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A GSO-based multi-objective technique for performance optimization of blockchain-based industrial Internet of things

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International Journal of Communication Systems 2024年第15期37卷

作者： Zanbouri, Kouros Darbandi, Mehdi Nassr, Mohammad Heidari, Arash Navimipour, Nima Jafari Yalcın, Senay Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Pôle Universitaire Léonard de Vinci Paris France Communication Technology Engineering Department Tartous University Tartus Syria Department of Mathematics and Natural Sciences Gulf University for Science and Technology Mishref Campus Kuwait Department of Computer Engineering Faculty of Engineering and Natural Science İstanbul Atlas University Turkey Department of Software Engineering Haliç University Istanbul Turkey Department of Computer Engineering Kadir Has University Istanbul Turkey Research Center of High Technologies and Innovative Engineering Western Caspian University Baku Azerbaijan Future Technology Research Center National Yunlin University of Science and Technology Douliou Taiwan Department of Energy System Engineering School of Engineering and Natural Sciences Bahçeşehir University Istanbul Turkey

The latest developments in the industrial Internet of things (IIoT) have opened up a collection of possibilities for many industries. To solve the massive IIoT data security and efficiency problems, a potential approach is considered to satisfy the main needs of IIoT, such as high throughput, high security, and high efficiency, which is named blockchain. The blockchain mechanism is considered a significant approach to boosting data protection and performance. In the quest to amplify the capabilities of blockchain-based IIoT, a pivotal role is accorded to the Glowworm Swarm Optimization (GSO) algorithm. Inspired by the collaborative brilliance of glowworms in nature, the GSO algorithm offers a unique approach to harmonizing these conflicting aims. This paper proposes a new approach to improve the performance optimization of blockchain-based IIoT using the GSO algorithm due to the blockchain's contradictory objectives. The proposed blockchain-based IIoT system using the GSO algorithm addresses scalability challenges typically associated with blockchain technology by efficiently managing interactions among nodes and dynamically adapting to network demands. The GSO algorithm optimizes the allocation of resources and decision-making, reducing inefficiencies and bottlenecks. The method demonstrates considerable performance improvements through extensive simulations compared to traditional algorithms, offering a more scalable and efficient solution for industrial applications in the context of the IIoT. The extensive simulation and computational study have shown that the proposed method using GSO considerably improves the objective function and blockchain-based IIoT systems' performance compared to traditional algorithms. It provides more efficient and secure systems for industries and corporations. © 2024 John Wiley & Sons Ltd.

关键词： Multiobjective optimization

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Multi-Path Attention Inverse Discrimination Network for Offline Signature Verification

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Intelligent Automation & Soft Computing 2023年第6期36卷 3057-3071页

作者： Xiaorui Zhang Yingying Wang Wei Sun Qi Cui Xindong Wei School of Computer and Software Nanjing University of Information Science&TechnologyNanjing210044China Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing 210044China Department of Electrical and Computer Engineering University of WindsorWindsorN9B 3P4Canada School of Teacher Education Nanjing University of Information Science&TechnologyNanjing210044China

Signature verification,which is a method to distinguish the authenticity of signature images,is a biometric verification technique that can effectively reduce the risk of forged signatures in financial,legal,and other business ***,compared with ordinary images,signature images have the following characteristics:First,the strokes are slim,i.e.,there is less effective ***,the signature changes slightly with the time,place,and mood of the signer,i.e.,it has high intraclass *** challenges lead to the low accuracy of the existing methods based on convolutional neural net-works(CNN).This study proposes an end-to-end multi-path attention inverse dis-crimination network that focuses on the signature stroke parts to extract features by reversing the foreground and background of signature images,which effectively solves the problem of little effective *** solve the problem of high intraclass variability of signature images,we add multi-path attention modules between discriminative streams and inverse streams to enhance the discriminative features of signature ***,a multi-path discrimination loss function is proposed,which does not require the feature representation of the samples with the same class label to be infinitely close,as long as the gap between inter-class distance and the intra-class distance is bigger than the set classification threshold,which radically resolves the problem of high intra-class difference of signature *** addition,this loss can also spur the network to explore the detailed infor-mation on the stroke parts,such as the crossing,thickness,and connection of *** respectively tested on CEDAR,BHSig-Bengali,BHSig-Hindi,and GPDS Synthetic datasets with accuracies of 100%,96.24%,93.86%,and 83.72%,which are more accurate than existing signature verification *** is more helpful to the task of signature authentication in justice and finance.

关键词： Offline signatures biometric verification multi-path discrimination loss attention mechanisms inverse discrimination

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Digital Event-Based Stabilization of Nonlinear Time-Delay Systems

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IFAC-PapersOnLine 2023年第2期56卷 5969-5974页

作者： M. Di Ferdinando S. Di Gennaro A. Borri P. Pepe Department of Information Engineering Computer Science and Mathematics Center of Excellence DEWS University of L'Aquila L'Aquila Italy Institute of Systems Analysis and Computer Science National Research Council Rome Italy

In this paper, the stabilization problem of nonlinear time-delay systems via quantized sampled-data event-based controllers is investigated. Fully nonlinear (i.e., possibly non-affine in the control) systems affected by state delays are studied. Sufficient conditions are provided for the existence of a suitably fast sampling and of an accurate quantization of the input/output channels such that the digital implementation of the continuous-time controller at hand, updated through a proposed event-triggered mechanism, ensures the semi-global practical stability property, with arbitrarily small final target ball of the origin, of the related closed-loop system. A spline approximation methodology is used in order to cope with the problem of the possible non-availability in the buffer of suitable past values of the system variables needed for the digital implementation of the controller. The stabilization in the sample-and-hold sense theory is used as a tool to prove the results. In the theory here developed, the case of non-uniform quantization of the input/output channels and the case of aperiodic sampling are both included. The proposed theoretical results are validated through an application concerning the plasma glucose regulation problem in type-2 diabetic patients.

关键词： Quantized Sampled-Data Controllers Event-triggered Control Spline approximation Nonlinear Time-Delay Systems Stabilization in the Sample-and-Hold sense Glucose-Insulin model

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Reverse engineering of Mobile Banking Applications

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computer Systems Science & engineering 2021年第9期38卷 265-278页

作者： Syeda Warda Asher Sadeeq Jan George Tsaramirsis Fazal Qudus Khan Abdullah Khalil Muhammad Obaidullah National Center for Cyber Security Department of Computer Science&Information TechnologyUniversity of Engineering&TechnologyPeshawar25120Pakistan Higher Colleges of Technology Abu Dhabi Women’s CollegeAbu DhabiUAE Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Software Engineering University of Engineering&TechnologyMardan23200Pakistan

software reverse engineering is the process of analyzing a software system to extract the design and implementation *** engineering provides the source code of an application,the insight view of the architecture and the third-party *** a security perspective,it is mostly used for finding vulnerabilities and attacking or cracking an *** process is carried out either by obtaining the code in plaintext or reading it through the binaries or ***,reverse engineering is widely used for mobile applications and is considered a security *** Open Web Application Security Project(OWASP),a leading security research forum,has included reverse engineering in its top 10 list of mobile application *** applications are used in many sectors,e.g.,banking,education,*** particular,the banking applications are critical in terms of security as they are used for financial transactions.A security breach of such applications can result in huge financial losses for the customers as well as the *** exist various tools for reverse engineering of mobile applications,however,they have deficiencies,e.g.,complex configurations,lack of detailed analysis *** this research work,we perform an analysis of the available tools for reverse engineering of mobile *** dataset consists of the mobile banking applications of the banks providing services in *** results indicate that none of the existing tools can carry out the complete reverse engineering process as a standalone *** addition,we observe significant differences in terms of the execution time and the number of files generated by each tool for the same file.

关键词： Reverse engineering mobile banking applications security analysis

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Review of Inference Time Prediction Approaches of DNN: Emphasis on Service robots with cloud-edge-device architecture

Review of Inference Time Prediction Approaches of DNN: Empha...

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2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023

作者： Xiang, Tian Meng, Qiwei Zhang, Ji Zhang, Beibei Song, Wei Xie, Anhuan Gu, Jason Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Research Center for Intelligent Robotics Hangzhou311100 China The Chinese University of Hong Kong T Stone Robotics Institute Department of Mechanical and Automation Engineering Hong Kong Research Institute of Intelligent Computing Zhejiang Laboratory Research Center for Intelligent Computing Software Hangzhou311100 China Dalhousie University Electrical and Computer Engineering Halifax Canada

ISBN: (纸本)9798350325706

In recent years, the global robot market has witnessed substantial growth, particularly in the domain of service robots. Despite their expanding presence, service robots encounter limitations when operating autonomously in unstructured environments, primarily due to their constrained computational capacities. As a solution, the fusion of cloud and edge computing resources becomes imperative to expedite task inference and enhance scenario perception capabilities. The integration of cloud-edge-device models holds significant promise in bolstering the operational efficiency of robots. This entails the dynamic partitioning of intricate robotic tasks, executed collaboratively across cloud, edge, and device resources. In this landscape, deep neural network (DNN) models play a pivotal role in facilitating a wide array of robotic tasks. The inference time for each layer of a DNN model in actual deployment, emerges as a critical determinant in the model's partitioning strategy. It also serves as an important metric influencing the model's suitability for a specific hardware platform. This article presents an overview of recent advancements in predicting inference and training time of DNN models, summarizes the related methods, and finally discusses the challenges in this field and the research that can be studied in the future. © 2023 IEEE.

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Human Machine Interface with Wearable Electronics Using Biodegradable Triboelectric Films for Calligraphy Practice and Correction

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Nano-Micro Letters 2022年第12期14卷 630-644页

作者： Shen Shen Jia Yi Zhongda Sun Zihao Guo Tianyiyi He Liyun Ma Huimin Li Jiajia Fu Chengkuo Lee Zhong Lin Wang Jiangsu Engineering Technology Research Center for Functional Textiles Jiangnan UniversityNo.1800 Lihu AvenueWuxiP.R.China Department of Electrical and Computer Engineering National University of Singapore4 Engineering Drive 3Singapore 117576Singapore CAS Center for Excellence in Nanoscience Beijing Key Laboratory of Micro-Nano Energy and SensorBeijing Institute of Nanoenergy and NanosystemsChinese Academy of SciencesBeijing 100083P.R.China China National Textile and Apparel Council Key Laboratory of Natural Dyes Soochow UniversitySuzhou 215123People’s Republic of China School of Material Science and Engineering Georgia Institute of TechnologyAtlantaGA 30332-0245USA

Letter handwriting,especially stroke correction,is of great importance for recording languages and expressing and exchanging ideas for individual behavior and the *** this study,a biodegradable and conductive carboxymethyl chitosan-silk fibroin(CSF)film is prepared to design wearable triboelectric nanogenerator(denoted as CSF-TENG),which outputs of V_(oc)≈165 V,I_(sc)≈1.4μA,and Q_(sc)≈72 mW cm^(−2).Further,in vitro biodegradation of CSF film is performed through trypsin and *** results show that trypsin and lysozyme have stable and favorable biodegradation properties,removing 63.1%of CSF film after degrading for 11 ***,the CSF-TENG-based human-machine interface(HMI)is designed to promptly track writing steps and access the accuracy of letters,resulting in a straightforward communication media of human and *** CSF-TENG-based HMI can automatically recognize and correct three representative letters(F,H,and K),which is benefited by HMI system for data processing and *** CSF-TENG-based HMI can make decisions for the next stroke,highlighting the stroke in advance by replacing it with red,which can be a candidate for calligraphy practice and ***,various demonstrations are done in real-time to achieve virtual and real-world controls including writing,vehicle movements,and healthcare.

关键词： Letter handwriting Triboelectric nanogenerator Biodegradable Human-machine interface Calligraphy practice

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D³APTS: Design of ECC-Based Authentication Protocol and Data Storage for Tactile Internet Enabled IoD System With Blockchain

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IEEE Transactions on Consumer Electronics 2023年第1期70卷 4239-4248页

作者： Sanjeev Kumar Dwivedi Mohammad Abdussami Ruhul Amin Muhammad Khurram Khan VIT-AP School of Computer Science and Engineering VIT-AP University Amaravati India Computer Science and Engineering Department International Institute of Information Technology Naya Raipur India Center of Excellence in Information Assurance King Saud University Riyadh Saudi Arabia

Tactile Internet, a fast and low-latency communication network, plays a crucial role in IoD applications for providing various features like precision control and realistic telepresence. The Internet of Drones (IoD) is a rapidly evolving concept that envisions a network of collaborating and communicating interconnected drones. Drones have diverse applications, including traffic monitoring, medicine, battlefield surveillance, logistics, and agriculture. However, the widespread deployment of drones raises significant security and privacy concerns. In this paper we have proposed secure data delivery authentication protocol for tactile Internet enabled IoD system $(\text {D}^{3}$ APTS). Also, using blockchain along with IoD for storing data will provide additional characteristics like data immutability. To secure communication among user, ground station server, and drone D3APTS employs one-way hash function and ECC cryptosystem. It resists various attacks like impersonation attacks, capturing attacks, session-key disclosure attacks, especially desynchronisation attacks which is important in case of IoD scenario. D3APTS also provides anonymity and unlinkability features. It is formally verified using the scyther security tool and ROR model. The performance analysis and comparison shows that D3APTS is better in terms of computation, communication overheads, security and functionality features.

关键词： Drones Security Blockchains Authentication Tactile Internet Computational modeling Servers

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Deep Reinforcement Learning for Multi-Objective Resource Allocation in Multi-Platoon Cooperative Vehicular Networks

Deep Reinforcement Learning for Multi-Objective Resource All...

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作者： Xu, Yuanyuan Zhu, Kun Xu, Hu Ji, Jiequ Hohai University College of Computer and Information Nanjing211100 China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing210016 China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing211106 China University of Victoria Department of Electrical and Computer Engineering VictoriaV8P 5C2 Canada

Grouping vehicles into platoons is a promising cooperative driving scenario to enhance the traffic safety and capacity of future vehicular networks. However, fast changing channel conditions in multi-platoon vehicular networks cause tremendous uncertainty for resource allocation. In addition, the unprecedented proliferation of various emerging vehicle-to-infrastructure (V2I) applications may result in some service demands with conflicting quality of experience. In this paper, we formulate a multi-objective resource allocation problem, which maximizes the transmission success ratio of intra-platoon communications and the mean opinion score (MOS) of V2I communication links. To efficiently solve this multi-objective optimization problem, we resort to a deep reinforcement learning (DRL) framework. Specifically, we divide it into a set of scalar optimization subproblems based on the weighted sum approach and model each one as a partially observable stochastic game (P-OSG), where each platoon acts as an agent and the actions taken by all platoons correspond to the resource allocation solution. We further propose a contribution-based dual-clip proximal policy optimization (CD-PPO) algorithm to deal with each subproblem, which is a DRL algorithm based on the actor-critic framework. The network parameters of all subproblems are then optimized collaboratively by using the proposed training algorithm and the neighborhood parameter transfer strategy. The desired Pareto front is obtained when all subproblems are solved. Simulation results reveal that the proposed algorithm can outperform other algorithms in terms of the MOS and transmission success ratio. © 2002-2012 IEEE.

关键词： Reinforcement learning

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Adaptive Autopilot: Constrained Drl for Diverse Driving Behaviors

Adaptive Autopilot: Constrained Drl for Diverse Driving Beha...

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International Conference on Intelligent Transportation

作者： Dinesh Cyril Selvaraj Christian Vitale Tania Panayiotou Panayiotis Kolios Carla Fabiana Chiasserini Georgios Ellinas CARS@Polito and Politecnico di Torino Torino Italy Department of Electrical and Computer Engineering KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus Department of Computer Science KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus Chalmers University of Technology Sweden

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

In pursuit of autonomous vehicles, achieving human-like driving behavior is vital. This study introduces adaptive autopilot (AA), a unique framework utilizing constrained-deep reinforcement learning (C-DRL). AA aims to safely emulate human driving to reduce the necessity for driver intervention. Focusing on the car-following scenario, the process involves (i) extracting data from the highD natural driving study and categorizing it into three driving styles using a rule-based classifier; (ii) employing deep neural network (DNN) regressors to predict human-like acceleration across styles; and (iii) using C-DRL, specifically the soft actor-critic Lagrangian technique, to learn human-like safe driving policies. Results indicate effectiveness in each step, with the rule-based classifier distinguishing driving styles, the regressor model accurately predicting acceleration, outperforming traditional car-following models, and C-DRL agents learning optimal policies for humanlike driving across styles.

关键词： Adaptation models Artificial neural networks Reinforcement learning Predictive models Semisupervised learning Autopilot Safety Driver behavior Intelligent transportation systems Vehicles

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Explainable AI for Healthcare: An Approach Towards Interpretable Healthcare Models

Explainable AI for Healthcare: An Approach Towards Interpret...

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Information Technology (ACIT)

作者： Kanwal Zahoor Narmeen Zakaria Bawany Usman Ghani Department of Computer Science and Software Engineering Center for Computing Research Jinnah University for Women Institute of Business Administration Karachi Pakistan

Artificial intelligence (AI) along with deep learning techniques has become an integral part of almost all aspects of life. One of the domains significantly impacted by this technological revolution is healthcare. Deep learning-based AI systems assist clinicians and medical professionals in disease diagnosis, personalized treatment, and monitoring through wearables, among other applications. Despite its expedient integration in healthcare, the trustworthiness of deep learning models remains a concern, primarily due to a lack of understanding of their underlying processes. However, Explainable AI (XAI) offers explanations through various methods, including Local Interpretable Model-agnostic Explanations (LIME), Shapley Additive explanation (SHAP), and GRAD-CAM. XAI is utilized to enhance transparency, allowing users to understand and trust AI decisions. In this study, we present deep learning models for the classification of pneumonia disease in Chest X-ray Images followed by their explanations. Convolutional Neural Networks (CNNs) and other pre-trained models, including VGG16, MobileNetV3, and ResNet50, were used for classification of images as ‘normal’ or ‘pneumonia’. The VGG16 model, known for its exceptional image understanding capabilities, achieved the highest accuracy, with an impressive 93% score. Further, we used XAI techniques including SHAP, LIME, and Grad-CAM for explanation of models. LIME and Grad-CAM provided more accurate results than SHAP in our experiments. This approach was taken to evaluate the fairness and transparency of the model. The insights gained from XAI can be used to refine and improve machine learning models by identifying areas of weakness or misinterpretation which increases overall model robustness.

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