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arXiv 2025年

作者： Chen, Chen Guan, Peiyuan Chen, Ziru Taherkordi, Amir Hou, Fen Cai, Lin X. Department of Computer Science and Technology University of Cambridge United Kingdom Department of Informatics University of Oslo Norway Department of Electrical and Computer Engineering Illinois Institute of Technology United States Department of Electrical and Computer Engineering University of Macau China

Serverless computing adopts a pay-as-you-go billing model where applications are executed in stateless and short-lived containers triggered by events, resulting in a reduction of monetary costs and resource utilization. However, existing platforms do not provide an upper bound for the billing model which makes the overall cost unpredictable, precluding many organizations from managing their budgets. Due to the diverse ranges of serverless functions and the heterogeneous capacity of edge devices, it is challenging to receive near-optimal solutions for deployment cost in a polynomial time. In this paper, we investigated the function scheduling problem with a budget constraint for serverless computing in wireless networks. Users and IoT devices are sending requests to edge nodes, improving the latency perceived by users. We propose two online scheduling algorithms based on reinforcement learning, incorporating several important characteristics of serverless functions. Via extensive simulations, we justify the superiority of the proposed algorithm by comparing with an ILP solver (Midaco). Our results indicate that the proposed algorithms efficiently approximate the results of Midaco within a factor of 1.03 while our decision-making time is 5 orders of magnitude less than that of Midaco. © 2025, CC BY.

关键词： Budget control

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Secure and Reliable 6G Communications with BCH-Based GLDPC and Kyber

Secure and Reliable 6G Communications with BCH-Based GLDPC a...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Linh Nguyen Cheol-Hong Min Hanho Lee Tuy Tan Nguyen School of Informatics Computing and Cyber Systems Northern Arizona University USA Department of Electrical. and Computer Engineering University of St. Thomas USA Department of Electrical and Computer Engineering Inha University Republic of Korea

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Channel coding in sixth-generation (6G) networks must attain exceptionally low bit error rate (BER), typically in the range of 10– 6 to 10– 9 , to ensure the requisite level of reliability. While fifth-generation (5G) New Radio (NR) coding techniques have made substantial progress, the inherent limitations of the low error floor impede the attainment of such stringent BER targets. Compared to the low-density parity check (LDPC) codes employed in 5G communications, generalized low-density parity check (GLDPC) codes offer a more advantageous tradeoff between computational complexity and error correction performance in 6G networks. GLDPC codes enhance traditional LDPC structures by incorporating more complex local decoding units within their check nodes. When coupled with cryptographic techniques, GLDPC codes can establish a robust information security framework. In this paper, we propose a new security-based GLDPC that uses a number of BCH matrices for the GLDPC decoding process combined with the Kyber algorithm (BCH-based KyGLDPC) before transmitting data to increase reli-ability and solve the security problem against quantum computer attacks. The system's performance is assessed using both image data and data encrypted prior to transmission in a white additive Gaussian noise environment. The results indicate that the system provides strong error correction and data security.

关键词： 6G mobile communication Quantum computing computer network reliability System performance Bit error rate Parity check codes Error correction codes Decoding Reliability Channel coding

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Feature Analysis and Selection for BGP Anomaly Detection

Feature Analysis and Selection for BGP Anomaly Detection

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International Conference on Intelligence in Next Generation Networks (ICIN)

作者： Rifqy Hakimi Martin J. Reed School of Computer Science and Electronic Engineering University of Essex Colchester United Kingdom School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (数字)9798331542399

ISBN: (纸本)9798331542405

Automated incidence reporting requires accurate and timely anomaly detection. This paper considers this in the context of the Border Gateway Protocol (BGP). BGP is crucial for Internet routing but is vulnerable to attacks due to a lack of widespread authentication. While rare, BGP disruptions can be highly detrimental to Internet performance and security. Detecting BGP anomalies helps network operators protect their networks and improve Internet reliability. In this work, we investigate and develop anomaly detection for BGP using Machine-Learning. We extract relevant features of BGP control plane messages from RIPE RIS and RouteViews public datasets. After extracting features, we employ various feature selection algorithms to extract the most relevant features and explore balancing the datasets. Finally, we explore detection latency, an important operating parameter for automated anomaly detection. The results show that BGP anomalies can be detected in the order of seven minutes when using real attack data and that the observation point for the BGP data has a significant effect on anomaly detection.

关键词： Technological innovation Soft sensors Machine learning Feature extraction Routing Border Gateway Protocol Security Reliability Anomaly detection Faces

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Integrated terahertz transceiver front-end utilizing spatiotemporal modulation

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Journal of the Optical Society of America B 2025年第5期42卷 1114-1122页

作者： Mahsa Valizadeh Leila Yousefi MirFaez Miri School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran *** Iran School of Engineering and Informatics University of Sussex Falmer BN1 9RH UK Department of Physics University of Tehran Tehran 14395547 Iran

The paper proposes a method for developing spatiotemporally modulated structures operating at THz frequencies. In this technique, electromagnetic waves propagating inside microwave waveguides modulate the chemical potential of a graphene sheet both temporally and spatially. The modulated graphene sheet interconnects these waveguides with a plasmonic waveguide carrying a THz wave. This approach has been used to create an integrated device that encapsulates all components of a THz transceiver front-end in a single unit. The device leverages the extraordinary features of spatiotemporally modulated structures, such as nonreciprocal response and frequency mixing, to integrate all functionalities of a THz transceiver front-end. We have implemented a leaky-wave antenna, mixer, and duplexer with beam steering capabilities, all within a spatiotemporal graphene-based structure. The performance of the device is investigated both analytically and numerically in transmit and receive modes. Additionally, the modulating waveguides have undergone both electromagnetic and thermal numerical simulations to ensure they can handle the power required for efficient modulation of graphene without overheating, which could hinder their performance.

关键词： Beam steering Crosstalk Frequency modulation Mode conversion Numerical simulation Plasmon waveguides

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Optimizing Heart-Kidney Interaction for Cancer Detection through Physiological Process Simulation as a Decision Support System

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IEEE Access 2025年 13卷 79169-79179页

作者： Jaddi, Najmeh Sadat Abdullah, Salwanj Othnman, Zalinda Yahya, Yazrina Nazri, Mohd Zakree Ahmad Goh, Say Leng Alvankarian, Fatemeh Alvankarian, Jafar Iranian eUniversity Faculty of Computer Engineering Tehran Iran Faculty of Information Science and Technology Center for Artificial Intelligence Technology Selangor Bangi43600 Malaysia Center for Software Technology and Management Faculty of Information Science and Technology UniversitiKebangsaan Malaysia Selangor Bangi43600 Malaysia Universiti Malaysia Sabah KampusAntarabangsa Labuan Optimisation and Analytics Research Group Faculty of Computing and Informatics Labuan87000 Malaysia University of Maryland Department of Electrical & Computer Engineering College ParkMD20742 United States Tehran University Science and Technology Park RayanMiniFab Tehran Iran

Decision support system (DSS) is a computer-based tool used to improve decision-making capabilities for any organization, using analysis of the available *** heart-kidney (HK) model proposed in this paper as a DSS simulates the physiological processes of the heart and kidneys in the human body to support cancer detection. The first part of the HK algorithm is inspired by the functions of the heart, where blood molecules (solutions) are sent to the heart, simulating the processes of oxygenation and the pumping of oxygenated blood to the kidneys as a local search. The solutions generated by the heart are then fed into the kidney-inspired algorithm (KA), which performs filtration, reabsorption, secretion, and excretion. The heart component enhances the KA's local search capability, guiding the algorithm toward optimal solutions. The HK model is evaluated on eight test functions, with results compared to those of the heart algorithm and KA alone. Additionally, the HK algorithm's effectiveness as a decision support system is tested on benchmark classification and time series prediction problems, with statistical analysis against existing methods in the literature. Finally, the HK algorithm is applied to cancer detection from microRNA data, demonstrating its potential in optimizing decision-making processes in healthcare. © 2013 IEEE.

关键词： Diseases

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Curious Administrator’s Misbehaviors in Message Layer Security (MLS)

Curious Administrator’s Misbehaviors in Message Layer Secur...

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International Conference on Computing, Networking and Communications (ICNC)

作者： Hyoung-Kee Choi Songhui Kwon College of Computing and Informatics Sungkyunkwan University Suwon Korea Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea

ISBN: (数字)9798331520960

ISBN: (纸本)9798331520977

A key factor of the MLS group protocol is that it provides asynchronously efficient group key establishment with forward secrecy and post-compromise security for many groups in a standardized way. End-to-end encryption (E2EE) is essential to fulfilling the correctness of security in a messenger application. The correctness heavily depends on the trustworthiness of a middleman server in the center where a curious administrator may break E2EE. If this happened the administrator as a nonmember could read messages and impersonate a member by intervening with group-key management. This paper presents an in-depth explanation of the MLS protocol and demonstrates a proof-of-concept of curious administrator’s misbehaviors by analyzing the protocol. Our research outputs suggest that unconditionally trustworthy relationships on the central server in designing network protocols for security needs to prepare for any incidents caused by misbehaving administrator of the central server.

关键词： Protocols Oral communication Message services Encryption Security Servers

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Deep Learning and IoT-Enabled Accident Detection and Reporting for Smart Cities Domain

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KSII Transactions on Internet and Information Systems 2025年第4期19卷 1140-1166页

作者： Javed, Rawal Mazhar, Tehseen Aoun, Muhammad Shahzad, Tariq Al-AlShaikh, Halah A. Ghadi, Yazeed Yasin Saudagar, Abdul Khader Jilani Khan, Muhammad Amir School of Automation Central South University Changsha China School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Science and Information Technology School Education Department Government of Punjab Layyah31200 Pakistan Department of CS IT Ghazi University Punjab Dera Ghazi Khan Pakistan School of Electrical Engineering Dept. of Electrical and Electronic Eng. Science University of Johannesburg Johannesburg2006 South Africa Riyadh11432 Saudi Arabia Department of Computer Science and Software Engineering Al Ain University Abu Dhabi12555 United Arab Emirates School of Computing Sciences College of Computing Informatics and Mathematics University Technology MARA Selangor Shah Alam40450 Malaysia

Even though numerous ICT-based solutions have been put forth for the detection of accidents and rescue missions, they often suffer from compatibility issues with different vehicles and are accompanied by high costs. This paper introduces a cutting-edge intelligent system designed for accident detection and rescue, mirroring human cognitive functions through integrating the Internet of Things (IoT) and artificial intelligence. A specialized kit for IoT has been created to identify accidents and gather comprehensive incident-related data, such as pressure, position, gravitational force, and speed. This information is then transmitted to the cloud for processing. A deep learning model is implemented in the cloud environment to verify the IoT module's output and activate the rescue mechanism when an accident is detected. The DL module, employing ensemble transfer learning with dynamic weights, aims to minimize false detection rates. Because there isn't an appropriate dataset available, a personalized dataset is generated from diverse online videos. To validate the proposed method, a comparative analysis is conducted on Yolo V4. Experimental results indicate that Yolo V4 outperforms, achieving training, validation, and test accuracies of 98.2% each. To assess the system's real-world applicability, it undergoes validation using a robot car model. Copyright © 2025 KSII.

关键词： Deep learning

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Heart Sound Classification Using Weka for Machine Learning

Heart Sound Classification Using Weka for Machine Learning

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IEEE Southeastcon

作者： Tanner Mergist T. Maheswara Reddy Yenumula Rafeeq Muhammad Tytiana James Ali Harimi Kasem Khalil Ashok Kumar School of Computing and Informatics University of Louisiana at Lafayette Lafayette USA Electrical and Computer Engineering University of Mississippi Mississippi USA

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

The accurate and efficient classification of heart sounds represents a crucial challenge in cardiac care, with significant implications for early diagnosis and timely intervention. This study investigates the application of machine learning techniques, utilizing the Weka framework, to address this problem and used PhysioNet dataset, which comprises 3,240 heart sound recordings, and extracted 238 features to capture the essential spectral and temporal characteristics of heartbeats. Considering the high dimensionality of the dataset, various feature selection methods were evaluated, including information gain ranking, Principal Component Analysis (PCA), and Recursive Feature Elimination (RFE). Ultimately, the InfoGain algorithm was applied, selecting the 50 most significant features from the initial set of 238. The performance of five machine learning models—Multilayer Perceptron, Random Forest, Decision Tree, k-Nearest Neighbor (k-NN), and Support Vector Machine (SVM) — on both the full and reduced feature sets, using 80-20 split and 10-fold cross-validation was evaluated. The results demonstrate that the Random Forest model achieved the highest accuracy of 92.1% on the full feature set, while the k-NN model exhibited superior computational efficiency on the reduced set, with an accuracy of 89.4% and significantly faster training times. These findings highlight the trade-offs between model complexity and efficiency, providing valuable insights for clinicians and researchers in selecting appropriate algorithms for real-world heart sound classification applications.

关键词： Heart Deep learning Training Support vector machines Accuracy Machine learning algorithms Computational modeling Feature extraction Real-time systems Computational efficiency

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Secure Lattice-Based Signature Scheme for Internet of Things Applications

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IEEE Access 2025年 13卷 75985-75999页

作者： Prajapat, Sunil Gautam, Deepika Kumar, Pankaj Jangirala, Srinivas Das, Ashok Kumar Sikdar, Biplab Srinivasa Ramanujan Department of Mathematics Central University of Himachal Pradesh Dharamshala 176206 India Jindal Global Business School O. P. Jindal Global University Haryana 131001 India Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad 500 032 India Department of Computer Science and Engineering College of Informatics Korea University Seoul 02841 South Korea Department of Electrical and Computer Engineering National University of Singapore 117583 Singapore

The enormous benefits of Internet of Things (IoT) technology have driven its deployment in various applications. Additionally, the development of quantum computers has directed attention towards lattice-based cryptography. Consequently, the computational capabilities of quantum computers pose a threat to the security of the existing IoT signature mechanisms. Quantum computers are proficient at unraveling the complexity bound of computationally hard problems like the integer factorization problem (IFP) and the discrete logarithm problem (DLP). As a result, security is an essential requirement for the IoT communication network against quantum attacks. The amalgamation of certificateless public key cryptosystems (CL-PKC) and lattice-based cryptography (LBC) is one of the solution for alleviating these security menaces. Lucidly, CL-PKC prevents key escrow issues and key management problems;LBC prevents quantum attacks. The Shortest Integer Solution (SIS) problem, which the NTRU lattices offer, serves as the basis for this paper’s introduction of a certificateless signature mechanism for IoT environments. By adopting the Random Oracle Model, we demonstrated the security of the suggested mechanism against Type 1 and Type 2 attackers. Furthermore, security analysis and performance evaluation demonstrate robust communication, as evidenced by metrics such as the computational cost of CL-Sign and CL-Verify phases at 536µs, 376.81µs and communication cost of KGC at 418 bits, CL-Sign at 532 bits and CL-Verify at 446 bits. Also, we calculate the cost of single-message signature generation and verification on an IoT device. These results show that the suggested mechanism’s security and computational efficiency are more reliable, and efficient than other relevant competing frameworks. © 2013 IEEE.

关键词： Certificateless public key cryptosystem Internet of Things (IoT) lattice-based cryptography Security Signature

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Core Temperature Estimation of Lithium-Ion Batteries Using Long Short-Term Memory (LSTM) Network and Kolmogorov-Arnold Network (KAN)

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IEEE Transactions on Transportation Electrification 2025年

作者： Karnehm, Dominic Samanta, Akash Rosenmuller, Christian Neve, Antje Williamson, Sheldon University of Bundeswehr Munich Electrical Engineering and Technical Informatics Department Neubiberg85577 Germany Ontario Tech University Electrical Computer and Software Engineering ONL1G 0C5 Canada Munich80335 Germany

Health-conscious battery management systems (BMS) that rely on surface temperature measurements are insufficient for managing automotive lithium-ion batteries (LIBs). Experimental studies have shown temperature differences of up to 10°C between surface and core of cylindrical LIBs. BMSs that consider only surface temperature overlook critical thermal information. The missing monitoring can delay detecting thermal events within the cell, accelerating battery degradation and increasing the risk of thermal runaway. This paper introduces two deep learning algorithms to address this: Kolmogorov-Arnold Network (KAN) and interconnected Long Short-Term Memory (LSTM) network. Both approaches estimate the core temperature of LIBs without requiring surface temperature feedback to the neural network. Experimental validation revealed a core temperature mean absolute error (MAE) of 0.55 °C with a computational cost of 2.9 ms to 3.2 ms for KAN. The proposed interconnected LSTM reached a MAE of 0.80 °C. The performance of the two core temperature estimation techniques was further evaluated under dynamic loading profile using UDDS drive cycle. The KAN method achieved a MAE of 0.325 °C, demonstrating its adaptability to dynamic operating conditions. The two proposed methods, primarily KAN, are both adaptive and computationally efficient, making them suitable for integrating onboard BMS and cloud-enabled digital-twin-based BMS systems. © 2015 IEEE.

关键词： Long short-term memory

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