1,347 research outputs found
Towards Data Sharing across Decentralized and Federated IoT Data Analytics Platforms
In the past decade the Internet-of-Things concept has overwhelmingly entered all of the fields where data are produced and processed, thus, resulting in a plethora of IoT platforms, typically cloud-based, that centralize data and services management. In this scenario, the development of IoT services in domains such as smart cities, smart industry, e-health, automotive, are possible only for the owner of the IoT deployments or for ad-hoc business one-to-one collaboration agreements. The realization of "smarter" IoT services or even services that are not viable today envisions a complete data sharing with the usage of multiple data sources from multiple parties and the interconnection with other IoT services.
In this context, this work studies several aspects of data sharing focusing on Internet-of-Things. We work towards the hyperconnection of IoT services to analyze data that goes beyond the boundaries of a single IoT system. This thesis presents a data analytics platform that: i) treats data analytics processes as services and decouples their management from the data analytics development; ii) decentralizes the data management and the execution of data analytics services between fog, edge and cloud; iii) federates peers of data analytics platforms managed by multiple parties allowing the design to scale into federation of federations; iv) encompasses intelligent handling of security and data usage control across the federation of decentralized platforms instances to reduce data and service management complexity.
The proposed solution is experimentally evaluated in terms of performances and validated against use cases. Further, this work adopts and extends available standards and open sources, after an analysis of their capabilities, fostering an easier acceptance of the proposed framework. We also report efforts to initiate an IoT services ecosystem among 27 cities in Europe and Korea based on a novel methodology.
We believe that this thesis open a viable path towards a hyperconnection of IoT data and services, minimizing the human effort to manage it, but leaving the full control of the data and service management to the users' will
A Lightweight Multi-Attack CAN Intrusion Detection System on Hybrid FPGAs
Rising connectivity in vehicles is enabling new capabilities like connected
autonomous driving and advanced driver assistance systems (ADAS) for improving
the safety and reliability of next-generation vehicles. This increased access
to in-vehicle functions compromises critical capabilities that use legacy
invehicle networks like Controller Area Network (CAN), which has no inherent
security or authentication mechanism. Intrusion detection and mitigation
approaches, particularly using machine learning models, have shown promising
results in detecting multiple attack vectors in CAN through their ability to
generalise to new vectors. However, most deployments require dedicated
computing units like GPUs to perform line-rate detection, consuming much higher
power. In this paper, we present a lightweight multi-attack quantised machine
learning model that is deployed using Xilinx's Deep Learning Processing Unit IP
on a Zynq Ultrascale+ (XCZU3EG) FPGA, which is trained and validated using the
public CAN Intrusion Detection dataset. The quantised model detects denial of
service and fuzzing attacks with an accuracy of above 99 % and a false positive
rate of 0.07%, which are comparable to the state-of-the-art techniques in the
literature. The Intrusion Detection System (IDS) execution consumes just 2.0 W
with software tasks running on the ECU and achieves a 25 % reduction in
per-message processing latency over the state-of-the-art implementations. This
deployment allows the ECU function to coexist with the IDS with minimal changes
to the tasks, making it ideal for real-time IDS in in-vehicle systems.Comment: 5 pages, 2 figures, 6 table
Definition and specification of connectivity and QoE/QoS management mechanisms â final report
This document summarizes the WP5 work throughout the project, describing its functional architecture and the solutions that implement the WP5 concepts on network control and orchestration. For this purpose, we defined 3 innovative controllers that embody the network slicing and multi tenancy: SDM-C, SDM-X and SDM-O. The functionalities of each block are detailed with the interfaces connecting them and validated through exemplary network processes, highlighting thus 5G NORMA innovations. All the proposed modules are designed to implement the functionality needed to provide the challenging KPIs required by future 5G networks while keeping the largest possible compatibility with the state of the art
Emerging research directions in computer science : contributions from the young informatics faculty in Karlsruhe
In order to build better human-friendly human-computer interfaces,
such interfaces need to be enabled with capabilities to perceive
the user, his location, identity, activities and in particular his interaction
with others and the machine. Only with these perception capabilities
can smart systems ( for example human-friendly robots or smart environments) become posssible. In my research I\u27m thus focusing on the
development of novel techniques for the visual perception of humans and
their activities, in order to facilitate perceptive multimodal interfaces,
humanoid robots and smart environments. My work includes research
on person tracking, person identication, recognition of pointing gestures,
estimation of head orientation and focus of attention, as well as
audio-visual scene and activity analysis. Application areas are humanfriendly
humanoid robots, smart environments, content-based image and
video analysis, as well as safety- and security-related applications. This
article gives a brief overview of my ongoing research activities in these
areas
A Cognitive Routing framework for Self-Organised Knowledge Defined Networks
This study investigates the applicability of machine learning methods to the routing protocols for achieving rapid convergence in self-organized knowledge-defined networks. The research explores the constituents of the Self-Organized Networking (SON) paradigm for 5G and beyond, aiming to design a routing protocol that complies with the SON requirements. Further, it also exploits a contemporary discipline called Knowledge-Defined Networking (KDN) to extend the routing capability by calculating the âMost Reliableâ path than the shortest one.
The research identifies the potential key areas and possible techniques to meet the objectives by surveying the state-of-the-art of the relevant fields, such as QoS aware routing, Hybrid SDN architectures, intelligent routing models, and service migration techniques. The design phase focuses primarily on the mathematical modelling of the routing problem and approaches the solution by optimizing at the structural level. The work contributes Stochastic Temporal Edge Normalization (STEN) technique which fuses link and node utilization for cost calculation; MRoute, a hybrid routing algorithm for SDN that leverages STEN to provide constant-time convergence; Most Reliable Route First (MRRF) that uses a Recurrent Neural Network (RNN) to approximate route-reliability as the metric of MRRF. Additionally, the research outcomes include a cross-platform SDN Integration framework (SDN-SIM) and a secure migration technique for containerized services in a Multi-access Edge Computing
environment using Distributed Ledger Technology.
The research work now eyes the development of 6G standards and its compliance with Industry-5.0 for enhancing the abilities of the present outcomes in the light of Deep Reinforcement Learning and Quantum Computing
Developing a distributed electronic health-record store for India
The DIGHT project is addressing the problem of building a scalable and highly available information store for the Electronic Health Records (EHRs) of the over one billion citizens of India
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