Article
Engineering, Electrical & Electronic
Malgorzata Wasilewska, Hanna Bogucka, H. Vincent Poor
Summary: This article explores reliable and secure spectrum sensing in cognitive radio using federated learning. It discusses the motivation, architectures, and algorithms of federated learning in spectrum sensing. It provides an overview of security and privacy threats on these algorithms and presents possible countermeasures. The article also includes illustrative examples and offers design recommendations for future cognitive radios.
IEEE COMMUNICATIONS MAGAZINE
(2023)
Article
Chemistry, Analytical
Yanqueleth Molina-Tenorio, Alfonso Prieto-Guerrero, Rafael Aguilar-Gonzalez
Summary: This work discusses a novel multiband spectrum sensing technique for cognitive radios using wavelets, machine learning, and fractal dimensions. The proposed method was tested in real-time scenario by linking multiple affordable software-defined radios. The results showed promising performance with a 95% success rate for signal-to-noise ratio values greater than 0 dB.
Article
Chemistry, Analytical
Jun Wang, Weibin Jiang, Hongjun Wang, Yanwei Huang, Riqing Chen, Ruiquan Lin
Summary: As part of an IoT framework, the Smart Grid relies on advanced communication technologies, with Cognitive Radio technology improving communication quality. This paper proposes a new system architecture and optimization methods for multiband-CR-enabled SG communication. Simulation results validate the effectiveness of the proposed methods.
Article
Engineering, Multidisciplinary
Abdalaziz Mohammad, Faroq Awin, Esam Abdel-Raheem
Summary: This study utilizes machine learning algorithms to design a spectrum sensing model, and applies support vector machine, k-nearest neighbor, and decision tree methods to detect the existence of primary users. Principal Component Analysis is incorporated to speed up the learning process of classifiers, and an ensemble classification-based approach is employed to enhance classifier performance.
AIN SHAMS ENGINEERING JOURNAL
(2022)
Article
Telecommunications
S. Lakshmikantha Reddy, M. Meena
Summary: The increasing interest in wireless networks has made spectrum availability a challenge. Cognitive radio, a promising technology, can help overcome this issue. Finding available spectrum holes is one of the most challenging tasks in this technique. The use of machine learning techniques, particularly support vector machine with kernel transformation, has improved spectrum sensing performance.
WIRELESS PERSONAL COMMUNICATIONS
(2023)
Article
Chemistry, Analytical
Malgorzata Wasilewska, Hanna Bogucka, Adrian Kliks
Summary: This paper proposes a method using federated learning algorithm for distributed processing of spectrum sensing. Devices are grouped based on mean signal-to-noise ratio (SNR) and a common deep learning model is created for each group in the iterative process, achieving the goal of simplifying the spectrum sensing process in the network.
Article
Energy & Fuels
Mohammad Asif Hossain, Rafidah Md Noor, Kok-Lim Alvin Yau, Saaidal Razalli Azzuhri, Muhammad Reza Z'aba, Ismail Ahmedy, Mohammad Reza Jabbarpour
Summary: This paper proposes a segment-based CR-VANET architecture to improve spectrum sensing performance by dividing roads and using machine learning algorithms.
Review
Telecommunications
Muhammad Umair Muzaffar, Rula Sharqi
Summary: Cognitive radio network (CRN) is an advanced technology that improves spectrum utilization efficiency by exploiting unused portions of the spectrum. Spectrum sensing, the ability to determine the status of the target spectrum, is the most important capability of a cognitive radio. This work presents the state of the art in spectrum sensing techniques for different types of primary user signals, including both conventional and modern methods. Machine learning algorithms show promise in improving performance, but the selection of features remains a challenge. Additionally, there is a need for further research in spectrum sensing techniques for 5G cognitive radio networks.
TELECOMMUNICATION SYSTEMS
(2023)
Article
Computer Science, Information Systems
Qasem Abu Al-Haija, Ammar Odeh, Hazem Qattous
Summary: Portable document format (PDF) files are commonly used and often targeted by hackers. This paper presents a new detection system that utilizes machine learning methods, specifically the AdaBoost decision tree, to effectively identify malware PDF files. The proposed system achieves high detection performance and low detection overhead, outperforming other state-of-the-art models.
Article
Computer Science, Information Systems
Chen Wang, Yizhen Xu, Zhuo Chen, Jinfeng Tian, Peng Cheng, Mingqi Li
Summary: The paper investigates the issue of spectrum sensing, where classical methods and machine learning methods fail to adapt to new signal-to-noise ratio environments. The authors propose a new adversarial learning method that improves model adaptability by designing coupled neural networks. Simulation results demonstrate significant improvement in spectrum sensing error rate compared to existing methods.
IEEE WIRELESS COMMUNICATIONS LETTERS
(2022)
Article
Engineering, Electrical & Electronic
Zihang Song, Yue Gao, Rahim Tafazolli
Summary: This paper highlights the importance of cognitive radio in addressing spectrum scarcity, reviews spectrum sensing and learning algorithms, and discusses the application of sub-sampling framework and recovery algorithms based on compressed sensing theory in 5G and 6G communication systems. The paper also investigates recent progress in machine learning for spectrum sensing technology.
IEICE TRANSACTIONS ON COMMUNICATIONS
(2021)
Article
Engineering, Electrical & Electronic
Wenjing Zhao, Syed Sajjad Ali, Minglu Jin, Guolong Cui, Nan Zhao, Sang-Jo Yoo
Summary: This paper focuses on the design of optimal or near-optimal detectors using extreme eigenvalues. A general framework involving model-driven and data-driven approaches is introduced. The extreme eigenvalues based likelihood ratio test (LRT) and Naive Bayesian detector are derived via the model-driven and merged approaches, respectively. Two near-optimal detectors called alpha-SMME and alpha-PMME are further designed for practicality. Theoretical performance analysis is provided and optimal weight selection is obtained for the alpha-SMME and alpha-PMME algorithms. Simulation experiments demonstrate the improved performance of the proposed detectors using extreme eigenvalues.
IEEE TRANSACTIONS ON COMMUNICATIONS
(2022)
Article
Telecommunications
Abbass Nasser, Hussein Al Haj Hassan, Ali Mansour, Koffi-Clement Yao, Loutfi Nuaymi
Summary: This paper investigates the impact of deploying Intelligent Reflecting Surface (IRS) on spectrum sensing in cognitive radio networks, considering two different scenarios. The results show that deploying IRS can significantly enhance spectrum sensing performance.
IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING
(2022)
Article
Engineering, Electrical & Electronic
Yoel Bokobza, Ron Dabora, Kobi Cohen
Summary: This paper investigates the problem of dynamic spectrum access (DSA) in cognitive wireless networks, where secondary users (SUs) can only obtain partial observations due to narrowband sensing and transmissions. The objective is to maximize the SU's long-term throughput by developing a novel algorithm called Double Deep Q-network for Sensing and Access (DDQSA) that learns both access and sensing policies via deep Q-learning. The proposed algorithm achieves near-optimal performance and outperforms existing approaches in certain scenarios.
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Mehran Golvaei, Mohammad Fakharzadeh
Summary: Cooperative Spectrum Sensing addresses the Hidden Primary User problem by leveraging the spatial diversity of spectrum sensors, while introducing a novel fast soft decision algorithm based on Machine Learning theory for wideband Cooperative Spectrum Sensing, which outperforms traditional algorithms with a faster solution.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2021)
Article
Computer Science, Information Systems
Ali Hassan Sodhro, Sandeep Pirbhulal, Zongwei Luo, Khan Muhammad, Noman Zahid
Summary: This article focuses on capturing energy-efficient communication and user's QoE level through UT devices during multimedia transmission. It proposes a QoS-based joint energy and entropy optimization (QJEEO) algorithm, develops a 6G-driven multimedia data structure model for QoE evaluation with acquisition time, and establishes the relationship between subjective test score and objective performance metrics for IoT-based multimedia services.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Engineering, Civil
Ali Hassan Sodhro, Joel J. P. C. Rodrigues, Sandeep Pirbhulal, Noman Zahid, Antonio Roberto L. de Macedo, Victor Hugo C. de Albuquerque
Summary: The study proposes a novel reliable connectivity framework with the SSLO algorithm for optimization of vehicular networks, demonstrating high stability and reliability in different test scenarios. Experimental results on the software-defined Internet of Vehicle platform show the superior performance of the SSLO algorithm in vehicle-to-vehicle, vehicle-to-infrastructure, and vehicle-to-anything communications.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Article
Engineering, Civil
Ali Hassan Sodhro, Sandeep Pirbhulal, Gul Hassan Sodhro, Muhammad Muzammal, Luo Zongwei, Andrei Gurtov, Antonio Roberto L. de Macedo, Lei Wang, Nuno M. Garcia, Victor Hugo C. de Albuquerque
Summary: This research proposes 5G-based algorithms and frameworks for intelligent transportation systems, aiming to improve energy efficiency and reliability, and optimizing signal strength and packet loss ratio. Experimental results show significant progress in energy and reliability aspects.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Article
Computer Science, Information Systems
Ali Hassan Sodhro, Andrei Gurtov, Noman Zahid, Sandeep Pirbhulal, Lei Wang, Muhammad Mahboob Ur Rahman, Muhammad Ali Imran, Qammer H. Abbasi
Summary: The convergence of artificial intelligence (AI) and the Internet of Things (IoT) promotes energy-efficient communication in smart homes. This research focuses on optimizing Quality-of-Service (QoS) during video streaming through wireless micro medical devices (WMMDs) in smart healthcare homes. The proposed lazy video transmission algorithm (LVTA), novel video transmission rate control algorithm (VTRCA), and cloud-based video transmission framework contribute to significant energy reduction and performance improvement.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Geochemistry & Geophysics
Muhammad Ahmad, Adil Mehmood Khan, Manuel Mazzara, Salvatore Distefano, Mohsin Ali, Muhammad Shahzad Sarfraz
Summary: This study proposes a 3-D CNN model that utilizes both spatial-spectral feature maps to improve the performance of HSIC. By processing small overlapping 3-D patches and generating 3-D feature maps, the model demonstrates remarkable performance in terms of accuracy and computational time.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Chemistry, Analytical
Ali Hassan Sodhro, Charlotte Sennersten, Awais Ahmad
Summary: Secure and reliable sensing is crucial for cognitive tracking and authentication. This article highlights the importance of cognitive authentication and the use of electroencephalogram (EEG) as a unique performance indicator. The experimental setup and analysis show that the Random Forest (RF) classifier performs well in testing EEG data.
Review
Computer Science, Information Systems
Soomaiya Hamid, Narmeen Zakaria Bawany, Ali Hassan Sodhro, Abdullah Lakhan, Saleem Ahmed
Summary: The Internet of Medical Things (IoMT) has played a crucial role in the healthcare sector during the COVID-19 outbreak. IoMT systems have been implemented to support traditional healthcare systems in providing remote medical services. This research conducts a systematic literature review (SLR) on IoMT systems for COVID-19 and other medical applications, and proposes a framework called 'cov-AID' for remote disease monitoring and diagnosis.
Article
Chemistry, Analytical
Samuel Mcmurray, Ali Hassan Sodhro
Summary: Software Defect Prediction (SDP) is an integral aspect of the Software Development Life-Cycle (SDLC). This article investigates various Machine Learning (ML) techniques and their impact on SDP, including feature extraction and selection techniques, as well as different ML algorithms. The results show that certain techniques, such as Principal Component Analysis (PCA) and Partial Least Squares Regression (PLS), can provide significant improvements in predicting software defects.
Article
Computer Science, Information Systems
Mohsin Ali, Muhammad Naveed Yasir, Dost Muhammad Saqib Bhatti, Haewoon Nam
Summary: Cognitive radio (CR) is a key technology used to overcome spectrum scarcity in wireless applications. Efficient spectrum utilization is the core purpose of CR systems, and this study focuses on optimizing sensing time to maximize spectrum utilization efficiency (SUE) while minimizing interference to primary users (PUs). A trade-off between sensing time and SUE is analyzed, and the proposed system shows a 45% improvement in optimal sensing time compared to conventional systems.
IEEE WIRELESS COMMUNICATIONS LETTERS
(2023)
Article
Computer Science, Interdisciplinary Applications
Shuaib K. Memon, Kashif Nisar, Mohd Hanafi Ahmad Hijazi, B. S. Chowdhry, Ali Hassan Sodhro, Sandeep Pirbhulal, Joel J. P. C. Rodrigues
Summary: IEEE 802.11 WLAN is widely deployed around the world for real-time multimedia applications and emergency services. Time-sensitive applications and emergency traffic require strict requirements for packet delays, jitter, and losses. Providing a strict QoS guarantee and supporting emergency traffic under high loads in WLANs is a challenging task that requires further research.
JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION
(2021)
Article
Mathematical & Computational Biology
Abdullah Lakhan, Mazhar Ali Dootio, Ali Hassan Sodhro, Sandeep Pirbhulal, Tor Morten Groenli, Muhammad Saddam Khokhar, Lei Wang
Summary: This paper aims to address the issues of data security and cost efficiency in the Internet of Medical Things (IoMT) system, by designing cost-efficient service selection and a blockchain-enabled serverless network. Simulation results show that the proposed scheme outperforms existing schemes in terms of data security and application execution cost.
MATHEMATICAL BIOSCIENCES AND ENGINEERING
(2021)
Proceedings Paper
Engineering, Electrical & Electronic
Noman Zahid, Ali Hassan Sodhro, Mabrook S. Al-Rakhami, Lei Wang, Abdul Gumaei, Sandeep Pirbhulal
Summary: This paper proposes an adaptive duty-cycle optimization algorithm and a joint Green and sustainable healthcare framework to enhance energy saving and reliability. Theoretical and experimental analysis shows that these methods can improve energy saving and reliability by 24.43% and 36.54% respectively. This suggests that the proposed algorithm has great potential for energy constrained sensor devices in smart and connected healthcare platform.
2021 IEEE 93RD VEHICULAR TECHNOLOGY CONFERENCE (VTC2021-SPRING)
(2021)
Proceedings Paper
Engineering, Electrical & Electronic
Ali Hassan Sodhro, Mabrook S. Al-Rakhami, Lei Wang, Hina Magsi, Noman Zahid, Sandeep Pirbhulal, Kashif Nisar, Awais Ahmad
Summary: This study focuses on the energy efficiency in the IoMT system, proposing the adaptive Energy efficient (EEA) algorithm to extend battery life by exploiting the recovery effect. Compared with the BRLE algorithm, the proposed algorithm shows better performance in terms of battery lifetime and energy consumption.
2021 IEEE 93RD VEHICULAR TECHNOLOGY CONFERENCE (VTC2021-SPRING)
(2021)
