Joint Active and Passive Beamforming Design in Intelligent Reflecting Surface(IRS)-Assisted Covert Communications:A Multi-Agent DRL Approach

Intelligent Reflecting Surface(IRS),with the potential capability to reconstruct the electromagnetic propagation environment,evolves a new IRSassisted covert communications paradigm to eliminate the negligible detection of malicious eavesdroppers by coherently beaming the scattered signals and suppressing the signals leakage.However,when multiple IRSs are involved,accurate channel estimation is still a challenge due to the extra hardware complexity and communication overhead.Besides the crossinterference caused by massive reflecting paths,it is hard to obtain the close-formed solution for the optimization of covert communications.On this basis,the paper improves a heterogeneous multi-agent deep deterministic policy gradient(MADDPG)approach for the joint active and passive beamforming(Joint A&P BF)optimization without the channel estimation,where the base station(BS)and multiple IRSs are taken as different types of agents and learn to enhance the covert spectrum efficiency(CSE)cooperatively.Thanks to the‘centralized training and distributed execution’feature of MADDPG,each agent can execute the active or passive beamforming independently based on its partial observation without referring to others.Numeral results demonstrate that the proposed deep reinforcement learning(DRL)approach could not only obtain a preferable CSE of legitimate users and a low detection of probability(LPD)of warden,but also alleviate the communication overhead and simplify the IRSs deployment.

Fuzzy Geometric Programming in Multivariate Stratified Sample Surveys in Presence of Non-Response with Quadratic Cost Function

In this paper, the problem of non-response with significant travel costs in multivariate stratified sample surveys has been formulated of as a Multi-Objective Geometric Programming Problem (MOGPP). The fuzzy programming approach has been described for solving the formulated MOGPP. The formulated MOGPP has been solved with the help of LINGO Software and the dual solution is obtained. The optimum allocations of sample sizes of respondents and non respondents are obtained with the help of dual solutions and primal-dual relationship theorem. A numerical example is given to illustrate the procedure.

A Review of Human Vulnerabilities in Cyber Security: Challenges and Solutions for Microfinance Institutions

This review examines human vulnerabilities in cybersecurity within Microfinance Institutions, analyzing their impact on organizational resilience. Focusing on social engineering, inadequate security training, and weak internal protocols, the study identifies key vulnerabilities exacerbating cyber threats to MFIs. A literature review using databases like IEEE Xplore and Google Scholar focused on studies from 2019 to 2023 addressing human factors in cybersecurity specific to MFIs. Analysis of 57 studies reveals that phishing and insider threats are predominant, with a 20% annual increase in phishing attempts. Employee susceptibility to these attacks is heightened by insufficient training, with entry-level employees showing the highest vulnerability rates. Further, only 35% of MFIs offer regular cybersecurity training, significantly impacting incident reduction. This paper recommends enhanced training frequency, robust internal controls, and a cybersecurity-aware culture to mitigate human-induced cyber risks in MFIs.

A prototype model of zero trust architecture blockchain withEigenTrust-based practical Byzantine fault tolerance protocol to managedecentralized clinical trials

The COVID-19 pandemic necessitated the emergence of Decentralized Clinical Trials (DCTs) due to patientretention, accelerating trials, improving data accessibility, enabling virtual care, and facilitating seamlesscommunication through integrated systems. However, integrating systems in DCTs exposes clinical data to potentialsecurity threats, making them susceptible to theft at any stage, a high risk of protocol deviations, andmonitoring issues. To mitigate these challenges, blockchain technology serves as a secure framework, acting as adecentralized ledger, creating an immutable environment by establishing a zero-trust architecture, where dataare deemed untrusted until verified. In combination with Internet of Things (IoT)-enabled wearable devices,blockchain secures the transfer of clinical trial data on private blockchains during DCT automation and operations.This paper proposes a prototype model of the zero-Trust Architecture Blockchain (z-TAB) to integratepatient-generated clinical trial data during DCT operation management. The EigenTrust-based PracticalByzantine Fault Tolerance (T-PBFT) algorithm has been incorporated as a consensus protocol, leveragingHyperledger Fabric. Furthermore, the IoT has been integrated to streamline data processing among stakeholderswithin the blockchain platforms. Rigorous evaluation has been done for immutability, privacy and security,mutual consensus, transparency, accountability, tracking and tracing, and temperature‒humidity controlparameters.

Intelligent geospatial maritime risk analytics using the Discrete Global Grid System

Each year,accidents involving ships result in significant loss of life,environmental pollution and economic losses.The promotion of navigation safety through risk reduction requires methods to assess the spatial distribution of the relative likelihood of occurrence.Yet,such methods necessitate the integration of large volumes of heterogenous datasets which are not well suited to traditional data structures.This paper proposes the use of the Discrete Global Grid System(DGGS)as an efficient and advantageous structure to integrate vessel traffic,metocean,bathymetric,infrastructure and other relevant maritime datasets to predict the occurrence of ship groundings.Massive and heterogenous datasets are well suited for machine learning algorithms and this paper develops a spatial maritime risk model based on a DGGS utilising such an approach.A Random Forest algorithm is developed to predict the frequency and spatial distribution of groundings while achieving an R2 of 0.55 and a mean squared error of 0.002.The resulting risk maps are useful for decision-makers in planning the allocation of mitigation measures,targeted to regions with the highest risk.Further work is identified to expand the applications and insights which could be achieved through establishing a DGGS as a global maritime spatial data structure.

Global Reference Grids for Big Earth Data

The emerging field of Discrete Global Grid Systems(DGGS)provides a way to organise,store and analyse spatio-temporal data at multiple resolutions and scales(from near global scales down to microns).DGGS partition the entire planet into a discrete hierarchy of global tessellations of progressively finer resolution zones(or cells).Data integration,decomposition and aggregation are optimised by assigning a unique spatio-temporal identifier to each zone.These identifiers are encodings of both the zone’s location and its resolution.As a result,complex multi-dimensional,multi-resolution spatio-temporal operations are simplified into sets of 1D array and filter operations.