International Journal of Autonomous and Adaptive Communications Systems (8 papers in press)

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  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
   
  
• 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