International Journal of Ad Hoc and Ubiquitous Computing (22 papers in press)

Regular Issues

• Open Access

  Intelligent Monitoring of Patient Vital Signs Based on Adaptive Attention Fusion Spatiotemporal Graph Neural Network ( Free Full-text Access )

  by Shunda Cheng, Jie Zhu, Shengjiang Guan, Jie Cheng, Tong Dou 
  Abstract: This study proposes a vital signs monitoring framework that addresses the limitations of traditional threshold-based alarms and existing deep-learning models in capturing multimodal physiological interactions and spatiotemporal dynamics. The method integrates an adaptive attention fusion mechanism that dynamically adjusts the importance of heterogeneous physiological parameters, a spatiotemporal graph neural network that jointly models inter-parameter correlations and temporal evolution using multi-scale windows, and a reinforcement learning module that enables active, strategy-driven early warning and clinical decision support. Evaluated on the MIMIC-III and eICU datasets, the proposed system achieves 96.3% anomaly detection accuracy, 38.5-minute early warning capability, and a 0.912 F1-score, outperforming existing methods. Ablation studies confirm the contributions of adaptive fusion, spatiotemporal graph modelling, and policy optimisation.
  Keywords: Adaptive attention mechanism; Spatiotemporal graph neural network; Vital sign monitoring; Deep reinforcement learning; Multimodal fusion.
  DOI: 10.1504/IJAHUC.2026.10076081
   
  

• Open Access

  Adaptive Deep Reinforcement Learning-Based Error Analysis Model for Strength Training ( Free Full-text Access )

  by Guobao Zhang, Haotian Li, Xurui Liu 
  Abstract: To tackle limitations of conventional strength training movement evaluation, such as inadequate movement stage segmentation and insufficient fine-grained error perception, this study proposes ARLE-Net. This framework integrates improved YOLOv8 with CBAM attention mechanism to enhance local movement feature detection, HR-Net for high-resolution human keypoint extraction, and adaptive reinforcement learning for dynamic action segmentation and standardized score prediction. Experiments on Fitness-AQA and ARFit datasets show it outperforms state-of-the-art methods in action segmentation accuracy and score prediction mean square error, with strong stability. Ablation studies confirm component effectiveness, providing support for intelligent fitness guidance and fitting the journal’s enhanced ubiquitous computing focus.
  Keywords: Strength Training; Pose Estimation; Reinforcement Learning; Action Quality Assessment.
  DOI: 10.1504/IJAHUC.2026.10076102
   
  

• Open Access

  Intelligent Automotive Trade Market Forecasting and Decision-Making System: Multi-Source Data Fusion under Meta-Learning ( Free Full-text Access )

  by Kan Lu, Mingting Huang, Min Song 
  Abstract: With the continuous advancement of intelligent vehicle technology, the smart automotive trade market has become increasingly complex and dynamic. This study proposes an intelligent forecasting framework that integrates multi-source data through a meta-learning-based approach. Specifically, a Meta-Attention Gated Recurrent Unit (MA-GRU) model is developed to enhance the accuracy and robustness of market predictions. The model first extracts key automotive performance indicators and market-related features using a GRU network, and then applies an attention mechanism to capture the most informative temporal dependencies. To address the challenge of data scarcity in the emerging smart vehicle market, meta-learning is introduced to improve the model's adaptability and generalisation across diverse datasets. Experimental evaluations demonstrate that the proposed MA-GRU framework achieves superior predictive performance even under limited data conditions, providing a solid technical foundation for market trend analysis and strategic decision-making in the intelligent automotive trade.
  Keywords: Intelligent marketing prediction; Intelligent cars; GRU; Meta-learning.
  DOI: 10.1504/IJAHUC.2026.10076104
   
  

• Open Access

  Integrating Multi-modal Emotion Recognition with Strategy Generation: A Transformer Approach to Sustainable Marketing ( Free Full-text Access )

  by Anqi Wu, Jing Wang, Jie Zhang, Wei Qiu 
  Abstract: This paper proposes the MEPSO-Transformer (Multi-modal Emotion Perception and Strategy Optimization Transformer), a dual-task architecture designed to jointly perform emotion classification and green marketing strategy recommendation based on heterogeneous user data. The model incorporates modality-specific encoders, hierarchical gated cross-attention fusion, and two parallel decoders, and is trained under a multi-task learning scheme. To evaluate its effectiveness, experiments are conducted on three datasets: CMU-MOSEI, MELD, and a constructed domain-specific dataset, GreenPromo-Emotion, which contains 6,200 multi-modal samples annotated with seven emotional categories and five predefined marketing strategies. Results show that MEPSO-Transformer achieves an average accuracy of 84.3% and a macro-F1 score of 81.2% on CMU-MOSEI, outperforming the best baseline by +2.3% and +2.6% respectively. On MELD, the model attains a 75.0% macro-F1 and a Hit@1 score of 65.7%. These findings demonstrate the model's applicability to sustainable marketing tasks requiring real-time affective understanding and strategy selection.
  Keywords: Multi-modal Emotion Recognition; Green Marketing; Strategy Optimization; Transformer; Sustainable AI; Affective Computing; Multi-task Learning.
  DOI: 10.1504/IJAHUC.2026.10076105
   
  

• Open Access

  Design and Optimisation of Multi-Party Secure Computing Protocols Supporting Dynamic Participant Switching ( Free Full-text Access )

  by Zhipeng Li, Haiping Zhao, Shaobo Liu, Hao Yang, Shuo Shen, Rui Dong, Yu Zhang 
  Abstract: Most existing SMPC protocols assume static participants, unsuitable for dynamic scenarios where participant numbers and identities change. This paper proposes an SMPC protocol enabling dynamic participant switching, introducing a dynamically joinable model with variable committee sizes per round and supporting arbitrary member counts. It uses secret sharing to transfer computations between participants without revealing intermediates and consolidates all validations into a dedicated phase to reduce communication complexity. Experiments with 7-member committees show its communication costs (for 10,000,000 entries) outperform existing protocols. A platform based on Spring Boot and MyBatis, integrating libscapi for core crypto operations, is developed with optimised architecture and visualisation tools. The protocol offers efficient, secure solutions for data sharing and collaboration, with significant theoretical and practical value.
  Keywords: multiparty secure computing; secret sharing; participant role switching.
  DOI: 10.1504/IJAHUC.2026.10076106
   
  

• Open Access

  Dynamic Load Balancing Model for Data Centres Based on Bidirectional Network Edge Detection Algorithm ( Free Full-text Access )

  by Diangang Hu, Xi Song, Bingling Gu, Kai Song, Xiaodong Wei 
  Abstract: To resolve link congestion and QoS deterioration caused by Elephant Flow conflicts in data centre networks under conventional multipath routing, this study proposes an innovative dynamic load balancing model. It combines Software-Defined Networking (SDN) for centralised control with a Transformer-BiLSTM-DNN deep learning framework: the Transformer encoder grasps global spatial correlations among traffic flows, BiLSTM retrieves intricate temporal variations, and DNN achieves deep feature integration for precise load state categorisation, enabling refined proactive traffic regulation. Evaluated on Google Borg Cluster Trace v2, the model excels in key metrics like Load Balancing Degree, Job Completion Time, and resource utilization, outperforming traditional baselines significantly. Ablation tests verify the essentiality of each component, providing a sturdy intelligent approach to enhance data center network efficiency and dependability.
  Keywords: Data center; Load balancing; Bidirectional network; Transformer; BiLSTM; SDN scheduling.
  DOI: 10.1504/IJAHUC.2026.10076107
   
  
• 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
   
  
• 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
   
  
• 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
   
  
• Hierarchical Key Rotation, Isolation and Sensor-based Resilient Protocol for Wireless Sensor Networks  
  by Rounak Raman, Ayush Yadav, Vatsal Chhabra, Nisha Kandhoul, S.K. Dhurandher, Isaac Woungang, Han-Chieh Chao 
  Abstract: Ensuring a robust and secure wireless sensor network (WSN) remains a critical challenge, particularly against both internal and external threats. This paper presents a novel protocol hierarchical key rotation, isolation, and sensor-based resilient protocol (HKRISRP) designed to address the inherent security vulnerabilities in wireless sensor networks (WSNs). This approach tackles the critical challenges of dynamic threat detection and resource constraints by employing a hierarchical network architecture that integrates dynamic key rotation, lightweight encryption, and an adaptive cluster head re-election mechanism. This methodology not only enhances data confidentiality and integrity but also ensures energy efficiency and rapid threat isolation. Empirical results from both OMNeT++ simulations and hardware prototype evaluations demonstrate a throughput of 10,000 bps, an average remaining energy level of 82%, and a mean resolution time of 0.8 sec, significantly outperforming existing solutions. By introducing a robust framework for proactive threat prevention and isolation, HKRISRP offers a scalable and resilient solution for applications in military surveillance, border security, and industrial IoT environments.
  Keywords: Wireless sensor networks (WSNs); IoT security; Hierarchical network; Arduino; NRF module; Omnet++; Border security.
  DOI: 10.1504/IJAHUC.2025.10075499
   
  
• Energy Harvesting-Based Routing for Prolonging Sensor Node Lifespan in Underwater Networks Using Stochastic Network Calculus  
  by Vignesh S. R, Rajeev Sukumaran 
  Abstract: Underwater wireless sensor networks (UWSNs) are crucial for marine monitoring, disaster prediction, and underwater surveillance, but their performance is limited by energy constraints and harsh, unpredictable environments. Conventional deterministic models fail to capture underwater randomness, leading to inefficient energy management and unreliable communication. This work proposes an adaptive framework integrating stochastic network calculus (SNC) with piezoelectric energy harvesting (PEH) to enhance energy efficiency and network reliability. The model incorporates pH-based environmental variations within the SNC framework to dynamically regulate energy utilisation. Performance is evaluated against depth-based routing (DBR) and vector-based forwarding (VBF) using metrics such as: packet delivery ratio (PDR), packet loss ratio (PLR), end-to-end delay (E2E), throughput, path loss, and energy consumption. Results show significant improvements in network lifespan, energy efficiency, latency reduction, and data reliability. The proposed probabilistic approach provides a scalable and sustainable solution for real-time, next-generation UWSNs operating in dynamic aquatic environments.
  Keywords: Underwater Wireless Sensor Networks (UWSNs); Stochastic Network Calculus (SNC); Piezoelectric Energy Harvesting; pH-Based Modeling; Network Sustainability.
  DOI: 10.1504/IJAHUC.2025.10076088
   
  
• An Efficient and Secure Undeniable Signature Scheme based on Ring Learning with Error  
  by Akanksha Singh, Harish Chandra, Saurabh Rana 
  Abstract: This paper, proposes a novel undeniable signature scheme based on the Ring Learning with Errors (RLWE) problem, a complex lattice problem. In our scheme, authentic signatures can be validated via an interactive protocol, which guarantees that adversaries cannot forge signatures. Moreover, it ensures that honest participants consistently finish the verification procedure. Thus, the scheme is a strong and effective quantum-resistant undeniable signature scheme. Our security analysis, demonstrates resilience against both classical and quantum adversaries, based on the RLWE problem's hardness assumptions. Additionally, the Random Oracle model is utilised to evaluate our protocol's security, showing that our scheme satisfies important cryptographic properties like completeness, soundness, and unforgeability. Our work advances the feasibility of postquantum undeniable signature schemes for real-world cryptographic applications.
  Keywords: Digital signature; Undeniable Signature; RLWE; Lattice based Cryptography.
  DOI: 10.1504/IJAHUC.2026.10076212
   
  
• Wireless Channel Allocation in Smart Building using Cross-Interference Model  
  by Gabriel Galdino, Francisco Cardoso, Rafael Gomes 
  Abstract: IoT paradigm allowed a proliferation of connected wireless devices, that utilize various technologies such as Bluetooth, Zigbee, and Wi-Fi, all operating within the common 2.4GHz frequency band. However, sharing the same frequency poses challenges due to significant interference among these devices, making their coexistence in the same environment quite daunting mainly in smart buildings. Calculating interference between devices on different floors necessitates factoring in signal loss as it passes through ceilings or floors, along with distance-related signal attenuation between the devices. Within this context, this article proposes an interference model that is applied in an algorithm to allocate the channels of the devices in a smart building environment the model proposed aims to minimize the total interference of the environment between devices using different access technologies, improving the performance of the network and the applications that rely on it leading to a suitable expectation of the network capabilities.
  Keywords: Wireless Channel; Smart Building; Cross-Interference; Internet of Things.
  DOI: 10.1504/IJAHUC.2025.10076213