TII's secure systems research centre collaborates with global universities on secure mesh communications research projects

Synergy to improve communication resilience, ensure dependable secure mesh networks, integral to enhancing communication infrastructure, enabling secure autonomous systems

  
Shreekant Thakkar

Shreekant Thakkar

Abu Dhabi-UAE: Technology Innovation Institute (TII), the applied research pillar of Abu Dhabi’s Advanced Technology Research Council (ATRC), today announced that its Secure Systems Research Centre (SSRC) has partnered with three prestigious universities on  secure mesh communications research projects.

Mesh Network is a network topology in which the infrastructure nodes connect directly, dynamically, and non-hierarchically to as many other nodes as possible, and cooperate with one another to efficiently route data to and from third parties. The project is set to strengthen the security of the Mesh network technology that TII is currently developing for machine to machine and human to human communications.

The three partner universities include the UAE’s Khalifa University of Science and Technology, a world-class research institution that nurtures critical thinkers in applied science and engineering, University of Turku in Turku, Finland, and Graz University of Technology (TU Graz) in Styria, Austria. The Khalifa University project is titled ‘A Secure and Resilient Chat/VoIP Application over Private Mesh Networks’. Meanwhile, the University of Turku project focuses on ‘R3Swarms: Robust, Resilient and Reconfigurable Swarms’, and the project with TU Graz is titled ‘SPiDR: Secure, Performant, Dependable, and Resilient Wireless Mesh Networks’.

Speaking on the significance of this development for the UAE and the wider region, Dr Shreekant (Ticky) Thakkar, Chief Researcher at SSRC, said: “In the UAE we conduct research to provide technology that improves life through making systems and communications safer. This is of the utmost importance when technologies combine human, physical systems, and software interactions. Mesh Network Research conducted with our partners in Turku University, TU Graz, and Khalifa University will accelerate the deployment of safe communication for the benefit of people living in the UAE and the wider region.”

Dr Thakkar said the collaborations with Turku University and TU Graz will span the domain of communication resilience improvement through exploring, among other aspects, UWB (Ultra-wideband) transmissions, while the partnership with Khalifa University focuses on PHY (Physical layer) security and secure routing. A total of four research programmes are getting underway to provide a significant improvement in the ability of Mesh networks to resist eavesdropping, scanning, malware propagation and jamming, among other threats. As a direct application, these benefits are critical for cyber physical system swarm communications to become credible and resilient when operating autonomously, Dr Thakkar added.

Reiterating the significance of the new synergies, Jean- Pierre Giacalone, Vice President, Secure Connectivity at SSRC, said: “This project enables us to realise our ambition to provide the highest level of security and resilience through our Mesh network technology. Through leveraging a flexible approach in providing the requisite security features as open-source contributions, we are also targeting future users of such networks that want to deploy them easily. Therefore, the wider population will be able to benefit from this technology that accelerates the development of safer systems for all.”

He added: “We are conducting advanced research in the domain of machine to machine and human to human communication for secure mesh networks deployment through partnering with universities that offer the best competencies and track records in communication resilience as well as new forms of secrecy using radio PHY-layer. Our goal is to provide world-class leading security technology for Mesh network deployment released as open-source software.”

He said dependable and secure Mesh networks are an integral part of overall communication infrastructure and will enable secure autonomous systems.

Dr Carlo Boano, Associate Professor, Institute of Technical Informatics, TU Graz, said: “The technologies developed within SPiDR will empower off-the-shelf drones and smartphones with the ability to autonomously detect and mitigate malicious attacks and coexistence problems, thereby increasing their availability and functionality without adding to the costs of operating them.”

Dr Tomi Westerlund, Associate Professor, Smart Systems at University of Turku, said: “The project with SSRC focuses on exploiting and advancing state-of-the-art technologies including, but not limited to, DL, DLT, UWB, as the basis to design and build more secure, flexible, robust, resilient, and reconfigurable swarms.”

For his part, Dr Hadi Otrok, Associate Professor, Department of Electrical Engineering and Computer Science at Khalifa University, said: “Through this project, we aim to build efficiencies and address earlier shortcomings by developing efficient chat and VoIP applications within secure and resilient mesh networks.”

One of seven initial dedicated research centres at TII, SSRC was established to create a global centre of excellence in the development of end-to-end security and resilience to protect cyber-physical and autonomous systems.

-Ends-

About Technology Innovation Institute (TII)

Technology Innovation Institute (TII) is the dedicated ‘applied research’ pillar of Advanced Technology Research Council (ATRC). TII is a pioneering global research and development centre that focuses on applied research and new-age technology capabilities. The Institute has seven initial dedicated research centres in quantum, autonomous robotics, cryptography, advanced materials, digital security, directed energy, and secure systems. By working with exceptional talent, universities, research institutions, and industry partners from all over the world, the Institute connects an intellectual community and contributes to building an R&D ecosystem reinforcing Abu Dhabi and the UAE’s status as a global hub for innovation.

For more information, visit www.tii.ae 

About Secure Systems Research Centre (SSRC)

Secure Systems Research Centre (SSRC) – at Technology Innovation Institute (TII) – is a global centre of excellence developing end-to-end security and resilience innovations to protect cyber-physical and autonomous systems. These developments aim to reduce security vulnerabilities and threats in a global community dependent on billions of physical and digital points of contact.

For more information, visit https://securesystems.tii.ae/ 

For media enquiries, please contact:
Technology Innovation Institute
comms@atrc.ae

Technology University Graz - SPiDR: Secure, Performant, Dependable, and Resilient Wireless Mesh Networks

SPiDR brings together the latest advances in wireless networking, localization, benchmarking, collaborative awareness, and machine learning, towards the development of secure, resilient, and highly-performant wireless mesh networks. Within this context, the researchers will work on benchmarking infrastructures supporting experimentation on wireless networks based on Wi-Fi, Bluetooth Low Energy (BLE), and Ultra-wideband (UWB) technology; they will design dependable and scalable networking protocols that are resilient to malicious agents; they will provide autonomous entities such as drones with RF context- and location-awareness, as well as with the ability to identify and mitigate security threats, network anomalies, and coexistence issues.

The technologies developed within SPiDR will enrich off-the-shelf drones and smartphones with the ability to autonomously detect and mitigate malicious attacks and coexistence problems, thereby increasing their availability and functionality without additional costs. In this regard, the focus on commodity Wi-Fi/BLE/UWB chipsets will provide a more practical and scalable solution than the reliance on software-defined radios. The technologies developed within SPiDR will also enable secure teleoperation over a mesh-cloud continuum, for example towards the remote inspection and control of pipelines and drone networks. The performance of the devices within the mesh network will further be improved by exploiting the availability of multiple radios and various physical layers on modern embedded devices. Furthermore, SPiDR will develop scalable and privacy-preserving UWB localization systems that can complement the collaborative framework developed within R3Swarms by Turku University. Such UWB localization systems, based on networking protocols making useof concurrent ranging and of the new IEEE 802.15.4z standard, will enable asecure localization of mobile entities (such as drones) at much higher update rates than traditional schemes based on two-way ranging. Thus, SPiDR paves the way for the creation of secure, performant, dependable, and resilient swarms of autonomous entities.

University of Turku - R3Swarms: Robust, Resilient and Reconfigurable Swarms

R3Swarms brings together the latest advances in ultra-wideband (UWB)-based wireless connectivity and localization (situated communication), collaborative autonomy and localization, and distributed perception. In all these areas, the researchers will work in cooperation with the ARROWSMITH project towards secure communication, safe interfacing and control, and identification of anomalies and malicious or byzantine agents.

The project delivers tangible research results in the form of (i) a novel framework for simultaneous localization and communication in secured UWB-based mesh networks; (ii) innovative multi-tier cloud-fog-edge autonomy stack with fallback and autonomous reconfiguration algorithms for self-healing when connectivity is disrupted or entities attacked;

(iii) deep-learning-enhanced predictive situational awareness at the swarm level and anomaly detection; and (iv) DLT-powered trustable and safe collaboration, interfacing and control. The project focuses on exploiting and advancing the state-of-the-art across various technological fields (including, but not limited to, DL, DLT, UWB) as the basis to design and build more secure, flexible, robust, resilient and reconfigurable swarms. Using a scalable UWB-mesh network as a connectivity and collaborative localization solution, the research work will also open the door to boost further integration of large-scale distributed robotic systems within the IIoT, and interoperability with smart city infrastructure.

Khalifa University: A Secure and Resilient Chat/VoIP Application over Private Mesh Networks

The proposal aims to incorporate different technologies and techniques such as mesh ad-hoc connections and routing protocols, machine learning to provide security and resiliency.

In this proposal, the researchers will focus on designing and implementing a QoS-OLSR protocol as part of TII’s LibreMesh for routing messages among the mesh members. While some existing applications and libraries are available such as Bridgefy and RightMesh library, they do not take into consideration important characteristics such as:

  • Battery capacity of relay devices.
  • The Quality of Service (QoS) of the relay devices such as bandwidth available for routing, connectivity, and mobility.
  • The misbehavior of devices through the network.
  • The mobility patterns of the user and their impact on the network topology.
  • Security and privacy of users.

In this project, the researchers aim to address these shortcomings by developing efficient chat and VoIP applications on top of mesh networks.

PHY-layer Security for Heterogeneous UAV-Ground Wireless Networks

This project aims at holistically evaluating the feasibility of adopting physical layer security (PLS) techniques for unmanned aerial vehicles (UAVs) aided wireless communications networks. Although PLS have been thoroughly investigated in the literature, and proved to be very effective, integrating UAVs with wireless networks has created new threats to such techniques which allows malicious parties to exploit the system vulnerabilities to cause deliberate or accidental harm. More specifically, PLS highly depends on the stochastic and uniqueness of the wireless link between the communicating devices. Nevertheless, in the case of UAV-aided networks, the wireless channel may not have sufficient position-related randomness to allow random key generation process. Moreover, it might be possible for an intruding UAV to clone the key by flying in proximity of a legitimate UAV. Therefore, one of goals of this project is to evaluate the vulnerabilities of UAV-aided networks and propose efficient solutions to mitigate the system weakness. Furthermore, the concept of physical unclonable functions (PUFs) will be applied in the context of wireless communications to improve the immunity of conventional PLS techniques.

Secure Communications for Power Constrained Wireless Mesh Networks

The main objective of  this project is to present scalable solutions that can accommodate power-constrained (also RF-powered) wireless mesh networks (MWN), e.g., a swarm of nano-drones, including: 1) the design of routing protocols which will be energy aware and customized to the characteristic of energy harvesting, 2) the development of lightweight security solutions taking in account the resource constraints, and 3) the development of a testbed to verify the developed wireless communication and signal processing algorithms and rapid prototyping. 

Neural Networks for Signal Processing in Secure IoT Mesh Networks

In this project, theaim is to adopt and develop machine learning (ML) approaches on which the performance of future IoT connectivity can be optimized. The researchers propose research to address key challenges in mobile wireless mesh networks (with particular emphasis on UAV networks) in order to realize their potentials in dynamic environments. The work leverages the team’s diverse experience in different areas, where radical performance improvements through ML-based routing protocols and M-based PLS techniques, can be achieved. In this respect, artificial neural networks (ANN) and deep learning (DL) paradigms of ML have been considered as good candidates, as they offer flexibility to learn complex functions with high accuracy and online self-optimization to attain the best possible performance in a practical scenario.

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