International Journal of Autonomous and Adaptive Communications Systems (14 papers in press)

Regular Issues

• Open Access

  Motion Capture and Damage Recognition Method Based on Memetic Algorithm for Edge AI in Industry 5.0 Human Computer Collaboration ( Free Full-text Access )

  by Yuhang Li 
  Abstract: Traditional motion capture systems typically rely on optical or inertial sensors, which have limitations such as difficulty adapting to lightweight deployments at the edge. This article proposes a motion capture and recognition method based on the Memetic algorithm. First, the meme algorithm is combined with computer vision technology and edge computing architecture to capture human image sequences through edge deployed cameras. And use the location processing advantage of edge computing to reduce data transmission delay. Then, the Memetic algorithm is used to preprocess the image sequence and accurately extract key skeletal points of the human body. Solved the problem of low accuracy and poor anti-interference ability in extracting bone points in public scenes. Subsequently, standardised human motion data is generated by calculating the relative positions and angles between key skeletal points. Provide technical support for innovative applications of soft computing and edge intelligence in industrial and urban ecosystems.
  Keywords: Edge Artificial Intelligence; Soft Computing; Motion Capture; Industry 5.0; Lightweight Algorithms; Computer Collaboration.
  DOI: 10.1504/IJAACS.2026.10079240
   
  

• Open Access

  Application of Wearable Biosensors for Motion and Health Monitoring of Industrial Human-Machine Collaboration Operators Based on Edge Artificial Intelligence and Soft Computing ( Free Full-text Access )

  by Liguang Xu, Wei Zhang 
  Abstract: The acceleration of digital transformation is driving the iteration of Industry 5.0 towards human-machine collaboration, safety and efficiency, and the demand for real-time monitoring of operator physiological health and exercise status is becoming increasingly urgent. Traditional monitoring methods suffer from pain points such as high latency, insufficient accuracy, and poor adaptability. This study is based on the industrial 5.0 human-machine collaboration scenario, exploring the application of wearable biosensors in real-time monitoring of operator physiological health and exercise status. This article selects 30 industrial human-machine collaborative operators as experimental subjects and uses soft computing related pattern recognition techniques such as filtering, feature extraction, and fuzzy support vector machine (fuzzy SVM) for data analysis. Introducing edge intelligence to achieve local real-time data processing, avoiding cloud transmission delays and privacy leakage risks. Experiments have shown that combining multi-sensor fusion algorithms with lightweight optimization techniques can effectively reduce noise interference in complex industrial environments
  Keywords: Wearable Biosensors; Sports Health Monitoring; Industrial Human-Machine Collaboration; Human-Machine Collaboration; Edge Intelligence; Soft Computing; Lightweight Algorithm.
  DOI: 10.1504/IJAACS.2026.10080135
   
  

• Open Access

  Research on Cognitive Behavioral Education Interaction in Industry 5.0 Human Computer Collaboration Based on Soft Computing in Smart City Ecology ( Free Full-text Access )

  by Xiaoxia Wang 
  Abstract: This paper examines the integration of soft computing, edge intelligence, and Edge AI into human-computer interaction (HCI) for cognitive behaviour and education of human-machine collaborative talents in Industry 5.0 and smart cities. It analyses cognitive processes using intelligent interactive devices powered by these technologies. Results show that such HCI accurately matches cognitive patterns, with localisation and real-time responses facilitating cognitive cultivation. Case studies confirm its capability to capture and optimise cognitive behaviours through soft computing analytics, edge responsiveness, and fuzzy logic adaptability. Finally, a talent training approach is proposed, building cognitive environments with soft computing and Edge AI to enhance students cognitive abilities, thereby advancing both cognitive improvement and industrial/smart city development.
  Keywords: Cognitive Behavior; Soft Computing; Edge Intelligence; Industry 5.0; Human-Machine Collaboration; Smart City; Cognitive Education.
  DOI: 10.1504/IJAACS.2026.10080295
   
  
• Cross-Modal Attention for Fake News Detection: Integrating Text and Image Features with Multi-Modal Fusion and Advanced Methods  
  by Yamini Devi Jonnala, J. Sirisha Devi 
  Abstract: The rise of digital media accelerates the spread of fake news, impacting public opinion, politics, and health. Traditional detection methods analyse text and images separately, failing to capture cross-modal relationships. This study proposes a multimodal framework that processes text and image to address these challenges. The text data is first processed using bidirectional long short-term memory (BiLSTM) and CapsNet, where BiLSTM captures sequential dependencies and CapsNet with self-attention enhances spatial feature extraction. Simultaneously, image data is processed using ResNet and vision transformer (ViT) to extract meaningful features, creating a single representation. The extracted text and image features are then refined through multi-head self-attention. These refined features are fed into a cross-modal attention mechanism, which integrates and fuses the enhanced representations from both modalities the fused representation passes through a fully connected layer for classification. The specified models achieves a high accuracy of 96.2% and 95.2% for text and image inputs.
  Keywords: BiLSTM; CapsNet; ResNet,ViT; Fully connected Layer; Multiple Multi-Model; Multi-head self-attention and Cross-Modal Attention Network.
  DOI: 10.1504/IJAACS.2026.10077164
   
  
• A comprehensive review on DV-hop-based localisation and flooding routing protocols in underwater acoustic sensor networks  
  by Pankaj Singh Yadav, Pabitra Mohan Khilar 
  Abstract: Underwater acoustic sensor networks (UASN) tackle various marine applications through their essential tasks even though they deal with major hurdles including acoustic propagation delays alongside limited bandwidth and additional energy expenditures. UASN dependability relies heavily on accurate node localisation and efficient data dissemination. The distance vector-hop (DV-Hop) algorithm represents a popular location identification system because it provides straightforward implementation and requires minimal hardware deployment. Underwater conditions that are unpredictable often prompt operators to select flooding-based routing because of its reliability. Most approaches develop independently from each other even though the systems work together in practice. The paper delivers a comprehensive evaluation of DV-Hop-based localisation together with flooding-based routing protocols in UASNs while identifying their individual advances and remaining gaps and potential integration prospects. Multiple state-of-the-art algorithms currently suffer from three main shortcomings as they fail to handle network mobility, ignoring acoustic signal limitations and lack performance enhancement through cross-layer integration.
  Keywords: UASNs; underwater acoustic sensor networks; DV-hop localisation; flooding-based routing; mobility-aware protocols; energy-efficient communication; acoustic channel modelling.
  DOI: 10.1504/IJAACS.2026.10078117
   
  
• Public Cultural Policy Opinion Analysis Based on Multimodal Joint Attention Mechanism  
  by Dandan Liu 
  Abstract: The research on public opinion analysis of public cultural policies is helpful for timely warning of potential contradictions and risks in policy implementation. Therefore, a public cultural policy opinion analysis method based on multimodal joint attention mechanism is proposed. By improving the K-means algorithm to cluster the data collected by form focused web crawlers, a data anomaly detection model based on multimodal joint attention mechanism is established. Abnormal data is determined and removed through anomaly scores. Using the data after removing anomalies as input, output the results of public opinion classification and recognition, establish a BERT-BDCA model, and achieve public cultural policy public opinion analysis through multiple steps such as word embedding layer and attention processing. The experimental results show that the maximum recall rate of the proposed method for public opinion data reaches 98.75%, the maximum accuracy exceeds 98%, and the maximum time is only 16.31 minutes
  Keywords: Multimodal joint attention mechanism; Public cultural policies; Public opinion analysis; Improving the K-means algorithm; BERT-BDCA model.
  DOI: 10.1504/IJAACS.2026.10079211
   
  
• Graphene-Enabled Reconfigurable 2x2 MIMO dielectric resonator antenna for multiband THz applications  
  by Ritesh Kushwaha 
  Abstract: This work proposes a 2x2 frequency-reconfigurable multiple-inputmultiple- output (MIMO) dielectric resonator antenna (DRA) utilising graphene for multi-band operation in terahertz (THz) applications. The design integrates monolayer graphene into a novel feed structure, where chemical potential tuning enables four discrete switching states: OFFOFF, OFFON, ONOFF, and ONON. The antenna maintains efficient impedance matching across the 0.451.0 THz range, enabling effective dynamic control of the resonant frequencies. The DRA achieves high inter-element isolation (>30 dB), totalactive- reflection-coefficient (TARC) below 0.7 dB, and notable frequency agility with minimal reconfiguration complexity. MIMO performance metrics include an envelope correlation coefficient (ECC) below 0.003, diversity gain (DG) close to 10 dB, and balanced mean effective gain (MEG) at both ports. The channel capacity loss (CCLremains below 0.5 bits/s/Hz throughout most of the operating band, confirming the antennas potential for high-data-rate THz links. The antenna is suitable for THz adaptive wireless communication.
  Keywords: Reconfiguration Dielectric Resonator; Graphene; MIMO; TARC,CCL; THz band.
  DOI: 10.1504/IJAACS.2026.10079623
   
  
• Deep Learning Optimized Cloverleaf MIMO Antenna for Enhanced Wireless Connectivity in Smart Agriculture  
  by Ogirala Pranitha, K.V. Prashanth, Jagadeesh Chandra Prasad Matta, Hema Chandra Rao Bitra, Ramesh Babu Juturi 
  Abstract: Smart farming systems demand reliable, high-speed wireless networks for seamless data transmission and real-time monitoring. However, existing solutions face limitations such as narrow bandwidth, high mutual coupling, low gain, and poor spectral and radiation efficiency. To overcome these challenges, this research presents a Deep Learning Optimized Cloverleaf MIMO antenna integrated with an Extended Long Short-Term Memory (E-LSTM) model, fine-tuned using the Gazelle Optimization Algorithm (GOA). The Cloverleaf antenna delivers compact, multi-band operation with high isolation, achieving a broad 214.8 GHz bandwidth, 9.1 dB peak gain, 52% radiation efficiency, and mutual coupling below 15 dB. Additionally, the system achieves a spectral efficiency of 10 bps/Hz at 15 dB signal-to-noise ratio and channel capacity loss (CCL) below 0.35 bits/s/Hz. This framework enhances predictive performance for agricultural data. Collectively, the proposed system offers an intelligent, scalable wireless infrastructure tailored to meet the advanced communication needs of smart agriculture.
  Keywords: Smart Agriculture; Deep learning; Gazelle Optimization Algorithm; MIMO Antenna; Long Short-Term Memory.
  DOI: 10.1504/IJAACS.2026.10079653
   
  
• A New Approach to Energy-Efficient Routing in WSNs Using a Modified LEACH Protocol  
  by Mahendra Dongare, Satish Jondhale, Balasaheb Agarkar 
  Abstract: In wireless sensor networks (WSNs), hierarchical clustered routing protocols are pivotal in optimising energy utilisation. The low-energy adaptive clustering hierarchy (LEACH) contributes to higher energy depletion if rotation of cluster heads is not systematically managed. To address this limitation, we introduce an enhanced routing strategy as average energy and residual energy-based modified LEACH (aerem-LEACH); designed to enhance the energy efficiency of WSNs by simultaneously considering both the average network energy and the residual energy of individual nodes during CH selection process. It determines ideal number of cluster heads, restricts nodes located near the sink from forming clusters to avoid excessive energy burden, and introduces a novel threshold mechanism for more effective CH selection. Additionally, the protocol leverages a hybrid communication model including free space propagation, multi-hop routing, and adaptive data transmission ensuring minimal energy usage. Proposed aerem-LEACH achieves a network lifetime enhancement from 9% to 57% compared to existing protocols.
  Keywords: Low-energy adaptive clustering hierarchy(LEACH); average energy residual energy based modified LEACH (aerem-LEACH); Stable Energy Efficient Network (SEEN); LEACH-Mobile (LEACH-M); LEACH-Centralized.
  DOI: 10.1504/IJAACS.2026.10079657
   
  
• Priority based Neighboring following Adaptive Reliable Clustering for Multi Hop Communication for Cooperative Vehicular Networks  
  by Sravani Potula, Sreenivasa Rao Ijjada 
  Abstract: In the heterogeneous vehicular cellular networks, cluster design significantly augments substantial performance metrics like routing. Effect of mobility and vehicle direction plays a major performance metric of any wireless network. Dynamic cluster head (CH) selection with optimal complexity and quick adaption is always an important paradigm. Any mobility change in CH, will effect reliability and connectivity with the other vehicles in the cluster (Cluster Members-CM). This research suggests a novel adaptive reliable clustering (ARC) method in multi- hop networks and the priority-based adaptive reliable Selection (P-ARC) algorithm introduces a passive approach for enhancing the stability and reliability of clusters. Priority-driven neighbour following strategy is used for optimal CM selection. The dependability and heftiness of the cluster are further boosted during the cluster maintenance phase with cluster merging mechanism. The projected algorithms effectiveness is verified with CH and CM durations in cluster, number of CH changes and packet delivery ratio.
  Keywords: Cluster head selection; Cluster Member; Multi-hop relaying; Cooperative Vehicular Network; Non-orthogonal Multiple Access; Cluster merging.
  DOI: 10.1504/IJAACS.2026.10080052
   
  
• Secure Mobile Device Communication in Fog Environments Through Advanced Data Encryption and Merkle Tree-Based Blockchain Integration  
  by Jenifa Sabeena S, Thevahi B, MarySelvi S, Kumaran U, Hemasilviavinothini S, Singaravelan S, Sukumar M, Srinivasan R 
  Abstract: Many people employ cloud-based applications to save and analyse data for various reasons. Fog computing has become a key enabler for latencysensitive Internet of Things (IoT) applications; however, ensuring secure authentication, data confidentiality, and integrity remains challenging due to resource constraints and decentralised environments. This proposed work secure fog-based communication framework that integrates advanced data encryption with Merkle treebased authentication (ADE-MT) and blockchain technology. The proposed scheme employs a lightweight hierarchical key management mechanism, where Merkle Trees enable efficient integrity verification and blockchain provides decentralised trust and tamper resistance. These results indicate that the ADE-MT framework is well suited for largescale fog-enabled IoT environments requiring secure, efficient, and low-latency data communication. It offers a variety of keys to enable secure fog-cloud communication. It is a cost effective, lightweight cryptography key management system, hierarchical structure system that can sustain a high volume of device connectivity without exceeded user nodes storage constraints.
  Keywords: Blockchain; Merkle Tree Hash Function; Advanced Data Encryption; Lightweight Cryptography; Fog Computing.
  DOI: 10.1504/IJAACS.2026.10080181
   
  
• Bald Eagle Enclosed Greylag Goose Optimization Based Load Balancing in the Computational Storage Devices  
  by Sushama Annaso Shirke, Naveenkumar Jayakumar, Suhas Patil, Satish Kumbhar 
  Abstract: This work aims to address the challenges associated with load balancing by proposing a robust and efficient framework explicitly tailored for distributing workload across computation storage devices (CSD). Initially, the data is generated for implementation in both dynamic and static environments. Once the input data is gathered, it needs to be allocated to the available CSD in a manner that optimises resource utilization and minimizes response time. This allocation process ensures that each device operates efficiently without becoming overloaded or underutilised. To achieve this, the framework employs the Genetic fused Falcon Optimization (GFO) scheduling algorithm. Once the initial allocation is complete, the framework implements a load balancing mechanism called Bald Eagle Enclosed Greylag Goose Optimisation (BEGGO) to optimise resource utilisation. The algorithm provides optimal resource utilisation and minimal response times. In the evaluation, the proposed approach scores 3859.22 mbps and 3969.86 mbps in both static and dynamic datasets.
  Keywords: Computational storage device; bald eagle optimization; Genetic algorithm; load balancing; Falcon optimization algorithm.
  DOI: 10.1504/IJAACS.2026.10080185
   
  
• An Optimisation Approach for Path Prediction and Detection in Autonomous Vehicles  
  by Swati Jaiswal, Renu Kachhoria, Rupali Chopade, Trupti Pawase, Neha Joshi, Spandan Surdas 
  Abstract: Intelligent transportation systems are rapidly evolving with the rise of autonomous vehicles, emphasising travel safety. However, challenges such as road damage, occlusion, low illumination, shadows, and complex road conditions often result in unclear images, making accurate vehicle decisions difficult. This study proposes an ensemble framework for lane line and traffic sign detection, integrating metaheuristic optimisation with a modified Hough-enabled Lane Generative Adversarial Network (GAN) designed to process fuzzy road images. To strengthen decision-making in dynamic urban crossings, the model incorporates the features of Grey Wolf and Starling bird optimization, enhancing classifier performance. A hybrid ensemble classifier combining CNN and BiLSTM improves traffic sign recognition, while synthetic data generation significantly reduces lane detection misclassifications. The proposed system demonstrates improved accuracy, sensitivity, and specificity, with the lane detection model showing a lower error rate than existing methods. This approach advances autonomous driving by offering a more reliable, adaptive, and robust decision-making framework.
  Keywords: Optimization; decision-making; autonomous vehicles; generative adversial network; convolutional neural network.
  DOI: 10.1504/IJAACS.2026.10080285
   
  
• Copyright Protection of Enhanced Low-Light Image Based on Degradation-Aware Conditional Diffusion Model  
  by Hengbo Li, Yeling Ma, Yang Song, Ting Luo 
  Abstract: Low-light images often suffer from poor luminance, low contrast, and distorted colour, making low-light image enhancement (LLIE) crucial for vision tasks. However, enhanced images represent valuable assets requiring copyright protection via watermark embedding. To protect copyright, we propose Diff_EWL, a degradation-aware conditional diffusion model. Considering varying degradation across low-light images, we design the degradation-aware feature modulation module (DFMM) to focus on severely degraded regions for recovery and embed watermarks into less degraded areas, minimising quality loss and reducing noise interference during enhancement. A cross-attention mechanism builds long-range dependencies between channel and spatial features for effective feature extraction. To address colour distortion, we introduce the color-guided hierarchical feature fusion module (CHFFM) to incorporate colour priors into multi-scale feature fusion and mine global contextual information to restore colour fidelity and fine details. Experimental results demonstrate Diff_EWL effectively decreases interference between LLIE and watermarking and outperforms existing methods.
  Keywords: Low-light Image Enhancement; Robust Watermarking; Conditional Diffusion Model; Degradation-Aware.
  DOI: 10.1504/IJAACS.2026.10080341