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

Regular Issues

• ONCP: An Optimised Node Clustering Protocol using PSO for Enhanced Multimedia IoT Network Performance  
  by Prakruti Kulkarni, S.S. Lokhande 
  Abstract: High-performance and reliable multimedia communication is critical for modern IoT applications such as smart healthcare, wearables, homes, and industrial automation. Cross-layer protocol designs have emerged to improve network performance by enabling coordination across protocol layers. The IoMT protocol applies cross-layer optimisation to reduce energy usage and latency but faces scalability and real-time issues in dense networks due to increased computational complexity. To address this, enhancements like directional antennas and the Dual Sensing MAC (DSDMAC) protocol were introduced at the MAC layer to improve channel access and reduce hidden terminal issues, forming the basis for the MCROSS protocol. Building on this, the Optimised Node Clustering Protocol introduces a modified PSO-based clustering method for intelligent selection of cluster heads and relay nodes. It uses a fitness function that considers multiple parameters (PFR, RER, CR), resulting in improved energy efficiency, load balancing, and reliability. Comparative analysis confirms ONCP's superior performance across diverse metrics.
  Keywords: IoT; cross-layer protocol; IoMT; MCROSS; ONCP; DSDMAC; Particle Swarm Optimisation; cluster head selection; adaptive clustering.
  DOI: 10.1504/IJAACS.2026.10073918
   
  
• Automatic Monitoring Approach for Continuous Usability Evaluation  
  by Mouna Jarraya, Faouzi Moussa, Meriem Riahi 
  Abstract: Resilience engineering aims to embed resilience into socio-technical systems by addressing unforeseen situations and ensuring that systems can operate effectively under both expected and unexpected conditions. To this end, we propose an automatic monitoring approach for continuous usability evaluation, designed to detect and alert users to potential risks arising from usersystem interactions. In our study, a proxy is integrated into an academic simulator of an onboard automated car system to monitor user actions through the generation of a Petri net-based activity trace. Usability evaluation is then carried out by comparing this trace with a real-time deduced task model in order to identify deviations from expected interaction patterns. Furthermore, machine learning techniques are employed to analyse the current situational context and to determine which tasks are permissible and which should be restricted. The overarching objective is to enhance system resilience by proactively monitoring user deviations and anticipating unexpected situations.
  Keywords: Usability evaluation; Human-Computer interaction; Task modelling; Monitoring; Petri net.
  DOI: 10.1504/IJAACS.2026.10074890
   
  
• New Relaxed Conditions of Continuous Delayed Takagi-Sugeno Positive Systems Controlled by State Feedback and Memory State Feedback by LP Approach: Application to Buck Converter T-S Model  
  by Rkia Oubah, Ouahiba Benmessaouda, Layla Wakrim 
  Abstract: This paper investigates the new relaxed stability conditions in open loop and closed loop of time-varying delay Takagi-Sugeno models while maintaining positivity within closed loop. New delay-dependent conditions of stabilization in terms of LP approach will be derived by using a Lyapunov-Krasovskii Functional (LKF). The proposed stabilization conditions are less restrictive than some major published stabilization conditions. Memoryless state feedback control is also employed in cases when the system in open loop is not positive (for example an electrical system where either a positive or negative input current is possible), that in feedback is necessary to make stable and positive. The benefits of the suggested stabilization conditions will be demonstrated with a numerical and practical (Buck Converter) examples.
  Keywords: LP approach; Lyapunov-Krasovskii Functional; Fuzzy Takagi-Sugeno system; Time-varying delay; Positive systems; State feedback; Memory state feedback; Buck Converter.
  DOI: 10.1504/IJAACS.2026.10074989
   
  
• A QR Code Secret Sharing Scheme Based on Extended Hamming Code  
  by Xuehua Cao, Xinwei Zhong, Genlin Ji 
  Abstract: Secret sharing, as a crucial method for privacy protection, has seen rapid development in fields such as cloud computing and the Internet of Things in recent years. As a very popular information cover at present, QR code is used far more frequently than traditional images, so the application of secret sharing in QR code has highly promising. However, because the QR code is often presented in paper form, it is easy to be stained or damaged; At the same time, stego-QR codes may be tampered with by attackers during transmission. These issues will inevitably lead to bit errors in stego-QR code, and the existing research schemes are unable to recover the secret losslessly. To address this, this paper proposes a QR code secret sharing scheme based on Extended Hamming Code. This scheme not only ensures high embedding capacity but also implements error correc tion capability of stego-QR codes, which can accurately detect and correct bit errors in the stego-QR code, and then recover the secret. Both theoretical analysis and experimental results demonstrate the effectiveness and practicality of this scheme.
  Keywords: secret sharing; QR code; bit errors; Extended Hamming Code; error correction.
  DOI: 10.1504/IJAACS.2026.10075089
   
  
• Design, Development and Analysis of CPW Fed Circular patch MIMO Antenna with Miniaturised Ground Plane for n78 Band Applications  
  by Bhargava D. S, Seetharaman G 
  Abstract: This research paper, presents a coplanar waveguide (CPW) feed Circular Slotted Microstrip Patch Antenna (CS-MPA) with miniaturised ground structure, which operates at the n78 band (3.23.8 GHz) and resonates at LTE-42 Band (3.55 GHz) is presented. The antenna structure (single-element) designed is of 55
  Keywords: Microstrip Patch Antenna; Coplanar Waveguide (CPW); n78 Band; Optimized Ground structure; 5G Communication; WiMax Services.
  DOI: 10.1504/IJAACS.2026.10075474
   
  
• Overview of Power Line Communication: Innovations and the Integration of Artificial Intelligence  
  by Samir Laksir 
  Abstract: This paper provides a comprehensive review of the evolution, standardisation, applications, and advancements of power line communication (PLC) technologies. It presents a historical overview of key standards, from EN 50065 to the latest IEEE 1901c amendment, alongside an examination of significant patents and licensing efforts. The analysis highlights the diverse roles of PLC in smart grids, industrial automation, electric vehicle infrastructure, and broadband access. A central contribution of this work is its focus on the emerging integration of Artificial Intelligence (AI) into PLC systems, a topic not yet extensively synthesised in the literature. We show how AI augments PLC by enabling adaptive fault detection, intelligent security, noise mitigation, and performance optimisation under variable conditions. By consolidating global research and practical implementations, this review identifies a unique gap at the intersection of PLC and AI, offering insights into how their convergence can enhance versatility, resilience, and scalability. Finally, the study outlines future research avenues, including hybrid communication architectures, sustainable designs, and AI-driven frameworks for secure and scalable PLC innovations.
  Keywords: Power Line Communication; PLC Standards; Artificial Intelligence; Smart Grids; Noise Mitigation; Hybrid Communication; IoT Integration; Sustainable Networks; PLC Security.
  DOI: 10.1504/IJAACS.2026.10075666
   
  
• Spectrum Allocation in 6G Communication Networks Using Enhanced Dual Deep Q-Network  
  by Monika Gupta, Ishani Mishra, Sonika Katta, Pavithra G, Swapnil S. Ninawe, Preeti Khanwalkar 
  Abstract: In 6G communication networks, spectrum allocation process is crucial for effectively managing and utilising the limited spectrum resources available. However, traditional spectrum allocation approaches encounter significant challenges, particularly in dynamic environments; often result in suboptimal spectrum utilisation and delays in the allocation process. To overcome this issue, we propose an Enhanced Dual Deep Q-Net (EDDQN) scheme for spectrum allocation. EDDQN is an advanced version of DQN that makes use of two neural networks: an online network for action selection and a target network for Q-value estimation. By using two separate neural networks, EDDQN overcomes overestimation bias in Q-value estimation, a significant drawback of the original DQN. The zebra optimisation algorithm is used to optimise the hyper-parameters of EDDQN in order to improve its performance. Through extensive simulations, we prove the superiority of proposed spectrum allocation compared with traditional spectrum allocation approaches.
  Keywords: 6G communication network; spectrum allocation; enhanced dual deep Q-Net; online network; target network; and zebra optimization algorithm.
  DOI: 10.1504/IJAACS.2026.10075893
   
  
• Advanced Deep Learning-enabled Effective Framework for the Segmentation and Classification of Skin Disease employing Dermatological Images  
  by Priya Jayakanth, Rosline Nesa Kumari G 
  Abstract: A computer-based framework leveraging deep learning was developed for automated skin disease diagnosis, addressing the inaccuracies and inconsistencies of traditional manual methods. The system employs a two-stage process. First, an Adaptive Refined UNetV4 (ARUNetV4) performs disease segmentation by focusing on fine-grained lesion details while suppressing noise. The ARUNetV4's hyperparameters are optimised using the Enhanced Random Variable-based Red Panda Optimization (ERV-RPO) algorithm. In the second stage, the segmented images are classified using a hybrid Vision Transformer with Residual DenseNet (ViT-RDNet). This model combines ViT's global contextual understanding with RDNet's local feature extraction to overcome visual similarities between different diseases. The framework demonstrated superior performance against existing models, achieving 96% accuracy on Dataset-1 for classification and 95.04% accuracy on Dataset-2 for segmentation.
  Keywords: Skin Disease Segmentation and Classification; Dermatological Images; Adaptive Refined UnetV4; Enhanced Red Panda Optimisation; Residual DenseNet.
  DOI: 10.1504/IJAACS.2026.10076020