Project Coordinator (EU) :Technological University Dublin
Country of the EU Coordinator :Ireland
Organisation Type :Academia
Project participants :
Team members of TUD - UCD (EU)
Dr Sachin Sharma, Role: Project Coordinator
Dr Avishek Nag, Role: ML Lead
Snehal Dey, Role: Data Scientist
Tiarnan Rush, Role: WP2, WP3 and WP4
Dr Catherine Mulwa, Role: WP3 and WP4
Aviejay Paul, Role: WP3 and WP4
Team members of UNIVERSITY OF NEBRASKA (US)
Prof Byrav Ramamurthy, Role: WP1 and WP4 Lead
Boyang Hu, Role: WP 4
Shideh Yavary Mehr, Role: WP3 and WP4
Sai Suman, Role: WP3 and WP4
State of US partner :Nebraska
Starting date :
ATLANTIC-eVISION: Cross-Atlantic Experimental Validation of Intelligent SDN-controlled IoT Networks
This experiments does consider that IoT devices will send data to an IoT application in the cloud over the Internet via a gateway that is enabled with security and latency by running network functions (Figure 1). Note that the devices have a limited range, and they connect to the gateway through other IoT devices in the network. IoT network's major Key Performance Indicators (KPIs) are (1) high reliability, (2) low latency, (3) high security, (4) programmability, and (5) intelligent decision-making. It is a challenge to meet such KPIs subject to the limited processing and storage capacity and limited bandwidth of the IoT devices.
Therefore, we will combine Software-Defined Networking (SDN), Machine Learning (ML), and Cloud Computing to bridge the gap between the physical limitations of the IoT devices and the target KPIs of the IoT network. We propose five experiments: (1) automatic configuration/discovery of SDN in wireless IoT sensor networks, (2) ML-assisted control and data traffic path discovery experiments, (3) GPU cluster assisted experiments for ML algorithms, (4) Failure recovery intercity experiments, and (5) Scalability experiments.
Implementation plan :
There are four work packages (WP) in this project:
WP1: Project Management (Leader: Dr Sharma and Prof. Ramamurthy): This deals with the day-to-day management of the project, meeting contractual obligations and filling cost statements. It will also address the dissemination of project outcomes through publications and workshops at IEEE/ACM conferences, social media, and personal websites. This WP will have one initial deliverable due at the end of the first month and the rest will be included in the final deliverable. Initially, this will result in deliverable 1 (D1), which will provide detailed implementation and experimentation plans. Further, all the dissemination activities will be reported in the second (D2) and the final deliverable (D3). As a part of this work package, the project team has decided to meet bi-weekly on Fridays via Zoom to discuss updates and plan the tasks ahead as the project progresses. The EU and the US teams already met two times in the first month and discussed the next steps of the project. Further, we have also created a shared folder on Google Drive to share all project-related information among the partners.
WP2: Testbeds Preparation (Leader: Dr Nag): Contains two tasks as depicted in Figure 2. Initially, TU Dublin and UCD will prepare the testbeds on Fed4Fire, and the University of Nebraska will prepare the COSMOS/POWDER. Later, all these teams will work together to integrate multiple testbed experiments. There will be one initial deliverable due at the end of the second month. Currently, AERPAW is not available for experimentation. They are aiming to declare general availability with the initial platform features in November 2021 . So, the team will restrict US experimentation to COSMOS and POWDER.
The requested resources for emulations from the testbeds side include:
- 10 wireless nodes from W-iLab1.t, W-iLab2.t and CityLab of Fed4Fire at the EU
- 10 nodes from the virtual wall testbed of Fed4Fire for cloud server functionality.
- 10 wireless nodes at the POWDER testbeds in the US.
- 4 cloud server nodes with GPU functionality from COSMOS.
- GPULAB at Fed4Fire (a maximum of 10 simultaneous jobs at a time.
As the above testbeds are public, it would be difficult to request a large number of nodes at once. Therefore, initially, the team will use a small number of nodes and additional nodes will be reserved or released based on availability and requirement.
The team will connect all sensor nodes in an ad-hoc fashion (Figure 1), install an Ubuntu OS and other required software (e.g., Open vSwitch), and run the emulations. Gateways for accessing the Internet will be configured using access point (AP) nodes of the testbeds. The controller and the IoT application will run on the cloud servers of COSMOS and Fed4Fire respectively. These servers would access the sensor networks through the Internet (Figure 1). Furthermore, the controller and the IoT application will use GPULAB clusters of Fed4Fire and/or COSMOS to run ML algorithms and process large real-time data (power consumption, battery usage, buffer capacity, etc.) collected from the sensor nodes. The Command Line Interface (CLI) of the GPULAB will be used to execute commands from the controller and the IoT application.
Sensors that gather and send data related to temperature, humidity, etc. to an IoT application, will be used in our research. Different sensor network topologies (tree, mesh, ring, etc.) will be created so that only a few sensor nodes will have Internet connectivity.
WP3: Prototyping (Leader: Dr Sharma). This Work Package contains six tasks:
- Task 3.1 - Automatic Configuration Method
- Task 3.2 - Data Collection
- Task 3.3 - ML-based path selection
- Task 3.4 - ML with GPULab/Hadoop clusters
- Task 3.5 - IoT Application Emulation
- Task 3.6 - Failure Recovery
WP4: Validation, Testing and Result Analysis:
This work package will handle the integration of the components developed by the previous work package, and will perform various tests to validate the reliability, interoperability and integration ability of the developed technology enablers. It will then lead the use cases implementation and tests, including the end user validation and evaluation. It contains the following tasks: (1) validation of each functionality built in WP3, (2) scalability testing of each of the functionality, (3) integration testing with all the functionality and( 4) scalability testing with more nodes.
Our project will address the impacts in relation to the NGI initiative, namely:
Impact 1: Enhanced EU – US cooperation in Next Generation Internet, including policy cooperation
This project will enable researchers at the University of Nebraska-Lincoln (UNL), USA to collaborate with this multi-university EU team experienced with numerous testbeds including Fed4Fire. The EU team, while developing and performing their experiments on US testbeds, will help to build up new SDN capabilities related to IoT and machine learning that will enhance these testbeds. Simultaneously, the US team will provide expertise in SDN control and testbed experimentation, enabling the EU team to perform testbed experiments. This partnership will mutually benefit and forge strong relationships that promise to drive further collaboration in the future.
Going forward, we will also explore other testbeds funded by NSF and the Platforms for Advanced Wireless Research (PAWR) program including POWDER and COSMOS. Furthermore, this will be an exemplary instance of integrating so many diverse testbeds i.e., in terms of capabilities (pure wireless, wireless/optical, or standard TCP/IP, IoT protocol stack using 6LoWPAN/IPv6/RPL/CoAP), platforms, and functionalities, and being managed by ML-assisted SDN. Both EU and US researchers will benefit from the volume of new knowledge created through this large-scale inter-continental experimental framework.
Through the project, European partners will have the unique opportunity to perform experimentation on one of the world’s largest and most advanced wireless testbeds, which complement those available to them locally within Europe. The ability to remotely run experimentation across the Atlantic puts the researchers in the project to “stress-test” their novel algorithms and achieve the project’s KPIs in one of the most challenging scenarios in terms of round-trip latency and network heterogeneity.
Additionally, developing and experimenting with novel algorithms on such an advanced trans- Atlantic testbed is going to be an invaluable and truly unique experience for postdocs and research assistants working on the project, with a strong impact on the advancement of their future research career.
Impact 2: Reinforced collaboration and increased synergies between the Next Generation Internet and the Tomorrow's Internet programmes.
This project establishes collaboration between three PI's Dr Sachin Sharma and Dr Avishek Nag from the EU and Dr Byrav Ramamurthy from the US. Dr. Sharma has worked for several EU and Flemish projects: FP7-SPARC, FP7-OFELIA, FP7- CityFlow, FP7-UNIFY, FP7-CleanSky and MECANO where he extensively used the virtual wall testbed within Fed4Fire. In the FP7-OFELIA project, Dr. Sharma has also worked with W-iLab.t to deploy OpenWrt in wireless testbeds. Dr Nag on the other hand has worked on the FP7-DISCUS project with many EU collaborators. Dr Nag was also a researcher in Ireland’s biggest telecom networks research center CONNECT where he worked on developing an LTE testbed with YouTube. CONNECT currently houses one of the biggest 5G testbeds that has links with Fed4Fire and the US testbed COSMOS.
Dr Nag is also the alumnus of the same research group (i.e., Prof Biswanath Mukherjee’s networks research lab) at UC Davis as Prof Ramamurthy who is the US PI of this project. Both Dr Nag and Prof Ramamurthy, therefore, are connected to a rich alumni network of UC Davis working in the telecom networks industry. This project will, therefore, reach a very wide network of established professionals spanning over North America and the EU and would reinforce the chances of collaboration and result in increased synergies between the Next-Generation Internet and Tomorrow's Internet programmes.
Impact 3: Developing interoperable solutions and joint demonstrators, contributions to standards.
The mere philosophy of this project is based on interoperability as it establishes the feasibility of OpenFlow and SDN for unified control of wireless testbeds spanning over two continents. OpenFlow and SDN were originally suited for wide-area wired networks and we are emulating it for the first time on practical-scale networks. Different technologies and protocols need to be interoperated with a certain scope of creating new standards. The scope of new standards lies in the fact that SDN/OpenFlow has to be redefined to support machine learning algorithms and policies as well as support automatic discovery of wireless devices.
Impact 4: An EU - US ecosystem of top researchers, hi-tech start-ups / SMEs and Internet-related communities collaborating on the evolution of the Internet
The PIs of this project, owing to their combined professional network, has reachability to a lot of tech startups and SMEs. For example, Dr Avishek Nag and Dr Byrav Ramamurthy’s PhD advisor Prof Biswanath Mukherjee is the founder of a startup based in Northern California, called Ennetix Inc. which specializes in AIOps and Network Analytics. Our automatic IoT device discovery methodologies and machine-learning-based optimum path discovery in IoT networks can help them extend their solutions beyond enterprise-wide-area networks. Moreover, we can also collaborate with them in future NGI calls to extend our proposal to incorporate security and advanced and accurate data collection techniques from a live operational network. Furthermore, Dr Nag has worked with a US-based startup called MotoJeannie Inc. and an Irish SME called Overcast which are closely working on multimedia content delivery over edge networks. Though this project focuses on the use-case of healthcare, our methodologies can also be utilized for use cases that would interest MotoJeannie and Overcast.
Dr Nag is also professionally connected to the founders of a few Irish startups working on the synergy between IoT, AI, and healthcare. The findings of this project may also be of use to them and can foster future collaborations. Dr Sharma on the other hand has worked in NEC Labs Europe and collaborated with several well-known companies in Europe such as Ericsson, Hungary, Ericsson Stockholm, Deutsche Telecom and Onesource, Portugal. The findings of the project will also be noticed by these organisations and this can stimulate future collaborations.
This project compared a number of EU and US testbeds based on their (1) architecture, (2) resources available, (3) IoT capabilities, (4) data that can be collected, (5) limitations, (6) Software-Defined Networking (SDN) capabilities, (7)) machine learning capabilities, and (8) practical experimental results. Benchmark and failure recovery experiments are performed and results are shown. Further, issues faced from each testbed are reported. Different testbeds have different resources available for wireless experiments, as shown by the results. In addition, because nodes in different testbeds have different resources available with respect to CPU, memory, bandwidth available, we achieved different results from each testbed experimentation. Furthermore, as the selection of nodes is highly dependent on the availability of nodes at the time of experimentation, results vary depending on the type of node selected. In addition, testbed experiments provide a realistic environment for experimentation. Therefore, it makes sense to use these nodes as
Apart from that, we also achieved the objectives of this NGIAtlantic project namely:
- We experimentally demonstrated automatic configuration of SDN/OpenFlow in Wireless Ad hoc networks. This was achieved by implementing an automatic configuration method on testbeds. The efficiency of the method is calculated by measuring the automatic discovery time and data plane latency.
- We achieved the best data-plane latency for an e-healthcare application. This was done by applying machine learning to find the best path from an IoT device to an IoT application which meets the latency and bandwidth requirements.
- Recover from a failure when it occurs in different network topologies (ring, grid and mesh). This was achieved by implementing a restoration scheme and calculating the failure-recovery time. The failure-recovery time was calculated after the failure is introduced in the network. Most of the results in the literature are based on simulations. The results gathered using our emulations are unique, as these are measured in a set-up emulated on real testbeds.
We tested inter-testbed connectivity by performing experiments on EU and US testbeds. The inter-testbed connectivity was achieved by running different modules (IoT applications and sensor nodes) on different testbeds and using the public internet for connection. For example, we ran the controller at the COSMOS testbed and an IoT application at the virtual wall testbed. Further, wireless IoT scenarios will were created on the W-iLab.t, CityLab and POWDER testbeds.
We tested our secure IoT application using the GPULAB testbed. Further, our e-healthcare application was tested using a setup created on POWDER, COSMOS, and AWS servers.
The RL application for route optimization can be extended in several multiple ways.
Future Plan :
Due to resource limitations (as discussed in section 4.2), we could not test our IoT application with the inter-testbed environment where w-ilab1.t, w-ilab2.t and POWDER testbed are connected to each other as discussed in previous section. In the future, we will perform this experiment when the POWDER testbed is more developed.
Going forward, we will use the findings of this project to implement more advanced edge-computing use cases and run more robust machine learning algorithms (e.g., considering the tradeoff between algorithm accuracy vs energy efficiency) on the EU-US inter-testbed topologies. Furthermore, we would like to explore the security and trust aspect of connecting these software-controlled nodes by using the principles of Blockchain.
Six papers published or accepted:
- S. Sharma, S. Urumkar, G. Fontanesi, B. Ramamurthy, and A. Nag, "Future Wireless Networking Experiments Escaping Simulations", Future Internet. 2022; 14(4):120. https://doi.org/10.3390/fi14040120
- V. Tomer and S. Sharma, "Detecting IoT Attacks Using an Ensemble Machine Learning Model" Future Internet," 2022; 14(4):102. https://doi.org/10.3390/fi14040102
- S. Sharma, A. Nag and B. Ramamurthy, "Cross-Atlantic Experiments on EU-US Test-beds," IEEE Networking Letters, doi: 10.1109/LNET.2022.317771
- V. Tomer and S. Sharma, "Experimenting an Edge-Cloud Computing Model on the GPULab Fed4Fire Testbed," 2022 IEEE International Symposium on Local and Metropolitan Area Networks (LANMAN), 2022, pp. 1-2, doi: 10.1109/LANMAN54755.2022.9820006.
- S. Urumkar, G. Fontanesi, A. Nag and S. Sharma, "Demonstrating Configuration of Software Defined Networking in Real Wireless Testbeds," 2022 IEEE International Symposium on Local and Metropolitan Area Networks (LANMAN), 2022, pp. 1-2, doi: 10.1109/LANMAN54755.2022.9819994.
- S. Sharma, S. Urumkar, G. Fontanesi, V. S. Karanam, B. Hu, B. Ramamurthy and A. Nag, 1) S. Sharma, S. Urumkar, G. Fontanesi, B. Ramamurthy, and A. Nag, "Future Wireless Networking Experiments Escaping Simulations", Future Internet. 2022; 14(4):120. https://doi.org/10.3390/fi14040120
Open source contributions:
Experimenting an Edge-Cloud Computing Model on the GPULab Fed4Fire Testbed, https://github.com/VikasTomar32/LANMAN
GNN+DQN code in https://github.com/GianFont/RL_routhOpt .
Controller Automatic Configuration Code: https://bitbucket.org/saish15/olsrd2/src/master/
Client node automatic configuration code: https://bitbucket.org/o2cmf-work/olsrd_client/src/master/
Demonstrating configuration of software defined networking in wireless testbeds: