International Journal of Ad Hoc and Ubiquitous Computing (16 papers in press)

Regular Issues

• Hybrid Cheetah and Artificial Rabbits Optimisation Algorithm for Micro Electro Mechanical System  
  by Sabitha Balasubramanian, Raffik Rasheed 
  Abstract: In this manuscript, a Hybrid Cheetah and Artificial Rabbits Optimization Algorithm fostered Digital Control Systems for Micro Electro Mechanical Systems Gyroscope(MEMS-GYS-HCO-ARO) is proposed. In this method the design Procedure starts by designing a closed loop control systems for Micro Electro Mechanical Systems Gyroscope and loop parameters are optimized using Hybrid algorithms. First the loop parameters are optimised by the help of HCO and ARO. Then Dwarf Mongoose Optimisation Algorithm and LMS demodulator utilized for demodulate noise signal. The performance of the proposed MEMS-GYS-HCO-ARO method attains amplitude analysis of 39.25%, 33.36% and 32.99% of lower amplitude compared with existing methods, like a design of micro electro mechanical systems gyroscope using Hybrid Optimisation Algorithm (MEMS-GYS-HOA), Digital Control and Readout of MEMS Gyroscope Using Second-Order Sliding Mode Control(MEMS-GYS-SOSMC) and A Digital Closed Loop Sense MEMS Disk Resonator Gyroscope Circuit Design Based on Integrated Analog Front-end (DCLS-MEMS-IAF) respectively.
  Keywords: MEMS gyroscope; closed-loop system; control systems; hybrid cheetah and Artificial Rabbits algorithm; Monte Carlo analysis; demodulator.
  DOI: 10.1504/IJAHUC.2024.10068845
   
  
• AI-Based Behaviour-Aware Path Risk Assessment for Wheelchair-Centric Smart Assistive Devices  
  by Chiao-Wen Kao, Shih-Tung Wang, Chi-Sheng Huang 
  Abstract: Mobility challenges in aging societies underscore the need for safer navigation solutions for wheelchair users, who often face hazards like uneven surfaces and indistinct curbs. Addressing these concerns, this study introduces an AI-based behavior-aware path risk assessment system designed for wheelchair-centric assistive devices. The system combines object detection and semantic segmentation to identify critical road features, such as sidewalks, curbs, and lanes. A Path Risk Assessment Module evaluates path safety using a scoring algorithm incorporating regional weighting and adjusting to varying environmental conditions. Integrating Internet of Behavior principles, the system adapts dynamically to user behaviors and conditions, offering personalized risk assessments. Tested on a custom dataset, the system demonstrated accurate real-time evaluations, with horizontal camera orientations excelling in dynamic scenarios and vertical setups suited for detailed analysis. These findings highlight its potential to enhance safety and mobility, with future work focusing on dynamic obstacles and multi-view integration for broader applications.
  Keywords: Behavior-Aware Systems; Smart Assistive Devices; Path Risk Assessment; Semantic Segmentation; Wheelchair-Centric.
  DOI: 10.1504/IJAHUC.2025.10070877
   
  
• Efficient SWIPT Transmission Optimisation in Decentralised FD-NOMA-V2X Systems  
  by Peiying Zhang, Shengpeng Chen, Yi Wang, Di Zhang, Lizhuang Tan, Jian Wang 
  Abstract: This paper investigates the effectiveness of Simultaneous Wireless Information and Power Transfer (SWIPT) in a decentralized Full-Duplex Non-Orthogonal Multiple Access Vehicle-to-Everything (FD-NOMA-V2X) system. The proposed model enables direct communication between devices, reducing the burden on cellular networks in vehicular communication. Additionally, the FD-NOMA technology further enhances the system's spectral efficiency (SE). Considering the limited battery capacity of devices, a SWIPT transmission framework is introduced based on the system model, formulated as an optimization problem. First, the optimal solution to this problem is derived using the CVX tool. To increase the model’s practical applicability, a suboptimal solution with lower computational complexity than the CVX-based method is also proposed, based on an analytical approach. Simulation results demonstrate that as the energy harvesting threshold increases, the power allocation coefficients in both methods converge to a common point. Furthermore, the analytical method significantly reduces computation time compared to the CVX tool.
  Keywords: Caching and sharing mechanism; 5G; Internet of things; Energy efficiency.
  DOI: 10.1504/IJAHUC.2025.10072018
   
  
• Intelligent Task Offloading in Vehicular Edge Computing using Federated Learning and Kolmogorov Arnold Networks (FL-KAN)  
  by Shabariram C. P, Shanthi N, Alisha Shinaz, Lakshana Ranganathan 
  Abstract: In recent times, Vehicular Edge Computing has risen to become a crucial paradigm to handle the increasing computational demands of Smart Transportation Systems. However, multiple challenges exist including the dynamic nature of vehicular environments, resource constraints of edge servers, and high privacy for data. To address these challenges, an intelligent task offloading framework integrates Kolmogorov Arnold Networks (KAN) and Federated Learning (FL) is proposed. The framework works within a multi-tier architecture, using two different modes Vehicle-to-Roadside Unit and Vehicle-to-Infrastructure to offload tasks. The KAN is trained with historical task data allows efficient task offloading, while FL ensures privacy and scalability across the local and global model. The experimental simulations were performed to optimize parameters like service latency, energy consumption and transfer delay. The results depict the proposed approach exceeds the existing algorithms such as Machine Learning, Selective Model Aggregation, Reinforcement Learning, Deterministic Policy Gradient by 18%, 25%, 13% and 21%.
  Keywords: Vehicular Edge Computing; Task offloading; Transport System; Kolmogorov Arnold Networks; Federated Learning.
  DOI: 10.1504/IJAHUC.2025.10072307
   
  
• Large Models for Fatigue Driving Detection in Future Vehicles: a Predictive Analytics with Ensemble Neural Networks  
  by Guangwu Hu, Tan Chen, Wei Liu, Yan Li, Dandan Hu 
  Abstract: In this paper, we introduce a system for fatigue driving detection via analyzing spatial-temporal electroencephalogram (EEG) features and employing ensemble neural networks. We extract time-domain EEG features and spatial-domain metric features related to the driving process from EEG data and brain functional network (BFN) data over time. To effectively utilize these features, we develop a feature contribution algorithm that assigns varying contribution coefficients to the time-domain EEG features and the spatial-domain BFN features based on their relationship with the target class. Subsequently, we utilize two sets of weighted features as inputs for two different neural networks: The long short-term memory (LSTM) network and the pseudo three-dimensional convolutional neural network, allowing to harness the complementary information of spatial-temporal EEG features and the data processing capabilities of these two neural network algorithms. Experimental results corroborate the superior performance of the proposed ensemble neural network model compared with the state-of-the-art methods.
  Keywords: Large models; Fatigue driving detection; Ensemble neural networks; LSTM; EEG.
  DOI: 10.1504/IJAHUC.2025.10072543
   
  
• Modelling of Reconfigurable Intelligent Surfaces (RIS) with Adaptive Power and Thermal Energy Harvesting  
  by Abdulrahman Alghamdi 
  Abstract: Reconfigurable Intelligent Surfaces (RIS) have emerged as transformative elements in the evolution of wireless communication systems, particularly within Cognitive Radio Networks (CRNs) These surfaces enable dynamic control over the wireless environment, allowing for enhanced signal propagation, interference mitigation, and spectral efficiency In this work, we explore the integration of RIS with adaptive transmit power control and thermal energy harvesting, where energy is harvested from ambient temperature gradients This approach enables the RIS to operate sustainably by converting environmental thermal differences such as those found in industrial or natural settings into usable electrical energy. We investigate the performance of RIS-enabled CRNs under various system configurations through comprehensive analytical modelling and simulation, we demonstrate that the proposed RIS-assisted CRN with thermal energy harvesting significantly outperforms conventional systems, offering notable improvements in throughput. These results underscore the potential of RIS, powered by ambient thermal gradients.
  Keywords: Cognitive Radio Networks (CRN); thermal energy harvesting; adaptive transmit power; Rayleigh channels.
  DOI: 10.1504/IJAHUC.2025.10072666
   
  
• Efficient Hardware Implementation of KLEIN Cipher with Power Analysis Attack Resistance  
  by Parthasarathy R, Saravanan Paramasivam 
  Abstract: Lightweight hardware implementation of block ciphers is critical to ensure the safety of data in resource-constrained environments. In this work, two iterative architectures are designed for Klein-64/80/96 and implemented in both the FPGA devices and the ASIC platform. The first architecture is of 64-bit datapath size, utilized a minimum number of slices in FPGAs and exhibited very good throughput compared to the existing works in the literature. The second architecture utilised a 32-bit datapath with a minimum number of S-boxes and mixcolumns equation which resulted in an area of 1451 GE in the ASIC platform, determined using the Cadence tool and 45 nm technology library gpdk045. A Correlation power analysis attack is mounted on the FPGA implementation of KLEIN-64 and successfully extracted the 64-bit key. Threshold implementation is defined as a countermeasure to mitigate the power analysis attack and secure the hardware implementation against the power analysis attack.
  Keywords: KLEIN Cipher; Lightweight Cryptography; Low cost implementation; Power analysis attack; Threshold implementation.
  DOI: 10.1504/IJAHUC.2025.10073177
   
  
• Energy Optimisation Model Based On Ant-Mating Optimisation for Collaborative Fog Computing In Internet Of Drones  
  by Dillon Leong Lon Zan, Muhammad Umair Munir, Rafidah Binti Md Noor, Ismail Ahmedy, Rami Sihwail, Husam Ahmed Al Hamad 
  Abstract: Rapid advancements in drone technology have facilitated their deployment in various applications, including aerial surveys through the Internet of Drones (IoD). Given that IoD operations are resource-intensive, efficient management is crucial to avoid overloading drones and reducing power consumption. This study introduces an energy-aware task scheduling model for IoD operations within fog networks, enhancing resource allocation by offloading tasks from drones to fog devices. This method optimises drone data handling and significantly reduces IoD energy usage. We implemented the model in a simulated environment using an augmented version of iFogSim, with a focus on minimising energy expenditure in fog devices. Our findings reveal that the proposed Ant-Mating Optimisation (AMO) algorithm markedly outperforms traditional genetic algorithms in efficiency, presenting a viable solution for energy optimisation in IoD systems.
  Keywords: Internet of Drones (IoD); Fog Computing; Energy-aware Task Scheduling; Resource Allocation; Power Consumption Optimisation.
  DOI: 10.1504/IJAHUC.2025.10073276
   
  
• Learning Boosting: Enhancing Predictive Modelling in Blended Learning Environments  
  by Zhihong Xu, Chin-Hwa Kuo, Chih-Yung Chang, Jinjun Liu, Chunyan Yu 
  Abstract: Accurate prediction of student learning outcomes is critical for early intervention and instructional decision-making in blended learning environments. This study proposes learning boosting, a structure-enhanced predictive framework integrating community-aware Louvain clustering with a gradient boosting classification. Student activity graphs are clustered to detect latent behavioural communities, and the resulting structural labels are embedded as features for final prediction. Experiments on real-world data from a blended learning course with 102 students evaluate the method under multiple classification granularities, data modalities, and clustering strategies. Results show that learning boosting consistently outperforms 11 baseline models, achieving an F1-score of 0.892, AUC of 0.883, and recall of 0.903 in the three-class task. Ablation studies confirm the complementary benefits of structural feature extraction and clustering. The findings demonstrate that combining graph-based structural modelling with boosting classifiers offers a robust and interpretable approach to learning analytics, especially in sparse and multimodal conditions.
  Keywords: Learning Analytics; Blended Learning; Learning Outcome Prediction; Graph-Based Modeling; Gradient Boosting.
  DOI: 10.1504/IJAHUC.2025.10073446
   
  
• SCS-DSSS: a Compressive Sensing and Deep Semantic Segmentation Framework for Cooperative Spectrum Sensing in 5G Cognitive Radio Networks  
  by Jebamalar Leavine E, Azhagu Subha M 
  Abstract: The rapid proliferation of wireless devices and the advent of advanced 5G New Radio (NR) standards have intensified the need for efficient spectrum utilization. Cognitive Radio (CR) with Dynamic Spectrum Access (DSA) offers a solution, yet existing wideband sensing methods face a trade-off between accuracy and data overhead. This work proposes SCS-DSSS, an end-to-end framework integrating Spectrogram Compressive Sensing (SCS) with DeepLabv3+ semantic segmentation using a ResNet-50 backbone. This framework system performs joint spectral occupancy detection with signal-type classification (5G-NR/LTE) directly from compressively sampled spectrograms, reducing sensing load without sacrificing accuracy. Unlike traditional approaches, SCS-DSSS eliminates the need for handcrafted features or prior signal knowledge. Experimental results show a sub-band detection accuracy of 96.85%, with notably low false negative rates, even under 25% compression. The framework demonstrates strong robustness in low-SNR settings and is well-suited for cooperative CRNs in edge-deployed IoT and future 6G systems, enabling scalable and intelligent DSA.
  Keywords: 5G New Radio (NR); Cognitive Radio (CR); Dynamic Spectrum Access (DSA); Spectrogram Compressive Sensing; Deep Spectrum Sensing Segmentation; Wideband Spectrum Sensing; Semantic Segmentation.
  DOI: 10.1504/IJAHUC.2025.10073449
   
  
• Cooperative and Multi-criteria Control Algorithm for Future mmWave SWIPT UAV Networks  
  by Sungwook Kim 
  Abstract: In this paper, we focus on unmanned aerial vehicles (UAVs) to realise energy-transferring and information-transmitting simultaneously in the millimetre wave (mmWave) aware network system. By using the power splitting technique, we design a new cooperative control scheme for the mmWave based simultaneous wireless information and power transfer (SWIPT) UAV system. The control approach in our proposed scheme combines the cooperative bargaining principles and multi-criteria decision-making (MCDM) concepts to achieve the efficient usage of mmWave spectrum bands in the UAV integrated SWIPT network platform. According to the reference point and aspiration point based cooperative solutions, our proposed algorithms dynamically allocate the mmWave spectrum band. Based on the MCDM method, the allocated mmWave spectrum band is split into downlink and uplink phases, and we can harvest the energy during the downlink phase. By integrating two different control mechanisms, our comprehensive control framework facilitates balanced system performance.
  Keywords: Unmanned aerial vehicles; mmWave spectrum band; Simultaneous wireless information and power transfer; Cooperative game solutions; Multi-criteria decision.
  DOI: 10.1504/IJAHUC.2025.10073625
   
  
• Enhancing Cellular Network-Based Localisation using CNN-CellImage Method  
  by Ngoc Hung Pham, Dinh Thuan Nguyen, Huy Dinh Nguyen, The Vinh La, Hai Tung Ta, Van Hiep Hoang 
  Abstract: Location-based services (LBS) are essential in daily applications. However, GPS-based localisation often suffers from high power consumption, added hardware costs, and unreliable performance in weak or jammed signal environments. Cellular network-based localisation offers a practical alternative by exploiting existing mobile infrastructure. In our earlier work, we developed methods to collect and process cell information datasets using received signal strength (RSS) and evaluated traditional approaches such as centroid, weighted centroid, linear regression, support vector regression, multilayer perceptron, and fingerprinting. In this study, we propose CNN-CellImage, a novel technique that converts RSS and geographical cell relationships into image data for convolutional neural network processing. Using a dataset of 21,155 measurements collected over multiple days and environments, our method achieved a mean localisation error of 119.7 metres, significantly outperforming Cell-ID, centroid, and other machine learning approaches. These results highlight the effectiveness of CNN-CellImage for accurate and robust cellular-based localisation.
  Keywords: Location-based services; Localisation; Cellular Network; Signal Fingerprint; Received Signal Strength; CNN - Cell Image.
  DOI: 10.1504/IJAHUC.2025.10073864
   
  
• Document-Level Event Extraction via Gated Cross-Sentence Attention Network with Global Memory  
  by Shunyu Yao, Dan Liu, Jie Hu 
  Abstract: Event extraction aims to detect events from text, and identify arguments of the event and its role. When event-related information is distributed in a document, event arguments are always scattered across different sentences, and multiple events may co-exist in the same document. In order to meet these challenges, we propose a novel end-to-end model Gated Cross-sentence Attention Network with Global Memory called GCANGM. To address the multi-events challenge, we introduce a global memory unit to store the current path state and event information extracted in the past. In order to handle the argument-scattering challenge, we construct a gated cross-sentence attention layer so that the entity embedding can obtain the cross-sentence contextual information in the document. To prove the effectiveness of GCANGM, we conduct experiments on the widely used large-scale document-level event extraction dataset. The experimental results show that our method is effective in the challenging document-level event extraction task.
  Keywords: Event Extraction; Neural Network; Attention Mechanism.
  DOI: 10.1504/IJAHUC.2025.10074272
   
  
• Spectrum Sensing with Energy Harvesting from Vibrations  
  by Majed Abdouli 
  Abstract: In cognitive radio (CR) networks, efficient and reliable spectrum sensing is essential for opportunistic spectrum access However, energy consumption remains a critical limitation, particularly in environments where power sources are constrained This paper investigates a novel spectrum sensing framework where the primary source is powered via energy harvesting from ambient vibrations The harvested energy is used to transmit a signal to the primary user (PU), which then performs spectrum sensing using an energy detection technique We develop an analytical model to capture the behavior of the vibration-powered primary transmitter and its impact on the received signal's energy characteristics at the PU The proposed system combines two key technologies energy harvesting and cognitive sensing and presents a sustainable, low-power sensing mechanism suitable for remote or infrastructure-less deployments Simulation results validate the theoretical findings, demonstrating how the energy availability, determined by the vibration profile, affects the detection probability.
  Keywords: Spectrum sensing; energy harvesting from vibrations; Rayleigh and Nakagami channels.
  DOI: 10.1504/IJAHUC.2025.10074983
   
  
• Reconfigurable Intelligent Surfaces with Thermal Energy Harvesting  
  by Eman Bouazizi 
  Abstract: Reconfigurable Intelligent Surfaces (RIS) have emerged as transformative technology in wireless communications, capable of dynamically manipulating electromagnetic waves to enhance signal propagation and communication efficiency. Integrating thermal energy harvesting into RIS introduces a novel dimension, enabling these surfaces to not only optimise communication links but also harness ambient thermal energy for sustainable power generation. This abstract explores the integration of thermal energy harvesting mechanisms with RIS, discussing the technological challenges, opportunities, and potential applications. By leveraging thermal gradients in the environment, RIS-based systems can achieve dual functionality, significantly advancing the feasibility of autonomous and energy-efficient wireless networks. This abstract provides a foundational overview and highlights future research directions in this evolving field at the intersection of wireless communications and renewable energy technologies.
  Keywords: RIS; 6G; thermal energy harvesting.
  DOI: 10.1504/IJAHUC.2025.10075027
   
  
• Lightweight and Resilient Blockchain-Enabled Mutual Authentication and Key Establishment Protocol for Smart Health Devices in IoMT  
  by Rachida Hireche, Houssem Mansouri, Yasmine Harbi, Al-Sakib Khan Pathan 
  Abstract: Rapid expansion of wireless body area networks (WBANs) has advanced the healthcare services. However, open communication channels pose significant security challenges. This study presents a lightweight mutual authentication and key agreement protocol for blockchain-enabled Internet of-Medical-Things (IoMT) systems. The scheme integrates biometric-based sensor management and employs smart contracts with system revocation lists to ensure the timely exclusion of compromised entities. By combining fog computing with blockchain, the framework offloads intensive computations from IoMT devices, thereby enhancing their scalability and efficiency. Security is strengthened through elliptic curve cryptography, cryptographic hash functions, and lightweight primitives, while formal validation using Burrows-Abadi-Needham (BAN) logic and the automated-validation-of-internet-security (AVISPA) tools confirms resistance to adversarial attacks. Moreover, it was implemented using the Ethereum platform and evaluated using Hyperledger-Caliper, demonstrating lower latency and higher throughput. A comparative analysis with relevant recent works in terms of performance and security requirements also proves its reliability and robustness.
  Keywords: E-Healthcare; Elliptic Curve Cryptography; Hash Function; IoMT; Smart Contract; Wireless Body Area Network.
  DOI: 10.1504/IJAHUC.2025.10075031