war driving tools is that it has a more complete Wi-Fi Network Manager that supports logging into WEP/WPA networks, remembering passwords for these networks, supports login through AP portal/splash pages, and performs a battery of measurement tests after you login.
- 2005-05-10 tools/collect/802.11/wrapi_plus
A tool for monitoring wireless statistics in real-time.
Contributed by Feng Li, Mingzhe Li, Mark Claypool, Bob Kinicki.
WRAPI+ is a tool to monitor wireless statistics, including received signal strength, transmitted frame count, and failed frame transmissions and acknowledgments on a Windows XP end hosts' IEEE 802.11b/g network device. WRAPI+ was built upon the freely available WRAPI C++ library.
- 2007-09-14 tools/collect/location/loclib
Loclib - a collection tool for 802.11-based positioning systems.
Contributed by Thomas Butter, Wolfgang Effelsberg, Thomas Haenselmann, Stephan Kopf, Thomas King, Hendrik Lemelson.
Loclib is a research tool for 802.11-based positioning systems. Loclib is a connector between applications and sensor hardware. Its task is to collect data from the sensor hardware and pre-process it for further usage.
- 2007-09-14 tools/collect/location/loctrace
Loctrace - a collection tool for 802.11-based positioning systems.
Contributed by Thomas King, Stephan Kopf, Thomas Butter, Hendrik Lemelson, Thomas Haenselmann, Wolfgang Effelsberg.
Loctrace is a research tool for 802.11-based positioning systems. Loctrace gathers data offered by Loclib and stores it in a file.
- 2009-11-13 tools/collect/multihop/EXC
EXC is a software toolkit to control and steer experiments with wireless multihop networks.
Contributed by Wolfgang Kiess, Thomas Ogilvie, Andreas Tarp, Markus Kerper.
EXC is a software toolkit to control and steer experiments with wireless multihop networks. EXC enables researchers to evaluate their algorithms in a real-world environment without having to deal with too much thought on how to coordinate an experiment.
- 2006-09-21 tools/process/pads/snmp_parser
A PADS-based C library for processing snmp traces.
Contributed by Jihwang Yeo.
snmp_parser provides a C library for processing snmp traces, and several tool implementations. Using this library, users can develop their own snmp tools. The PADS system (http://www.padsproj.org) needs to be installed to build and use the library and tools.
- 2010-01-13 tools/process/pcap/WiPal
IEEE 802.11 traces manipulation software
Contributed by Thomas Claveirole, Marcelo Dias de Amorim.
WiPal is a piece of software dedicated to IEEE 802.11 traces manipulation. It comes as a set of programs and a C++ library. A distinctive feature of WiPal is its merging tool, which enables merging multiple wireless traces into a unique global trace. This tool works offline on PCAP traces that do not need to be synchronized. WiPal also provides statistics extraction and anonymization tools, and its authors plan to extend it. WiPal&#x2019;s key features are flexibility, ease of use, and efficiency.
- 2008-02-01 tools/process/pcap/Wifipcap
A simple C++ wrapper around libpcap for parsing 802.11 frames from the pcap file.
Contributed by Jeffrey Pang.
A simple C++ wrapper around libpcap that allows applications to selectively demultiplex 802.11 frames, and the most common layer 2 and layer 3 protocols contained within them. Basically, the wifipcap library handles all the parsing of 802.11 frames (and/or layer 2/3 packets) from the pcap file (or stream). wifipcap is now embedded in tcpflow, a TCP/IP session reassembler maintained by Simson Garfinkel.
- 2008-08-23 tools/process/pcap/pcapsync
Tool to synchronize the timestamps of packets of 802.11-based experiments to a common time basis.
Contributed by Björn Scheuermann, Wolfgang Kiess, Daniel Marks.
Pcapsync is a tool to time-synchronize tracefiles recorded in libpcap format. Its main application area are logfiles of real-world experiments with wireless multihop networks like MANETs (mobile ad-hoc networks), VANETs (vehicular ad-hoc networks) or mesh networks. The underlying algorithm, however, is more general and can be applied to any network with local broadcast characteristic.
- 2007-11-05 tools/process/pcap/wifi_parser
A tool for selectively printing out header fields/flags from either live or recorded 802.11 traffic.
Contributed by Doug Madory.
This tool is able to select out header fields/flags from either live or recorded 802.11 traffic. Tcpdump and Tethereal have limited flexibility to tease out specific fields without printing out the entire packet.
- 2006-11-01 tools/process/syslog/syslog_parser
A tool for parsing Cisco and Aruba 802.11 syslog traces.
Contributed by Tristan Henderson.
syslog_parser is a script to parse the syslog traces from Cisco VxWorks, Cisco IOS and Aruba access points. This script was designed to parse the syslog traces in the dartmouth/campus/syslog tracesets, but should be useful for other traces as well.
- 2006-09-26 tools/sanitize/generic/AnonTool
AnonTool - an open-source implementation of Anonymization API.
Contributed by D. Koukis, Spiros Antonatos, Demetris Antoniades, E. P. Markatos, P. Trimintzios, Michalis Fukarakis.
AnonTool, an open-source implementation of Anonymization API, provides an easy to use, flexible and efficient library of functions that can be used to anonymize live traffic, or packet traces in libpcap file format. Currently, IP, TCP/UDP, HTTP, FTP and Netflow v5 and v9 are supported. Three ready-to-use applications have been implemented on top of this library; one provides basic anonymization functionality for the IP/TCP/UDP protocols, and two more which can perform anonymization on every field of a Netflow datagram, one for v5 and one for v9 respectively.
- 2006-11-01 tools/sanitize/generic/FLAIM
FLAIM - anonymize system and network logs.
Contributed by Kiran Lakkaraju, Xiaolin Luo, Adam Slagell.
FLAIM is a multi-level, multi-log anonymization tool. FLAIM-Core comprises the anonymization engine and XML based policy manager. FLAIM-Core loads dynamic libraries responsible for I/O and parsing at runtime. There is a library for each type of log flaim supports. The XML policy, the I/O module, input file and output files are all specified on the command line.
- 2016-04-21 tools/simulate/uoi/adyton
Adyton: A Network Simulator for Opportunistic Networks
Contributed by Nikolaos Papanikos, Dimitrios-Georgios Akestoridis, Evangelos Papapetrou.
Adyton is an event-driven network simulator, written in C++, for Opportunistic Networks (a.k.a. Delay-Tolerant Networks) that is capable of processing contact traces. The Adyton simulator supports a plethora of routing protocols and real-world contact traces, while also providing several congestion control mechanisms and buffer management policies.
- 2006-08-29 toronto/bluetooth
Traces of Bluetooth activity in different urban environment and in some controlled setting.
Contributed by Jing Su, Stefan Saroiu.
To investigate whether a large-scale Bluetooth worm outbreak is viable in practice, we conducted controlled experiments and we gathered traces of Bluetooth activity in different urban environments to determine the feasibility of a worm infection.
- 2019-10-30 tuc/mysignals
Community RF Sensing via iPhones for Source Localization and Coverage Maps
Contributed by Emmanouil Alimpertis, Aggelos Bletsas.
MySignals dataset was collected by a network of approx. 10 mobile smartphone (iPhones) users via the MySignals iPhone App (www.mysignals.gr) for a period of approximately 8 months. MySignal App records the received signal strength indicator (RSSI), in dBm, of the mobile serving cell, as well as their own location, through the GPS module of their smartphone and other contextual information (timestamp, deviceID etc.). Measurements and relevant information (e.g. c ell ID, downlink carrier frequency and mobile user location) are saved temporarily in a local sqlite DB and uploaded periodically at a central web server DB for permanent storage. The systems allowed crowdsourcing in an automated and user transparent-way. The systems allowed crowdsourcing in an automated and user transparent-way and no personal identifiers were collected. The published data contains approximately 3 million GSM measurements and they are a subset of the entire MySignals dataset, utilized primarily for RF Source (e.g. GSM Base Station) localization case study and localization algorithm developme nt. The reported measurements were collected in Chania, Greece for one central Base Stations (BS) for a major Greek Cellular provider. The massive dataset along with the novel Particle Filtering localization algorithm, allowed RF source location estimation error on the order of 50m, with the phones-sensors located in a grid of approx. 1000m x 1000m. Although the reported data were primarily used for BS localization, they were also used as mobile coverage maps; for example, cove rage holes were able to be discovered. At a glance, each of the recorded measurements contains the following fields: timestamp, iPhoneUUID, RSS1, RSS5, latitude, longitude, accuracy, cellID, lac, mac, arfcn, etc. DOI: 10.15783/c7-3bk9-9t96 http://doi.org/10.15783/c7-3bk9-9t96 Start date: 2012-09-01, end date: 2013-04-15
- 2020-04-08 tuda/ubicompzis
Dataset of diverse context information (e.g., ambient audio) collected by multiple devices in different environments (e.g., office).
Contributed by Mikhail Fomichev, Max Maass, Lars Almon, Alejandro Molina, Matthias Hollick.
The dataset contains 10 types of context information (i.e., audio restricted access, Wi-Fi and Bluetooth Low Energy (BLE) beacons, barometric pressure, humidity, luminosity, temperature, accelerometer, gyroscope, and magnetometer) collected by multiple devices in three scenarios: car, office, and mobile. The collected context information is used to evaluate the performance of five zero-interaction pairing or authentication schemes the results of this evaluation are also part of the dataset. Institution that owns the data: TU Darmstadt DOI: 10.15783/c7-n3q8-xr73 URL links to data: http://crawdad.org/download/tuda/ubicompzis/url.txt and https://zenodo.org/record/2537707#.XpRnFepKgdV
- 2020-02-18 tum/proximityness
Dataset for evaluation of co-presence detection
Contributed by Michael Haus, Aaron Yi Ding, Jorg Ott.
We conducted a study with 126 subjects, over three months, collecting data from various sensors, that resulted in a multimodal dataset for co-presence detection. We publish a subset of the original data set in the period between 01.06.2018 and 15.06.2018 including Wi-Fi scans as proximity verification set, magnetometer as sensor data, the positions of Wi-Fi access points, and magnetometer's sensor hardware. This study has been published at IEEE PerCrowd 2020, M. Haus, A. Y. Ding, J. Ott, "Multimodal Co-Presence Detection with Varying Spatio-Temporal Granularity", in Proceedings of IEEE 3rd International Workshop on Context-awareness for Multi-device Pervasive and Mobile Computing (PerCrowd), 2020. DOI: http://doi.org/10.15783/c7-x43g-h794
- 2006-11-01 ucdavis/unitrans
Bluetooth connectivity dataset collected on a bus system at UC Davis.
Contributed by Jason LeBrun, Chen-Nee Chuah.
This data set includes several traces about the available Bluetooth connectivity during a typical day on the Unitrans bus system at University of California, Davis.
- 2016-03-04 uclouvain/mptcp_smartphone
Multipath TCP traces from real smartphone users
Contributed by Quentin De Coninck, Matthieu Baerts, Benjamin Hesmans, Olivier Bonaventure.
Multipath TCP is a recent TCP extension that enables multihomed hosts like smartphones to send and receive data over multiple interfaces. Despite the growing interest in this new TCP extension, little is known about its behavior with real applications in wireless networks. Our paper "A First Analysis of Multipath TCP on Smartphones" analyzes a trace from a SOCKS proxy serving smartphones using Multipath TCP. This first detailed study of real Multipath TCP smartphone traffic reveals several interesting points about its behavior in the wild. It confirms the heterogeneity of wireless and cellular networks which influences the scheduling of Multipath TCP. The analysis shows that most of the additional subflows are never used to send data. The amount of reinjections is also quantified and shows that they are not a major issue for the deployment of Multipath TCP. With our methodology to detect handovers, around a quarter of the connections using several subflows experience data handovers.
- 2005-10-19 ucsb/ietf2005
Dataset collected by wireless monitoring at 2005 IETF meeting.
Contributed by Amit Jardosh, Krishna N. Ramachandran, Kevin C. Almeroth, Elizabeth Belding.
This dataset includes the traces collected by wireless monitoring at the 62nd Internet Engineering Task Force (IETF) meeting held in Minneapolis, MN, March, 2005.
- 2007-02-01 ucsb/meshnet
Dataset for detailed link quality information collected over several days from the UCSB MeshNet.
Contributed by Irfan Sheriff, Elizabeth Belding, Kevin C. Almeroth, Krishna N. Ramachandran.
Detailed link quality information was collected over several days from the UCSB MeshNet for characterizing routing stability in wireless mesh networks.
- 2008-08-25 ucsd/cse
Dataset of comprehensive traces of wireless activity in the UCSD Computer Science building.
Contributed by Yu-Chung Cheng.
To characterize the sources of delay in 802.11 production network, we collected comprehensive traces of wireless activity in the UCSD Computer Science building.
- 2002-04-23 ucsd/sigcomm2001
SNMP and tcpdump records from 4 access points at a three-day computer-science conference.
Contributed by Anand Balachandran, Geoffrey M. Voelker, Paramvir Bahl, P. Venkat Rangan.
This dataset includes SNMP and tcpdump records from 4 access points at a three-day computer-science conference.
- 2019-08-06 ues/emespy
Measurements for PhD: Surface density of radiation energy as an integral measure for the characterization of exposure to electromagnetic emissions
Contributed by Darko S. Suka, Predrag V. Pejovic, Mirjana I. Simic-Pejovic.
The measurement results provided here are part of work on PhD thesis connected with measurement results variability reduction (main focus was on GSM/UMTS system. but other technologies were measured at the same time). All measurements were of indoor type. The duration of collecting data samples was 24h per day, with 10 seconds sampling interval. At some places it took one, two or four weeks to complete the measurements. Equipment used is the dosimeter (or exposimeter) EME Spy 140 (manufactured by Satimo). Similar to the procedure described in (Vermeeren, 2013; Markakis et al., 2013), the exposimeter was placed at available position in the investigated rooms and were standing alone. It was, thus, not worn by adults. Also, no influence due to shielding occured, like when exposimeters are carried on the body (where underestimations up to 6.5 dB are possible according to Iskra et al. (2010)). During measurement period, all location were secured, and only authorized technical personnel that performed measurements had access to such places in order to provide measurement conditions of unperturbed field, according to EN 50492:2010 and EN 50413:2010. All measurements were made within the range of temperature and humidity stated by the manufacturer of the meter (www.satimo.com). Finally, during our measurement campaign, the networks were not forced to operate in a specific mode in order to observe traffic variations in real conditions. All results of measurements are in unit V/m (for electric field). In addition to the dataset,here is a link to the published research article based on the dataset analysis https://academic.oup.com/rpd/advance-article/doi/10.1093/rpd/ncz154/5531430?searchresult=1# DOI: 10.15783/c7-ry9z-m812 https://doi.org/10.15783/c7-ry9z-m812 EME Sp y 140 Measurements of RF Signals - one week measurements
- 2012-01-24 uiuc/uim
Dataset of Bluetooth and Wi-Fi traces collected from Android phone users at University of Illinois.
Contributed by Klara Nahrstedt, Long Vu.
This is the dataset of MACs of Bluetooth and Wi-Fi access points collected by the University of Illinois Movement (UIM) framework using Google Android phones.
- 2008-10-21 umass/diesel
The bus-based DTN (Disruption-tolerant networks) traces from UMass Amherst campus.
Contributed by John Burgess, John Zahorjan, Ratul Mahajan, Brian Neil Levine, Aruna Balasubramanian, Arun Venkataramani, Yun Zhou, Bruce Croft, Nilanjan Banerjee, Mark Corner, Don Towsley.
This dataset includes the real mobility and real transfers of the bus-based DTN (Disruption-tolerant-network) testbed, called UMassDieselNet, operating from the UMass Amherst campus and the surrounding county.
- 2007-06-01 umass/long_distance
Dataset of 802.11g long-distance measurements over ad-hoc nodes using directional antennas.
Contributed by Timothy Ireland, Adam Nyzio, Michael Zink, Jim Kurose.
Our experiments consisted of 802.11g wireless network throughput measurements in various overlapping ad-hoc node configurations in order to better understand interference when using yagi antennas to extend the range of the wireless transmission.
- 2009-03-02 umd/sigcomm2008
Dataset of wireless network measurement in the SIGCOMM 2008 conference.
Contributed by Aaron Schulman, Dave Levin, Neil Spring.
We collected a trace of wireless network activity at SIGCOMM 2008. The subjects of the traced network chose to participate by joining the traced SSID. The release contains 3 types of anonymized traces: 802.11a, Ethernet and Syslog from the Access Point. We anonymized the trace data using a modified version (http://www.cs.umd.edu/projects/wifidelity/sigcomm08_traces/sigcomm08-tcpmkpub.tar.gz) of the tcpmkpub tool (http://www.icir.org/enterprise-tracing/tcpmkpub.html) The packet traces include anonymized DHCP and DNS headers.
- 2011-08-10 umich/rss
Dataset of RSS measurements of a Mica2 sensor network deployed at the University of Michigan.
Contributed by Alfred O. Hero III, Neal Patwari, Kumar Sricharan.
This is a dataset of RSS measurements collected by Mica2 sensor nodes deployed inside and outside a lab room, with anomaly patterns occurring when students walked into and out of the lab. A web camera recorded the activity that could be matched with detected anomalies.
- 2008-03-28 umich/virgil
War-walking data set collected in different cities in the United States for the field study and evaluation of an access point selection system.
Contributed by Anthony J. Nicholson, David Wetherall, Yatin Chawathe, Mike Chen, Brian Noble.
We collected the data set through war walking, i.e., collecting Wi-Fi beacons by walking around the neighborhoods in different cities in the United States, for the field study and evaluation of Virgil, an access point selection system.
- 2015-02-08 unical/socialblueconn
Traces of Bluetooth encounters, Facebook friendships and interests of a set of users collected through SocialBlueConn application at University of Calabria
Contributed by Antonio Caputo, Annalisa Socievole, Floriano De Rango.
The dataset contains Bluetooth device proximity data collected by an ad-hoc Android application called SocialBlueConn. This application was used by 15 students at University of Calabria campus in Rende, Cosenza (Italy) and logged both the internal contacts between the participants and contacts with other 20 external mobile nodes. The dataset includes the social profiles (Facebook friends and self-declared interests) of the participants.
- 2008-12-01 unimi/pmtr
Dataset of mobility traces collected by Pocket Mobility Trace Recorder devices at University of Milano.
Contributed by Paolo Meroni, Sabrina Gaito, Elena Pagani, Gian Paolo Rossi.
This dataset contains mobility traces from 44 mobile devices at University of Milano. The data was collected in November 2008.
- 2019-03-12 unm/blebeacon
A Real-Subject Trial Dataset from Mobile BLE-beacons
Contributed by Dimitrios Sikeridis, Ioannis Papapanagiotou, Michael Devetsikiotis.
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.
- 2016-08-28 uoi/haggle
Bluetooth encounters from the cambridge/haggle dataset (v. 2009-05-29) have been converted into the StandardEventsReader format for use in the ONE simulator.
Contributed by Dimitrios-Georgios Akestoridis.
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.
- 2008-07-23 up/rf_recordings
Data set of RF recordings of several communication signals captured by a real time spectrum analyzer.
Contributed by Joseph Hoffbeck, Andrew Melton.
In order to be used for examples or projects in communication systems or digital signal processing courses, the radio frequency (RF) signal was recorded from several commercial communication systems and stored in a database. This database contains recordings of radio frequency (RF) signals from several commercial communication systems including AM and FM radio, high definition AM and FM radio, analog and digital TV, Bluetooth, WiFi, GPS, WWV time signal, garage door opener, remote control for toy cars, wireless thermometers, and a wireless serial cable replacement system.
- 2016-10-17 upb/hyccups
Trace of wireless contacts, social connections, and user interests, performed in an academic environment for 63 days, with 72 participants
Contributed by Radu I. Ciobanu, Ciprian Dobre.
Wireless contacts trace collected at the University Politehnica of Bucharest in the spring of 2012, using an application entitled HYCCUPS Tracer (http://hyccups.hpc.pub.ro), with the purpose of collecting contextual data from Android smartphones. It was run in the background and collected availability and mobile interaction information such as usage statistics, user activity, battery statistics, or sensor data, but it also gathered information about a device's encounters with other nodes or with wireless access points. Encounter collection was performed using AllJoyn. The data was collected by constructing and deleting wireless sessions using the AllJoyn framework based on WiFi. Tracing was executed asynchronously. The duration of the tracing experiment was 63 days, between March and May 2012, and had 72 participants, out of which only 42 had at least one contact. By analyzing the participants' Facebook profiles, the social connections matrix was extracted, as well as the users' interests. The trace (and others from the CRAWDAD collection) is parsed within the MobEmu simulator (used in all UPB's papers), publicly available at https://github.com/raduciobanu/mobemu.
- 2012-06-18 upb/mobility2011
Bluetooth encounter trace collected from Android phones in an academic environment.
Contributed by Radu I. Ciobanu, Ciprian Dobre.
This is the data from an Android Bluetooth tracing experiment performed for a period of 35 days in an academic environment (University Politehnica of Bucharest) between November 18 and December 22 2011.
- 2006-11-17 upmc/content
Traces of Bluetooth sightings by groups of users carrying iMotes.
Contributed by Jérémie Leguay, Pan Hui, Jon Crowcroft, James Scott, Anders Lindgren, Timur Friedman.
This data includes a number of traces of Bluetooth sightings by groups of users carrying small devices (iMotes) at locations around the city of Cambridge, UK.
- 2009-02-02 upmc/rollernet
Traces of Bluetooth sightings by groups of rollerbladers carrying iMotes.
Contributed by Farid Benbadis, Jeremie Leguay.
This data includes traces of any opportunistic sighting of Bluetooth devices by groups of rollerbladers carrying iMotes in the roller tour in Paris, France.
- 2014-05-12 uportorwthaachen/vanetjamming2012
This dataset contains traces of 802.11p packets, collected in an anechoic chamber and outdoors in Porto (Portugal) in 2011, with and without the presence of an RF jamming signal with constant, reactive, and pilot jamming patterns.
Contributed by Oscar Puñal, Carlos Pereira, Ana Aguiar, James Gross.
This dataset contains several tracesets of 802.11p communications with and without the presence of a RF jamming signal. The RF jammer has different patterns of operation, namely constant, reactive and pilot jamming. First, the observations were performed inside an anechoic chamber and, finally, outdoor, in two relevant outdoor scenarios, a straight road in an open space and a dense building scenario.
- 2014-05-12 uportorwthaachen/vanetjamming2014
This dataset contains traces of 802.11p packets, collected in a rural area located in the periphery of Aachen (Germany) in 2012, with the presence of an RF jamming signal with constant, periodic, and reactive jamming patterns.
Contributed by Oscar Puñal, Carlos Pereira, Ana Aguiar, James Gross.
This dataset contains several tracesets of 802.11p communications with the presence of an RF jamming signal. The RF jammer has different patterns of operation, namely constant, periodic, and reactive jamming. The observations were performed in a rural area located in the periphery of Aachen in Germany.
- 2006-04-12 uprm/wireless
Dataset of wireless signal strength measurements from the University of Puerto Rico.
Contributed by Brian C. Donovan, Jim Kurose, Michael Zink, Adam Nyzio, Timothy Ireland.
This data set contains a collection of wireless traces from the University of Puerto Rico. Wireless signal strength measurements for Dell and Thinkpad laptops were performed over distances of 500 feet and one mile. The data is presented in .cap files giving TCP dump packet headers.
- 2008-07-24 usc/mobilib
VPN session, DHCP log, and trap log data from wireless network at USC.
Contributed by Wei-jen Hsu, Ahmed Helmy.
This dataset includes VPN session, DHCP log, and tcap log data, for 79 access points and several thousand users at USC.
- 2007-09-10 utah/CIR
Measured CIR (Channel Impulse Response) Data Set.
Contributed by Neal Patwari.
This dataset contains over 9300 measured CIR (channel impulse responses) in a 44-node wireless network.
- 2006-05-02 uw/places
Location-aware dataset for extracting significant places.
Contributed by Jong Hee Kang, Gaetano Borriello, William Welbourne, Benjamin Stewart.
Real, long-term data collected from three participants using a Place Lab client, from which the authors extract significant places.
- 2006-10-17 uw/sigcomm2004
Dataset of wireless network measurement in SIGCOMM 2004 conference.
Contributed by Maya Rodrig, Charles Reis, Ratul Mahajan, David Wetherall, John Zahorjan, Ed Lazowska.
We are trying to understand how well 802.11 networks work in practice and how they can be improved. This dataset includes the traces collected by wireless monitoring and wired monitoring using tcpdump.
- 2007-06-06 vanderbilt/interferometric
Localization data set collected from a radio interferometric tracking system.
Contributed by Branislav Kusy, Janos Sallai.
We collected localization traces from a radio interferometric tracking system, which is implemented on mote-class wireless sensor nodes.
- 2008-11-01 vt/maniac
Dataset of routing and topology traces collected during MANIAC Challenge.
Contributed by Amr Hilal, Jawwad N Chattha, Vivek Srivastava, Michael S Thompson, Allen B MacKenzie, Luiz A DaSilva, Pallavi Saraswati.
The dataset comprises routing and topology traces collected during the Mobile Ad hoc Networks Interoperability And Cooperation (MANIAC) Challenges, held on November 25-26th 2007 in conjunction with IEEE Globecom 2007 and on March 8, 2009 in conjunction with IEEE PerCom 2009.
- 2011-10-20 wisc/airshark
Dataset of RF device usage measurements collected using a signal analyzer for use by Airshark.
Contributed by Shravan Rayanchu, Ashish Patro, Suman Banerjee.
This is a dataset of RF device usage measurements collected at University of Wisconsin-Madison for demonstration of functionality of Airshark.
- 2012-08-03 wisc/wiscape
Network performance data collected with WiScape framework.
Contributed by Jongwon Yoon, Sayandeep Sen, Joshua Hare.
Dataset of network performance data collected with WiScape framework from three commercial cellular wireless networks.
- 2012-01-03 yonsei/lifemap
Mobility data collected by LifeMap monitoring system at Yonsei University in Seoul.
Contributed by Yohan Chon, Elmurod Talipov, Hyojeong Shin, Hojung Cha.
We deployed our mobility monitoring system, called LifeMap, to collect fine-grained mobility data from commercial mobile phones over two months in Seoul, Korea. The dataset contains location information (latitude and longitude) with accuracy (error bound), Wi-Fi fingerprints (MAC address and signal strength of surrounding Wi-Fi APs), user-defined types of places (workplace, cafeteria, etc.). Our system continuously collected this information every 2 to 5 minutes for everyday location monitoring.