Post-quantum blockchain for internet of things domain

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonIn the evolving realm of quantum computing, emerging advancements reveal substantial challenges and threats to existing cryptographic infrastructures, particularly impacting blockchain technologies. These are pivotal for securing the Internet of Things (IoT) ecosystems. The traditional blockchain structures, integral to myriad IoT applications, are susceptible to potential quantum computations, emphasizing an urgent need for innovations in post-quantum blockchain solutions to reinforce security in the expansive domain of IoT. This PhD thesis delves into the crucial exploration and meticulous examination of the development and implementation of post-quantum blockchain within the IoT landscape, focusing on the incorporation of advanced post-quantum cryptographic algorithms in Hyperledger Fabric, a forefront blockchain platform renowned for its versatility and robustness. The primary aim is to discern viable post-quantum cryptographic solutions capable of fortifying blockchain systems against impending quantum threats enhancing security and reliability in IoT applications. The research comprehensively evaluates various post-quantum public-key generation and digital signature algorithms, performing detailed analyses of their computational time and memory usage to identify optimal candidates. Furthermore, the thesis proposes an innovative lattice-based digital signature scheme Fast-Fourier Lattice-based Compact Signature over NTRU (Falcon), which leverages the Monte Carlo Markov Chain (MCMC) algorithm as a trapdoor sampler to augment its security attributes. The research introduces a post-quantum version of the Hyperledger Fabric blockchain that integrates post-quantum signatures. The system utilizes the Open Quantum Safe (OQS) library, rigorously tested against NIST round 3 candidates for optimal performance. The study highlights the capability to manage IoT data securely on the post-quantum Hyperledger Fabric blockchain through the Message Queue Telemetry Transport (MQTT) protocol. Such a configuration ensures safe data transfer from IoT sensors directly to the blockchain nodes, securing the processing and recording of sensor data within the node ledger. The research addresses the multifaceted challenges of quantum computing advancements and significantly contributes to establishing secure, efficient, and resilient post-quantum blockchain infrastructures tailored explicitly for the IoT domain. These findings are instrumental in elevating the security paradigms of IoT systems against quantum vulnerabilities and catalysing innovations in post-quantum cryptography and blockchain technologies. Furthermore, this thesis introduces strategies for the optimization of performance and scalability of post-quantum blockchain solutions and explores alternative, energy-efficient consensus mechanisms such as the Raft and Stellar Consensus Protocol (SCP), providing sustainable alternatives to the conventional Proof-of-Work (PoW) approach. A critical insight emphasized throughout this thesis is the imperative of synergistic collaboration among academia, industry, and regulatory bodies. This collaboration is pivotal to expedite the adoption and standardization of post-quantum blockchain solutions, fostering the development of interoperable and standardized technologies enriched with robust security and privacy frameworks for end users. In conclusion, this thesis furnishes profound insights and substantial contributions to implementing post-quantum blockchain in the IoT domain. It delineates original contributions to the knowledge and practices in the field, offering practical solutions and advancing the state-of-the-art in post-quantum cryptography and blockchain research, thereby paving the way for a secure and resilient future for interconnected IoT systems

A sustainable ultrafiltration of sub-20 nm nanoparticles in water and isopropanol: experiments, theory and machine learning

This research focused on ultrafiltration (UF) for particles down to 2 nm against membranes with larger pore size in water and IPA, which has the potential to save up to 90% of energy. This study developed electrospray (ES) - scanning mobility particle sizer (SMPS) method to fast and effective measure retention efficiencies for small particles (ZnS, Au and PSL) on polytetrafluoroethylene (PTFE), polyvinylidene fluoride (PVDF) and polycarbonate (PCTE) in different liquids. Theoretical models that could quantitatively explain the experimental results for small particles in medium-polarity organic solvents were also developed. Results showed that the highest efficiency was up to ~80% with 10 nm Au nanoparticle challenged on 100 nm rated PTFE, which demonstrated the feasibility of the proposed sustainable UF. The theoretical models were validated by experimental results and indicated that a higher efficiency was possible by enhancing material properties of membranes, particles, or liquids. Therefore, optimization on filtration condition was performed. A hybrid artificial neural network (ANN) and particle swarm optimization algorithm (PSO) models was firstly applied in this case. The dataset includes all the experimental results and some additional calculated retention efficiencies. Optimization parameters include membrane zeta potential, pore size, particle size, particle zeta potential, and Hamaker constant. The ANN model provided highly correlated predicted values with target values. The PSO model showed that a filtration efficiency of 99.9% could be achieved by using a 52.2 nm filter with a -20.3 mV zeta potential, 5.5 nm nanoparticles with a 41.4 mV zeta potential, and a combined Hamaker constan

3D printing of Aluminium alloys: Additive Manufacturing of Aluminium alloys using selective laser melting

© 2019 The Authors Metal Additive Manufacturing (AM) processes, such as selective laser melting (SLM), enable the fabrication of arbitrary 3D-structures with unprecedented degrees of freedom. Research is rapidly progressing in this field, with promising results opening up a range of possible applications across both scientific and industrial sectors. Many sectors are now benefiting from fabricating complex structures using AM technologies to achieve the objectives of light-weighting, increased functionality, and part number reduction, among others. AM also lends potential in fulfilling demands for reducing the cost and design-to-manufacture time. Aluminium alloys are of the main material systems receiving attention in SLM research, being favoured in many high-value applications. However, processing them is challenging due to the difficulties associated with laser-melting aluminium where parts suffer various defects. A number of studies in recent years have developed approaches to remedy them and reported successful SLM of various Al-alloys and have gone on to explore its potential application in advanced componentry. This paper reports on recent advancements in this area and highlights some key topics requiring attention for further progression. It aims to develop a comprehensive understanding of the interrelation between the various aspects of the subject, as this is essential to demonstrate credibility for industrial needs

2018 Symposium Brochure

This dissertation explores the mean field Heisenberg spin system and its evolution in time. We first study the system in equilibrium, where we explore the tool known as Stein's method, used for determining convergence rates to thermodynamic limits, both in an example proof for a mean field Ising system and in tightening a previous result for the equilibrium mean field Heisenberg system. We then model the evolution of the mean field Heisenberg model using Glauber dynamics and use this method to test the equilibrium results of two previous papers, uncovering a typographical error in one. Agreement in other aspects between theory and our simulations validates our approach in the equilibrium case. Next, we compare the evolution of the Heisenberg system under Glauber dynamics to a number of forms of Brownian motion and determine that Brownian motion is a poor match in most situations. Turning back to Stein's method, we consider what sort of proof regarding the behavior of the mean field Heisenberg model over time is obtainable and look at several possible routes to that path. We finish up by offering a Stein's method approach to understanding the evolution of the mean field Heisenberg model and offer some insight into its convergence in time to a thermodynamic limit. This demonstrates the potential usefulness of Stein's method in understanding the finite time behavior of evolving systems. In our efforts, we encounter several holes in current mathematical and physical knowledge. In particular, we suggest the development of tools for Markov chains currently unavailable and the development of a more physically based algorithm for the evolution of Heisenberg systems. These projects lie beyond the scope of this dissertation but it is our hope that these ideas may be useful to future research

Emergent properties of microbial activity in heterogeneous soil microenvironments:Different research approaches are slowly converging, yet major challenges remain

Over the last 60 years, soil microbiologists have accumulated a wealth of experimental data showing that the usual bulk, macroscopic parameters used to characterize soils (e.g., granulometry, pH, soil organic matter and biomass contents) provide insufficient information to describe quantitatively the activity of soil microorganisms and some of its outcomes, like the emission of greenhouse gases. Clearly, new, more appropriate macroscopic parameters are needed, which reflect better the spatial heterogeneity of soils at the microscale (i.e., the pore scale). For a long time, spectroscopic and microscopic tools were lacking to quantify processes at that scale, but major technological advances over the last 15 years have made suitable equipment available to researchers. In this context, the objective of the present article is to review progress achieved to date in the significant research program that has ensued. This program can be rationalized as a sequence of steps, namely the quantification and modeling of the physical-, (bio)chemical-, and microbiological properties of soils, the integration of these different perspectives into a unified theory, its upscaling to the macroscopic scale, and, eventually, the development of new approaches to measure macroscopic soil characteristics. At this stage, significant progress has been achieved on the physical front, and to a lesser extent on the (bio)chemical one as well, both in terms of experiments and modeling. In terms of microbial aspects, whereas a lot of work has been devoted to the modeling of bacterial and fungal activity in soils at the pore scale, the appropriateness of model assumptions cannot be readily assessed because relevant experimental data are extremely scarce. For the overall research to move forward, it will be crucial to make sure that research on the microbial components of soil systems does not keep lagging behind the work on the physical and (bio)chemical characteristics. Concerning the subsequent steps in the program, very little integration of the various disciplinary perspectives has occurred so far, and, as a result, researchers have not yet been able to tackle the scaling up to the macroscopic level. Many challenges, some of them daunting, remain on the path ahead