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.
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.
last modified : 2008-09-24
release date : 2006-04-12
date/time of measurement start : 2006-01-24
date/time of measurement end : 2006-01-24
Dataset of wireless network measurement in SIGCOMM 2004 conference.
Note: This dataset has multiple versions. The dataset file names of the data associated with this version are listed below, under the 'Traceset' heading and can be downloaded under 'Dataset Files' on the right-hand side of the page.
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.
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.
Dataset of mobility traces collected by Pocket Mobility Trace Recorder devices at University of Milano.
This dataset contains mobility traces from 44 mobile devices at University of Milano. The data was collected in November 2008.
date/time of measurement start: 2008-11-13
date/time of measurement end: 2008-12-01
We have created a Deep Learning model for 5G and Network Slicing. (eMBB, URLLC, IoT).
I encourage developers and researchers working on the 4G/LTE, 5G, 6G and similar interest to use and provide feedback:
Our research can be found at
1. IEEE paper "DeepSlice: A Deep Learning Approach towards an Efficient and Reliable Network Slicing in 5G Networks" (https://ieeexplore.ieee.org/document/8993066)
Dataset of RSS measurements of a Mica2 sensor network deployed at the University of Michigan.
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.
date/time of measurement start: 2006-04-21
date/time of measurement end: 2006-04-21