These datasets are part of Community Resource for Archiving Wireless Data (CRAWDAD). CRAWDAD began in 2004 at Dartmouth College as a place to share wireless network data with the research community. Its purpose was to enable access to data from real networks and real mobile users at a time when collecting such data was challenging and expensive. The archive has continued to grow since its inception, and starting in summer 2022 is being housed on IEEE DataPort.

Questions about CRAWDAD? See our CRAWDAD FAQ. Interested in submitting your dataset to the CRAWDAD collection? Get started, by submitting an Open Access Dataset.

Measurement trace from wireless network at Dartmouth College.

This dataset includes measurement trace for over 450 access points and several thousand users at Dartmouth College.

last modified: 2006-11-14

version: 2004-08-05

reason for most recent change: Infocom 2004 trace is added.

short description: Two-year records showing the location (AP association) of each wireless card seen on campus.


network type: bluetooth

network type: DTN (Delay Tolerant Network)

collection environment:

Four iMote-based experiments were conducted. 

The first included eight researchers and interns working at Intel Research in Cambridge. 

The second obtained data from twelve doctoral students and faculty comprising a research group at the University of Cambridge Computer Lab. 


This dataset is used for the identification of video in the internet traffic. The dataset was prepared by using Wireshark. It comprises of two types of traffic data, VPN (Virtual Private Network) or encrypted traffic data and Non-VPN or unencrypted traffic. The dataset consist of the data streams (.pcap) of 43 videos. Each video is played 50 times in both VPN and Non-VPN mode. The streams were obtained by setting-up a dummy client on a PC which plays a YouTube video and Wireshark is used to capture the internet traffic.


Emulating a RT task and measuring the response latency of its thread by means of the high-resolution testing tool Cyclictest. The thread was clocked at 10ms, and a FIFO scheduling policy was used, with the thread being assigned the highest priority. Measurements were performed in distinct testing environments, some of which had best effort concurrent threads competing for the machine resources. For this purpose, the workload generator tool stress was used.


Machine learning methods are poised to drastically improve the performance of many aspects of communication engineering, across all layers of the communication stack: from the physical layer to the application one.  In this competition, we focus on the problem of federated training of a deep CSI compressor for massive MIMO in 5G protocols and beyond.

Last Updated On: 
Mon, 10/24/2022 - 04:43

Network topologies with link parameters; some statistical data.


This dataset contains the raw data of the measurements/simulations presented in "Modulation Scheme Analysis for Low-Power Leadless Pacemaker Synchronization Based on Conductive Intracardiac Communication" by A. Ryser et al. This work analyzed the bit error rate (BER) performance of a prototype dual-chamber leadless pacemaker both in simulation and in-vitro experiments on porcine hearts.


This dataset was created using Wireshark. The dataset contains a total of 30 encrypted communication records, 3 records (.pcap) were created for each application. The records were obtained from a mobile device that was connected to the laptop using wifi technology. The laptop was connected to the Internet and contained a running instance of Wireshark to create a record. The telephone had been restarted before each record was created. After connecting to the network, the device was left without user interaction for 5 minutes.


Extensive use of unmanned aerial vehicles (UAVs) is expected to raise privacy and security concerns among individuals and
communities. In this context, detection and localization of UAVs will be critical for maintaining safe and secure airspace in the
future. In this work, Keysight N6854A radio frequency (RF) sensors are used to detect and locate a UAV by passively monitoring
the signals emitted from the UAV. First, the Keysight sensor detects the UAV by comparing the received RF signature with various


Port scanning attack is popular method to map a remote network or identify operating systems and applications. It allows the attackers to discover and exploit the vulnerabilities in the network.