Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
13
datasets available to search
ShareScore release 0.9.0
Dataset results
13 results for “Fed4FIRE+”
Experimental Results for the AERO 5G project (Fed4FIRE+)
<p>The corresponding results refer to the experiments conducted during the life of the Fed4FIRE+ project entitled: "AERO 5G (Augmented Reality Tour Guide Architecture for 5G)". The public availability of the results aim to help future experimenters and researchers to obtain some intuition with regard to the benefits of 5G for content-based, bandwitdh consuming, MAR applications.</p> <p>----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>Short Description of the Experiments: All values have been rounded to two decimal places. Our team has conducted ten experimental runs for each of the following experimental scenarios.</p> <p>o <strong>4G SDR srsLTE-to-AWS</strong>: A 4G SDR srsLTE network deployed over the Fed4FIRE+ Iris testbed, between a Xiaomi Mi Mix 2S handset and an Amazon EC2 node at Amazon Cloud (AWS) that hosts the AR video content.</p> <p>o<strong> 4G SDR srsLTE-to-IMEC</strong>: A 4G SDR srsLTE network deployed over the Fed4FIRE+ Iris testbed, between a Xiaomi Mi Mix 2S handset and a bare metal machine at Fed4FIRE+ IMEC's VirtualWall that hosts the AR video content.</p> <p>o <strong>4G SDR srsLTE-to-Iris-MEC</strong>: A 4G SDR srsLTE network deployed over the Fed4FIRE+ Iris testbed, between a Xiaomi Mi Mix 2S handset and a MEC storage node located at the edge of the network infrastructure at the Iris testbed that hosts the AR video content. Although MEC is considered to be a 5G technology, our aim in this scenario is to explore the benefit of deploying edge storage nodes in wireless mobile telecommunication technologies in general. For this purpose, we assume that the video content has been stored at the MEC storage node a priori to the end-user's requests.</p> <p>o <strong>Commercial Three.ie 4G-to-AWS</strong>: A Commercial 4G network deployed by the Three.ie mobile operator in Ireland, between a Xiaomi Mi Mix 2S handset and an Amazon EC2 node that hosts the AR video content. Even though a MEC storage node could not be deployed in this scenario, our intention is to estimate the benefit of deploying edge storage nodes empirically by consulting the results concluded for the 4G LTE-to-MEC scenario.</p> <p>o <strong>2.4GHz Wi-Fi-to-IMEC</strong>: A 2.4GHz Wi-Fi (802.11 n) network deployed over the Fed4FIRE+ Iris testbed, between a Xiaomi Mi Mix 2S handset and a bare metal machine at Fed4FIRE+ IMEC's VirtualWall that hosts 4K AR video. This scenario serves us as a proof-of-concept that a 2.4GHz Wi-Fi network is not capable of supporting the delivery of high quality 4K AR content in areas where there are a lot of 2.4GHz Wi-Fi networks.</p> <p>o <strong>5GHz Wi-Fi-to-AWS</strong>: A 5GHz Wi-Fi (802.11 ac) network deployed over the Fed4FIRE+ Iris testbed, between a Xiaomi Mi Mix 2S handset and an Amazon EC2 node at Amazon Cloud (AWS) that hosts the AR video content.</p> <p>o <strong>5GHz Wi-Fi-to-IMEC</strong>: A 5GHz Wi-Fi (802.11 ac) network deployed over the Fed4FIRE+ Iris testbed, between a Xiaomi Mi Mix 2S handset and a bare metal machine at Fed4FIRE+ IMEC's VirtualWall that hosts the AR video content.</p> <p>o<strong> 5GHz Wi-Fi-to-Iris-MEC</strong>: A 5GHz Wi-Fi (802.11 ac) network deployed over the Fed4FIRE+ Iris testbed, between a Xiaomi Mi Mix 2S handset and a MEC storage node located at the edge of the network infrastructure that hosts the AR video content. Similar to the 4G LTE-to-MEC scenario, we assume that the video content has been stored at the MEC storage node a priori to the end-user's requests. </p>
HAMMER Experiment Dataset 1 - Fed4FIRE+
<p>In this dataset, we provide the measurement results (DataSet1) conducted in the course of the <strong>HAMMER</strong> experiment for Fed4FIRE+.</p> <p>The objective of the Experiments is provided in the uploaded reports, the HAMMER experiment presentation and the HAMMER poster. At the report and the documents, the interested researcher may find details about the conducted measurments and their objectives.</p> <p>The specific data set corresponds to the measurements conducted for the Training of the HAMMER deep learning scheme. </p> <p>More specifically, in HAMMER:</p> <ul> <li>a reconfigurable MuPAR antenna array was implemented with 4 RF chains and 8 parasitic elements.</li> <li>the antenna is able to implement 81 different patterns.</li> <li>the project performed extensive measurments inside the w-iLab.t testbed facilities - testing the various patterns, in order to train a Deep Learning model for hybrid beamforming measuments.</li> </ul> <p>The measurements were extracted as follows:</p> <ol> <li>NR reference signals (DMRS) where transmitted by the HAMMER BS positioned at [9.5, 7.25] coordinates inside the w-iLab.t space. The HAMMER BS is implemented with two X310 USRPs</li> <li>The mobile node measures the signal (USRP B200) and estimates the radio channel.</li> <li>The procedure is repeated continuously sweeping periodically all beam patterns.</li> <li>The radio channel transfer function and the radio channel impulse response are saved.</li> </ol> <p>The dataset contains mat files (MATLAB). Meta-data with details for the measurement setup are included in the mat files.</p> <ul> <li>Position of the mobile user</li> <li>Position of the HAMMER BS</li> <li>Center Frequency (3.5GHz)</li> <li>Orientation of the mobile user</li> <li>Speed and Waiting time (at each point) for the mobile user. (the mobile user should be considered static when analyzing the measurements).</li> </ul> <p>The main dataset contains:</p> <ul> <li>The transfer function for 20MHz bandwidth for all 4 active antenna elements.</li> <li>The Channel Impulse Responsefor all 4 active antenna elements.</li> <li>The RSSI in dB (you can consider it as dBm - however, it is not calibrated for the Rx USRP, so it should not be used for propagation analysis vs. distance).</li> </ul> <p>The transfer function has dimensions:</p> <ul> <li>No Rx (1)</li> <li>No Tx (4)</li> <li>FFT size = 512 (it was downsampled from 2048 to reduce file size)</li> <li>Time index of measurements.</li> </ul> <p>It is noted that:</p> <ul> <li>Meaurements <em><strong>1:81:end</strong></em> represent measurements with pattern index #1 (pattern #1 is all OMNI)</li> <li>Meaurements <em><strong>2:81:end</strong></em> represent measurements with pattern index #2</li> <li>...</li> <li>Meaurements <em><strong>41:81:end</strong></em> represent measurements with pattern index #41 (pattern #41 is all elements looking in-front)</li> <li>...</li> <li>Meaurements <em><strong>81:81:end</strong></em> represent measurements with pattern index #41 (pattern #41 is all elements looking back)</li> </ul> <p>The channel estimates are raw.</p> <p>In this specific dataset:</p> <ul> <li>the training measurements for the points of map1.mat are provided. </li> <li>the map1 points can be seen in the screenshot of map1.png, in order to exactly understand the geometry of the environment.</li> </ul> <p>Additional files provided include:</p> <ul> <li>the pattern of the ESPAR antenna simulated at the azimuth. (Four ESPARs are used)</li> <li>pattern indexes (0 is omni, 1 is looking front, -1 looking back).</li> </ul> <p>Details can be found in the HAMMER report.</p> <p>For any question, contact kmaliat@unipi.gr</p> <p><em>Kostas Maliatsos</em></p> <p> </p>
Experimental Results of the MANET4E experiment (Fed4FIRE+)
<p>This dataset contains measured values from the MANET4E experiment, which was funded as part of the Fed4FIRE+ F4Fp-09 call. Aim of the experiment was to research a new communication approach using mobile peer-to-peer network technologies in combination with a new blockchain solution and services. The combination of both technological parts should enable the development of future decentralized and self-organizing energy management systems. In this Fed4FIRE+ Stage-2 experiment, we analysed the blockchain behaviour in the peer-to-peer network and identified the optimal blockchain configuration (block size, distribution rates, distribution of the blocks, packet loss, etc.) in the imec w-iLab.1 testbed. The measurements were carried out in the imec w-iLab.1 testbed in Ghent, Belgium. More details can be found in the deliverables and final report of the MANET4E project.</p>
Lightweight Self-adaptive Cloud-IoT Monitoring across Fed4FIRE+ Testbeds (LiSCIo)
<p>Monitoring will be crucial to properly orchestrate next-gen services. Indeed, monitoring’s output can be exploited to choose where to deploy application services for the first time and to decide when and where to migrate them in case their QoS and contextual requirements cannot be satisfied by the current deployment and infrastructure state. However, only a few works have focused so far on the design and prototyping of monitoring tools for next-gen Cloud-IoT computing platforms.</p> <p>In this context, <a href="https://github.com/di-unipi-socc/FogMon">FogMon</a>, described in (Brogi et al., 2019) and (Forti et al., 2021), is an open-source C++ distributed monitoring service targeting heterogeneous infrastructures along the Cloud-IoT continuum, e.g. Fog computing. FogMon monitors hardware and virtualised resources at different Cloud-IoT computing nodes, end-to-end network QoS between such nodes, as well as available IoT devices. Besides, it features a self-organising peer-to-peer overlay topology with self-restructuring mechanisms and differential monitoring updates, which feature scalability, fault-tolerance, and low communication overhead.</p> <p>The LiSCIo project aimed at assessing FogMon over increasing infrastructures from 20 to 40 Cloud and Edge nodes, spanning two testbeds within the Fed4Fire+ federated infrastructure. Particularly, LiSCIo implemented a new version of the service, i.e. <a href="https://github.com/di-unipi-socc/FogMon-LiSCIo/tree/2.0">FogMon 2.0</a>, which was thoroughly fixed and tuned over a large number of experiments carried on Fed4Fire+ facilities. Throughout the project, data have been collected on all the measurements performed by FogMon 1.x and by FogMon 2.0 (viz. node hardware, IoT, latency, bandwidth) to assess their footprint on hardware resources and bandwidth in all settings, and the relative error on its estimates of latency and bandwidth against ground-truth configurations, enforced via GRE tunnels.</p>
Experimental Results of the REWIRE FED4FIRE+ Open Call (OC) 9 Project
<p>This repository contains the detailed experimental results of the <strong>REWIRE <em>"Experimenting with SDN-based Adaptable Non-IP Protocol Stacks in Smart-City Environments"</em></strong> project.</p> <p>This work has received funding from the EU's Horizon 2020 research and innovation programme through the 9th open call scheme of the FED4FIRE+ (grant agr. no 732638)</p>
Results of the DYNAMO (Dynamic MEC Orchestration of Cellular Networks) experiment in the Fed4FIRE+ testbeds
<p>The main objective of the DYNAMO Fed4FIRE+ experiment was to perform Network Function Virtualization (NFV) Management and Network Orchestration (MANO) of a cellular network on top of cloud infrastructures, exploring one of the key enabling technologies for 5G systems and beyond. DYNAMO used cloud and radio access facilities at the IRIS testbed and cloud facilities at the University of Vigo (UVIGO) to deploy an end-to-end (E2E) cellular network and perform elastic changes on it if needed. The geographic distance in between facilitated the setup of a realistic Multi-Access Edge Computing (MEC) use case, where the virtual Evolved Packet Core (vEPC) was deployed at UVIGO (Spain) and the access network, i.e., the User Equipment (UE), the e-Node-B (eNB) and edge cloud, were implemented on IRIS testbed (Ireland).<br> <br> While the initial deployment of the E2E cellular network may be considered as static, DYNAMO showcases the elasticity that an E2E cellular network may need in runtime. Hence, we presented a use case consisting of a latency sensitive E2E cellular network (network slice), where the endpoint of the UE connection was initially located in the core (UVIGO) but then migrated to the edge (IRIS), in case the UE's latency ranges were unacceptable.<br> <br> In this regard, the UE reported the experienced latency to Open Network Automation Platform (ONAP), which is responsible to trigger specific policy-driven control actions if a predefined Service-Level Agreement (SLA) is violated.<strong> <em>This datased includes the reports provided by the UE to ONAP.</em></strong><br> <br> As a result, the endpoint of the data plane of the UE is automatically moved to the access network (IRIS) thus reducing significantly the latency for the UE. For the access part of the network, we implemented one srsLTE e-Node-B (eNB), one srsLTE User Equipment (UE) and a Devstack (Edge Cloud) in virtual machines on IRIS testbed. In addition, we also implemented an SDN switch controlled by an ONOS SDN controller. For the core part of the network, we considered a disaggregated vEPC from Open Air Interface (OAI) on a Devstack (Core Cloud) at UVIGO.<br> <br> DYNAMO has succeeded in the integration of a broad set of network elements and technologies between the two different domains (UVIGO and IRIS testbed) and fulfilled all initial objectives: (i) establishing communication between ONAP and IRIS testbed to deploy generic VNFs on core and edge clouds, (ii), deployment of an E2E cellular network with UE and eNB in IRIS and the vEPC at UVIGO, (iii), sending telemetry of the UE to ONAP and (iv) designing and testing closed-loop control actions in ONAP to migrate the data plane of the UE to the edge in case of unsatisfactorily SLA. DYNAMO paves the way to a broad set of future 5G experiments that will require resource orchestration, such as the deployment of network slices or the automatic scheduling of services in the limited resources of Edge Clouds.</p> <p>This repository contains the information sent from the UE to ONAP, in order to decide if the latency between the UE and the PGW is OK or if an action has to be considered to reduce such latency.<br> </p> <p> </p>
Results of the MECinFire experiment in Fed4FIRE+ EU project
<p><strong>Multi-access Edge Computing (MEC)</strong> has been proposed as the means to drastically minimize the service access latency, by bringing computational resources and services closer to the wireless network edge. Edge resources are planned to be extensively used in the 5G network deployments, as they are able to meet stiff latency demands required from services being developed around this ecosystem (e.g. AR/VR, e-Health, Industry 4.0, etc.), by providing computational capabilities where such services can be executed close to the network edge. At the same time, 5G networks redefine the operation of traditional base station units, by disaggregating them and operating part of them in the Cloud, thus creating Cloud-RANs. These Cloud-RANs can also be heterogeneous, allowing users to access the network through multiple wireless technologies (e.g. dual access through 5G-NR and LTE). In this project, we blend the novel disaggregated and heterogeneous base station architecture with the MEC concept, and develop and experiment with the deployment of the edge computing services even closer to the network edge. In MECinFIRE we developed a software prototype that allows services to be executed close or over the machines hosting the radio access services for the network access. Our experiment provides several proof-of-concept experiments that illustrate the applicability and benefits of our solution in real 5G networks. The experiment was evaluated in a real testbed environment, while measuring KPIs regarding the end-to-end user to service latency.</p> <p>This repository contains the dataset of the experimental results produced by the MECinFIRE Project within the FED4FIRE+.</p>
[FED4FIRE+] [MMT-IoT] 6LoWPAN - IoT Traffic Dataset
<p>The dataset contains the pcap files of IoT network traffic captured in the experiments with w-iLab.t and Log-a-Tec testbeds in the context of FED4FIRE+ open call project. </p>
Experimental Results of the UNIC OC4 FED4FIRE+ Project
<p>This repository contains the detailed experimental results of the Unikernel-based CDNs for 5G Networks (UNIC) project. This work has received funding from the EU's Horizon 2020 research and innovation programme through the 4th open call scheme of the FED4FIRE+ (grant agr. no 732638).</p> <p> </p>
FlowBlaze on Fed4FIRE+ FB-FIRE Final Results of the Experiment
<p>This repository contains the results of the FB-FIRE experiment. The aim of FB-FIRE is to test, validate and assess in the Fed4FIRE+ Virtual Wall testbed the performance of FlowBlaze, a novel stateful programmable dataplane engine that can simplify network functions (NF) design and implementation, while providing high performance packet forwarding, and support for future hardware offloading to SmartNICs. FlowBlaze is an abstraction that can be implemented in an efficient DPDK-accelerated software engine, conceived to rapidly design and deploy complex network functions hiding the complexity of software optimizations associated to the development of network functions.</p>
Internet on FIRE, a Fed4FIRE+ Experiment
<p>Log files related to three different experiments done on the Fed4FIRE TestBeds within the Internet on Fire project.</p> <p>Experiment 1: BGP Tests related to Fabrikant topology.</p> <p>Experiment2: BGP Tests related to Elmokashfi topologies, 1000 Nodes.</p> <p>Experiment3: BGP Tests related to Elmokashfi topologies, 2000 Nodes.</p> <p>Experiment4: BGP Tests related to Elmokashfi topologies, 4000 Nodes.</p> <p>Experiment5: BGP Tests related to Elmokashfi topologies, 12000 Nodes.</p>
DRAFT experiment data of Fed4FIRE+ project
<p>The draft_experiment_data.zip contains four .json files with data generated during the DRAFT experiment which is a part of the Fed4FIRE+ Project - <a href="https://www.digiotouch.com/digiotouch-2nd-open-call-grant">https://www.digiotouch.com/digiotouch-2nd-open-call-grant</a></p> <p>1. bytebuffer - it contains data in JSON format that has been used as a part of a simulated DDoS attack to Cloud based<br> Web Services.</p> <p>2. denm - it contains Decentralized Environmental Notification Message (DENM) providing information related to a road <br> hazard or an abnormal traffic condition, including its type and position in JSON format. For more info, check the ETSI <br> draft - https://www.etsi.org/deliver/etsi_en/302600_302699/30263703/01.02.01_30/en_30263703v010201v.pdf</p> <p>3. senml_temp - it contains representation of an IoT temperature sensor metadata in JSON format. The format conforms with<br> Sensor Measurement Lists (SenML) standard from IETF - https://tools.ietf.org/pdf/rfc8428.pdf.</p> <p>4. subscription - it also contains data in JSON format used to perform a DDoS attack on the Cloud based Web Services.</p>
Temperature sensor data for locations in Smart-Santander testbed of Fed4FIRE+
<p>This is a database dump with temperature sensor-values (called "phenomenons" in testbed API), collected during the Fed4FIRE+ project SECTOR (Algorithm to determine a cost-effective, optimal SpatiaL-dEployment for smart-City environmenTal sensOr netwoRks).</p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.