LLM-enabled adaptive scheduling in IoT sensing for optimized network performance Muhammad Nawaz Khan, Sokjoon Lee, Sang Su Lee, Mohsin Shah, Inam Ullah, Shakila Basheer, Ali Kashif Bashir Scientific Reports, 2026 The use of numerous sensors on edge devices, combined with the emergence of AI techniques, makes the IoT environment more intelligent and interactive. The resulting paradigm encompasses device-centric systems that operate instantly and remotely with zero clicks. However, with these advantages, many functional challenges affect remote sensing, including incomplete data, communication delay, lack of context awareness, and dynamically switching topology. To address these challenges, we have proposed a novel scheme, "LLM-Enabled Adaptive Scheduling in IoT Sensing for Optimized Network Performance (LLM-AS)." This scheme uses LLM to adjust the system's sensing to avoid redundant and useless data sending and enhance decision-making for optimized network resources. First, LLM-AS is trained with a defined data set for different parameters, such as packet loss trends, time-based fluctuations, event triggers, network failure patterns, and congestion signals with contextual decisions. Then, this scheme is deployed in a dynamic remote monitoring system for learning and updating task descriptions to utilize the feedback for future decisions and enhance the system performance. Evaluation of LLM-AS on various parameters using the CASAS dataset shows that the optimization functions of LLM are useful and make the IoT more usable. The LLM-AS optimization function confirms an improvement of 57.8% to 60% in MTP, a decrease of 26% to 60% in median delay, and an optimized energy solution with a confidence interval of 95% and a very small error margin. It also indicates that the precision score is about 0.86, the recall score is about 0.82, and the RMSE is about 0.21; all these values suggest high separability for varying conditions of IoT systems in dynamically changing situations.
XAI-driven Data Mining for Self-defending IoT Systems: Enhancing Cybersecurity Transparency in the Age of Smart Cities Fida Muhammad Khan, Asim Zeb, Taj Rahman, Mahmoud Ahmad Al-Khasawneh, Yousef Ibrahim Daradkeh, Isma Farah Siddiqui, Ali Kashif Bashir, Inam Ullah Cognitive Computation, 2026 The rapid expansion of Internet of Things (IoT) technologies in smart cities, healthcare, and industrial automation has intensified the need for cybersecurity frameworks capable of operating at scale and in real time under increasingly sophisticated threat conditions. Traditional security mechanisms and opaque AI-based models are no longer adequate for protecting interconnected urban infrastructures, especially as regulatory and societal expectations move toward transparency and accountability. Although prior surveys have examined IoT security and general AI techniques, they rarely address the emerging role of Explainable Artificial Intelligence (XAI) in data mining for IoT cybersecurity or integrate recent advances in cognitively inspired and human-aligned explainability methods. This survey provides an up-to-date review of XAI-driven data mining approaches applied to IoT ecosystems, highlighting their ability to detect anomalies, interpret complex sensor-driven behaviours, and support automated security decisions through transparent and interpretable reasoning. The review identifies critical challenges, including data privacy, scalability, computational constraints, and the interpretability limitations of modern AI models. It examines how biologically inspired learning paradigms and cognitively grounded explanation techniques can enhance trust and situational awareness in IoT environments. Emerging technologies such as edge intelligence, federated learning (FL), blockchain integration, and quantum-assisted analytics are discussed as promising enablers of scalable and transparent IoT security. The survey underscores the importance of trustworthy, ethically aligned AI, advocating for XAI frameworks that enable fair, auditable, and reliable decision-making in safety-critical infrastructure. By addressing gaps in the literature and synthesizing recent developments, this study presents a timely perspective on XAI, data mining, and IoT cybersecurity, outlining future directions for building resilient, interpretable, and human-centric smart city systems.
Integration of Neural Architecture Search With Fuzzy Deep Neural Network Model for Emotion AI in Public Health Emergencies Gopalakrishnan Chandran, Ganesh Gopal Devarajan, Judgi T., Mohamed Mallick M. S., Theyazn H. H. Aldhyani, Ali Kashif Bashir IEEE Transactions on Computational Social Systems, 2026 Social networks, particularly Twitter, significantly influence public emotions during health crises, often amplifying distress and misinformation. Effective sentiment analysis is crucial for mitigating social unrest and enabling timely interventions. This study introduces a novel two-stage framework, the fuzzy-integrated case-based and adaptive deep-belief neural network (2-SCBAADBNN) model, for real-time sentiment classification in public health emergencies. The framework integrates neural architecture search (NAS) and large machine learning models (LMMs) such as bidirectional encoder representations from transformers (BERT) to optimize fuzzy logic components and enhance feature extraction, improving sentiment detection accuracy. The two-stage classification first distinguishes between personal and news-related tweets using a fuzzy clue-based method, followed by sentiment classification of personal tweets into positive or negative categories. By combining fuzzy logic with deep learning, this multimodal approach aligns with advancements in emotion AI, offering greater scalability, adaptability, and precision in sentiment analysis. Comparative evaluations show that 2-SCBAADBNN outperforms existing models, providing a robust solution to monitor emotional distress and combat misinformation during crises. This research advances emotion AI by integrating NAS and LMM, allowing more context-sensitive real-time sentiment analysis. It contributes to developing AI-driven empathy-based systems capable of understanding and responding to public sentiment more effectively in critical social scenarios.
Intent-Based Networking with Deep Reinforcement Learning for Detecting Decreased Rank Attacks in Low-Power and Lossy IoT Networks Muhammad Haqdad, Muhammad Fayaz, Pervez Khan, Farman Ali, Theyazan H. H. Aldhyani, Ali Kashif Bashir, Daehan Kwak IEEE Internet of Things Journal, 2026 The routing protocol for low-power and lossy networks (RPL) is a specialized routing protocol designed for optimized data routing, specifically for resource-constrained Internet of Things (IoT) networks with unreliable links and high packet loss. However, RPL is highly vulnerable to significant security challenges, particularly the decrease rank attack (DRA), in which malicious nodes attract child nodes by falsely advertising lower ranks, leading to routing inefficiencies, unnecessary retransmissions, and increased energy consumption. To address this problem, we propose a novel intent-based networking-driven centralized real-time reinforced detection scheme (CRRDS), which translates high-level security intents into policy-driven automated control strategies for DRA detection. In the proposed CRRDS, a resource-rich root node acts as a deep reinforcement learning agent that collects critical information from the child nodes, including the node ID, end-to-end delay, received signal strength indicator, and hop count, to detect suspicious behavior accurately and intelligently. Initially, we implemented a deep Q-network (DQN)-assisted CRRDS in detecting DRA. Subsequently, we utilized double DQN (DDQN) and dueling DDQN due to their enhanced capabilities in value estimation and policy learning. The dueling DDQN performed optimally because of its deeper architecture. Simulation results demonstrate that the proposed dueling DDQN-assisted CRRDS achieves the highest detection accuracy of 98% with notable gains in true positive and false positive rates, even in complex scenarios with up to 30% malicious nodes.
SIM-IBN: Surgical Event Time Imputation in Intent-Based Networking for Internet of Medical Things Yixian Chen, Zhaocheng He, Sen Deng, You Zhou, Ali Kashif Bashir, Norah Saleh Alghamdi, Lin Yao, Yuhuan Lu, Wei Wang IEEE Internet of Things Journal, 2026 The rapid development of the Internet of Medical Things (IoMT) enables automatic recording of surgical reports via interconnected medical devices. However, the reliability of these data is often compromised by missing data, frequently stemming from intermittent IoT communication issues like network disruptions or device malfunctions. This incomplete data critically hinders downstream medical applications and violates implicit network intents related to data integrity and timeliness within an Intent-based Networking (IBN), essential for supporting proactive resource allocation in operating rooms and optimized surgical scheduling. While existing studies focus on addressing missing event types, event time imputation remains a significant, underexplored challenge due to the need to capture implicit temporal contexts and complex cross surgical procedures dependencies. To tackle this for IoMT, we propose a novel Surgical event time IMputation in Intent-Based Networking(SIM-IBN) model. SIM-IBN employs continuous-time LSTMs with attention mechanisms to learn intra-and inter-sequence correlations, effectively recovering missing timestamps. By enhancing data reliability at the source, SIM-IBN serves as a crucial component enabling IBN systems to better fulfill intents for dependable IoMT operations. Rigorous evaluation on real-world surgical event datasets demonstrates SIM-IBN’s superiority over state-of-the-art baselines by up to 11.88% across various missing data scenarios, validating its potential to enable more reliable IoMT systems and enhance operational efficiency in smart healthcare environments.
Conv-MTD: A CNN Based Multi-Label Medical Tubes Detection and Classification Model to Facilitate Resource-Constrained Point-of-Care Devices Moneeb Abbas, Wen-Chung Kuo, Khalid Mahmood, Waseem Akram, Sajid Mehmood, Ali Kashif Bashir IEEE Journal of Biomedical and Health Informatics, 2026 Computer-aided detection through deep learning is becoming a prevalent approach across various fields, including detection of anomalies in medical procedures. One such medical procedure involves the placement of medical tubes to provide nutrition or other medical procedures in critically ill patients. Medical tube placement can be highly complex and prone to subjective errors. Malposition of medical tubes is often observed, and associated with significant morbidity and mortality. In addition, continuous verification using manual procedures such as capnography, pH testing, auscultation, and visual inspection through chest X-ray (CXR) imaging is required. In this paper, we propose a Conv-MTD, a medical tube detection (MTD) model that detects the placement of medical tubes using CXR images, assisting radiologists with precise identification and categorizing the tubes into normal, abnormal, and borderline placement. Conv-MTD leverages the state-of-the-art EfficientNet-B7 architecture as its backbone, enhanced with auxiliary head in the intermediate layers to mitigate vanishing gradient issues common in deep neural networks. The Conv-MTD is further optimized using post-training 16-bit floating-point (FP16) quantization, which effectively reduces memory consumption and inference latency on resource-constrained devices. Conv-MTD provided the best performance, with an average area under the receiver-operator curve AUC-ROC of 0.95. The proposed Conv-MTD has the potential to operate on resource-constrained point-of-care devices, enabling low-cost and automated assessments in various healthcare settings.
BioTwinXAI-Eye: A Consumer-Centric Digital Twin for Explainable and Personalized Risk Prediction of Diabetic Retinopathy and Glaucoma Jing Yang, Vijay Govindarajan, Sunil Prajapat, Ali Kashif Bashir, Theyazn H. H. Aldhyani, Fu Chenxi, Lip Yee Por, Qianjie Yang IEEE Transactions on Consumer Electronics, 2026 Diabetic retinopathy (DR) and glaucoma cause preventable blindness in over 600 million people worldwide. Early detection is critical, but challenging due to limited specialist access. We present BioTwinXAI-Eye, a digital twin system that creates a virtual model of each patient’s eye health by combining retinal images, eye pressure data, OCT scans, and health indicators from consumer devices such as smartwatches and glucose monitors. Our system uses intelligent algorithms that track how diseases progress over time, and how different eye structures relate to each other. To help doctors trust the system, we include tools that explain predictions by highlighting important image regions and identifying key risk factors. Tests on 88,702 retinal images and 15,234 glaucoma patients show that our method improves accuracy by 21% compared to existing approaches. Crucially, it increased doctor confidence in AI predictions by 58% through transparent explanations. BioTwinXAI-Eye enables personalized care by continuously adapting to each patient’s unique health profile, supporting modern precision medicine goals in preventing vision loss.
Spatial Reasoning and Risk Assessment for Autonomous Vehicles on Consumer Electronics Platforms Using a Customized Vision–Language Model With Data Augmentation Ziyu Song, Jing Yang, Lei Fang, Muhammad Umair Ali, Gyanendra Kumar, Ali Kashif Bashir, Nazik Alturki, Lip Yee Por, Seung-Won Lee IEEE Transactions on Consumer Electronics, 2026 Accurate spatial reasoning and risk assessment from monocular video on consumer electronics platforms are prerequisites for safe decision-making in autonomous vehicles, yet general-purpose vision–language model (VLM) remains unreliable at lane-level localization and temporally grounded risk estimation. We present a deployment-oriented, spatially enhanced VLM that learns implicit 3D reconstructive information from monocular sequences via a reconstructive 3D tokenization pipeline and a spatial–visual fusion encoder built on MobileVLM. Concretely, the model fuses 3D reconstructive tokens with CLIP visual tokens through cross-attention to produce 3D-aware visual tokens, enabling lane-level localization and depth-aware reasoning about object orientation and inter-object relations. The unified decoder jointly generates scenario descriptions, object identities and positions, and converts spatial estimates into interpretable multi-level risk scores using a Time-to-Collision (TTC) mapping. Experimental results show that our system outperforms strong VLM baselines for multi-level risk classification on the nuScenes dataset. The generalization in CARLA and CoVLA further corroborates robust spatial reasoning and risk assessment. The findings indicate that coupling a lightweight VLM with monocular 3D cues via cross-attentional spatial–visual fusion yields accurate spatial localization and interpretable, transferable risk estimation that is robust under shift and practical for edge deployment in downstream tasks.
A Blockchain-Assisted Quantum Encryption Scheme for Secure Communication in Internet of Things Networks Sunil Prajapat, Ali Kashif Bashir IEEE Internet of Things Magazine, 2026 With the rapid expansion of intelligent devices in the Internet of Things (IoT), ensuring secure and autonomous communication has become increasingly critical. This paper presents a quantum-assisted encryption framework that safeguards between users and IoT devices. By combining quantum cryptographic primitives with blockchain-based identity verification, the scheme achieves confidentiality, authenticity, and resistance to quantum-era threats. Experimental implementation on a real quantum backend validates its feasibility and demonstrates high fidelity and communication efficiency, highlighting its suitability for real-time IoT environments. This work provides a step toward integrating quantum technologies with practical IoT security frameworks under current technological constraints.
Preface S. Sountharrajan, M. Karthiga, Balamurugan Balusamy, Ali Kashif Bashir Applied Mathematical Modeling for Biomedical Robotics and Wearable Devices, 2025
Neuro-VAE-Symbolic Dynamic Traffic Management Jiaming Pei, Jinhai Li, Zhenyu Song, Maryam Mohamed Al Dabel, Mohammed J. F. Alenazi, et al. IEEE Transactions on Intelligent Transportation Systems, 2025
GreenCom 2024 Message from the General Chairs Proceedings IEEE Congress on Cybermatics 2024 IEEE International Conferences on Internet of Things Ithings 2024 IEEE Green Computing and Communications Greencom 2024 IEEE Cyber Physical and Social Computing Cpscom 2024 IEEE Smart Data Smartdata 2024, 2024
Message from the Program Chairs Proceedings IEEE Congress on Cybermatics 2023 IEEE International Conferences on Internet of Things Ithings 2023 IEEE Green Computing and Communications Greencom 2023 IEEE Cyber Physical and Social Computing Cpscom 2023 and IEEE Smart Data Smartdata 2023, 2023
Multi sensor-based implicit user identification Muhammad Ahmad, Rana Aamir Raza, Manuel Mazzara, Salvatore Distefano, Ali Kashif Bashir, Adil Khan, Muhammad Shahzad Sarfraz, Muhammad Umar Aftab Computers Materials and Continua, 2021
In-network RFID data filtering scheme in RFID-WSN for RFID applications Ali Kashif Bashir, Myong-Soon Park, Sang-Il Lee, Jinseop Park, Wongryol Lee, et al. Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2013
Efficient in-network redundancy filtering in RFID system integrated with wireless sensor networks Inc2010 the International Conference on Networked Computing Proceeding, 2010
Location based multi-queue scheduler in wireless sensor network International Conference on Advanced Communication Technology Icact, 2010
Mobile Ad hoc computational grid for low constraint devices Sayed Chhattan Shah, Ali Kashif Bashir, Sajjad Hussain Chauhdary, Chen Jiehui, Myong-Soon Park Proceedings 2009 International Conference on Future Computer and Communication Icfcc 2009, 2009
Reliable cache memory design for sensor networks Hyung Beom Jang, Ali Kashif, Myong-Soon Park, Sung Woo Chung Proceedings 3rd International Conference on Convergence and Hybrid Information Technology Iccit 2008, 2008
A Context-Aware Service Discovery consideration in 6LoWPAN Sajjad Hussain Chauhdary, MinYu Cui, Jung Hwan Kim, Ali Kashif Bashir, Myong-Soon Park Proceedings 3rd International Conference on Convergence and Hybrid Information Technology Iccit 2008, 2008
Energy efficient in-network phase RFID data filtering scheme Dong-Sub Kim, Ali Kashif, Xue Ming, Jung-Hwan Kim, Myong-Soon Park Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2008
GARPAN: Gateway-assisted inter-PAN routing for 6LoWPANs Ali Hammad Akbar, Ki-Hyung Kim, Won-Do Jung, Ali Kashif Bashir, Seung-Wha Yoo Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2006
3.3 V 50 MHz synchronous 16 Mb flash memory Digest of Technical Papers IEEE International Solid State Circuits Conference, 1995
RECENT SCHOLAR PUBLICATIONS
XAI-driven Data Mining for Self-defending IoT Systems: Enhancing Cybersecurity Transparency in the Age of Smart Cities FM Khan, A Zeb, T Rahman, MA Al-Khasawneh, YI Daradkeh, IF Siddiqui, ... Cognitive Computation 18 (1), 16 , 2026 2026 Citations: 2
Autonomous Systems in the Internet of Vehicles B Balusamy, SK Mathivanan, P Jayagopal, SKB Sangeetha, AK Bashir John Wiley & Sons , 2026 2026
Consumer Data Privacy-Aware Federated Orchestration for Communication, Computing, and Control in 6G Consumer Services SDA Shah, AK Bashir, D Kwak, A Salhi, F Ali IEEE Transactions on Consumer Electronics , 2026 2026
Adaptive Negative Scheduling for Graph Contrastive Learning A Ali, J Li, SM Israr, AK Bashir arXiv preprint arXiv:2605.03076 , 2026 2026
Trust Mechanisms in IIoT Software Authenticity: Challenges and Emerging Solutions N Onumah, AK Bashir, MI Biswas, MAU Rehman SEJIC 1 (1) , 2026 2026
Intelligent and secure mobility management for 6g networks: A systematic review of AI-driven and physical-layer solutions T Ali, M Al-Khalidi, AK Bashir Alexandria Engineering Journal 145, 253-286 , 2026 2026
PPWCS: A Strategy for Enhancing Caching Performance in Content-Centric Networking Enabled Consumer IoT Systems S Kumar, R Tiwari, SS Singh, H Shakya, G Kumar, AK Bashir IEEE Transactions on Consumer Electronics , 2026 2026
LLM-enabled adaptive scheduling in IoT sensing for optimized network performance: MN Khan et al. MN Khan, S Lee, SS Lee, M Shah, I Ullah, S Basheer, AK Bashir Scientific Reports 16 (1), 13007 , 2026 2026 Citations: 1
Blockchain Incentivized Federated Learning for IoT Networks R Al-Zaidi, M Al-Khalidi, JC Woods, AK Bashir IEEE Internet of Things Magazine , 2026 2026
Robust μ-Channel Estimation for IoT and 6G Edge Devices via Defensive Distillation T Ali, M Al-Khalidi, AK Bashir, NS Alghamdi IEEE Internet of Things Journal , 2026 2026
PHyena–DurCRF: A Quantum-Inspired, Edge-Efficient Analytics Framework for Human-Centric Wearable Sensing RK Rai, DK Singh, SR Hukampal, RH Alsisi, AK Bashir IEEE Transactions on Consumer Electronics , 2026 2026
A Blockchain-Assisted Quantum Encryption Scheme for Secure Communication in Internet of Things Networks S Prajapat, AK Bashir IEEE Internet of Things Magazine , 2026 2026
Swarm Smarts: Enabling Real-Time Self-Healing With TinyML at the Edge H Wu, J Zhang, M Feng, M Dai, S Mohiuddin, AK Bashir, S Mumtaz, J Pei IEEE Communications Standards Magazine , 2026 2026
Root of trust: survey, taxonomy, and open challenges NF Al-Otaiby, M Hammoudeh, J Hassine, AK Bashir Telecommunication Systems 89 (1), 39 , 2026 2026
GenAI and LLMs for Beyond 5G Networks S Singh, MR Kanagarathinam, MR GNS, Y Wu, N Saxena, AKR Chavva, ... Springer Nature , 2026 2026 Citations: 1
Distributed Federated Learning-Based AIOT Framework for Secure High Speed H Byeon¹, A AlGhamdi, I Keshta, M Soni, M Shabaz, MA Khan, AK Bashir Proceedings of Fifth International Conference on Computing and Communication … , 2026 2026
Spatial Reasoning and Risk Assessment for Autonomous Vehicles on Consumer Electronics Platforms Using a Customized Vision–Language Model with Data Augmentation Z Song, J Yang, L Fang, MU Ali, G Kumar, AK Bashir, N Alturki, LY Por, ... IEEE Transactions on Consumer Electronics , 2026 2026
Next-Gen Networks for the Internet of Consumer Electronics: A Survey G Kumar, VK Sharma, V Chamola, AK Bashir IEEE Consumer Electronics Magazine , 2026 2026
Federated Deep Learning for Collision Avoidance in IoV With Digital Twin Integration FM Khan, A Zeb, T Rahman, I Ullah, N Alturki, AK Bashir, Y El Touati, ... Expert Systems 43 (1), e70168 , 2026 2026 Citations: 4
Verifiable Credential-Based Access Control for Interoperable Crowdsourced Drone Services J Akram, A Akram, A Anaissi, RH Jhaveri, AK Bashir, MM Al Dabel IEEE Transactions on Consumer Electronics , 2025 2025
MOST CITED SCHOLAR PUBLICATIONS
Investigating the Acceptance of Mobile Library Applications with an Extended Technology Acceptance Model (TAM) H Rafique, AO Almagrabi, A Shamim, F Anwar, AK Bashir Computers & Education 145 , 2020 2020 Citations: 788
Corrauc: a malicious bot-iot traffic detection method in iot network using machine learning techniques M Shafiq, Z Tian, AK Bashir, X Du, M Guizani IEEE Internet of Things Journal 8 (5), 3242 - 3254 , 2021 2021 Citations: 662
COVID-19 patient health prediction using boosted random forest algorithm C Iwendi, AK Bashir, A Peshkar, R Sujatha, JM Chatterjee, S Pasupuleti, ... Frontiers in public health 8, 357 , 2020 2020 Citations: 617
A critical cybersecurity analysis and future research directions for the internet of things: a comprehensive review U Tariq, I Ahmed, AK Bashir, K Shaukat sensors 23 (8), 4117 , 2023 2023 Citations: 465
DITrust Chain: Towards Blockchain-based Trust Models for Sustainable Healthcare IoT Systems EA Nasser, AM Iliyasu, PM Kafrawy, OH Song, AK Bashir, AAE Latif. IEEE Access 8, 111223-111238 , 2020 2020 Citations: 391
A Metaheuristic Optimization Approach for Energy Efficiency in the IoT Networks. C Iwendi, PK Reddy, T Reddy, K Lakshmanna, AK Bashir, MJ Piran Software: Practice and Experience , 2020 2020 Citations: 326
IoT malicious traffic identification using wrapper-based feature selection mechanisms M Shafiq, Z Tian, AK Bashir, X Du, M Guizani Computers & Security 94, 101863 , 2020 2020 Citations: 324
Learning-Based Context-Aware Resource Allocation for Edge Computing-Empowered Industrial IoT H Liao, Z Zhou, X Zhao, S Mumtaz, A Jolfaei, SH Ahmed, AK Bashir IEEE Internet of Things Journal , 2020 2020 Citations: 321
Securing critical infrastructures: Deep-learning-based threat detection in IIoT K Yu, L Tan, S Mumtaz, S Al-Rubaye, A Al-Dulaimi, AK Bashir, FA Khan IEEE Communications Magazine 59 (10), 76-82 , 2021 2021 Citations: 307
Realizing an efficient IoMT-assisted Patient Diet Recommendation System through Machine Learning Model. C Iweni, S Khan, JH Anajemba, AK Bashir, F Noor. IEEE Access , 2020 2020 Citations: 298
Data mining and machine learning methods for sustainable smart cities traffic classification: A survey M Shafiq, Z Tian, AK Bashir, A Jolfaei, X Yu Sustainable Cities and Society 60, 102177 , 2020 2020 Citations: 273
A Survey on Resource Management in IoT Operating Systems A Musaddiq, Y Bin Zikria, O Hahm, H Yu, AK Bashir, SW and Kim IEEE Access 6, 8459-8482 , 2018 2018 Citations: 261
Towards sFlow and adaptive polling sampling for deep learning based DDoS detection in SDN RMA Ujjana, Z Pervez, K Dahal, AK Bashir, R Mumtaz, J González Future Generation Computer Systems 111, 763-779 , 2020 2020 Citations: 254
Efficient and Secure Data Sharing for 5G Flying Drones: A Blockchain-Enabled Approach C Feng, K Yu, AK Bashir, YD Al-Otaibi, Y Lu, S Chen, D Zhang IEEE Network 35 (1), 130-137 , 2021 2021 Citations: 253
Energy-efficient random access for LEO satellite-assisted 6G internet of remote things L Zhen, AK Bashir, K Yu, YD Al-Otaibi, CH Foh, P Xiao IEEE Internet of Things Journal 8 (7), 5114-5128 , 2020 2020 Citations: 249
Toward real-time and efficient cardiovascular monitoring for COVID-19 patients by 5G-enabled wearable medical devices: a deep learning approach L Tan, K Yu, AK Bashir, X Cheng, F Ming, L Zhao, X Zhou Neural Computing and Applications 35 (19), 13921-13934 , 2023 2023 Citations: 231
Millimeter-Wave Communication for Internet of Vehicles: Status, Challenges and Perspectives. KZ Ghafoor, L Kong, S Zeadally, AS Sadiq, G Epiphniou, M Hammoudeh, ... IEEE Internet of Things Journal , 2020 2020 Citations: 225
Energy-aware marine predators algorithm for task scheduling in IoT-based fog computing applications M Abdel-Basset, R Mohamed, M Elhoseny, AK Bashir, A Jolfaei, N Kumar IEEE Transactions on Industrial Informatics 17 (7), 5068-5076 , 2020 2020 Citations: 224
A Review on Classification of Imbalanced Data for Wireless Sensor Networks. H Patel, DS Rajput, GT Reddy, C Iwendi, AK Bashir, O Jo. International Journal of Distributed Sensor Networks , 2020 2020 Citations: 216
Federated learning for the healthcare metaverse: Concepts, applications, challenges, and future directions AK Bashir, N Victor, S Bhattacharya, T Huynh-The, R Chengoden, ... IEEE Internet of Things Journal 10 (24), 21873-21891 , 2023 2023 Citations: 213