Wit is a non-intrusive tool that builds on passive monitoring to analyze the detailed MAC-level behavior of operational wireless networks.
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...
Measurement of the channel gain for multiple distances within a factory environment
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).
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...
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.
This dataset includes radiant light energy measurements from a study by Columbia University's EnHANTs (Energy Harvesting Active Networked Tags) project.
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.
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.
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.
This dataset includes syslog, SNMP, and tcpdump data for 3 years or more, for over 450 access points and several thousand users at Dartmouth College.
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...
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.
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.
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.
This is a dataset of real-world Bluetooth contact data colected from shop employees of a shopping mall over six days.
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.
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...
This dataset includes SNMP and tcpdump records from 4 access points at a three-day computer-science conference.
Dataset of BitTorrent traffic from Korea Telecom's mobile WiMAX network, collected in March 2010.
This data set contains 6 months of mobile phone records of German Green party politician Malte Spitz, stored by Deutsche Telekom in 2009-2010.
This is the traceset of a privacy study, including encounters, sharing preferences, and accelerometer readings. The study was conducted in St Andrews and London.
This dataset contains traces of the Stanford CS department's wireless network.
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...
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.