Wireless Networking

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)

2. IEEE paper "Secure5G: A Deep Learning Framework Towards a Secure Network Slicing in 5G and Beyond" (https://ieeexplore.ieee.org/document/9031158)

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

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).

Dataset of comprehensive traces of wireless activity in the UCSD Computer Science building.

To characterize the sources of delay in 802.11 production network, we collected comprehensive traces of wireless activity in the UCSD Computer Science building.

last modified : 2008-09-30

release date : 2008-08-25

date/time of measurement start : 2007-01-11

date/time of measurement end : 2007-01-11

Detailed link quality information was collected over several days from the UCSB MeshNet for characterizing routing stability in wireless mesh networks.

last modified : 2007-02-15

release date : 2007-02-01

date/time of measurement start : 2006-04-01

date/time of measurement end : 2006-04-07

Traces of Bluetooth encounters, opportunistic messaging, and social profiles of 76 users of MobiClique application at SIGCOMM 2009.

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.

This dataset includes the traces collected by wireless monitoring at the 62nd Internet Engineering Task Force (IETF) meeting held in Minneapolis, MN, March, 2005.

last modified : 2006-11-14

release date : 2005-10-19

date/time of measurement start : 2005-03-09

date/time of measurement end : 2005-03-10

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.

date/time of measurement start: 2006-01-24

date/time of measurement end: 2006-03-03

Dataset for evaluation of co-presence detection

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.

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.