157 research outputs found
Securing IoT Applications through Decentralised and Distributed IoT-Blockchain Architectures
The integration of blockchain into IoT can provide reliable control of the IoT network's
ability to distribute computation over a large number of devices. It also allows the AI
system to use trusted data for analysis and forecasts while utilising the available IoT
hardware to coordinate the execution of tasks in parallel, using a fully distributed
approach.
This thesis's  rst contribution is a practical implementation of a real world IoT-
blockchain application,
ood detection use case, is demonstrated using Ethereum proof
of authority (PoA). This includes performance measurements of the transaction con-
 rmation time, the system end-to-end latency, and the average power consumption.
The study showed that blockchain can be integrated into IoT applications, and that
Ethereum PoA can be used within IoT for permissioned implementation. This can be
achieved while the average energy consumption of running the
ood detection system
including the Ethereum Geth client is small (around 0.3J).
The second contribution is a novel IoT-centric consensus protocol called honesty-
based distributed proof of authority (HDPoA) via scalable work. HDPoA was analysed
and then deployed and tested. Performance measurements and evaluation along with
the security analyses of HDPoA were conducted using a total of 30 di erent IoT de-
vices comprising Raspberry Pis, ESP32, and ESP8266 devices. These measurements
included energy consumption, the devices' hash power, and the transaction con rma-
tion time. The measured values of hash per joule (h/J) for mining were 13.8Kh/J,
54Kh/J, and 22.4Kh/J when using the Raspberry Pi, the ESP32 devices, and the
ESP8266 devices, respectively, this achieved while there is limited impact on each de-
vice's power. In HDPoA the transaction con rmation time was reduced to only one
block compared to up to six blocks in bitcoin.
The third contribution is a novel, secure, distributed and decentralised architecture
for supporting the implementation of distributed arti cial intelligence (DAI) using
hardware platforms provided by IoT. A trained DAI system was implemented over the
IoT, where each IoT device hosts one or more neurons within the DAI layers. This
is accomplished through the utilisation of blockchain technology that allows trusted
interaction and information exchange between distributed neurons. Three di erent
datasets were tested and the system achieved a similar accuracy as when testing on a
standalone system; both achieved accuracies of 92%-98%. The system accomplished
that while ensuring an overall latency of as low as two minutes. This showed the secure architecture capabilities of facilitating the implementation of DAI within IoT
while ensuring the accuracy of the system is preserved.
The fourth contribution is a novel and secure architecture that integrates the ad-
vantages o ered by edge computing, arti cial intelligence (AI), IoT end-devices, and
blockchain. This new architecture has the ability to monitor the environment, collect
data, analyse it, process it using an AI-expert engine, provide predictions and action-
able outcomes, and  nally share it on a public blockchain platform. The pandemic
caused by the wide and rapid spread of the novel coronavirus COVID-19 was used as
a use-case implementation to test and evaluate the proposed system. While providing
the AI-engine trusted data, the system achieved an accuracy of 95%,. This is achieved
while the AI-engine only requires a 7% increase in power consumption. This demon-
strate the system's ability to protect the data and support the AI system, and improves
the IoT overall security with limited impact on the IoT devices.
The  fth and  nal contribution is enhancing the security of the HDPoA through
the integration of a hardware secure module (HSM) and a hardware wallet (HW). A
performance evaluation regarding the energy consumption of nodes that are equipped
with HSM and HW and a security analysis were conducted. In addition to enhancing
the nodes' security, the HSM can be used to sign more than 120 bytes/joule and
encrypt up to 100 bytes/joule, while the HW can be used to sign up to 90 bytes/joule
and encrypt up to 80 bytes/joule. The result and analyses demonstrated that the HSM
and HW enhance the security of HDPoA, and also can be utilised within IoT-blockchain
applications while providing much needed security in terms of con dentiality, trust in
devices, and attack deterrence.
The above contributions showed that blockchain can be integrated into IoT systems.
It showed that blockchain can successfully support the integration of other technolo-
gies such as AI, IoT end devices, and edge computing into one system thus allowing
organisations and users to bene t greatly from a resilient, distributed, decentralised,
self-managed, robust, and secure systems
LIPIcs, Volume 261, ICALP 2023, Complete Volume
LIPIcs, Volume 261, ICALP 2023, Complete Volum
Onboard Mission- and Contingency Management based on Behavior Trees for Unmanned Aerial Vehicles
Unmanned Aerial Vehicles (UAVs) have gained significant attention for their potential in various sectors, including surveillance, logistics, and disaster management. This thesis focuses on developing a novel onboard mission and contingency management system based on Behavior Trees for UAVs. The study aims to assert if behavior trees can be effectively applied to this domain and how they perform with respect to other modelling architectures. Furthermore, this document explores which tree structures are more efficient, good-design practices and behavior tree limitations. Overall, this thesis addresses the challenge of autonomous onboard decision-making of UAVs in complex and dynamic environments, particularly in the context of delivery missions in off-shore wind farms. The developed architecture is tested in a simulated environment. The research integrates a Skill Manager, a Mission Planner, and a Mission and Contingency Manager. The architecture leverages Behavior Trees to facilitate both mission execution and contingency management. The thesis also presents a quantitative analysis of key performance indicators, providing a comparative evaluation against traditional architectures like Finite State Machines. The results indicate that the proposed system is efficient in mission execution and effective in handling contingencies. This study offers a comprehensive structure targeting onboard planning, contingency management and concurrent actions execution. It also presents a quantitative analysis of Behavior Trees' performance in UAV mission execution and reactivity to contingent situations. It contributes to the ongoing discourse on UAV autonomy, offering insights beneficial for the broader deployment of UAVs in various industrial applications
Applications
Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications
Reconfigurable Computing Systems for Robotics using a Component-Oriented Approach
Robotic platforms are becoming more complex due to the wide range of modern applications, including multiple heterogeneous sensors and actuators. In order to comply with real-time and power-consumption constraints, these systems need to process a large amount of heterogeneous data from multiple sensors and take action (via actuators), which represents a problem as the resources of these systems have limitations in memory storage, bandwidth, and computational power.
Field Programmable Gate Arrays (FPGAs) are programmable logic devices that offer high-speed parallel processing. FPGAs are particularly well-suited for applications that require real-time processing, high bandwidth, and low latency. One of the fundamental advantages of FPGAs is their flexibility in designing hardware tailored to specific needs, making them adaptable to a wide range of applications. They can be programmed to pre-process data close to sensors, which reduces the amount of data that needs to be transferred to other computing resources, improving overall system efficiency. Additionally, the reprogrammability of FPGAs enables them to be repurposed for different applications, providing a cost-effective solution that needs to adapt quickly to changing demands. FPGAs' performance per watt is close to that of Application-Specific Integrated Circuits (ASICs), with the added advantage of being reprogrammable.
Despite all the advantages of FPGAs (e.g., energy efficiency, computing capabilities), the robotics community has not fully included them so far as part of their systems for several reasons. First, designing FPGA-based solutions requires hardware knowledge and longer development times as their programmability is more challenging than Central Processing Units (CPUs) or Graphics Processing Units (GPUs). Second, porting a robotics application (or parts of it) from software to an accelerator requires adequate interfaces between software and FPGAs. Third, the robotics workflow is already complex on its own, combining several fields such as mechanics, electronics, and software.
There have been partial contributions in the state-of-the-art for FPGAs as part of robotics systems. However, a study of FPGAs as a whole for robotics systems is missing in the literature, which is the primary goal of this dissertation. Three main objectives have been established to accomplish this. (1) Define all components required for an FPGAs-based system for robotics applications as a whole. (2) Establish how all the defined components are related. (3) With the help of Model-Driven Engineering (MDE) techniques, generate these components, deploy them, and integrate them into existing solutions.
The component-oriented approach proposed in this dissertation provides a proper solution for designing and implementing FPGA-based designs for robotics applications.
The modular architecture, the tool 'FPGA Interfaces for Robotics Middlewares' (FIRM), and the toolchain 'FPGA Architectures for Robotics' (FAR) provide a set of tools and a comprehensive design process that enables the development of complex FPGA-based designs more straightforwardly and efficiently. The component-oriented approach contributed to the state-of-the-art in FPGA-based designs significantly for robotics applications and helps to promote their wider adoption and use by specialists with little FPGA knowledge
Fundamentals
Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters
Edge Computing for Internet of Things
The Internet-of-Things is becoming an established technology, with devices being deployed in homes, workplaces, and public areas at an increasingly rapid rate. IoT devices are the core technology of smart-homes, smart-cities, intelligent transport systems, and promise to optimise travel, reduce energy usage and improve quality of life. With the IoT prevalence, the problem of how to manage the vast volumes of data, wide variety and type of data generated, and erratic generation patterns is becoming increasingly clear and challenging. This Special Issue focuses on solving this problem through the use of edge computing. Edge computing offers a solution to managing IoT data through the processing of IoT data close to the location where the data is being generated. Edge computing allows computation to be performed locally, thus reducing the volume of data that needs to be transmitted to remote data centres and Cloud storage. It also allows decisions to be made locally without having to wait for Cloud servers to respond
Tools and Algorithms for the Construction and Analysis of Systems
This open access book constitutes the proceedings of the 28th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2022, which was held during April 2-7, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 46 full papers and 4 short papers presented in this volume were carefully reviewed and selected from 159 submissions. The proceedings also contain 16 tool papers of the affiliated competition SV-Comp and 1 paper consisting of the competition report. TACAS is a forum for researchers, developers, and users interested in rigorously based tools and algorithms for the construction and analysis of systems. The conference aims to bridge the gaps between different communities with this common interest and to support them in their quest to improve the utility, reliability, exibility, and efficiency of tools and algorithms for building computer-controlled systems
Online learning on the programmable dataplane
This thesis makes the case for managing computer networks with datadriven methods automated statistical inference and control based on measurement data and runtime observations—and argues for their tight integration with programmable dataplane hardware to make management decisions faster and from more precise data. Optimisation, defence, and measurement of networked infrastructure are each challenging tasks in their own right, which are currently dominated by the use of hand-crafted heuristic methods. These become harder to reason about and deploy as networks scale in rates and number of forwarding elements, but their design requires expert knowledge and care around unexpected protocol interactions. This makes tailored, per-deployment or -workload solutions infeasible to develop. Recent advances in machine learning offer capable function approximation and closed-loop control which suit many of these tasks. New, programmable dataplane hardware enables more agility in the network— runtime reprogrammability, precise traffic measurement, and low latency on-path processing. The synthesis of these two developments allows complex decisions to be made on previously unusable state, and made quicker by offloading inference to the network.
To justify this argument, I advance the state of the art in data-driven defence of networks, novel dataplane-friendly online reinforcement learning algorithms, and in-network data reduction to allow classification of switchscale data. Each requires co-design aware of the network, and of the failure modes of systems and carried traffic. To make online learning possible in the dataplane, I use fixed-point arithmetic and modify classical (non-neural) approaches to take advantage of the SmartNIC compute model and make use of rich device local state. I show that data-driven solutions still require great care to correctly design, but with the right domain expertise they can improve on pathological cases in DDoS defence, such as protecting legitimate UDP traffic. In-network aggregation to histograms is shown to enable accurate classification from fine temporal effects, and allows hosts to scale such classification to far larger flow counts and traffic volume. Moving reinforcement learning to the dataplane is shown to offer substantial benefits to stateaction latency and online learning throughput versus host machines; allowing policies to react faster to fine-grained network events. The dataplane environment is key in making reactive online learning feasible—to port further algorithms and learnt functions, I collate and analyse the strengths of current and future hardware designs, as well as individual algorithms
Proceedings of the 22nd Conference on Formal Methods in Computer-Aided Design – FMCAD 2022
The Conference on Formal Methods in Computer-Aided Design (FMCAD) is an annual conference on the theory and applications of formal methods in hardware and system verification. FMCAD provides a leading forum to researchers in academia and industry for presenting and discussing groundbreaking methods, technologies, theoretical results, and tools for reasoning formally about computing systems. FMCAD covers formal aspects of computer-aided system design including verification, specification, synthesis, and testing
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