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CEEO: an innovative Coulomb-enhanced Equilibrium Optimizer for task scheduling of cloud services in IoT environments

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Computing 2025年第6期107卷 1-24页

作者： Ebrahimi Mood, Sepehr Souri, Alireza İnanç, Nihat Li, Kuan-Ching Department of Computer Science Yazd University Yazd Iran Department of Computer Engineering Faculty of Engineering Haliç University Istanbul34060 Turkey Department of Electrical and Electronics Engineering Faculty of Engineering Haliç University Istanbul34060 Turkey Department of computer science and information engineering Providence University Taichung43301 Taiwan

In this paper, we address the multi-objective task scheduling problem in cloud computing environments for IoT-generated tasks, focusing on minimizing makespan, load imbalance, energy consumption, and CO2 emissions. We propose a novel load imbalance metric tailored for this specific context and introduce an enhanced Equilibrium Optimizer (EO) algorithm, termed CEEO. The CEEO integrates a Coulomb operator inspired by Coulomb’s law to improve exploration and escape local optima in multimodal optimization problems. The algorithm’s performance is evaluated using standard benchmark functions from CEC2017, where it demonstrates significant improvements over existing optimization algorithms in terms of convergence speed and solution quality. In addition, the CEEO is applied to a simulated cloud computing task scheduling environment, considering varying numbers of tasks and virtual machines. The results reveal that CEEO consistently outperforms other scheduling algorithms, reducing makespan and improving load balancing, energy consumption, and CO2 emissions. Statistical analysis using the Friedman test confirms the statistical significance of these improvements. This research provides an efficient and robust solution for multi-objective task scheduling in cloud computing, offering substantial improvements in operational costs, execution time, and overall service quality. © The Author(s) 2025.

关键词： Scheduling algorithms

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Ontology-Based Semantic Reasoning for Multi-Source Heterogeneous Industrial Devices Using OPC UA

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IEEE Internet of Things Journal 2025年第13期12卷 25020-25032页

作者： Bi, Jing Wu, Rina Yuan, Haitao Wang, Ziqi Zhang, Jia Zhou, MengChu Beijing University of Technology College of Computer Science Beijing100124 China Beihang University School of Automation Science and Electrical Engineering Beijing China Southern Methodist University Department of Computer Science Lyle School of Engineering DallasTX75205 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

The advent of smart manufacturing in Industry 4.0 signifies the era of connections. As a communication protocol, Object linking and embedding for Process Control Unified Architecture (OPC UA) can address most semantic heterogeneity issues. However, its semantics are not formally defined at the application layer. To address the information silo problem caused by semantic heterogeneity, an integration framework named Querying of Ontology Mapping-based OPC UA (QOMOU) is proposed. QOMOU extracts information models of OPC UA servers into resource description framework triples and utilizes web ontology language for semantic enrichment and inference. Then, an Event Class Semantic Similarity Calculation (ECSSC) method is proposed for device type identification, enabling the classification of semantically heterogeneous OPC UA devices. The effectiveness of ECSSC is validated through queries with the RDF query language (SPARQL) protocol in Apache Jena. Experimental results demonstrate that ECSSC improves the accuracy of device identification by approximately 7% compared to benchmark device identification models. Specifically, compared with graph embedding-based methods, QOMOU’s query performance is approximately 13% higher, and its query efficiency is 5% higher on average compared to both structured query and extensible markup languages. Moreover, by employing a keyword-matching algorithm, the query accuracy of the existing heterogeneous data integration scheme is improved by 4% on average. This enhancement can boost the operational efficiency of Internet of Things systems based on the OPC UA architecture. © 2014 IEEE.

关键词： Structured Query Language

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Square Binary Patterns: A Non-Circular Approach for Local Binary Patterns

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第6期52卷 1616-1626页

作者： Putra, Muhammad Ardi Harjoko, Agus Wahyono Jo, Kanghyun Department of Computer Science and Electronics Universitas Gadjah Mada Yogyakarta Indonesia Department of Computer Science and Electronics Universitas Gadjah Mada Yogyakarta Indonesia Department of Electrical Electronic and Computer Engineering University of Ulsan Ulsan Korea Republic of

Local Binary Patterns (LBP) is one of many image features suitable for enhancing textures within an image. Despite its popularity, this feature as well as its variants actually still has some drawbacks: requiring the use of bilinear interpolation mechanism which leads to a computational bottleneck, being unable to capture macrostructures, and being prone to noise. The objective of this research is to propose a novel image feature named Square Binary Patterns (SBP) which is intended to solve the mentioned problems. Experiments were conducted using four texture datasets, namely KTH-TIPS, KTH-TIPS2-b, ALOT, and the texture dataset we created on our own. The existing LBP-based features including LBPriu2, LBPu2, LBPri, MS-LBP, LTP, FbLBP and ILBP are directly compared with SBP. The quality of these features is obtained by observing the classification accuracy of separate SVM models. The classifier which produces higher accuracy implies that its corresponding feature contains a more important information regarding the textures. Experimental results showed that our best-performing SBP variant was able to work 2.98 times faster than the conventional LBP while at the same time capable of producing accuracy comparable to that of the existing LBP-based features. This paper makes a significant contribution through the introduction of the SBP algorithm and the creation of a novel texture dataset. © (2025), (International Association of Engineers). All rights reserved.

关键词： Feature Selection

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Bidirectional Photoelectric Response of Perovskite Oxide-Based Synaptic ReRAM Device

IEEE Electron Device Letters

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IEEE Electron Device Letters 2024年第3期45卷 384-387页

作者： Shrivastava, Saransh Limantoro, Stephen Ekaputra Juliano, Hans Tseng, Tseung-Yuen Hsinchu30010 Taiwan Department of Electrical Engineering and Computer Science Hsinchu30010 Taiwan

Photo sensing capability of an artificial synaptic device make it more valuable for brain-inspired computing systems, which can conquer the von Neumann bottleneck. In this letter, Ba0.7 Sr0.3 TiO3 (BST) based single layered resistive random access memory (ReRAM) device is proposed with its four different photoelectric applications. First, bidirectional photoelectric response (BPR) is achieved via 405 and 633 nm wavelengths stimulation. Second, this novel BPR contributes to the emulation of light potentiation, and light depression behaviors. Third, an important neurological behavior 'experience dependent plasticity (EDP)' is mimicked by frequency modulation of 405 nm light pulse. Fourth, ultra-high (>200%) indices of post-tetanic potentiation and depression (PTP/PTD) are achieved by amplifying the device conductance as a result of two different pulse train stimulations. A convolutional neural network is designed to classify Zalando's article images with a test accuracy of 87.7%. These results provide an opportunity for the development of multi-wavelength dependent multifunctional artificial brain-inspired systems. © 1980-2012 IEEE.

关键词： Neural networks

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A feature selection-aided deep learning based deepfake video detection method

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Multimedia Tools and Applications 2025年 1-24页

作者： Mohiuddin, Sk Roy, Ayush Pani, Saptarshi Malakar, Samir Sarkar, Ram Department of Computer Science Asutosh College Kolkata700026 India Department of Electrical Engineering Jadavpur University Kolkata700032 India Department of Computer Science UiT The Arctic University of Norway Tromsø9019 Norway Department of Computer Science and Engineering Jadavpur University Kolkata700032 India

The rapid advancements in deepfake technology are imposing significant challenges in detecting manipulated media contents. In this work, we have introduced a deepfake detection method that utilizes three pre-trained convolutional neural network models, namely DenseNet-121, EfficientNet-B0, and Xception to extract deep learning features from preprocessed extracted video frames. Next, these features are stacked to generate diverse feature representations, and thus end up with high-dimensional features. After that, a feature selection strategy is employed on the combined features to eliminate redundant and less effective features. The selected features are then fed into the K-nearest neighbors classifier to decide whether a questioned video is a fake video or not. Two widely used deepfake video datasets, namely FaceForensics++ and Celeb-DF (V2) are used to evaluate the performance of the proposed technique following two experimental setups: intra- and inter-datasets. The results show that the model achieves area under curve scores of 98.50% (85.70%) and 97.70% (79.00%) in the intra-dataset (inter-dataset) setups while trained on the FaceForensics++ and Celeb-DF (V2) datasets, respectively. The performance of the proposed model is comparable to that of state-of-the-art methods. Source code of the proposed method can be found at: here. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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A Strong PUF-Based Security Protocol to Protect AI Model Parameters Against Privacy Information Leakage

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IEEE Internet of Things Journal 2025年第12期12卷 20815-20827页

作者： Zhou, Ziyu Li, Gang Zhang, Yuejun Zheng, Ziyang Yuan, Tengfei Wang, Pengjun Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China Wenzhou University College of Electrical and Electronic Engineering Wenzhou325000 China

In the era of intelligent computing, with the aid of Internet of Things (IoT) technology, artificial intelligence (AI) chips can be embedded at the terminal, object, edge, and cloud levels, ultimately achieving the vision where there is computation, there is AI intelligence. This not only enhances the efficiency of production and daily life but also exponentially increases the risk of privacy information leakage within AI models. This article leverages the characteristics of strong physical unclonable functions (PUFs), in which the inherent feature information is hidden in physical variations and difficult to steal, to design a security protocol based on strong PUFs that provides effective protection for AI model parameters in the IoT environment. The protocol treats AI model parameters as responses and selects challenges capable of generating these responses. Since the responses generated by the challenges can be considered as randomly generated, transmitting the challenges does not disclose the response information, thus avoiding the risk of parameter hacking. Additionally, the protocol utilizes machine-learning modeling techniques and lightweight encryption technologies to reduce the storage costs for identity information and the computational overhead of encryption operations. Through a security analysis of the protocol, it demonstrates that even under ideal attack conditions, the proposed protocol can resist various attacks. By using formal verification with the ProVerif tool, it confirms the security of the protocol flow and the effective protection of private information. © 2014 IEEE.

关键词： Authentication Protocol

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Resolution-aware beam scanning for joint detection and communication in ISAC systems

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science China(Information sciences) 2025年第5期68卷 250-266页

作者： Yiming XU Dongfang XU Zhanyuan XIE Zheng JIANG Shenghui SONG Derrick Wing Kwan NG Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology China Telecom Research Institute School of Electrical Engineering and Telecommunications University of New South Wales

Beam scanning for joint detection and communication in integrated sensing and communication(ISAC) systems plays a critical role in continuous monitoring and rapid adaptation to dynamic environments. However, the design of sequential scanning beams for target detection with the required sensing resolution has not been tackled in the *** bridge this gap, this paper introduces a resolution-aware beam scanning design. In particular, the transmit information beamformer, the covariance matrix of the dedicated radar signal, and the receive beamformer are jointly optimized to maximize the average sum rate of the system while satisfying the sensing resolution and detection probability requirements.A block coordinate descent(BCD)-based optimization framework is developed to address the non-convex design problem. By exploiting successive convex approximation(SCA), S-procedure, and semidefinite relaxation(SDR), the proposed algorithm is guaranteed to converge to a stationary solution with polynomial time complexity. Simulation results show that the proposed design can efficiently handle the stringent detection requirement and outperform existing antenna-activation-based methods in the literature by exploiting the full degrees of freedom(DoFs) brought by all antennas.

关键词： integrated sensing and communication (ISAC) detection beam scanning resolution beamwidth

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Fast Routing With Rule Caching in Lossy Mobile SDN

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Journal of Institute of Control, Robotics and Systems 2023年第11期29卷 854-858页

作者： Kim, Janghan Park, Hyung-Seok Park, Kyung-Joon Department of Electrical Engineering and Computer Science DGIST Korea Republic of

Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) have been actively employed for tasks that are challenging for humans, such as scenarios like battlefield and disaster. Swarm unmanned systems, comprising UAVs and UGVs, experience dynamic additions or removals of unmanned aircraft. In this paper, we propose a rule caching-based recovery technique for achieving rapid forwarding rule establishment for additional UAVs deployed for recovery in wireless Software-defined Networking (SDN) based swarm unmanned systems. To validate the proposed algorithm, we conducted experiments by setting up a testbed using ODROID and a controller. Our results demonstrate that the proposed algorithm improves forwarding rule establishment performance by up to 50.3%. © ICROS 2023.

关键词： Ground vehicles

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Improving the Stability of APUF to 100% Without Extra Hardware Overhead for Enhancing the Performance of Security Authentication Protocols

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IEEE Internet of Things Journal 2025年第12期12卷 19818-19832页

作者： Zhou, Ziyu Wang, Pengjun Li, Gang Hu, Shuang Zhang, Yuejun Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China Wenzhou University College of Electrical and Electronic Engineering Wenzhou325000 China

With the increasing number of devices in the Internet of Things (IoT), security has become a necessary feature. Compared to traditional key encryption methods, IoT device authentication protocols based on strong Physically unclonable function (PUF) have the advantage of being difficult to leak and tamper with. In addition, the protocol can enhance the resistance of strong PUFs to machine learning (ML) attacks by encrypting private information. However, the quality of the authentication is strongly related to the stability of the generated responses. If a large cost is incurred to improve the stability of the strong PUF, it will consume the already scarce resources of the device. This article proposes a challenge screening strategy to improve the response stability of a commonly used strong PUF, arbiter PUF (APUF). First, a precise modeling method of APUF is carried out using the logistic-regression ML algorithm. Subsequently, random noise is injected into the challenges or PUF mathematical model and then calculated to estimate whether a stable response can be produced. Finally, stable challenges are used for IoT device authentication, while the threshold of the protocol is increased in parallel. Experimental verification shows that the proposed strategy can increase the stability of APUF responses to 100% at different temperatures. Furthermore, it does not consume hardware overhead at the device end, which is of particular significance for applications in resource-constrained conditions. © 2014 IEEE.

关键词： Authentication Protocol

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Optimized Visual Recognition Through a Deep Convolutional Neural Network With Hierarchical Modular Organization

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IEEE Access 2024年 12卷 95517-95528页

作者： Clinton Sitepu, Ade Liu, Chuan-Ming National Taipei University of Technology College of Electrical Engineering and Computer Science Taipei10608 Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei10608 Taiwan

Artificial Intelligence, including machine learning and deep convolutional neural networks (DCNNs), relies on complex algorithms and neural networks to process and analyze data. DCNNs for visual recognition often require access to high-performance hardware, such as GPUs and cloud-based computing resources, to perform tasks efficiently. Visual recognition requires DCNNs with numerous layers. Fully connected layers in DCNNs are often the most computationally intensive. These layers connect every neuron in one layer to every neuron in the next layer, resulting in a large number of parameters to compute. To mitigate redundancy and make DCNNs more efficient, this article implements and demonstrates the concept to identifying and removing redundant or low-impact connections from fully connected layers using convolution neural network with hierarchical modular organization. The modularity of the DCNNs is built based on the cluster hierarchy of the similar image. These clusters are created based on a novel similarity metric, which measures how closely related images are to each other. The architecture uses multiple smaller DCNNs, referred to as modules, designed to progressively classify images into super clusters according to their similarity. Experimental results using popular image datasets show that the proposed DCNNs model to optimized number of operations by 49% to 99% and keeps its performance comparable. © 2024 The Authors.

关键词： Convolution

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