International Journal of Ad Hoc and Ubiquitous Computing (24 papers in press)

Regular Issues

• Spectrum sensing using Reconfigurable Intelligent Surfaces with Acoustic Energy Harvesting  
  by Faisal Alanazi  
  Abstract: This paper investigates a novel spectrum sensing framework that integrates Reconfigurable Intelligent Surfaces (RIS) and acoustic energy harvesting in a cognitive radio environment. Specifically, a primary user (PU) harvests ambient acoustic energy to power its data transmission to a primary destination. The transmitted signal is reflected by a strategically placed RIS and subsequently received at the secondary user (SU), which performs spectrum sensing using an energy detection technique. The proposed system model captures the interplay between acoustic energy harvesting dynamics, RIS-assisted signal reflection, and the impact on the detection performance of the energy detector at the SU. Analytical expressions for detection probability is derived, taking into account acoustic energy harvesting. Simulation results validate the analytical findings and demonstrate the potential of the proposed architecture to enable efficient and green spectrum sensing in energy-constrained environments.
  Keywords: Spectrum sensing; RIS; acoustic energy harvesting.
  DOI: 10.1504/IJAHUC.2025.10077632
   
  
• DDPG-Based Joint Energy and Offloading Optimisation in UAV-Aided Mobile Edge Computing  
  by Changchun Qin, Yongzhi Ran, Fei Wang, Junwei Luo 
  Abstract: In this paper, we consider an unmanned aerial vehicles (UAV)-aided mobile edge computing (MEC) system, where a fixed-wing UAV provides computation resources for terminal devices (TDs). The UAV can effectively establish line-of-sight communication links with TDs. However, the limitations of energy capacity and transmission coverage of UAV and TDs are still challenges in UAV-aided MEC. The energy capacity affects the service lifetime of the UAV-aided MEC and the transmission coverage has an impact on the quality of service. To address these issues, we study a joint energy and offloading optimisation problem, where we aim to minimise the energy consumption of UAV and maximise the total offloaded data volume of TDs by optimising TDs transmission power and UAVs flight angle and speed. We propose a deep deterministic policy gradient (DDPG)-based algorithm to solve this problem. Simulation results show that our proposed algorithm has good convergence and is better than other algorithms.
  Keywords: Mobile edge computing (MEC); unmanned aerial vehicles (UAV); energy consumption; offloaded data volume; flight trajectory; deep deterministic policy gradient (DDPG).
  DOI: 10.1504/IJAHUC.2025.10077741
   
  
• Optimizing Intrusion Detection Systems Using Ensemble Voting Classifiers: A Multi-Dataset Performance Analysis  
  by Sayantan Singha Roy, Amrita Bhadra, Munesh Chandra Trivedi, Awnish Kumar 
  Abstract: As cyberattacks continue to increase in size and complexity, so does the need to protect sensitive data. Ensemble voting classifier has risen as one of the most extensive methods to improve the performance of intrusion detection systems which play a pivotal role in securing networks. The basis of this study is to use six common classifiers [logistic regression (LR), random forest (RF), decision tree (DT), AdaBoost (ADB), Gaussian Naive Bayes (GNB), and K-nearest neighbours (KNN)] on three benchmark datasets (KDD-CUP, CIC-DDoS2019, UNSW-NB15) and compare the classifiers given their final metrics (accuracy, recall, precision and F1-score). This study has explored 62 ensemble configurations to assess their suitability as base classifiers. Results show that ADB and RF ensembles perform best on KDD-CUP, ADB-DT-GNB-RF combinations excel on CIC-DDoS2019, and ADB-RF achieve top results on UNSW-NB15. The study provides practical guidance for developing data-driven IDS solutions and enhances ensemble-based cybersecurity research.
  Keywords: Ensemble Machine Learning; Intrusion Detection Systems ; Ensemble Voting Classifiers; Benchmark Datasets; Comparative Analysis.
  DOI: 10.1504/IJAHUC.2026.10077881
   
  
• A Hybrid Ant Colony Genetic Optimisation Framework for Automated CNN Architecture and Hyperparameter Tuning in Image Classification  
  by Hong-Ren Chen, Mu-Yen Chen, Jia-Lang Xu, Yi-Syuan Wang, Po-Yen Hsu 
  Abstract: Hyperparameter and architectural optimisation remain critical challenges in deep learning, directly affecting model accuracy, generalisation, and efficiency. This study proposes a hybrid ant colony genetic optimisation (ACGO) algorithm for image classification, integrating the pheromone-guided search of ant colony optimisation with the genetic diversity mechanisms of crossover and mutation. A novel encoding scheme for convolutional neural network (CNN) hyperparameters enables automated tuning across datasets of varying complexity. The proposed ACGO is evaluated against five metaheuristic algorithms genetic algorithm, ant colony optimisation, coral reef optimisation, particle swarm optimisation, and Grey Wolf optimiser on CIFAR-10 and CIFAR-100. ACGO achieved 81.1% accuracy on CIFAR-10 and 52.7% on CIFAR-100 (an absolute improvement of approximately 4% and 3% against competing methods), demonstrating strong adaptability and computational efficiency. These results highlight ACGOs potential as a scalable, robust optimisation framework for deep learning-based image classification.
  Keywords: metaheuristic algorithms; hyperparameter optimization; image classification; model encoding; convolutional neural networks.
  DOI: 10.1504/IJAHUC.2026.10078301
   
  
• Resource Bargaining and Server Selection Framework in Multi-Access Edge Computing Platform  
  by Sungwook Kim 
  Abstract: In this study, we consider both the allocation of different types of resources in heterogeneous MEC servers and server selection. To solve these two control problems, we formulate a joint control framework based on the interactive mechanism. For the multi-resource allocation problem, we design the new compromise bargaining solution (CBS) to distribute different type resources. By combining well-known bargaining solutions, the CBS can provide a comprised consensus solution in a fair-efficient manner. To address the server selection problem, the novel multi-criteria selection (MCS) method is developed. Based on the combination of ideal solution and weighted average, the MCS method selects an appropriate MEC server for each device. In the heterogeneous MEC dynamics, the key insight of our proposed approach is to provide a collaborative control paradigm through the interaction of CBS and MCS processes
  Keywords: Multi-access edge computing; Multi-resource allocation problem; Compromise bargaining solutions; Multi-criteria selection; Cooperative game theory.
  DOI: 10.1504/IJAHUC.2025.10078326
   
  
• Path Planning of Multi Drone System for Disaster Analysis and Management  
  by Abhijeet Pandey, Bhavi Khator, J. Sathish Kumar 
  Abstract: Disasters often cause unpredictable damage that complicates emergency response efforts. While drones (UAVs) offer promise for accessing hard-to-reach areas, current systems lack robust autonomous navigation and 3D situational awareness needed for efficient disaster management. This paper presents a novel approach using multiple drones to collaboratively generate detailed 3D models and employs an advanced deep learning-based obstacle detection and 3D path-planning system. Extending traditional 2D algorithms into 3D, our method integrates new cost functions to optimise drone swarm navigation considering environmental and operational constraints. Using pre-disaster 3D maps and real-time data, the system enables faster, safer, and more efficient search and rescue operations. Validated in AirSim simulations, this framework significantly enhances UAV autonomy and coordination, addressing key limitations in current disaster response technologies and improving both pre- and post-disaster management strategies.
  Keywords: Path Planning; Disaster Management; Unmanned Aerial Vehicles.
  DOI: 10.1504/IJAHUC.2026.10078440
   
  
• A Federated Blockchain Framework for Secure and Scalable Remote Patient Monitoring Using IoT Devices  
  by Sai Dheeraj Peketi, Pranav Sanand Puligandla, S.S.R. Sri Harsha Kedarisetty, Praveen Raj Kanickairaj, J. Sathish Kumar 
  Abstract: The increasing demand for secure, efficient, and scalable healthcare solutions has led to the integration of advanced technologies such as blockchain, IoT, and AI in Remote Patient Monitoring (RPM). Traditional RPM systems suffer from security vulnerabilities, lack of interoperability, and limited scalability. In this paper, we propose a novel Federated Blockchain-based RPM system leveraging Hyperledger Fabric, designed to provide decentralized, secure, and efficient patient data management. Our approach ensures data integrity, privacy, and controlled accessibility through smart contract automation. Comparative performance analysis highlights substantial improvements in transaction throughput, latency reduction, and data reliability. Furthermore, we discuss practical deployment challenges, regulatory compliance, and interoperability with EHRs and the Internet of Medical Things (IoMT). This work aims to revolutionize remote healthcare by delivering an intelligent, secure, and patient-centric approach, fostering innovation in real-time health monitoring and predictive medical interventions.
  Keywords: Remote Patient Monitoring (RPM); Federated Blockchain; Hyperledger Fabric; Decentralized Data Management; Electronic Health Records (EHRs).
  DOI: 10.1504/IJAHUC.2026.10078635
   
  
• Edge-Cloud Integrated Fraud Detection for E-Commerce Transactions Using Hybrid Siamese-LSTM Model  
  by Winner Pulakhandam, Vallu Visrutatma Rao, Pramod Begur Nagaraj, Archana Chaluvadi, Priyan Malarvizhi Kumar 
  Abstract: E-commerce systems are increasingly vulnerable to fraud, requiring efficient detection methods capable of handling large-scale transaction data. However, existing approaches struggle to adapt to evolving fraud patterns and often incur high computational costs. This study proposes a novel edge-cloud integrated fraud detection framework using a hybrid Siamese neural network with long short-term memory (Siamese-LSTM). The e-commerce transaction dataset is pre-processed using K-nearest neighbours for data imputation and Tukeys Fences for outlier removal. The processed data is analysed in an edge-cloud environment, where behavioural patterns and their frequencies are extracted at the edge. Additional indicators, such as unusual transactions and login location mismatches, are forwarded to the cloud for further analysis. The Siamese-LSTM model detects deviations from normal spending behaviour to identify fraudulent transactions. Experimental results demonstrate high performance, achieving 99.47% accuracy, 99.55% precision, and 99.38% recall, while ensuring efficiency and scalability
  Keywords: Fraud Detection; E-Commerce; Edge-Cloud Integration; Deep Learning; Siamese Neural Network; Long Short-Term Memory.
  DOI: 10.1504/IJAHUC.2026.10078656
   
  
• Efficient Edge Resource Allocation Using Deep Neural Network-XGBoost for Distributed Computing in Edge Networks  
  by Venkataramesh Induru, Karthik Kushala, Vijai Anand Ramar, Priyadarshini Radhakrishnan, Premalatha R 
  Abstract: The proposed Deep Neural Network (DNN) and XGBoost combined with Pufferfish Optimization Algorithm (POA) improve the performance of edge computing substantially with better outcomes on the most important metrics. In terms of latency (22 ms), resource usage (95%), and energy efficiency (75 J), the model performs exceptionally well, finishing jobs in 2.8 seconds with 98% accuracy. With 96% performance and 98% adaptability, it is perfect for power-constrained, dynamic IoT and edge computing scenarios that need scalability, accuracy, and speed.This total performance reflects the model's promise in making edge computing a more efficient, effective, and sustainable platform.
  Keywords: Edge Computing; Resource Allocation; IoT; Puffer fish optimization; Deep Neural network; XG Boost.
  DOI: 10.1504/IJAHUC.2026.10078661
   
  
• Building Resilient AI Architecture with MLOps, Chaos Engineering, and SRE for Scalable and Reliable ML Deployments  
  by Pramod Begur Nagaraj, Archana Chaluvadi, Winner Pulakhandam, Vallu Visrutatma Rao, Padmavathy R 
  Abstract: Machine Learning Operations (MLOps) has proved an important area for developing models, deploying them, and monitoring them in machine learning (ML) production environments. ML systems face challenges in scalability and reliability. This work introduces MLOps to optimize models, with Chaos Engineering and Site Reliability Engineering (SRE) ensuring resilience. Data from e-commerce, healthcare, and finance is processed using Matrix Factorization Imputation for missing values and Quantile Clipping for outliers. Features are derived via Quantile Binning and Feast. Lasso Regression aids feature selection, Quantile Regression Forests capture non-linearities, and Evidently AI monitors drift, while SRE ensures reliable deployment.The experimental findings indicate that the ML model works very well across many industries, such as E-Commerce, Healthcare and Finance, obtaining high levels of predictive accuracy and low levels of error metrics. This assists in developing machine learning systems that become more scalable and continuously improve efficiency throughout a wide range of applications.
  Keywords: Machine Learning Operations; Chaos Engineering; Site Reliability Engineering; Machine Learning; Observability; Automation; E-Commerce; Healthcare; and Finance.
  DOI: 10.1504/IJAHUC.2026.10078670
   
  
• Optimising Job Scheduling in Apache Spark: A Simulation Framework for Algorithm Comparison and Performance Prediction  
  by Vishnu Prasad Verma, Santosh Kumar, Srinivas Naik Nenavath 
  Abstract: Selecting the best scheduling algorithm is essential for optimising Apache Sparks performance and resource usage. This study conducts a comparative analysis of different scheduling algorithms, including first in, first out (FIFO), fair, earliest deadline first (EDF), round robin, shortest job next, least laxity first, priority scheduler, and multilevel feedback queue using a Python-based simulation framework. The analysis focuses on critical performance metrics such as turnaround time, waiting time, deadline adherence, and violation rates. Our findings highlight notable performance differences among scheduling algorithms when tested under various workload conditions. Among them, the multilevel feedback queue (MLFQ) consistently emerged as the top performer, achieving the lowest turnaround and waiting times, a remarkable earliest arrival ratio of 97.3%, and no deadline violations. The experimental findings guide researchers, cloud infrastructure designers, and Spark system architects in successfully choosing and refining scheduling techniques to manage various dynamic workload needs
  Keywords: Distributed Computing; Spark job scheduling; Big data; Scheduling algorithms; resource management; optimization.
  DOI: 10.1504/IJAHUC.2025.10078686
   
  
• Generative Artificial Intelligence in Ubiquitous IoT Systems: Advances, Applications and Challenges  
  by Yang Li, Leifeng Wei, Jing Nie, Joel Rodrigues, Muhammad Attique Khan, Margo Sulistio, Hua-Tsung Lin 
  Abstract: Ubiquitous IoT systems generate massive multi-modal data, yet transforming this deluge into actionable intelligence for real-time decision-making remains a fundamental challenge. The convergence of Generative AI and Large Language Models offers a transformative paradigm. This review proposes a generic cognitive orchestration architecture wherein the LLM functions as a central reasoning engine, composing specialised GAI models for simulation and content generation while mediating natural human-system interaction. We demonstrate this domain-agnostic framework across smart manufacturing, transportation, healthcare, built environments, and agriculture. Critical challenges persist, including data scarcity and heterogeneity, model hallucination and computational cost, and systemic ethical concerns including accountability, privacy, and the digital divide. Priority research directions include retrieval-augmented generation, domain-adapted foundation models, edge-native efficiency, and value-aligned governance. Realising dependable, equitable GAI-LLM systems is essential for the coming generation of truly intelligent ubiquitous computing environments.
  Keywords: Generative AI; Large Language Models; Ubiquitous computing; IoT intelligence; Edge AI; Cognitive IoT.
  DOI: 10.1504/IJAHUC.2026.10078834
   
  
• Query-Response generation system for reliable responses to agriculture-related queries  
  by Y.Y. Narayana Reddy, Thulasiram Narayanan, Adusumalli Balaji 
  Abstract: A smart deep learning based agricultural query response generation system that can provide trustworthy and semantically accurate answers in this research. The proposed system introduces two key innovations. Initially, a term inverse added Word2Vector (TIA-W2V) embedding model is developed to improve semantic representation by combining term importance with contextual embeddings. Second, a Cosine similarity enclosed triplet long shortterm memory (CT-LSTM) network is proposed to learn complex semantic relationships and predict responses. This enhances the model's ability to focus on pertinent parts of the query and response, which improves context comprehension and response generation. By effectively managing long sequences, understanding context, and capturing semantic connections, proposed framework can assist in producing more precise and relevant responses to questions about agriculture. The simulation results show that the proposed method performs better in terms of accuracy (97.9%), precision (96.58%), recall (96.12%), F1-score (96.98%), Query response time (15sec), BLEU (7.98%) and METEOR (8.59%), respectively.
  Keywords: Deep learning; Query-Response generation system; pre-processing; Term Inverse Added Word2vector; Cosine similarity enclosed triplet long short-term memory; text mining models.
  DOI: 10.1504/IJAHUC.2026.10078838
   
  
• KERSC: A Knowledge-Enhanced Reasoning Semantic Communication Framework for Text Transmission  
  by Lexuan Wang, Bo Shen 
  Abstract: Recently, deep learning-based semantic communication has attracted considerable attention. However, most existing approaches assume shared prior knowledge between transmitter and receiver, overlooking the role of external knowledge in semantic reasoning. To address this limitation, this paper proposes a Knowledge-Enhanced Reasoning Semantic Communication (KERSC) framework for text transmission, focusing on improving semantic reconstruction at the receiver. By modeling the semantic and structural features of external knowledge, we construct a semantically smooth embedding space to support reasoning. An effective interaction mechanism is introduced, comprising a contrastive learning module for precise knowledge alignment and a two-layer relation-aware graph attention network for structured reasoning. Finally, a knowledge-enhanced decoder with an adaptive gating mechanism fuses the transmitted semantics with knowledge representations to boost performance. Simulation results demonstrate that the proposed model effectively enhances semantic reconstruction accuracy under various channel conditions.
  Keywords: Semantic Communication; Knowledge-enhanced Reasoning; Knowledge Graph; Knowledge Representation Learning; Deep Learning.
  DOI: 10.1504/IJAHUC.2026.10078982
   
  
• A Psychology- and Emotion-Aware Multimodal Interaction Model for Depression Detection  
  by Yanping Fu, Fei Liu 
  Abstract: The escalating global prevalence of depression necessitates automated, scalable detection systems. While existing multimodal deep learning methods have made progress, they often overlook core psychological and emotional elements. To bridge this gap, this paper proposes PEAMI, a Psychology- and Emotion-Aware Multimodal Interaction model. PEAMI utilizes an LLM to extract psychological features from text and pre-trained emotion models for audio and visual cues. Furthermore, we introduce a Memorable Feature Enhanced (MFE) module based on MambaFormer to capture long-range contextual dependencies within each modality, effectively mitigating temporal asynchrony. For multimodal fusion, we design a Multimodal Feature Interaction (MFI) module that hierarchically models interactions to capture complex uni-modal, bi-modal, and tri-modal correlations. Evaluation on the popular AVEC-2014 and AVEC-2019 datasets demonstrates the validity of the proposed PEAMI model in depression detection tasks and its superiority of the structure in handling the comprehensive features.
  Keywords: Depression Detection; Multimodal Feature ; Emotion factors; Psychology Theory.
  DOI: 10.1504/IJAHUC.2026.10079171
   
  
• Modified Bulletproofs-based Zero-Knowledge Proof Scheme for the Ranges of Multiple Confidential Transactions  
  by Zhanlin Wang 
  Abstract: Zero-knowledge range proof is a privacy-protection approach to prove that the amount of money in a confidential transaction falls within a specified scope. Among the proposed strategies for zero-knowledge range proof, bulletproofs is the most efficient, and it does not need a trusted third party. Bulletproofs uses the inner product argument to hide a single secret value, and it can be improved to hide a vector of secret value by using the Pederson vector commitment. We construct a modified bulletproofs-based protocol of a single-value argument that can implement a zero-knowledge range proof for a single, confidential transaction. To achieve a zero-knowledge range proof for multiple confidential transactions, we first propose a parallel single-value argument protocol, which is a simple method. Then, we present an improved vector-value argument protocol that flats a matrix to a vector. The performance analysis reveals that the efficiency of the vector-value argument protocol is much better than that of the parallel single-value protocol. We provide proofs for the perfect completeness and computational knowledge soundness of our single-value protocol and vector-value argument protocol. We apply our protocols in the application of auditing confidential transactions through the blockchain interoperability oracle.
  Keywords: zero-knowledge argument; range proof; Bulletproofs; confidential transaction; Pederson vector commitment.
  DOI: 10.1504/IJAHUC.2026.10079174
   
  
• Energy-efficient and intelligent routing protocol design for next-generation wireless networks using vibration energy harvesting and RIS-assisted links  
  by Ayman Madbouly 
  Abstract: This paper investigates the design of energy-efficient and intelligent routing protocols for nextgeneration wireless networks by jointly exploiting vibration energy harvesting (VEH) and reconfigurable intelligent surface (RIS)-assisted communications. In the proposed framework, both the source and relay nodes harvest energy from ambient mechanical vibrations and dynamically adapt their transmission strategies based on the harvested energy and channel conditions. RIS-assisted links are employed to enhance signal propagation, mitigate severe fading, and extend network coverage while maintaining ultra-low power operation. We develop optimal and low-complexity suboptimal routing schemes that select the most suitable multi-hop path by jointly considering harvested energy availability, channel quality, and RIS phase configuration. Analytical expressions for end-to-end signalto-noise ratio, outage probability, and throughput are derived, providing fundamental insights into system performance. Extensive numerical results demonstrate that the proposed routing protocols significantly outperform conventional non-energy-harvesting and non-RIS-assisted routing schemes in terms of energy efficiency, reliability, and network lifetime.
  Keywords: Routing; vibrations; 6G; RIS.
  DOI: 10.1504/IJAHUC.2026.10079203
   
  
• An Energy Efficient Handoff through Joint Channel Probing and AP Selection for Mobile Wi-Fi Users  
  by Babul P. Tewari, Biplab Mandal, Poulomi Mukherjee 
  Abstract: This paper addresses a combined approach of channel probing and Access Point (AP) selection for an energy-efficient handoff model in high-speed Wi-Fi networks. We have established that the RSSI-based handoff model may fail to determine the best possible target AP in Wi-Fi network, even if the system has options with the same throughput but their energy consumption differs. We have focused on the design of a strategic handoff model that considers both throughput and energy dissipation by framing an energy efficiency handoff metric. Additionally, we addressed the issue of handoff delay in Wi-Fi network by limiting the probe count based on threshold throughput. The proposed combined approach is framed into an optimization problem, and a game theoretic model, with a greedy solution has been developed. A comparative performance evaluation shows that the proposed approach achieves over 60% energy efficiency and more than 25% throughput improvement compared to the benchmark algorithm.
  Keywords: Wi-Fi Handoff; Network selection; Energy efficiency; Channel probing; Handoff delay.
  DOI: 10.1504/IJAHUC.2026.10079342
   
  
• High-Resolution Remote Sensing Image Classification using Meta Learning deep fused Inception Network  
  by Judith Varshini Varshini, Vimal Shanmuganathan 
  Abstract: Remote sensing image classification has gained significant research attention. Existing approaches focus on improving accuracy but result in networks that are large, complex, and costly for real-time applications. To address this, a meta-learning fine-tuned inception network is proposed. The NWPU-RESISC45 dataset is pre-processed during normalisation techniques. Features are extracted using a deep fused Inception neural network with feature-level concatenation and dimensionality reduction. These fused features are input to a meta-learning classifier that dynamically weights and selects the most relevant features. The proposed model achieves 96% accuracy on NWPU-RESISC45, outperforming state-of-the-art methods, and supports disaster management, environmental monitoring, and urban planning applications.
  Keywords: Remote Sensing;NWPU-RESISC45;Inception Network;Transfer Learning;Meta Learning.
  DOI: 10.1504/IJAHUC.2026.10079422
   
  
• Federated Multi-Agent Framework for QoS-Aware Edge Orchestration of DAG-Based Microservices  
  by Amin Mohajer, Abbas Mirzaei, Mostafa Darabi, Xavier Fernando 
  Abstract: Edge platforms are increasingly required to support delay-sensitive services that are composed of multiple dependent microservices rather than a single monolithic task. In such systems, orchestration is difficult because each service must satisfy execution-order constraints while competing for limited communication and computing resources across distributed edge regions. This challenge becomes even greater when traffic patterns, resource availability, and service demand vary from one region to another, making centralised training costly and less practical. To address this problem, we propose a federated multi-agent framework for QoS-aware edge orchestration of DAG-based microservices. In the proposed framework, each application is modelled as a directed acyclic graph, where nodes denote microservices and edges represent execution dependencies. Regional edge agents learn local orchestration policies from their own observations, including queue status, resource usage, link conditions, and workflow progress, while a federated aggregation process periodically combines model knowledge without exchanging raw data. The framework jointly supports microservice placement, precedence-aware scheduling, and resource allocation with the goal of improving deadline satisfaction and end-to-end service quality. This design provides a scalable and privacy-aware solution for decentralised workflow orchestration in dynamic edge environments.
  Keywords: Mobile edge computing; DAG-based microservices; federated learning; precedence-aware scheduling; service placement.
  DOI: 10.1504/IJAHUC.2026.10079551
   
  
• Fault Monitoring and Life Prediction of Bearings in Automated Production Lines Based on Cloud-Edge Collaboration with Optimised Allocation of Algorithm  
  by Qianhan Zhang, QingHai Xie, Binyan Wei, Tao Ma, Jinping Du, Yingming Shi, Junxian Han, Le Geng 
  Abstract: This study develops a cloud-edge collaborative framework to optimize real-time monitoring and process control in automated production lines, focusing on the critical role of ball bearings. A comprehensive cloud-edge collaborative framework is developed to optimize task allocation between cloud and edge computing. At the edge level, an optimized deployment model for edge servers is established, taking into account computation time and workload balance to determine the optimal deployment nodes and required number of edge servers. For real-time bearing fault diagnosis, a lightweight hollow convolutional neural network (HCNN), which is designed to minimize latency while maintaining high diagnostic accuracy. On the cloud side, an enhanced HCNN model incorporating attention mechanisms and bidirectional long short-term memory (BiLSTM) units is employed for bearing lifespan prediction, improving predictive precision. The proposed framework is validated on an automobile pressure plate production line through real-time collection of rolling bearing signals. Experimental results confirm the effectiveness of the proposed approach in both fault diagnosis and lifespan prediction, demonstrating its feasibility for intelligent industrial applications.
  Keywords: Cloud-Edge Collaboration; Bearing Failure; Hollow Convolution.
  DOI: 10.1504/IJAHUC.2026.10079552
   
  
• Multi-Level Semantic Traceability for Power Grid Material Quality Management  
  by Yulin Luo, Xin Tan, Jianyun Shi 
  Abstract: This paper uses graph theory and semantic modelling to show complex supply chain interactions in a multi-level semantic traceability model for power grid material quality monitoring. A semantic quality rule engine and quality score propagation function assess and govern content quality throughout its lifecycle. The model distributes quality ratings among suppliers, manufacturers, components, products, and test records, where poor quality in any phase affects the final product quality score. Weighting parameters 1 and 2 significantly impact the final score by balancing intrinsic and upstream quality. Visualisation supports model interpretation and supplier performance assessment. The risk assessment matrix helps identify high-risk suppliers and components and supports personalised risk reduction strategies. The recommended model achieves the fastest query time and handles larger graphs, outperforming existing methods. With 94% quality score accuracy, 92% compliance detection, 91% risk prediction, and 93% efficiency, the model provides an effective power grid material quality control system.
  Keywords: Multi-Level Semantic Traceability; Power Grid Material Quality Management; Supply Chain Traceability; Material Quality Control; Semantic Modeling; Graph Theory; Quality Management in Power Grids.
  DOI: 10.1504/IJAHUC.2026.10079566
   
  
• Secure E-Voting using Homomorphic Encryption: An Experimental Analysis and Findings  
  by Swaraj Pal, Maroti Deshmukh, Sneha Chauhan 
  Abstract: Protecting voter privacy in cloud-based electronic voting systems remains a significant challenge, particularly during data processing. Homomorphic encryption addresses this by enabling secure computation on encrypted data. This paper presents a comparative evaluation of five homomorphic encryption schemes-RSA, ElGamal, Paillier, BFV, and CKKS within a basic plurality voting framework. Votes are encoded as binary vectors, and tallied using homomorphic aggregation. Each scheme is assessed on computation time, ciphertext uniqueness, and tally accuracy. ElGamal ensures strong privacy through probabilistic encryption but incurs higher decryption costs. RSA offers fast performance but lacks ciphertext diversity, undermining confidentiality. Paillier enables secure summation with moderate efficiency. Lattice-based schemes, BFV and CKKS, scale well for larger systems BFV delivers exact results, while CKKS provides faster, approximate outputs. Despite limited ciphertext visibility in BFV and CKKS, accurate decryption is maintained. Overall, scheme selection should balance privacy, performance, and precision, with CKKS favored for speed and ElGamal for robust anonymity.
  Keywords: E-voting; Homomorphic Encryption; RSA Scheme; Paillier Scheme; ElGamal Scheme; BFV Scheme; CKKS Scheme.
  DOI: 10.1504/IJAHUC.2026.10079761
   
  
• Design and Implementation of a Twin System for Distributed Control System Switching Network  
  by Yi Gao, Guorong Luo, Yuan Zhang, Nan Zhao 
  Abstract: Current industrial control systems lack high-fidelity, large-scale simulation environments suitable for deep testing and training, making it difficult to reproduce the complex network behaviour and operating conditions of Distributed Control System (DCS). To address this limitation, this paper proposes a design scheme for a DCS switching network twin system based on Open vSwitch (OVS) and Docker technology. The proposed scheme consists of several key components: an OVS switch network based on the Spanning Tree Protocol (STP) is created on a Linux system; actual controllers are simulated using Windows Docker and connected to the OVS network via Virtual Local Area Network (VLAN) technology; and a front-end console is created for overall network manipulation. The system supports multiple fault simulation and key performance metric acquisition functions, designed to simulate and evaluate the actual performance of industrial control systems, thereby ensuring their safety and reliability in complex environments.
  Keywords: Distributed control system; Open vSwitch; Docker; Spanning tree protocol; Twin system.
  DOI: 10.1504/IJAHUC.2026.10079765