Things Data Interoperability Through Annotating oneM2M resources for NGSI-LD Entities

In this era of Information and Communication Technology (ICT) and the Industrial Internet of Things (IIoT),semantic interoperability plays an important role in interworking among different standards. One such standard is oneM2M, which supports semantic interoperability between non-semantic oneM2M resource model and semantic data, but it is only limited to Resource Description Framework (RDF) triple data. Where Next Generation Service Interfaces – Linked Data (NGSI-LD) – provides information model and protocol for enhancing the capabilities to represent more complex structures of Linked Data, limited research has been conducted regarding such framework or protocol to support the interpretation and translation among these two different standards. This paper proposes a mapping protocol for interpreting and translating non-semantic oneM2M resource data to NGSI-LD interfaces

Get Your Foes Fooled: Proximal Gradient Split Learning for Defense Against Model Inversion Attacks on IoMT Data

The past decade has seen a rapid adoption of Artificial Intelligence (AI), specifically the deep learning networks, in Internet of Medical Things (IoMT) ecosystem. However, it has been shown recently that the deep learning networks can be exploited by adversarial attacks that not only make IoMT vulnerable to the data theft but also to the manipulation of medical diagnosis. The existing studies consider adding noise to the raw IoMT data or model parameters which not only reduces the overall performance concerning medical inferences but also is ineffective to the likes of deep leakage from gradients method. In this work, we propose proximal gradient split learning (PSGL) method for defense against the model inversion attacks. The proposed method intentionally attacks the IoMT data when undergoing the deep neural network training process at client side. We propose the use of proximal gradient method to recover gradient maps and a decision-level fusion strategy to improve the recognition performance. Extensive analysis show that the PGSL not only provides effective defense mechanism against the model inversion attacks but also helps in improving the recognition performance on publicly available datasets. We report 14.0 % , 17.9 % , and 36.9 % gains in accuracy over reconstructed and adversarial attacked images, respectively

High dynamic performance power quality conditioner for AC microgrids

This paper deals with power quality problems encountered in weak AC microgrids and solutions for mitigation. A power electronic converter can be used as an effective power quality conditioner to compensate non-idealities in currents drawn from the grid. A power quality conditioner consisting of three power converters connected to a common DC link is analysed. One of these converters acts as an active power filter for removing unwanted harmonics in grid currents feeding a non-linear load. The other two converters instead remove the harmonics from the voltage at the terminals of a sensitive load. The control of the shunt converter is designed to be fast enough for power quality servicing but also has a fast disturbance rejection capability. Simulation and experimental results validating the concept are provided along with obtained total harmonic distortion improvements