Shagufta Henna

Scopus Publications

Accelerating graph-based deep learning for self-calibration in large-scale uncontrolled wireless sensor networks for environmental monitoring
Asif Yar, Shagufta Henna, Marion McAfee
Array, 2026
Low-cost Wireless Sensor Networks (WSNs) have emerged as a valuable complement to high-performance monitoring stations for environmental monitoring. These low-cost networks significantly enhance spatial and temporal resolution, which is crucial given that many highly localised events require early warnings to mitigate their impacts. Recent advancements in machine learning (ML) have enabled self-calibration for WSNs, but existing approaches struggle to capture the spatiotemporal relationships among sensors. Graph Convolutional Networks (GCNs) have the ability to capture complex interactions of WSNs by recursively aggregating information from each node’s neighbours. However, as the network grows, this neighbourhood aggregation becomes computationally expensive, limiting scalability. This requires sampling strategies to reduce node count per aggregation step without sacrificing performance. To address computational inefficiency in GCNs, this work introduces an adaptive importance sampling (AIS) method that adjusts sampling probabilities based on node degree and node importance within the network. By prioritizing the most influential nodes during neighbourhood aggregation, this approach reduces the number of nodes sampled at each step, lowering computational complexity. To maintain layer connectivity and avoid disconnection of sampled nodes AIS-GCN samples from the set of 1-hop neighbours at each layer, ensure smooth information flow between layers. Experimental results show that AIS-GCN achieves a test accuracy of 0.9365, outperforming traditional GCN (0.9219), FastGCN (0.9288), HDSGNN(0.9317) and performing comparably to AS-GCN (0.9396 ), demonstrating better scalability without sacrificing performance. Notably, AIS-GCN reduces training time by over 69% compared to standard GCN, making it an effective solution for large-scale WSNs and resource-constrained settings.
Fast-gradient-guided generative adversarial learning for explainable cyber threat intelligence
Shagufta Henna, Upaka Rathnayake
Applied Soft Computing, 2026
The rapid evolution of Domain Generation Algorithm (DGA)-driven attacks and obfuscated DNS traffic exposes fundamental weaknesses in conventional machine learning-based threat detection systems, particularly under adversarial manipulation. This study introduces FGM-GAN, a hybrid adversarial learning framework that synergistically combines gradient-based Fast Gradient Method (FGM) perturbations with adaptive Generative Adversarial Network (GAN)-based perturbations to improve both robustness and interpretability of deep neural networks for DNS threat classification. Unlike existing adversarial defenses that rely on model-specific perturbations, FGM-GAN explicitly learns class-conditional adversarial distributions for benign, phishing, and malware domains. This design enables the generation of realistic, feature-aligned perturbations that exhibit strong cross-model transferability. Experiments were conducted on the 32-feature CIC-BELL-DNS-2021 dataset (approximately 7000 labeled samples) using 5-fold cross-validation, hybrid perturbations with and , and evaluated against baseline DNN, SVM, Random Forest, KNN, and Decision Tree classifiers using accuracy and robustness metrics. Comprehensive evaluation demonstrates that FGM-GAN consistently improves robustness across diverse adversarial attacks (FGM, PGD, MIM, C&W) while maintaining stable performance across folds. Ablation studies and reduced-capacity variants confirm that gains arise from the hybrid adversarial mechanism rather than over-parameterization or hyperparameter tuning, and statistical significance tests verify the reproducibility of results. To enhance transparency and operational trust, the framework integrates multi-level explainable AI analyses spanning feature, neuron, and layer representations. These analyses consistently identify a compact set of high-impact DNS features and reveal structured adversarial propagation patterns, showing that robustness emerges from semantically meaningful representation learning. Collectively, these findings position FGM-GAN as a scalable and interpretable adversarial learning solution that jointly addresses robustness, transferability, and explainability in real-world DNS-based cybersecurity environments. • FGM-GAN hybrid improves neural network robustness against adversarial attacks • GANs produce realistic, class-specific adversarial perturbations for DNS data • Adversarial transferability validated across KNN, SVM, Decision Trees, RF • Gradient-XAI interprets feature, neuron, and layer-level model vulnerabilities • Combines robustness and explainability for actionable cyber threat intelligence
Spatiotemporal score-based federated generative learning for multi-source remote sensing time series in environmental monitoring
Shagufta Henna, Upaka Rathnayake
ISPRS Journal of Photogrammetry and Remote Sensing, 2026
EnCo SupCon: Entropy-driven supervised contrastive learning for discriminative feature representations from remote sensing imagery
Kathy Bannigan, Shagufta Henna
Results in Engineering, 2026
• An entropy-based coordinate supervised contrastive learning approach is proposed to extract enhanced feature representations from remote sensing imagery. • ResNet50V2 achieved the highest accuracy of 99.74% with manual augmentations, reflecting the benefits of deeper architectures for capturing complex spatial and spectral shoreline patterns. • For computationally efficient deployment, MobileNetV2 combined with advanced augmentation strategies such as CutMix achieved 98.46% accuracy, approaching the performance of deeper networks while significantly reducing training time and resource requirements. Coastal erosion poses serious challenges, including damage to infrastructure, loss of farmland, and ecosystem degradation. Monitoring and predicting coastal changes are critical for mitigation, but existing machine learning approaches often struggle to generalize across heterogeneous coastal environments and require extensive labeled data for reliable performance. To address these challenges, we propose Entropy-based Coordinate Supervised Contrastive Learning (EnCo SupCon), a deep learning framework designed to extract robust, spatially-aware feature representations from satellite imagery. The model integrates three key components: (i) entropy weighting to emphasize informative, near-boundary regions, (ii) coordinate attention to capture horizontal and vertical spatial dependencies, and (iii) supervised contrastive learning to cluster similar features while separating dissimilar ones in the embedding space. Ablation studies demonstrate that entropy weighting and coordinate attention individually improve performance and act synergistically when combined, raising MobileNetV2 accuracy from 77.79% to 90.78%. Data augmentation further enhances performance, with CutMix achieving 98.46%, outperforming MixUp (83.51%) and manual augmentations (90.78%), by encouraging the model to attend to multiple discriminative regions simultaneously. Sensitivity analysis shows that moderate entropy weights and lower temperature values provide stable and high accuracy, while Grad-CAM visualizations confirm that CutMix combined with entropy-guided coordinate attention focuses on the most informative coastal features. EnCo SupCon generalizes effectively to geographically distinct coastal datasets, with high accuracy across deeper backbones, while lightweight architectures maintain competitive performance with lower computational cost. Statistical analysis indicates that augmentation strategies provide the greatest benefit for smaller models, highlighting the interaction between architecture, entropy weighting, and augmentation for robust coastal monitoring.
Hypergraph Representation Learning-Based xApp for Traffic Steering in 6G O-RAN Closed-Loop Control
Shagufta Henna, Upaka Rathnayake
IEEE Transactions on Network and Service Management, 2026
This paper addresses the challenges in resource allocation within disaggregated Radio Access Networks (RAN), particularly when dealing with Ultra-Reliable Low-Latency Communications (uRLLC), enhanced Mobile Broadband (eMBB), and Massive Machine-Type Communications (mMTC). Traditional traffic steering methods often overlook individual user demands and dynamic network conditions, while multi-connectivity further complicates resource management. To improve traffic steering, we introduce Tri-GNN-Sketch, a novel graph-based deep learning approach employing Tri-subgraph sampling to enhance link prediction in Open RAN (O-RAN) environments. Link prediction refers to accurately forecasting optimal connections between users and network resources using current and historical measurements. Tri-GNN-Sketch is trained on real-world 4G/5G RAN monitoring data. The model demonstrates robust performance across multiple metrics, including precision, recall, F1 score, and ROC-AUC, effectively modeling interfering nodes for accurate traffic steering. We further propose Tri-HyperGNN-Sketch, which extends the approach to hypergraph modeling, capturing higher-order multi-node relationships. Using link-level simulations based on Channel Quality Indicator (CQI)-to-modulation mappings and LTE transport block size specifications, we evaluate throughput and packet delay for Tri-HyperGNN-Sketch. Tri-HyperGNN-Sketch achieves an exceptional link prediction accuracy of 99.99% and improved network-level performance, including higher effective throughput and lower packet delay compared to Tri-GNN-Sketch (95.1%) and other hypergraph-based models such as HyperSAGE (91.6%) and HyperGCN (92.31%) for traffic steering in complex O-RAN deployments.
Explainable AI-driven conditional generative model for decision support and transparency in smart farming
Muhammad Jawad Bashir, Shagufta Henna, Eoghan Furey, Salem Gharbia
Journal of Decision Systems, 2026
Integration of Computer Vision and Physicochemical Parameters for Post-Harvest Ripeness Classification of TomEJC Mango
Savindi Thathsarani, Ashan Lakshitha, Pasindu Pramodya, Praveen Perera, Rasanjali Samarakoon, Shagufta Henna, Upaka Rathnayake
Phyton International Journal of Experimental Botany, 2026
Accurately determining the optimal post-harvest storage period is still a major challenge in mango processing, especially for the Tom EJC (TEJC) variety, due to reliance on subjective visual evaluations, leading to inconsiste... | Find, read and cite all the research you need on Tech Science Press
An interpretable deep learning framework for medical diagnosis using spectrogram analysis
Shagufta Henna, Juan Miguel Lopez Alcaraz, Upaka Rathnayake, Mohamed Amjath
Healthcare Analytics, 2025
Convolutional Neural Networks (CNNs) are widely utilized for their robust feature extraction capabilities, particularly in medical classification tasks. However, their opaque decision-making process presents challenges in clinical settings, where interpretability and trust are paramount. This study investigates the explainability of a custom CNN model developed for Covid-19 and non-Covid-19 classification using dry cough spectrograms, with a focus on interpreting filter-level representations and decision pathways. To improve model transparency, we apply a suite of explainable artificial intelligence (XAI) techniques, including feature visualizations, SmoothGrad, Grad-CAM, and LIME, which explain the relevance of spectro-temporal features in the classification process. Furthermore, we conduct a comparative analysis with a pre-trained MobileNetV2 model using Guided Grad-CAM and Integrated Gradients. The results indicate that while MobileNetV2 yields some degree of visual attribution, its explanations, particularly for Covid-19 predictions are diffuse and inconsistent, limiting their interpretability. In contrast, the custom CNN model exhibits more coherent and class-specific activation patterns, offering improved localization of diagnostically relevant features.
Game-Theoretic Explainable AI for Ensemble-Boosting Models in Early Malware Prediction for Computer Systems
Shagufta Henna, Mallikharjuna Rao Sakhamuri, Lakshya Gourav Moitra, Upaka Rathnayake
International Journal of Computational Intelligence Systems, 2025
Abstract Malware continues to pose a critical threat to computing systems, with modern techniques often bypassing traditional signature-based defenses. Ensemble-boosting classifiers, including GBC, XGBoost, AdaBoost, LightGBM, and CatBoost, have shown strong predictive performance for malware detection, yet their “black-box” nature limits transparency, interpretability, and trust, all of which are essential for deployment in high-stakes cybersecurity environments. This paper proposes a unified explainable AI (XAI) framework to address these challenges by improving the interpretability, fairness, transparency, and efficiency of ensemble-boosting models in malware and intrusion detection tasks. The framework integrates SHAP for global feature importance and complex interaction analysis; LIME for local, instance-level explanations; and DALEX for fairness auditing across sensitive attributes, ensuring that predictions remain both equitable and meaningful across diverse user populations. We rigorously evaluate the framework on a large-scale, balanced dataset derived from Microsoft Windows Defender telemetry, covering various types of malware. Experimental results demonstrate that the unified XAI approach not only achieves high malware detection accuracy but also uncovers complex feature interactions, such as the combined effects of system configuration and security states. To establish generalization, we further validate the framework on the CICIDS-2017 intrusion detection dataset, where it successfully adapts to different network threat patterns, highlighting its robustness across distinct cybersecurity domains. Comparative experiments against state-of-the-art XAI tools, including AnchorTabular (rule-based explanations) and Fairlearn (fairness-focused analysis), reveal that the proposed framework consistently delivers deeper insights into model behavior, achieves better fairness metrics, and reduces explanation overhead. By combining global and local interpretability, fairness assurance, and computational optimizations, this unified XAI framework offers a scalable, human-understandable, and trustworthy solution for deploying ensemble-boosting models in real-world malware detection and intrusion prevention systems.
Graph representation federated learning for malware detection in Internet of health things
Mohamed Amjath, Shagufta Henna, Upaka Rathnayake
Results in Engineering, 2025
The Internet of Health Things (IoHT) plays a crucial role in modern healthcare by integrating medical devices and patient data to enhance healthcare delivery. However, the increasing prevalence of malware threats presents significant security and privacy challenges. Although centralized Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) are effective in modeling complex interactions for malware detection, their dependence on centralized data introduces privacy and scalability issues. This research proposes a graph-based Federated Learning (FL) learning approach, which enables collaborative training across distributed IoHT devices while preserving data confidentiality. Experimental results show that Fed-MalGAT outperforms Fed-MalGCN, achieving ROC-AUC values of 0.926 for Fed-MalGAT and 0.912 for Fed-MalGCN, highlighting the superior malware detection capability of Fed-MalGAT's multi-head attention mechanism. Fed-MalGAT consistently maintains high classification accuracy across all rounds, demonstrating its robustness. In terms of performance, Fed-MalGAT achieves 93% accuracy, 92% precision, and 93% F1 score, balancing precision and recall effectively. GAT follows with 92% accuracy, 91% precision, and 91% F1 score, while GCN, with a high ROC-AUC of 0.95, shows strong class discrimination but lower accuracy (88%) and F1 score (87%). Fed-MalGCN, with 92% accuracy, 87% precision, and 91% F1 score, does not surpass Fed-MalGAT or GAT. The FL-based approach shows a minor trade-off in class discrimination, evidenced by slightly lower ROC-AUC scores in federated models compared to their non-federated counterparts. Fed-MalGAT (93%) and Fed-MalGCN (92%) achieve competitive accuracy compared to FedAvg (98.26%) and DW-FedAvg (98.28%), but with significantly fewer communication rounds, underscoring their efficiency in FL scenarios. This analysis emphasizes Fed-MalGAT's suitability for scenarios requiring high precision and robust classification, as it consistently outperforms others in key metrics despite the computational demands of its attention mechanism. • Developed Federated Learning-based GNN models, Fed-MalGAT and Fed-MalGCN, for improved malware detection in IoHT environments. • Leveraged Function Call Graphs to model and analyze intricate malware patterns through graph-based learning techniques. • Fed-MalGCN excels in capturing topological features of FCGs, while Fed-MalGAT employs advanced attention mechanisms to prioritize and extract relevant features, improving detection precision. • Achieved superior malware detection performance with Fed-MalGAT compared to Fed-MalGCN in FL, while preserving data privacy and scalability.
Understanding and classification of innate immune response through weighted edge representation learning with dual hypergraph transformation
Mallikharjuna Rao Sakhamuri, Shagufta Henna, Leo Creedon, Kevin Meehan, Marion McAfee
Results in Engineering, 2025
Counterfactual GraphLIME-Enabled Explainable Adversarial Defense for Graph-Based Intrusion Detection Using Residual GAN-FGSM Framework
Shagufta Henna
Proceedings 2025 27th IEEE International Conference on High Performance Computing and Communications 11th IEEE International Conference on Data Science and Systems 23rd IEEE International Conference on Smart City 11th IEEE International Conference on Dependability in Sensor Cloud and Big Data Systems and Applications and 21st IEEE International Conference on Embedded Software and Systems Hpcc Dss Smartcity Dependsys Icess 2025, 2025
A Spatiotemporal Generative AI Framework for Agronomic Forecasting Using Semi-Supervised Learning on Remote Sensing Time-Series
Shagufta Henna, Mallikharjuna Rao Sakhamuri
Proceedings 2025 IEEE International Conference on Smart Computing Smartcomp 2025, 2025
EdgeSecureDP: Strengthening IoHTs Differential Privacy Through Graphvariate Skellam
Mohamed Amjath, Shagufta Henna
IEEE Access, 2025
Few-Shot Learning for Brain Tumour Classification
Kathy Bannigan, Shagufta Henna
Proceedings 2025 IEEE International Conference on Smart Computing Smartcomp 2025, 2025
Differentially Private Federated Adversarial Learning for Robust Malware Detection in Internet of Health Things (IoHTs)
Mohamed Amjath, Shagufta Henna
Proceedings 2025 IEEE International Conference on Smart Computing Smartcomp 2025, 2025
Time-generative AI-enabled temporal fusion transformer model for efficient air pollution sensor calibration in IIoT edge environments
Shagufta Henna, , Mohammad Amjath, Asif Yar
Aims Environmental Science, 2025
Optimizing B2B customer relationship management and sales forecasting with spectral graph convolutional networks: A quantitative approach
Shagufta Henna, , Shyam Krishnan Kalliadan, Mohamed Amjath
Quantitative Finance and Economics, 2025
Explainable AI for Smart Greenhouse Control: Interpretability of Temporal Fusion Transformer in the Internet of Robotic Things
Muhammad Jawad Bashir, Shagufta Henna, Eoghan Furey
Irish Signals and Systems Conference Signalling Our Strength Issc 2025, 2025
Supervised Contrastive Learning for Coastal Resilience: Enhancing Climate Adaptation and Sustainability through Representation Learning
Kathy Bannigan, Shagufta Henna, Stephen Seawright
Irish Signals and Systems Conference Signalling Our Strength Issc 2025, 2025
Iterative Graph-Based Deep Learning for Modeling and Predicting Immunogenicity in Human Immune Response
Mallikharjuna Rao Sakhamuri, Shagufta Henna, Leo Creedon, Kevin Meehan
Proceedings 2025 IEEE International Conference on Smart Computing Smartcomp 2025, 2025
Advancing water quality assessment and prediction using machine learning models, coupled with explainable artificial intelligence (XAI) techniques like shapley additive explanations (SHAP) for interpreting the black-box nature
Randika K. Makumbura, Lakindu Mampitiya, Namal Rathnayake, D.P.P. Meddage, Shagufta Henna, Tuan Linh Dang, Yukinobu Hoshino, Upaka Rathnayake
Results in Engineering, 2024
Human activity recognition using ensemble machine learning classifiers
Shagufta Henna, David Aboga, Muhammad Bilal, Stephen Azeez
Aip Conference Proceedings, 2024
A data-augmented approach to transfer learning for Covid-19 detection
Shagufta Henna, Stephen Azeez, Muhammad Bilal, Aparna Reiji
Aip Conference Proceedings, 2024
Rényi Differential Privacy Analysis of Skellam under Federated Learning in Internet of Health Things
Mohamed Amjath, Shagufta Henna
Proceedings of the 2024 IEEE International Conference on Cyber Security and Resilience Csr 2024, 2024
Representation Learning for Spatial Reuse in IEEE 802.11ax-Compliance Edge Intelligence
Stephen Azeez, Shagufta Henna
Lecture Notes in Networks and Systems, 2024
Front-running Attack Detection in Blockchain using Conditional Packing Generative AI
Shagufta Henna, Mohamed Amjath
Proceedings 2024 IEEE International Conference on Big Data Bigdata 2024, 2024
Molecular Adversarial Generative Graph Network Model for Large-scale Molecular Networks
Mallikharjuna Rao Sakhamuri, Shagufta Henna, Leo Creedon, Kevin Meehan
Proceedings of the 35th Irish Systems and Signals Conference Issc 2024, 2024
Accelerating Deep Learning for Self-Calibration in Large-Scale Uncontrolled Wireless Sensor Networks for Environmental Monitoring
Asif Yar, Shagufta Henna, Marion McAfee, Salem S. Gharbia
Proceedings of the 35th Irish Systems and Signals Conference Issc 2024, 2024
Ensemble consensus representation deep reinforcement learning for hybrid FSO/RF communication systems
Shagufta Henna, Abid Ali Minhas, Muhammad Saeed Khan, Muhammad Shahid Iqbal
Optics Communications, 2023
Rényi Differential Privacy Analysis of Graph-Based Federated Learning Under Internet of Health Things
Mohamed Amjath, Shagufta Henna
2023 31st Irish Conference on Artificial Intelligence and Cognitive Science Aics 2023, 2023
Representation Learning with Attention for Spatial Reuse Optimization in Dense WLANs
Stephen Azeez, Shagufta Henna
Lecture Notes in Networks and Systems, 2023
Heterogeneous Graph Transformer for Advanced Persistent Threat Classification in Wireless Networks
Kazeem Saheed, Shagufta Henna
2023 IEEE Conference on Network Function Virtualization and Software Defined Networks Nfv Sdn 2023 Proceedings, 2023
Deep Reinforcement Learning for Advanced Persistent Threat Detection in Wireless Networks
Kazeem Saheed, Shagufta Henna
2023 31st Irish Conference on Artificial Intelligence and Cognitive Science Aics 2023, 2023
Multi-armed Bandit-based Channel Bonding for Off-body Communication in IEEE 802.15.6 Cognitive Radio Wireless Body Area Networks
Shagufta Henna, Kevin Meehan, Mallikharjuna Rao Sakhamuri
2023 34th Irish Signals and Systems Conference Issc 2023, 2023
Multi-Graph Convolutional Neural Network for Breast Cancer Multi-task Classification
Mohamed Ibrahim, Shagufta Henna, Gary Cullen
Communications in Computer and Information Science, 2023
A Graph Neural Network-based Security Posture-aware Cloud Service Provider Selection for Multi-cloud
D.S. Wijenayake, Shagufta Henna, William Farrelly
2023 31st Irish Conference on Artificial Intelligence and Cognitive Science Aics 2023, 2023
Extreme Learning Machines for Calibration and Prediction in Wireless Sensor Networks: Advancing Environmental Monitoring Efficiency
Asif Yar, Shagufta Henna, Marion McAfee, Salem S. Gharbia
2023 31st Irish Conference on Artificial Intelligence and Cognitive Science Aics 2023, 2023
Graph Modelling and Graph-Attention Neural Network for Immune Response Prediction
Mallikharjuna Rao Sakhamuri, Shagufta Henna, Leo Creedon, Kevin Meehan
2023 34th Irish Signals and Systems Conference Issc 2023, 2023
Wireless Sensor Networks Calibration using Attention-based Gated Recurrent Units for Air Pollution Monitoring
Shagufta Henna, Asif Yar, Kazeem Saheed, Paulson Grigarichan
Proceedings 2023 IEEE International Conference on Big Data Bigdata 2023, 2023
Air Pollution Monitoring Using Online Recurrent Extreme Learning Machine
Asif Yar, Shagufta Henna, Marion McAfee, Salem S. Gharbia
2023 31st Irish Conference on Artificial Intelligence and Cognitive Science Aics 2023, 2023
Global-Local Influence Maximization Subgraph Sampling-Based Graph Representation Learning for Innate Immune Response Classification
Mallikharjuna Rao Sakhamuri, Shagufta Henna, Leo Creedon, Kevin Meehan
2023 31st Irish Conference on Artificial Intelligence and Cognitive Science Aics 2023, 2023
Distributed and Collaborative High-Speed Inference Deep Learning for Mobile Edge with Topological Dependencies
Shagufta Henna, Alan Davy
IEEE Transactions on Cloud Computing, 2022
Correction: Performance Evaluation of LoRaWAN for Green Internet of Things (IEEE Access (2019) 7 (164102-164112) DOI: 10.1109/ACCESS.2019.2943720)
Zulfiqar Ali, Shagufta Henna, Adnan Akhunzada, Mohsin Raza, Sung Won Kim
IEEE Access, 2022
Attenuation-based hybrid RF/FSO link using soft switching
Abid Ali Minhas, Muhammad Saeed Khan, Shagufta Henna, Muhammad Shahid Iqbal
Optical Engineering, 2021
Modeling Human Innate Immune Response Using Graph Neural Networks
Shagufta Henna
IEEE Access, 2021
Collaborative Wireless Power Transfer in Wireless Rechargeable Sensor Networks
Azka Amin, Xi-Hua Liu, Muhammad Asim Saleem, Shagufta Henna, Taseer-ul Islam, Imran Khan, Peerapong Uthansakul, Muhammad Zeshan Qurashi, Seyed Sajad Mirjavadi, Masoud Forsat
Wireless Communications and Mobile Computing, 2020
Deep Reinforcement Learning for Topology-Aware VNF Resource Prediction in NFV Environments
Nikita Jalodia, Shagufta Henna, Alan Davy
IEEE Conference on Network Function Virtualization and Software Defined Networks Nfv Sdn 2019 Proceedings, 2019
An internet of things (IoT)-Based coverage monitoring for mission critical regions
Shagufta Henna, Alan Davy, Hasan Ali Khattak, Abid Ali Minhas
2019 10th IFIP International Conference on New Technologies Mobility and Security Ntms 2019 Proceedings and Workshop, 2019
Performance Evaluation of LoRaWAN for Green Internet of Things
Zulfiqar Ali, Shagufta Henna, Adnan Akhunzada, Mohsin Raza, Sung Won Kim
IEEE Access, 2019
An efficient precoding algorithm for mmWave massive MIMO systems
Imran Khan, Shagufta Henna, Nasreen Anjum, Aduwati Sali, Jonathan Rodrigues, Yousaf Khan, Muhammad Irfan Khattak, Farhan Altaf
Symmetry, 2019
An Adaptive Backoff Mechanism for IEEE 802.15.4 Beacon-Enabled Wireless Body Area Networks
Shagufta Henna, Muhammad Awais Sarwar
Wireless Communications and Mobile Computing, 2018
Evaluation of propagation path delay using 3D scattered model in LoRaWAN
Ad Hoc and Sensor Wireless Networks, 2018
A fair contention access scheme for low-priority traffic in wireless body area networks
Shagufta Henna, Muhammad Sajeel, Faisal Bashir, Muhammad Asfand-e-yar, Muhammad Tauqir
Sensors Switzerland, 2017
A survey on mobility management techniques in VANETs
Bilal Haider, Shagufta Henna, Ambreen Gul, Farhan Aadil
Proceedings 2016 16th IEEE International Conference on Computer and Information Technology CIT 2016 2016 6th International Symposium on Cloud and Service Computing IEEE Sc2 2016 and 2016 International Symposium on Security and Privacy in Social Networks and Big Data Socialsec 2016, 2017
HE-PRMAC: Hop extended pipelined routing enhanced MAC protocol for wireless sensor networks
Sundass Khaliq, Shagufta Henna
2016 6th International Conference on Innovative Computing Technology Intech 2016, 2017
AS-PW-MAC: An adaptive scheduling predictive wake-up MAC protocol for wireless sensor networks
Laraib Hamada, Shagufta Henna
2016 6th International Conference on Innovative Computing Technology Intech 2016, 2017
Energy Efficient Fault Tolerant Coverage in Wireless Sensor Networks
Shagufta Henna
Journal of Sensors, 2017
PW-MMAC: Predictive-Wakeup Multi-Channel MAC protocol for Wireless Sensor Networks
Muhammad Bilal Saleemi, Shagufta Henna
Proceedings 2016 Uksim Amss 18th International Conference on Computer Modelling and Simulation Uksim 2016, 2016
Sender assisted receiver initiated MAC with predictive-wakeup for wireless sensor networks
Shagufta Henna, Bilal Saleeemi
Lecture Notes in Electrical Engineering, 2016
Least channel variation multi-channel MAC (LCV-MMAC)
Shagufta Henna, Thomas Erlebach
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2013
Approximating maximum disjoint coverage in wireless sensor networks
Shagufta Henna, Thomas Erlebach
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2013
SA-RI-MAC: Sender-assisted receiver-initiated asynchronous duty cycle MAC protocol for dynamic traffic loads in wireless sensor networks
Shagufta Henna
Lecture Notes of the Institute for Computer Sciences Social Informatics and Telecommunications Engineering, 2012
CMAB: Cross layer mobility-adaptive broadcasting in mobile ad hoc networks
Shagufta Henna, Thomas Erlebach
Momm2010 8th International Conference on Advances in Mobile Computing and Multimedia, 2010
Congestion and network density adaptive broadcasting in mobile ad hoc networks
Shagufta Henna, Thomas Erlebach
Studies in Computational Intelligence, 2010
A throughput analysis of TCP variants in mobile wireless networks
Shagufta Henna
Ngmast 2009 3rd International Conference on Next Generation Mobile Applications Services and Technologies, 2009
A transformation framework for XML-based data transformation agent for data warehousing
Proceedings of the 2008 International Conference on Internet Computing Icomp 2008, 2008

Shagufta Henna

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Scopus Publications