FIRE facilities and components from eWINE
You will find in the 3 tabs below the FIRE facilities, software components or datasets. Click on the tab to deploy each list:
WiSHFUL and eWINE FIRE facilities (FIRE)
| TESTBEDS | Web link |
|---|---|
| w.iLab.t (Heterogeneous wireless testbed @ iMinds, Ghent, Belgium) Available technologies: Wi-Fi, 802.15.4, USRP, LTE 2.9GHz TDD, etc |
http://doc.ilabt.iminds.be/ilabt-documentation/wilabfacility.html |
| NITOS Testbed (network implementation testbed using open source platforms @ University of Thessaly, Volos, Greece)Available technologies: Wi-Fi, WiMAX, LTE, Bluetooth |
http://nitlab.inf.uth.gr/NITlab/nitos |
| IRIS (Software Defined Radio testbed @ TCD, Dublin, Ireland) Available technologies: USRP, etc |
http://iris-testbed.connectcentre.ie/ |
| TWIST (Sensor testbed and openWRT router testbed @ TU Berlin, Berlin, Germany) Available technologies: |
https://www.twist.tu-berlin.de/ |
| ORBIT (20 x 20 radio grid testbed @ Rutgers University, New Jersey, US) Available technologies: Wi-Fi, Bluetooth, ZigBee, Software Defined Radio platforms (USRP, WARP, RTL-SDR, USRP N210, USRP X310) |
http://www.orbit-lab.org |
| FIBRE@UFRJ (OMF testbed @ UFRJ, Rio de Janeiro, Brazil) Available technologies: Wi-Fi |
http://fibre.org.br/start-using-fibre/register/ufrj-island/ |
| LOG-a-TEC (testbed @ Jozef Stefan Institute, Ljubljana, Slovenia) Available technologies: LoRa, Sigfox, IEEE 802.15.4a, TVWS, clean slate non-IEEE 802.15.4, IEEE 802.15.4 |
http://log-a-tec.eu/ |
| WiSHFUL portable testbed Available technologies: Wi-Fi, 802.15.4, Bluetooth usb dongle, etc |
http://www.wishful-project.eu/node/16 |
Software components from eWINE
| S# | Software components | Brief description | eWINE partner name and Contact person | GitHub link |
|---|---|---|---|---|
| 1 | Surrogate model creation and optimization | Surrogate models create performance predicting models based on incomplete or noisy data. They can be used to efficiently identify optimal systems settings using a limited number of experiments. The reusable components for surrogate model developed in Node-RED (developed in MATLAB) can be used in various optimization problems | Adnan Shahid (imec) |
LINK |
| 2 | Generalized drag and drop (in Node-RED) | The generalized drag and drop Node-RED flow gives an idea about how the Node-RED can be used for designing systems by drag and drop components and most importantly makes it possible to interface with different software tools. | Adnan Shahid (imec) |
LINK |
| 3 | WiFi available airtime estimator | Passive estimation of available air time for WiFi in 5GHz ISM band being simultaneous used by LTE-U by utilizing MAC layer parameters of COTS 802.11 WiFi hardware. |
Niels Karowski (TUB) |
LINK |
| 4 | UniFlex – distributed framework for coordinated cross-layer wireless control | UniFlex, a framework enabling unified and flexible radio and network control. It provides an API enabling coordinated cross-layer control and management operation over multiple wireless network nodes. The controller logic may be implemented either in a centralized or distributed manner. This allows to place time-sensitive control functions close to the controlled device (i.e., local control application), off-load more resource hungry network application to compute servers and make them work together to control entire network. | Niels Karowski (TUB) |
LINK |
| 5 | Iris – Software-Defined Radio (SDR) software package | Iris is a software for building highly reconfigurable radio on USRP hardware with a component-based design. For instance, it includes OFDM Tx and Rx for PHY and simple MAC and higher layer components | Yi Zhang (TCD) |
LINK |
| 6 | GFDM flexible PHY | GFDM flexible PHY implementation running on USRP-RIO, which allows reconfiguration during run-time. Please note: Windows 7 and LabVIEW Communications is needed. |
Martin Danneberg (TUD) |
LINK |
| 7 | OFDM PHY | OFDM physical layer embedded implementation running on a Kalray MPPA2®-256 (Bostan) manycore. Please note: Linux CentOS 7.0 (Core) and Kalray AccessCore™ Software Development Kit (SDK) are needed. |
Somsaï Thao (TCS) |
LINK |
| 8 | Feature extractor for link quality estimation | Feature extraction script that can be used for machine learning approach for link quality estimation and prediction (for example using the Weka tool). The script uses packet reception data as input. Developed in Python. | Carolina Fortuna (JSI) |
LINK |
| 9 | Localization toolbox based on REMs | Localization toolbox which estimates the node location using parameters obtained from continuously complemented Radio Environment Maps. | Tomaz Javornik (JSI) |
LINK |
| 10 | SIGFOX experimentation toolbox | The toolbox allows for rapid development of experiments involving Low-power wide-area networks (LP-WAN) and ultra-narrowband (UNB) technology. Most components can be used with a visual editor and a flow-based programming approach in the Node-RED tool. |
Tomaž Šolc (JSI) |
LINK |
| 11 | AMS-DEMO | An implementation of AMS-DEMO algorithm for optimization of functions implemented as bash scripts. |
Matjaž Depolli (JSI) |
LINK |
| 12 | Flex_PHY_Optimization | The Flexible PHY parameter optimization is a MATLAB based optimization module which optimizes the GFDM PHY parameters based on the information regarding the real-time situation of the spectrum. The module configures the GFDM PHY for the upcoming transmit packet, such that it can fit the transmit signal into the white spaces of the spectrum, suitable for cognitive radio applications. | Dan Zhang, Martin Danneberg and Shahab Ehsanfar (TUD) |
LINK |
| 13 | I&Q data samples for WiFi and LTE (DS2) | The raw I&Q samples belonging to commercial LTE and WiFi transmissions, as collected using the B210 USRP (by Jerome Arokkiam), are given in the form of .bin files. These I&Q samples facilitate TCD’s work on the classification of LTE and WiFi technology using machine learning algorithms. | Farrukh Bhatti (TCD) |
LINK |
| 14 | Self-Organization framework for D2D communication in a three-tier cellular network | A self-organized framework for energy efficiency maximization for D2D communication in a three-tier cellular network is presented. The system model consists of D2D communication, small cell network, and macro cell network, and the problem is formulated using a non-cooperative game theoretic approach. The goal of the investigation is to enhance an energy efficiency of D2D network by the two less-complex independent methods: the resource block allocation and the power allocation, without creating a harmful impact on other network entities. | Adnan Shahid (IMEC) |
LINK |
| 15 | Standardized Localization Service | Standardized Localization Service is a modular localization service architecture that consists of location-based applications, an integrated location service enabling handover, fusion and integration of location information provisioning services, and resources for generating location information. A unified style of interaction among these components is enabled by a set of well-defined Application Programming Interfaces (APIs). | Filip Lemić (TUB) |
LINK |
| 16 | Indoor localization with multilateration | Evaluation of different indoor localization approaches with multilateration (least squares) using combination of NLoS classification and ranging error regression models for localization error mitigation. Scripts for training CNN classification and CNN ranging error regression models based on channel impulse response implemented in TensorFlow are included. UWB NLoS classification and ranging error regression datasets are included. | Klemen Bregar (JSI) |
LINK |
| 17 | UWB ranging error estimation | Evaluation of indoor UWB ranging error regression model using CNN in TensorFlow with dedicated dataset is presented as a part of larger repository. | Klemen Bregar (JSI) |
LINK |
| 18 | Fair coexistence between LTE-LAA & WiFi though cross-technology collaboration protocol (IMEC) | A software component for an adaptive LTE-U LBT scheme is presented that uses a variable LTE transmission opportunity (TXOP) followed by a variable muting period for realizing a fair coexistence between LTE-U and WiFi. | Vasilis Maglogiannis (imec) |
LINK |
| 19 | Interference classification for Sub-GHz (IMEC) | An optimized and flexible Convolutional Neural Network based classification scheme is presented for classification of the subGHz technologies including Sigfox, LoRA and IEEE 802.15.4 (subGHz). For now, the classifier is able to classify 8 different signal classes (from the three technologies) with an accuracy close to 98% and it can be extended to any number signal classes as required. | Adnan Shahid (imec) |
LINK |
| 20 | Dual access cross layer optimization framework | We provide a less complex non-cooperative game approach for subchannel and power allocation in licensed and unlicensed bands for ultra-dense small cell networks. The approach maximizes the energy efficiency of small cells without creating any harmful impact on the legacy WiFi devices, macro cell users and small cell users. | Adnan Shahid (imec) |
LINK |
| 21 | RSSI-based-OFDM-signal-classification | An RSSI-based manual feature extraction and autonomous feature machine learning schemes are presented for classification of LTE, WiFi and DVB-T. | Jaron Fontaine (imec) |
LINK |
| 22 | MAC protocol optimization | A WSN optimization scheme in dynamic environments using cloud repositories is presented. The scheme comprises of an offline phase and an online (cognitive loop) phase. In the former phase, various models that can closely approximate real environments are generated and stored in a cloud server. In the later phase, an optimized model is selected using model merging, selecting and building techniques based upon an environment characterization. | Michael Mehari (imec) |
LINK |
| 23 | Cooperative localization | The convex relaxation and belief propagation methods are applied to demonstrate cooperative location approach, meaning considering not only estimated agent to anchor distances, but also distances between agents. The distance between nodes is estimated from received signal strength measurements between nodes. | Tomaž Javornik (JSI) |
LINK |
Datasets from eWINE
| Sr# | Data sets | Brief description | eWINE partner name and contact person | GitHub link |
|---|---|---|---|---|
| 1 | 802.15.4 MAC layer performance data set | MAC performance dataset from experiments with 802.15.4 nodes on the wilab2 testbed. Two datasets will be provided: (i) fine-granularity dataset with short-term MAC statistics, and (ii) coarse-granularity dataset with long-term MAC statistics. Both datasets consist of the following environmental parameters and measured MAC performance metrics: node density, interference level Indication, traffic load, throughput, packet loss, packet loss rate (PLR) and packet reception rate (PRR). |
Adnan Shahid |
LINK |
| 2 | UWB LOS vs nLOS dataset |
Data set describing the UWB link quality in realistic industrial-like conditions. Traces are captured on the Wilab2 test bed by varying the position of the mobile nodes and correspondingly measure the entities: estimated signal power, estimated signal power in the first path, channel impulse response for analyzing the LOS and nLOS characteristics. | Adnan Shahid |
LINK |
| 3 | Technology classification dataset | Dataset comprised of LTE, WIFI and DVB signals captured via USRP from various locations in Ghent, Belgium. | Adnan Shahid |
LINK |
| 4 | WiFi available airtime estimator | Datasets consisting of different LTE-U transmission power levels, LTE-U duty-cycles and traces from 802.11 Atheros MAC state machine monitoring is provided. | Niels Karowski |
LINK |
| 5 | UWB LOS vs. nLOS dataset | 36.000 measurements using DecaWave DWM1000 module in line-of-sight and non-line-of-sight conditions taken in various in-door environments. Dataset includes channel impulse response and estimated RSSI. | Klemen Bregar |
LINK |
| 6 | SIGFOX dataset | Dataset consisting of SIGFOX transmissions using different configurations (gain, frequency hopping patterns, packet repetitions) recorded at JSI campus. Radio link was from an in-door device to an out-door SIGFOX base station. Includes packet loss data, RSSI, SNR and radio spectrum recordings. | Tomaz Solc |
LINK |
| 7 | Recorded datasets | Data Traces with interferences and sigfox signals | Stephane Chazal |
LINK |
| 8 | UWB ranging error estimation dataset | Indoor UWB dataset for ranging error estimation is presented as a part of a larger repository. | Klemen Bregar (JSI) |
LINK |
| 9 | sub-GHz technologies dataset (SigFox, LoRa, and IEEE802.15.4g (subGHz) ) | The dataset comprises of IQ samples from the three technologies such as sigfox, LoRA and IEEE802.15.4 (subGHz). From the technologies, 8 signal classes were generated including 6 classes of LoRA with spreading factors (SFs) 7, 8, 9, 10, 11 and 12 each of bandwidth 125KHz, 1 class of sigfox transmitting dynamically on the sigfox channels c0, c180,…c400 of bandwidth 100Hz, and 1 class of IEEE802.15.4g (subGHz) of bandwidth 600KHz. | Adnan Shahid (imec) |
LINK |
eWINE Grand Challenge components
| Grand Challenge components | Type | Brief description | Contact Person | Github link |
|---|---|---|---|---|
| 5G Flexible Transceiver for Physical Layer Evaluation – 5GFlexPHY | Software component | 5G is being widely researched nowadays. Several waveforms show features to be employed in 5G networks. However, performance analyses are based on theoretical or simulation results, with simplified channel model. Non-linearities, phase noise, interferences and other impairments present in a wireless channel are usually neglected. The eWINE flexible PHY component allows a first realtime study, however only for SISO transceivers. The aim of this proposal is to extend the eWINEinfrastructure with MIMO capabilities to improve and evaluate the performance of the flexible PHY system under real channel conditions. The MIMO processing blocks will be developed for the XILINX Kintex 7 FPGA inside the USRP software-defined radio platform. | Luciano Leonel Mendes (INATEL, Brazil) |
LINK |
| Enabling Agile Adaptation in Dense Heterogeneous Deployments | Dataset | In this demonstration, we will present the AGILE system providing for rapid deployment on top of COTS 802.11 hardware to address elastic Wi-Fi connectivity issues. The key novelty of AGILE lies in the adoption of an efficient cross-layer approach, combining the detail of information available at low networking layers with the ability of implementing sophisticated algorithms at the application layer. We will showcase three individual scenarios highlighting the advantages of applying autonomous mechanisms to provide distributed spectrum adaptation, low-quality link mitigation and centrally controlled traffic load distribution. The experiments will be deployed in NITOS testbed featuring compatible 802.11ac hardware. | Efstratios Kerandis (GRIDNET, Greece) |
LINK |
| Intelligent Network Control for the Internet of Things INTER-IOT | Software component | The CORAL framework expands the Software-Defined Networks (SDNs) concept to the Internet of Things (IoT). This integration enables efficient end-to-end wireless communication based on dynamically optimized routing for heterogeneous mobility-aware networks. CORAL brings elastic network adaptations, such as SDN-based network discovery, topology maintenance and routing, over IoT devices to improve performance, reduce cost and resource utilization; a novel visualization platform based on Node-RED is used for illustration. Exploiting the WiSHFUL and eWINE infrastructures is double beneficial for our framework: CORAL is based on a real test-bed with innovative facilities, whilst it is enhanced with intelligence to support our controller decisions. | Lefteris Mamatas (University of Macedonia, Greece) |
LINK |
| Network Access: Smart Detection And QoE-based selection – NASDAQ | Software component | In the context of heterogeneous and coexisting networks, the introduction of context-aware and cognitive mechanisms may allow the final user to optimize 1) the detection of surrounding access networks, and 2) the selection of the best one, thus improving Quality of Experience (QoE). The present proposal investigates this approach, by introducing the use of 1) MAC layer parameters for the detection of surrounding networks, and 2) application layer QoE parameters, namely Key Performance Indicators (KPIs), for optimizing the access network selection, respectively. MAC parameters and KPIs will be defined for different traffic types, and then used to 1) detect and recognize the access networks in the user surrounding area, 2) rank them, and select the one with highest estimated QoE, for each considered traffic type. |
Giuseppe Caso (Sapienza University of Rome, Italy) |
LINK |
| Design and Prototype of a Multi-Objective Environment Sensing Capability (ESC) System for Shared Access With Rotating Radars | Software component | Recent recommendations by regulatory bodies to use a distributed network of spectrum sensing/measurement devices, called environmental sensing capability (ESC) devices, which facilitates shared access (SA) between wireless communications and rotating radar systems has generated considerable research interest. We demonstrate a prototype of a multi-objective ESC system that facilitates a spectrum access system (SAS) to enable SA with ground-based fixed rotating radars. The implemented ESC device 1) detects radar signals; 2) measures interference from secondary users (Sus) for radar protection; and 3) also measures SUs’ airtime utilization (ATU) in a channel. The proposed ESC assisted SAS architecture can help in reducing the size of currently proposed large exclusion zones around the rotating radar systems. The prototype of the proposed ESC is implemented on Wireless Open Access Research Platform (WARP) nodes with Xilinx Field-programmable gate array (FPGAs). | Zaheer Khan (University of Oulu) |
LINK |
| Demonstration of a Database-assisted Shared Access System for Rotating Radars and Wireless Communications | Software component | To provide more frequency spectrum for smart wireless devices and new things in the IoT, there is an increasing research interest in systems that facilitate spectrum sharing between radars and wireless communications. We will demonstrate real implementation of a database assisted shared access (SA) system for rotating radars and wireless communications. Our implemented SA system includes: 1) a real-time rotating radar emulator which has been designed to generate real scanning patterns of three different rotating radar systems; 2) a real-time MySQL database which assigns rules for SA-based spectrum access; and 3) A real-time FPGA-based prototype of wireless users using frequency spectrum on SA-basis. The prototype is implemented on WARP (a scalable and extensible programmable wireless platform) nodes. | Zaheer Khan (Oulu) |
LINK |