This page will give you the opportunity to view all the scientific publications from ARCADIAN-IoT.

Enabling Autonomous Trust, Security and Privacy Management for IoT

Author: Sérgio Figueiredo, Paulo Silva, Alfonso Iacovazzi, Vitalina Holubenko, João Casal, Jose M Alcaraz Calero, Qi Wang, Pedro Colarejo, Shahid Raza, Ross Little Armitt, and Giacomo Inches

Publication: GIoT Summit, June 2022, Dublin

Publisher: Springer

Abstract. Cybersecurity incidents have been growing both in number and associated impact, as a result from society’s increased dependency in information and communication technologies – accelerated by the recent pandemic. In particular, IoT. technologies, which enable significant flexibility and cost-efficiency, but are also associated to more relaxed security mechanisms, have been quickly adopted across all sectors of the society, including critical infrastructures (e.g. smart grids) and services (e.g. eHealth). Gaps such as high dependence on 3rd party IT suppliers and device manufacturers increase the importance of trustworthy and secure solutions for future digital services.

This paper presents ARCADIAN-IoT, a framework aimed at holistically enabling trust, security, privacy and recovery in IoT systems, and enabling a Chain of Trust between the different IoT entities (persons, objects and services). It builds on features such as federated AI for effective and privacy-preserving cybersecurity, distributed ledger technologies for decentralized management of trust, or transparent, user-controllable and decentralized privacy.

Illumination-aware image fusion for around-the-clock human detection in adverse environments from Unmanned Aerial Vehicle

Author: Gelayol Golcarenarenji,Ignacio Martinez-Alpiste,Qi Wang,Jose Maria Alcaraz-Calero

Publication: Expert Systems with Applications

Publisher: Elsevier

Date: 15 October 2022

Abstract: This study proposes a novel illumination-aware image fusion technique and a Convolutional Neural Network (CNN) called BlendNet to significantly enhance the robustness and real-time performance of small human objects detection from Unmanned Aerial Vehicles (UAVs) in harsh and adverse operation environments. The proposed solution is particular useful for mission-critical public safety applications such as search and rescue operations in rural areas. The operation environments of such missions are featured with poor illumination condition and complex background such as dense vegetation and undergrowth in diverse weather conditions, and the missions have to address the challenges of detecting humans from UAVs at high altitudes, with a moving platform and from various viewing angles. To overcome these challenges, the proposed solution register and fuse the images using Enhanced Correlation Coefficient (ECC) and arithmetic image addition with customised weights techniques. The result of this fusion is fuelled with our new BlendNet AI model achieving 95.01 % of accuracy with 42.2 Frames Per Second (FPS) on Titan X GPUwith input size of 608 pixels. The effectiveness of the proposed fusion method has been evaluated and compared with other methods using the KAIST public dataset. The experimental results show competitive performance of BlendNet in terms of both visual quality as well as quantitative assessment of high detection accuracy at high speed.


XDP-Based SmartNIC Hardware Performance Acceleration for Next-Generation Networks

Author: Pablo Salva-Garcia, Ruben Ricart-Sanchez, Enrique Chirivella-Perez, Qi Wang, Jose M. Alcaraz-Calero

Publication: Journal of Network and Systems Management volume 30, Article number: 75 (2022)

Publisher: Springer

Abstract: Next-generation networks are expected to combine advanced physical and digital technologies in super-high-speed connected system infrastructures, gaining critical operation competitiveness of improved efficiency, productivity and quality of services. Towards a fully digital and connected world, these platforms will enable infrastructure virtualization and support of edge processing, making emerging sectors, such as Industry 4.0, ready to exploit its full potentials. Nevertheless, the fast growth of data-centric and automated systems may exceed the capabilities of the overall infrastructure beyond the radio access networks, becoming unable to fulfil the demands of vertical sectors and representing a bottleneck. To minimize the negative effects that could affect critical services in a heavily loaded network, it is essential for network providers to deploy highly scalable and prioritisable in-network optimisation schemes to meet industry expectations in next-generation networks. To this end, this work presents a novel framework that leverages extended Berkeley Packet Filter (eBPF) and eXpress Data Path (XDP) to offload network functions to reduce unnecessary overhead in the backbone infrastructure. The proposed solution is envisioned to be implemented as a Network Application (NetApp) service, which will greatly benefit the compatibility with next-generation networking ecosystem empowered by Artificial Intelligence (AI), advanced automation, multi-domain network slicing, and other related technologies. The achieved results demonstrate key performance improvements in terms of packet processing capacity as high as about 18 million packets per second (Mpps), system throughput up to 6.1 Mpps with 0% of packet loss, and illustrate the flexibility of the framework to adapt to multiple network policy rules dynamically on demand.