Multi-agent deep reinforcement learning-based key generation for graph layer security

All research work was conducted whilst all authors were at Cranfield University.Recently, the emergence of Internet of Things (IoT) devices has posed a challenge for securing information and avoiding attacks. Most of the cryptography solutions are based on physical layer security (PLS), whose idea is to fully exploit the properties of wireless channel state information (CSI) for generating symmetric keys between two communication nodes. However, accurate channel estimation is vulnerable for attackers and relies on powerful signal processing capability, which is not suitable for low-power IoT devices. In this paper, we expect to apply graph layer security (GLS) to exploit the common features of physical dynamics detected by IoT sensors placed in networked systems to generate keys for data encryption and decryption, which we believe is a new frontier to security for both industry and academic research. We propose a distributed key generation algorithm based on multi-agent deep reinforcement learning (MADRL) approach, which enables communication nodes to cooperatively generate symmetric keys based on their locally detected physical dynamics (e.g., water/gas/oil/electrical pressure/flow/voltage) with low computational complexity and without information exchange. In order to demonstrate the feasibility, we conduct and evaluate our key generation algorithm in both a simulated and real water distribution network. The experimental results show that the proposed algorithm has considerable performance in terms of randomness, bit agreement rate (BAR), and so on.This work has been supported by the PETRAS National Centre of Excellence for IoT Systems Cybersecurity, which has been funded by the UK EPSRC under grant number EP/S035362/1.ACM Transactions on Privacy and Securit

An analysis of factors that influence the spatial pattern of faecal matter flow in unsewered cities

The management of sanitation systems in unsewered cities in low and middle income countries is a critical issue, yet it is unclear where the risk hotspots are and where interventions should be focused. This study utilised a prototype model, developed by the authors, to map the spatial pattern of faecal flow in Rajshahi city, a secondary city in northwest Bangladesh with a population around a million. This city serves as a representative example of 60 such secondary cities in Bangladesh and hundreds more in the economically developing region in Asia, Africa and Latin America. The model relies on assumptions that carry significant uncertainties; hence, the study employed a sensitivity analysis with multiple plausible scenarios to characterise these uncertainties, aiming to identify ways to improve the model further. Five major influencing factors on the spatial pattern of faecal flow were identified: the emptying of septic tanks, the use of soak pits, and sludge removal from drains, variations in faecal matter production by building types, and the presence or absence of toilets. These factors were shown to collectively have a significant impact (almost 50 % changed) on the model outcome, depending upon the assumptions made. The study offers insights that will guide future data collection efforts by emphasising the need to understand these specific influencing factors and their spatial pattern. Consequently, this research has broader implications for urban sanitation management as well as associated public health research like wastewater surveillance, risk assessment, and disease dynamics in similar urban settings, offering insights into areas of uncertainty that need to be addressed in future modelling efforts.This work was supported by the UKRI Engineering and Physical Science Research Council (EPSRC) through a Ph.D. studentship received by the first author (M.S.S.) as part of the EPSRC Centre for Doctoral Training in Water and Waste Infrastructure and Services Engineered for Resilience (Water-WISER). EPSRC Grant No.: EP/S022066/1.Science of The Total Environmen

Robustness and resilience of different solid-liquid separation technologies for tertiary phosphorus removal to low levels by coagulation

In this study, three tertiary solid separation technologies were assessed on their robustness and resilience against an effluent phosphorus target of <0.3 mg P/L at steady state and dynamic conditions. The ballasted flocculation system was found to be very robust at delivering the low P target. Alternatively, cloth filtration provided a more sustainable option for less strict consents of sub 0.5 mg P/L. The effluent from the membrane system was more variable but it was shown to meet the low consents even with increased phosphorus and solids content in the feed. A molar ratio of 1.37 Fe: P was shown to be sufficient to meet the P target at short contact times as with the ballasted flocculation process. It was highlighted that optimisation of up-stream flocculation can be a considerable factor for consistent performance. Overall, the study determined key attributes of the different technologies tested providing valuable insights for technology selection at full scale.Funding for this study was gratefully received by Severn Trent Water.Science of The Total Environmen

Nanomaterials as a new frontier platform: metal-doped and hybrid carbon dots as enzyme mimics for environmental applications

Environmental pollution has become an inexorable problem for the planet Earth. The precise detection and degradation of heavy metals, pesticides, industrial-, pharmaceutical- and personal care- products is needed. Nanotechnology holds great promise in addressing global issues. Over the past decades, nanozymic nanomaterials have exceptionally overcome the intrinsic limitations of natural enzymes. Carbon dots (CDs) exhibit unique structures, surface properties, high catalytic activities, and low toxicity. Different techniques, such as doping or surface passivation, can enhance these exceptional properties. Doping modifies CDs’ electronic, magnetic, optical, and catalytic properties considerably. Metal doping, a more significant strategy, involves the introduction of metallic impurities, which offer insight into enhancing the physicochemical properties of CDs. Metal-doped CDs exhibit higher optical absorbance and catalytic performance than pristine CDs. The literature shows that researchers have utilized various synthetic approaches to fabricate CDs-Metal nanozymes. Researchers have reported the metal-doped and hybrid CDs’ peroxidase, catalase, laccase, and superoxide dismutase-like activities. These metal-doped nanozymes put forward substantial environmental remediations and applications such as sensing, photocatalytic degradation, adsorption, and removal of environmental contaminants. This review thoroughly discussed the metal-based functionalization of CDs, the enzyme-like properties, and the ecological applications of metal-doped and hybrid enzymes. The review also presents the current novelties, remaining challenges, and future directions with key examples.Frontiers in Material

Critical success factors for ICT integration in agri-food sector: pathways for decarbonization and sustainability

A decarbonized agri-food sector may provide consumers with nutritious, secure, and reasonably priced food with a lower carbon impact. Decarbonizing the agri-food sector is intricate and necessitates a holistic strategy. Technological advancements, like Information and Communication Technologies (ICT), might be the solution. This study analyses the critical success factors (CSFs) for ICT integration in the agri-food sector in the Western and North Western States of India based on empirical data collected and analyzed. The study proposes a framework that determines and ranks the significant factors for ICT integration in the agri-food sector to achieve the decarbonization goals by utilizing the fuzzy evidential reasoning approach (FERA) and the evidential reasoning approach (EFA). The factors are examined based on the Technological, Organization, and Environmental (TOE) criteria. The results show that the most significant factors contributing to the effective implementation of ICT in the agri-food sector are continuous innovation and R&D, supportive policies and regulations, and cost-effectiveness. The results will assist managers and decision-makers in creating effective policies and making knowledgeable choices that will support sustainable growth in the agri-food industry by lowering carbon emissions through effective ICT integration.Cleaner Engineering and Technolog

Quantitative microbial risk assessment of bioaerosol emissions from squat and bidet toilets during flushing

Bioaerosol emissions during toilet flushing are an often‐overlooked source of potential health risks in shared public facilities. This study systematically investigated the emission characteristics of Staphylococcus aureus and Escherichia coli bioaerosols in washrooms with squat and bidet toilets under varying flushing conditions and ventilation scenarios. Using Monte Carlo simulation–based quantitative microbial risk assessment and sensitivity analysis, the study estimated the disease burden and identified key factors influencing risk. The results showed that squat toilets generated 1.7–2.6 times higher concentrations of S. aureus bioaerosols and 1.2–1.4 times higher concentrations of E. coli bioaerosols compared to bidet toilets. After the first flush, bioaerosol concentrations were 1.3–1.8 times (S. aureus) and 1.2–1.4 times (E. coli) lower than those observed after the second flush. The second flush released a higher proportion of fine bioaerosol particles (<4.7 µm), increasing inhalation risks. The disease health risk burden was consistently one order of magnitude lower after the first flush than the second one. Ventilation with a turned‐on exhaust fan further reduced the risk by one order of magnitude. Sensitivity analysis identified exposure concentration as the most influential parameter, contributing up to 50% of the overall risk. This study highlights the importance of optimizing toilet design and ventilation systems to mitigate bioaerosol emissions and associated health risks. It provides actionable insights for improving public washroom hygiene and minimizing bioaerosol exposure.F.C., Z.A.N., and C.Y. gratefully acknowledge the support of the Environmental Microbiology and Human Health Programme (Grant Reference NE/M010961/1) and the SPF Clean Air Programme (Grant NE/V002171/1) in facilitating this collaborative study.Risk Analysi

From raw data to monotonic and trendable features reflecting degradation trends in turbofan engines

The performance of prognostic models relies heavily on the form and trend of the extracted features. However, the raw data collected from physical systems are inherently noisy, large in volume, and exhibit significant variability, which makes them unsuitable for direct use in prognostics. These characteristics poorly reflect the degradation behavior of physical systems and contribute to the uncertainty of prognostic outcome. Hence, transforming this data into relevant features and carefully selecting them is crucial for meeting the specific needs of prognostic models. This paper aims to address data processing challenges by focusing on extraction and selection of high-quality monotonic features which clearly reflect the degradation and can reduce prognostics uncertainty. The proposed framework comprises three main stages: Data pre-processing, feature extraction, and feature selection. It includes a fitness analysis to evaluate the monotonicity and trendability of features supplemented by visual inspections to identify relevant features. Applied to the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) dataset from the NASA Ames Prognostics Data Repository, the framework reduces noise, improves feature monotonicity and trendability, and facilitates the selection of useful features - essential aspects for effective prognostic methods.This research was supported by the Centre for Digital Engineering and Manufacturing, Cranfield University, United Kingdom, 10.13039/5011000008592024 IEEE 3rd Industrial Electronics Society Annual On-Line Conference (ONCON

EEG signal processing techniques and applications—2nd Edition

Electroencephalography (EEG), as a well-established, non-invasive tool, has been successfully applied to a wide range of conditions due to its many evident advantages, such as economy, portability, easy operation, easy accessibility, and widespread availability in hospitals. EEG signals, with ultra-high time resolution, are vital in understanding brain functions. Traditionally, considerable attention in EEG signal processing and analysis has been paid to understanding brain activities from various perspectives, such as the detection and identification of abnormal frequencies in specific biological states, spatial–temporal and morphological characteristics of neurological disorder behaviours (e.g., paroxysmal or persistent discharges), the response of the brain nervous/neurological system to external stimuli, and the effects and responses to intermittent photic stimulation [1].Sensor

Deep learning based secure transmissions for the UAV-RIS assisted networks: trajectory and phase shift optimization

This paper investigates the secure transmissions in the Unmanned Aerial Vehicle (UAV) communication network facilitated by a Reconfigurable Intelligent Surface (RIS). In this network, the RIS acts as a relay, forwarding sensitive information to the legitimate receiver while preventing eavesdropping. We optimize the positions of the UAV at different time slots, which gives another degree to protect the privacy information. For the proposed network, a secrecy rate maximization problem is formulated. The non-convex problem is solved by optimizing the RIS's phase shifts and UAV trajectory. The RIS phase shift optimization problem is converted into a series of subproblems, and a non-linear fractional programming approach is conceived to solve it. Furthermore, the first-order taylor expansion is employed to transform the UAV trajectory optimization into convex function, and then we use the deep Q-network (DQN) method to obtain the UAV's trajectory. Simulation results show that the proposed scheme enhances the secrecy rate by 18.7% compared with the existing approaches.King Saud University; JCYJ20190806160218174This work was supported in part by the National Natural Science Foundation of China under Grants 62271399 and 62206221, in part by National Key Research and Development Program of China under Grant 2020YFB1807003, in part by Foundation of the Science, Technology, and Innovation Commission of Shenzhen Municipality under Grant JCYJ20190806160218174, in part by Zhejiang Provincial Natural Science Foundation of China under Grant LQ24F010003, in part by the Distinguished Scientist Fellowship Program (DSFP) at King Saud University, Riyadh, Saudi Arabia, and in part by the Bournemouth University Qualiy research funding: Flying ad-hoc networking and its applications.GLOBECOM 2024 - 2024 IEEE Global Communications Conferenc

Guest editorial: transforming food supply chains: harnessing the potential of the digital era

The International Journal of Logistics Managemen