Pub Date : 2025-02-04DOI: 10.1109/OJCS.2025.3538557
K. Sasikumar;Sivakumar Nagarajan
The rapid expansion of cloud computing underscores the critical need for advanced security measures to protect sensitive data on remote servers. Authentication is crucial for safeguarding these data. Despite various proposed methods, vulnerabilities persist. This article introduces a novel multi-factor authentication system integrated with a hybrid cryptographic framework that dynamically changes encryption algorithms using machine learning techniques based on an intrusion-detection system. The proposed system employs passwords, conditional attributes, and fingerprint authentication to derive the encryption key from fingerprint data. It uses a dual-encryption strategy that combines five algorithm pairs: AES + HMAC (SHA-256), ECC + HMAC (SHA-512), HMAC-MD5 + PBKDF2, Twofish + Argon2, and Blowfish + HMAC SHA3-256. A Hybrid CNN-transformer model predicts and classifies attacks by dynamically adjusting an encryption algorithm to secure the data. The framework exhibited strong resilience against brute force, spoofing, phishing, guessing, and impersonation attacks. The proposed model achieved a commendable accuracy of 96.8%, outperforming other models. Implementing this framework in a cloud authentication environment significantly enhances data confidentiality and protects against unauthorized access. This study highlights the potential of combining multi-factor authentication and adaptive cryptography to obtain robust cloud security solutions.
{"title":"Enhancing Cloud Security: A Multi-Factor Authentication and Adaptive Cryptography Approach Using Machine Learning Techniques","authors":"K. Sasikumar;Sivakumar Nagarajan","doi":"10.1109/OJCS.2025.3538557","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3538557","url":null,"abstract":"The rapid expansion of cloud computing underscores the critical need for advanced security measures to protect sensitive data on remote servers. Authentication is crucial for safeguarding these data. Despite various proposed methods, vulnerabilities persist. This article introduces a novel multi-factor authentication system integrated with a hybrid cryptographic framework that dynamically changes encryption algorithms using machine learning techniques based on an intrusion-detection system. The proposed system employs passwords, conditional attributes, and fingerprint authentication to derive the encryption key from fingerprint data. It uses a dual-encryption strategy that combines five algorithm pairs: AES + HMAC (SHA-256), ECC + HMAC (SHA-512), HMAC-MD5 + PBKDF2, Twofish + Argon2, and Blowfish + HMAC SHA3-256. A Hybrid CNN-transformer model predicts and classifies attacks by dynamically adjusting an encryption algorithm to secure the data. The framework exhibited strong resilience against brute force, spoofing, phishing, guessing, and impersonation attacks. The proposed model achieved a commendable accuracy of 96.8%, outperforming other models. Implementing this framework in a cloud authentication environment significantly enhances data confidentiality and protects against unauthorized access. This study highlights the potential of combining multi-factor authentication and adaptive cryptography to obtain robust cloud security solutions.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"392-402"},"PeriodicalIF":0.0,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10872830","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-04DOI: 10.1109/OJCS.2025.3538579
MD AL Rafi;Gourab Nicholas Rodrigues;MD Nazmul Hossain Mir;MD Shahriar Mahmud Bhuiyan;M. F. Mridha;MD Rashedul Islam;Yutaka Watanobe
Accurate product sales forecasting is critical for inventory management, pricing strategies, and supply chain optimization in the retail industry. This article proposes a novel deep learning architecture that integrates Temporal Convolutional Networks (TCNs) with Transformer-based attention mechanisms to capture both short-term and long-term dependencies in time-series sales data. Utilizing the Favorita Grocery Sales Forecasting dataset, our hybrid TCN Transformer model demonstrates superior performance over existing models by incorporating external factors such as holidays, promotions, oil prices, and transaction data. The model achieves state-of-the-art results with a Mean Absolute Error (MAE) of 2.01, Root Mean Squared Error (RMSE) of 2.81, and a Weighted Mean Absolute Percentage Error (wMAPE) of 4.22%, significantly outperforming other leading models such as LSTM, GRU, and TFT. Extensive cross-validation confirms the robustness of our model, achieving consistently high performance across multiple folds.
{"title":"A Hybrid Temporal Convolutional Network and Transformer Model for Accurate and Scalable Sales Forecasting","authors":"MD AL Rafi;Gourab Nicholas Rodrigues;MD Nazmul Hossain Mir;MD Shahriar Mahmud Bhuiyan;M. F. Mridha;MD Rashedul Islam;Yutaka Watanobe","doi":"10.1109/OJCS.2025.3538579","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3538579","url":null,"abstract":"Accurate product sales forecasting is critical for inventory management, pricing strategies, and supply chain optimization in the retail industry. This article proposes a novel deep learning architecture that integrates Temporal Convolutional Networks (TCNs) with Transformer-based attention mechanisms to capture both short-term and long-term dependencies in time-series sales data. Utilizing the Favorita Grocery Sales Forecasting dataset, our hybrid TCN Transformer model demonstrates superior performance over existing models by incorporating external factors such as holidays, promotions, oil prices, and transaction data. The model achieves state-of-the-art results with a Mean Absolute Error (MAE) of 2.01, Root Mean Squared Error (RMSE) of 2.81, and a Weighted Mean Absolute Percentage Error (wMAPE) of 4.22%, significantly outperforming other leading models such as LSTM, GRU, and TFT. Extensive cross-validation confirms the robustness of our model, achieving consistently high performance across multiple folds.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"380-391"},"PeriodicalIF":0.0,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10870315","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-30DOI: 10.1109/OJCS.2025.3536562
Francesco Castro;Donato Impedovo;Giuseppe Pirlo
Federated Learning (FL) is a decentralized and collaborative learning approach that ensures the data privacy of each participant. However, recent studies have shown that the private data of each participant can be obtained from shared parameters of local models through reversal model and membership inference attacks leading to privacy leakage. Privacy-preserving federated learning strategies based on Homomorphic Encryption (PPFL-HE) have been developed to solve this issue. PPFL-HE methods require high communication and computational overheads, which are impractical for resource-limited devices. This work proposes an efficient PPFL-HE method to reduce communication and computational overheads. The proposed method is based on an innovative quantization process that introduces a dynamic range evaluation layer-for-layer (DREL) to encode the weights of the local models into long-signed integers. Compared to standard quantization approaches, the proposed method reduces the quantization errors and the communication overhead. Moreover, it enables the encryption of local weights with the Brakerski/Fan-Vercauteren Homomorphic Encryption scheme (BFV-HE), which is highly efficient on integers, reducing encryption, aggregation, decryption time, and ciphertext size. The experiments conducted with five popular datasets and four different Machine Learning (ML) models (three CNN models and a feedforward neural network) show that the proposed method is more efficient in communication and computational overheads than other PPFL-HE methods. Specifically, the proposed method requires fewer FL rounds to achieve global model convergence and leads to an average reduction in encryption time of 99.95% and 73.79%, in decryption time of 99.90% and 55.13%, and in ciphertext size of 5.78% and 75.17% compared to PPFL-HE methods based on Paillier and CKKS schemes, respectively.
{"title":"An Efficient and Privacy-Preserving Federated Learning Approach Based on Homomorphic Encryption","authors":"Francesco Castro;Donato Impedovo;Giuseppe Pirlo","doi":"10.1109/OJCS.2025.3536562","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3536562","url":null,"abstract":"Federated Learning (FL) is a decentralized and collaborative learning approach that ensures the data privacy of each participant. However, recent studies have shown that the private data of each participant can be obtained from shared parameters of local models through reversal model and membership inference attacks leading to privacy leakage. Privacy-preserving federated learning strategies based on Homomorphic Encryption (PPFL-HE) have been developed to solve this issue. PPFL-HE methods require high communication and computational overheads, which are impractical for resource-limited devices. This work proposes an efficient PPFL-HE method to reduce communication and computational overheads. The proposed method is based on an innovative quantization process that introduces a dynamic range evaluation layer-for-layer (DREL) to encode the weights of the local models into long-signed integers. Compared to standard quantization approaches, the proposed method reduces the quantization errors and the communication overhead. Moreover, it enables the encryption of local weights with the Brakerski/Fan-Vercauteren Homomorphic Encryption scheme (BFV-HE), which is highly efficient on integers, reducing encryption, aggregation, decryption time, and ciphertext size. The experiments conducted with five popular datasets and four different Machine Learning (ML) models (three CNN models and a feedforward neural network) show that the proposed method is more efficient in communication and computational overheads than other PPFL-HE methods. Specifically, the proposed method requires fewer FL rounds to achieve global model convergence and leads to an average reduction in encryption time of 99.95% and 73.79%, in decryption time of 99.90% and 55.13%, and in ciphertext size of 5.78% and 75.17% compared to PPFL-HE methods based on Paillier and CKKS schemes, respectively.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"336-347"},"PeriodicalIF":0.0,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10858339","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-30DOI: 10.1109/OJCS.2025.3536800
Farid Binbeshr;Muhammad Imam;Mustafa Ghaleb;Mosab Hamdan;Mussadiq Abdul Rahim;Mohammad Hammoudeh
The increasing sophistication of cyber threats has led to the evolution of Security Operations Centers (SOCs) towards more intelligent and adaptive systems. This review explores the integration of Artificial Intelligence (AI) in SOCs, focusing on their current state, challenges, opportunities, and advantages over traditional methods. We address three key questions: (1) What are the current AI approaches in SOCs? (2) What challenges and opportunities exist with these approaches? (3) What benefits do AI models offer in SOC environments compared to traditional methods? We analyzed 38 studies using a structured methodology involving database searches, quality checks, and data extraction. Our findings show that Machine Learning (ML) techniques dominate SOC research, with a trend towards multi-approach AI methods. We classified these into ML, Natural Language Processing, multi-approach, and others, forming a detailed taxonomy of AI applications in SOCs. Challenges include data quality, model interpretability, legacy system integration, and the need for constant adaptation. Opportunities involve task automation, enhanced threat detection, real-time analysis, and adaptive learning. AI-driven SOCs show better accuracy, reduced false positives, greater scalability, and predictive capabilities than traditional approaches. This review defines Cognitive SOCs, emphasizing their ability to mimic human-like processes. We offer practical insights for SOC designers and managers on implementing AI to improve security operations. Finally, we suggest future research directions in explainable AI, human-AI collaboration, and privacy-preserving AI for SOCs.
{"title":"The Rise of Cognitive SOCs: A Systematic Literature Review on AI Approaches","authors":"Farid Binbeshr;Muhammad Imam;Mustafa Ghaleb;Mosab Hamdan;Mussadiq Abdul Rahim;Mohammad Hammoudeh","doi":"10.1109/OJCS.2025.3536800","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3536800","url":null,"abstract":"The increasing sophistication of cyber threats has led to the evolution of Security Operations Centers (SOCs) towards more intelligent and adaptive systems. This review explores the integration of Artificial Intelligence (AI) in SOCs, focusing on their current state, challenges, opportunities, and advantages over traditional methods. We address three key questions: (1) What are the current AI approaches in SOCs? (2) What challenges and opportunities exist with these approaches? (3) What benefits do AI models offer in SOC environments compared to traditional methods? We analyzed 38 studies using a structured methodology involving database searches, quality checks, and data extraction. Our findings show that Machine Learning (ML) techniques dominate SOC research, with a trend towards multi-approach AI methods. We classified these into ML, Natural Language Processing, multi-approach, and others, forming a detailed taxonomy of AI applications in SOCs. Challenges include data quality, model interpretability, legacy system integration, and the need for constant adaptation. Opportunities involve task automation, enhanced threat detection, real-time analysis, and adaptive learning. AI-driven SOCs show better accuracy, reduced false positives, greater scalability, and predictive capabilities than traditional approaches. This review defines Cognitive SOCs, emphasizing their ability to mimic human-like processes. We offer practical insights for SOC designers and managers on implementing AI to improve security operations. Finally, we suggest future research directions in explainable AI, human-AI collaboration, and privacy-preserving AI for SOCs.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"360-379"},"PeriodicalIF":0.0,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10858372","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-29DOI: 10.1109/OJCS.2025.3536082
Aliya Tabassum;Ezieddin Elmahjub;Aasim I. Padela;Andrej Zwitter;Junaid Qadir
The metaverse, a pioneering digital realm merging virtual and augmented realities with Artificial Intelligence (AI), represents a transformative environment where digital and physical realities converge seamlessly. Generative AI (GenAI) is indispensable in powering the metaverse's dynamic and immersive experiences, enabling the autonomous generation of diverse digital content. Large Language Models (LLMs), as a component of GenAI, play a critical role by facilitating real-time communication, multilingual translation, and personalized interactions, enhancing user engagement in shared virtual spaces. This scoping review explores the interdependence between GenAI and the metaverse and the unique ethical and legal challenges that emerge from their integration. It identifies key ethical and legal issues, such as bias in AI-generated content, misinformation, and data privacy concerns, related to the deployment of GenAI and LLMs, and offers strategic recommendations for addressing these challenges responsibly. Emphasizing the transformative potential of these technologies, this review highlights the necessity of developing tailored ethical and legal frameworks to manage their convergence responsibly, ensuring equitable and sustainable growth within the metaverse.
{"title":"Generative AI and the Metaverse: A Scoping Review of Ethical and Legal Challenges","authors":"Aliya Tabassum;Ezieddin Elmahjub;Aasim I. Padela;Andrej Zwitter;Junaid Qadir","doi":"10.1109/OJCS.2025.3536082","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3536082","url":null,"abstract":"The metaverse, a pioneering digital realm merging virtual and augmented realities with Artificial Intelligence (AI), represents a transformative environment where digital and physical realities converge seamlessly. Generative AI (GenAI) is indispensable in powering the metaverse's dynamic and immersive experiences, enabling the autonomous generation of diverse digital content. Large Language Models (LLMs), as a component of GenAI, play a critical role by facilitating real-time communication, multilingual translation, and personalized interactions, enhancing user engagement in shared virtual spaces. This scoping review explores the interdependence between GenAI and the metaverse and the unique ethical and legal challenges that emerge from their integration. It identifies key ethical and legal issues, such as bias in AI-generated content, misinformation, and data privacy concerns, related to the deployment of GenAI and LLMs, and offers strategic recommendations for addressing these challenges responsibly. Emphasizing the transformative potential of these technologies, this review highlights the necessity of developing tailored ethical and legal frameworks to manage their convergence responsibly, ensuring equitable and sustainable growth within the metaverse.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"348-359"},"PeriodicalIF":0.0,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10857306","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143512793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-28DOI: 10.1109/OJCS.2025.3535663
Rola Abdallah;Mohamed Atef;Nasir Saeed
Hybrid Polymer Optical Fiber and Visible Light Communication (POF-VLC) systems are emerging as a promising solution for high-speed, interference-free connectivity, especially in environments where traditional RF communication is constrained. This paper investigates key nonlinear impairments in POF-VLC systems, such as chromatic dispersion (CD), self-phase modulation (SPM), cross-phase modulation (XPM), four-wave mixing (FWM), and stimulated scattering, which severely degrade signal quality and limit transmission range. We review advanced modulation techniques like Orthogonal Frequency Division Multiplexing (OFDM) and Discrete Multitone Modulation (DMT), alongside traditional methods like Non-Return-to-Zero (NRZ) and On-Off Keying (OOK), evaluating their effectiveness in overcoming these challenges. Furthermore, the application of machine learning, particularly neural network-based equalizers like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), is highlighted for their potential to enhance signal quality and system performance. This review emphasizes the transformative role these advanced strategies can play in optimizing hybrid POF-VLC systems, paving the way for their integration into high-demand communication environments. Moreover, this paper presents several promising research directions, such as optimizing training algorithms, exploring deeper neural network architectures, and integrating POF-VLC systems with emerging technologies like beyond 5G, improving energy efficiency, and addressing scalability and complexity in real-time adaptive POF-VLC systems.
{"title":"Hybrid POF-VLC Systems: Recent Advances, Challenges, Opportunities, and Future Directions","authors":"Rola Abdallah;Mohamed Atef;Nasir Saeed","doi":"10.1109/OJCS.2025.3535663","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3535663","url":null,"abstract":"Hybrid Polymer Optical Fiber and Visible Light Communication (POF-VLC) systems are emerging as a promising solution for high-speed, interference-free connectivity, especially in environments where traditional RF communication is constrained. This paper investigates key nonlinear impairments in POF-VLC systems, such as chromatic dispersion (CD), self-phase modulation (SPM), cross-phase modulation (XPM), four-wave mixing (FWM), and stimulated scattering, which severely degrade signal quality and limit transmission range. We review advanced modulation techniques like Orthogonal Frequency Division Multiplexing (OFDM) and Discrete Multitone Modulation (DMT), alongside traditional methods like Non-Return-to-Zero (NRZ) and On-Off Keying (OOK), evaluating their effectiveness in overcoming these challenges. Furthermore, the application of machine learning, particularly neural network-based equalizers like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), is highlighted for their potential to enhance signal quality and system performance. This review emphasizes the transformative role these advanced strategies can play in optimizing hybrid POF-VLC systems, paving the way for their integration into high-demand communication environments. Moreover, this paper presents several promising research directions, such as optimizing training algorithms, exploring deeper neural network architectures, and integrating POF-VLC systems with emerging technologies like beyond 5G, improving energy efficiency, and addressing scalability and complexity in real-time adaptive POF-VLC systems.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"317-335"},"PeriodicalIF":0.0,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10856321","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The increasing demand for high-precision real-time data processing in satellite clusters requires efficient algorithms to manage inherent uncertainties in space-based systems. We propose an innovative framework that integrates Quantum Neural Network (QNN) architecture into Kalman filtering processes, specifically tailored for Low Earth Orbit satellite clusters. Our quantum computing-based approach achieves a significant improvement in prediction accuracy and a reduction in mean absolute error compared to classical Kalman filtering techniques. These advances significantly improve computational efficiency and error handling, making the method highly scalable under varying noise levels. A comparative analysis demonstrates the superior performance of the Quantum Kalman Filter in processing speed, resource utilization, and prediction accuracy, all evaluated within the constraints of LEO satellite constellations. These findings highlight the potential of quantum computing to optimize data processing strategies for future missions, including deep space explorations.
{"title":"Quantum Computing-Accelerated Kalman Filtering for Satellite Clusters: Algorithms and Comparative Analysis","authors":"Shreyan Prakash;Raj Bhattacherjee;Sainath Bitragunta;Ashutosh Bhatia;Kamlesh Tiwari","doi":"10.1109/OJCS.2025.3535081","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3535081","url":null,"abstract":"The increasing demand for high-precision real-time data processing in satellite clusters requires efficient algorithms to manage inherent uncertainties in space-based systems. We propose an innovative framework that integrates Quantum Neural Network (QNN) architecture into Kalman filtering processes, specifically tailored for Low Earth Orbit satellite clusters. Our quantum computing-based approach achieves a significant improvement in prediction accuracy and a reduction in mean absolute error compared to classical Kalman filtering techniques. These advances significantly improve computational efficiency and error handling, making the method highly scalable under varying noise levels. A comparative analysis demonstrates the superior performance of the Quantum Kalman Filter in processing speed, resource utilization, and prediction accuracy, all evaluated within the constraints of LEO satellite constellations. These findings highlight the potential of quantum computing to optimize data processing strategies for future missions, including deep space explorations.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"307-316"},"PeriodicalIF":0.0,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10855618","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-20DOI: 10.1109/OJCS.2025.3531442
Yi Xu;Zhigang Chen;Ming Zhao;Fengxiao Tang;Yangfan Li;Jiaqi Liu;Nei Kato
High precision and robust indoor positioning system has a broad range of applications in the area of mobile computing. Due to the advancement of image processing algorithms, the prevalence of surveillance ambient cameras shows promise for offering sub-meter accuracy localization services. The tracking performance in dynamic contexts is still unreliable for ambient camera-based methods, despite their general ability to pinpoint pedestrians in video frames at fine-grained levels. Contrarily, ultra-wideband-based technology can continuously track pedestrians, but they are frequently susceptible to the effects of non-line-of-sight (NLOS) errors on the surrounding environment. We see a chance to combine these two most viable approaches in order to get beyond the aforementioned drawbacks and return to the pedestrian localization issue from a different angle. In this article, we propose UVtrack, a localization system based on UWB and ambient cameras that achieves centimeter accuracy and improved reliability. The key innovation of UVtrack is a well-designed particle filter which adopts UWB and vision results in the weight update of the particle set, and an adaptive distance variance weighted least squares method (DVLS) to improve UWB sub-system robustness. We take UVtrack into use on common smartphones and test its effectiveness in three different situations. The results demonstrated that UVtrack attains an outstanding localization accuracy of 7 cm.
{"title":"UVtrack: Multi-Modal Indoor Seamless Localization Using Ultra-Wideband Communication and Vision Sensors","authors":"Yi Xu;Zhigang Chen;Ming Zhao;Fengxiao Tang;Yangfan Li;Jiaqi Liu;Nei Kato","doi":"10.1109/OJCS.2025.3531442","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3531442","url":null,"abstract":"High precision and robust indoor positioning system has a broad range of applications in the area of mobile computing. Due to the advancement of image processing algorithms, the prevalence of surveillance ambient cameras shows promise for offering sub-meter accuracy localization services. The tracking performance in dynamic contexts is still unreliable for ambient camera-based methods, despite their general ability to pinpoint pedestrians in video frames at fine-grained levels. Contrarily, ultra-wideband-based technology can continuously track pedestrians, but they are frequently susceptible to the effects of non-line-of-sight (NLOS) errors on the surrounding environment. We see a chance to combine these two most viable approaches in order to get beyond the aforementioned drawbacks and return to the pedestrian localization issue from a different angle. In this article, we propose UVtrack, a localization system based on UWB and ambient cameras that achieves centimeter accuracy and improved reliability. The key innovation of UVtrack is a well-designed particle filter which adopts UWB and vision results in the weight update of the particle set, and an adaptive distance variance weighted least squares method (DVLS) to improve UWB sub-system robustness. We take UVtrack into use on common smartphones and test its effectiveness in three different situations. The results demonstrated that UVtrack attains an outstanding localization accuracy of 7 cm.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"272-281"},"PeriodicalIF":0.0,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10845877","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143107126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-14DOI: 10.1109/OJCS.2025.3527836
{"title":"2024 List of Reviewers*","authors":"","doi":"10.1109/OJCS.2025.3527836","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3527836","url":null,"abstract":"","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"1-3"},"PeriodicalIF":0.0,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10841813","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142975824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-10DOI: 10.1109/OJCS.2025.3525947
Vincenzo Piuri
{"title":"New Incoming EIC Editorial","authors":"Vincenzo Piuri","doi":"10.1109/OJCS.2025.3525947","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3525947","url":null,"abstract":"","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"2-3"},"PeriodicalIF":0.0,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10837004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142940903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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