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arXiv 2024年

作者： Xia, Yun Xue, Hai Zhang, Di Mumtaz, Shahid Xu, Xiaolong Rodrigues, Joel J.P.C. School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China School of Electrical and Information Engineering Zhengzhou University the Henan International Joint Laboratory of Intelligent Health Information System The National Telemedicine Center The National Engineering Laboratory for Internet Medical Systems and Applications Zhengzhou450001 China Department of Engineering Nottingham Trent University NottinghamNG1 4FQ United Kingdom The Department of Electronic Engineering Kyung Hee University Gyeonggi-do Yongin-si17104 Korea Republic of School of Software Nanjing University of Information Science and Technology Nanjing210044 China Amazonas State University AM Manaus Brazil

In mobile edge computing (MEC), task offloading can significantly reduce task execution latency and energy consumption of end user (EU). However, edge server (ES) resources are limited, necessitating efficient allocation to ensure the sustainable and healthy development for MEC systems. In this paper, we propose a dynamic pricing mechanism based near-optimal resource allocation for elastic edge offloading. First, we construct a resource pricing model and accordingly develop the utility functions for both EU and ES, the optimal pricing model parameters are derived by optimizing the utility functions. In the meantime, our theoretical analysis reveals that the EU’s utility function reaches a local maximum within the search range, but exhibits barely growth with increased resource allocation beyond this point. To this end, we further propose the Dynamic Inertia and Speed-Constrained particle swarm optimization (DISC-PSO) algorithm, which efficiently identifies the near-optimal resource allocation. Comprehensive simulation results validate the effectiveness of DISC-PSO, demonstrating that it significantly outperforms existing schemes by reducing the average number of iterations to reach a near-optimal solution by 92.11%, increasing the final user utility function value by 0.24%, and decreasing the variance of results by 95.45%. Copyright © 2024, The Authors. All rights reserved.

关键词： Particle swarm optimization (PSO)

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Distributed Trust Management for Secured Localization systems in Mobile Cloud Services

Distributed Trust Management for Secured Localization System...

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IEEE International Conference on Utility and Cloud Computing (UCC)

作者： Farouq Aliyu Mustafa Ghaleb Saud Mohammad Mostafa Sani Umar AbdalRahman Aburakhia Center of Excellence in Dev of NPO King Fahd University of Petr. & Min. Dhahran Saudi Arabia IRC for Intelligent Secure Systems King Fahd University of Petr. & Min. Dhahran Saudi Arabia Computer Engineering Department King Fahd University of Petr. & Min. Dhahran Saudi Arabia Dept. of Electrical Engr. and Computer Sci. Khalifa University Abu Dhabi UAE Information & Computer Science King Fahd University of Petr. & Min. Dhahran Saudi Arabia

ISBN: (数字)9798350367201

ISBN: (纸本)9798350367218

The market for the Internet of Things (IoT) is expanding at an exponential rate. As IoT devices increasingly rely on cloud computing for data processing, storage, and management, the security of cloud-based IoT services becomes paramount. Many of these services depend on the IoT device’s location. Therefore, secured localization systems are necessary. However, traditional security techniques are resource-consuming. An alternative method for detecting localization attacks in IoT is through trust models. This paper proposes a distributed trust management technique for localization in Mobile IoT, specifically designed to enhance the security and trustworthiness of cloud-based IoT services. The trust of a node is computed based on its availability and service quality. Nodes use this information to calculate their neighbors’ and anchor nodes’ trust values. We simulated the proposed system using the Cooja simulator. The results show that the proposed trust-based localization technique can help unknown nodes mobilize and reach a target location, reducing the control group’s error by 186.4% at the cost of 4.8 μJ only. The system finds application in indoor localization for location-based cloud services, such as locating shops of items in shopping malls.

关键词： Location awareness Cloud computing Costs Computational modeling Data processing Internet of Things Security Trust management

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Flexible Semantic-Aware Resource Allocation: Serving More Users Through Similarity Range Constraints

arXiv

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arXiv 2025年

作者： Gholami, Nasrin Moghim, Neda Ghahfarokhi, Behrouz Shahgholi Salavati, Pouyan Thomas, Christo Kurisummoottil Shetty, Sachin Rahmati, Tahereh Department of Computer Engineering University of Isfahan VA United States Center for Secure and Intelligent Critical Systems Old Dominion University VA United States Bradley Department of Electrical and Computer Engineering Virginia Tech ArlingtonVA United States

Semantic communication (SemCom) aims to enhance the resource efficiency of next-generation networks by transmitting the underlying meaning of messages, focusing on information relevant to the end user. Existing literature on SemCom primarily emphasizes learning the encoder and decoder through end-to-end deep learning frameworks, with the objective of minimizing a task-specific semantic loss function. Beyond its influence on the physical and application layer design, semantic variability across users in multi-user systems enables the design of resource allocation schemes that incorporate user-specific semantic requirements. To this end, a semantic-aware resource allocation scheme is proposed with the objective of maximizing transmission and semantic reliability, ultimately increasing the number of users whose semantic requirements are met. The resulting resource allocation problem is a non-convex mixed-integer nonlinear program (MINLP), which is known to be NP-hard. To make the problem tractable, it is decomposed into a set of sub-problems, each of which is efficiently solved via geometric programming techniques. Finally, simulations demonstrate that the proposed method improves user satisfaction by up to 17.1% compared to state of the art methods based on quality of experience-aware SemCom methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Integer programming

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Methods for Verifying the Security of Available IoT Devices

Methods for Verifying the Security of Available IoT Devices

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International Symposium on intelligent systems and Informatics (SISY)

作者： Jan Francisti Zoltán Balogh György Molnár Kopják József Sebestyén Gergely Enikő Nagy Department of Informatics Faculty of Natural Sciences and Informatics Constantine the Philosopher University in Nitra Nitra Slovakia Kandó Kálmán Faculty of Electrical Engineering Óbuda University Budapest Hungary János Csere Apáczai Faculty Széchenyi University Győr Hungary Antal Bejczy Center for Intelligent Robotics John von Neumann Faculty Institute of Cyberphysical Systems Óbuda University Budapest Hungary

In present, the field of Internet of Things (IoT) is experiencing significant development, as evidenced by the everincreasing number of connected devices equipped with various sensors and controllers. These devices have become an integral part of smart homes, offering numerous benefits. Paradoxically, their growing popularity also brings an increased risk of vulnerabilities that threaten the security and privacy of users. This paper focuses on analysing common IoT devices used in smart homes to identify weaknesses and propose measures for improving their protection. This will involve evaluating security tests and providing recommendations for securing the devices and safeguarding their users. The proposed solutions and recommendations will assist users in better understanding potential threats and protecting their devices from unauthorized access and the leakage of sensitive information. This research approach contributes to enhancing the security and privacy of IoT device users in smart homes.

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Towards Efficient Dynamic Uplink Scheduling over Multiple Unknown Channels

arXiv

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arXiv 2022年

作者： Wu, Shuang Ren, Xiaoqiang Jia, Qing-Shan Johansson, Karl Henrik Shi, Ling Huawei Noah’s Ark Lab Hong Kong The School of Mechatronic Engineering and Automation Shanghai University Shanghai200444 China The Center for Intelligent and Networked Systems Department of Automation BNRist Tsinghua University Beijing100084 China The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden The Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong

Age-of-Information (AoI) is a critical metric for network applications. Existing works mostly address optimization with homogeneous AoI requirements, which is different from practice. In this work, we optimize uplink scheduling for an access point (AP) over multiple unknown channels with heterogeneous AoI requirements defined by AoI-dependent costs. The AP serves N users by using M channels without the channel state information. Each channel serves only one user in each decision epoch. The optimization objective is to minimize the time-averaged AoI-dependent costs plus additional communication transmission costs over an infinite horizon. This decision-making problem can be formulated as a Markov decision process, but it is computationally intractable because the size of the state space grows exponentially with respect to the number of users. To alleviate the challenge, we reformulate the problem as a variant of the restless multi-armed bandit (RMAB) problem and leverage Whittle’s index theory to design an index-based scheduling policy algorithm. We derive an analytic formula for the indices, which reduces the computational overhead and facilitates online adaptation. Our numerical examples show that our index-based scheduling policy achieves comparable performance to the optimal policy and outperforms several other heuristics. Copyright © 2022, The Authors. All rights reserved.

关键词： Optimization

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Reputation and Pricing Dynamics in Online Markets

arXiv

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arXiv 2021年

作者： Ma, Qian Huang, Jianwei Bąsar, Tamer Liu, Ji Chen, Xudong The School of Intelligent Systems Engineering Sun Yat-sen University China The School of Science and Engineering Chinese University of Hong Kong Shenzhen Shenzhen518172 China The Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518129 China The Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign United States The Department of Electrical and Computer Engineering Stony Brook University United States The Department of Electrical University of Colorado Boulder Computer and Energy Engineering United States

We study the economic interactions among sellers and buyers in online markets. In such markets, buyers have limited information about the product quality, but can observe the sellers' reputations which depend on their past transaction histories and ratings from past buyers. Sellers compete in the same market through pricing, while considering the impact of their heterogeneous reputations. We consider sellers with limited as well as unlimited capacities, which correspond to different practical market scenarios. In the unlimited seller capacity scenario, buyers prefer the seller with the highest reputation-price ratio. If the gap between the highest and second highest seller reputation levels is large enough, then the highest reputation seller dominates the market as a monopoly. If sellers' reputation levels are relatively close to each other, then those sellers with relatively high reputations will survive at the equilibrium, while the remaining relatively low reputation sellers will get zero market share. In the limited seller capacity scenario, we further consider two different cases. If each seller can only serve one buyer, then it is possible for sellers to set their monopoly prices at the equilibrium while all sellers gain positive market shares;if each seller can serve multiple buyers, then it is possible for sellers to set maximum prices at the equilibrium. Simulation results show that the dynamics of reputations and prices in the longerterm interactions will converge to stable states, and the initial buyer ratings of the sellers play the critical role in determining sellers' reputations and prices at the stable state. Copyright © 2021, The Authors. All rights reserved.

关键词： Competition

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Creating Own AI Datasets from Different Language Sources Efficiently: Advantages, Disadvantages and Best Pracitces to Make Own Datasets

Creating Own AI Datasets from Different Language Sources Eff...

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International IEEE Conference and Workshop: Óbuda on electrical and Power engineering (CANDO-EPE)

作者： György Molnár József Cserkó Enikő Nagy Zoltán Balogh Kandó Kálmán Faculty of Electrical Engineering Óbuda University Budapest Hungary János Csere Apáczai Faculty Széchenyi University Győr Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemét Hungary Széchenyi University Doctoral School of Multidisciplinary Engineering Sciences (MMTDI) Győr Hungary Antal Bejczy Center for Intelligent Robotics John von Neumann Faculty Institute of Cyberphysical Systems Óbuda University Budapest Hungary Department of Informatics Faculty of Natural Sciences and Informatics Constantine the Philosopher University in Nitra Nitra Slovakia

Artificial Intelligence (AI) has become a ubiquitous technology that has the potential to revolutionize many industries. However, AI requires access to vast amounts of data to achieve its full potential. By understanding these data sources and the best practices for working with them, we can unlock the full potential of AI and create groundbreaking solutions that benefit society as a whole. Data sources can be utilized to train AI algorithms, including structured and unstructured data, but finding relevant datasets is challenging. The main problem is the international sources. Most of the datasets are available only in English or other common languages. However these are good sources, but translating them is expensive and time-consuming. This paper shows how to generate own datasets from open-source data with translation.

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Long and Short-Term Constraints Driven Safe Reinforcement Learning for Autonomous Driving

arXiv

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arXiv 2024年

作者： Hu, Xuemin Chen, Pan Wen, Yijun Tang, Bo Chen, Long School of Artificial Intelligence Hubei University Wuhan430062 China Key Laboratory of Intelligent Sensing System and Security Hubei University Ministry of Education Hubei Wuhan430062 China Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States State Key Laboratory of Multimodal Artificial Intelligence Systems State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing100190 China WAYTOUS Inc. Beijing100083 China Shenzhen518107 China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi’an710049 China

Reinforcement learning (RL) has been widely used in decision-making and control tasks, but the risk is very high for the agent in the training process due to the requirements of interaction with the environment, which seriously limits its industrial applications such as autonomous driving systems. Safe RL methods are developed to handle this issue by constraining the expected safety violation costs as a training objective, but the occurring probability of an unsafe state is still high, which is unacceptable in autonomous driving tasks. Moreover, these methods are difficult to achieve a balance between the cost and return expectations, which leads to learning performance degradation for the algorithms. In this paper, we propose a novel algorithm based on the long and short-term constraints (LSTC) for safe RL. The short-term constraint aims to enhance the shortterm state safety that the vehicle explores, while the long-term constraint enhances the overall safety of the vehicle throughout the decision-making process, both of which are jointly used to enhance the vehicle safety in the training process. In addition, we develop a safe RL method with dual-constraint optimization based on the Lagrange multiplier to optimize the training process for end-to-end autonomous driving. Comprehensive experiments were conducted on the MetaDrive simulator. Experimental results demonstrate that the proposed method achieves higher safety in continuous state and action tasks, and exhibits higher exploration performance in long-distance decision-making tasks compared with state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Lagrange multipliers

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Symbolic Models for Interconnected Impulsive systems

Symbolic Models for Interconnected Impulsive Systems

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IEEE Conference on Decision and Control

作者： Sadek Belamfedel Alaoui Adnane Saoud Pushpak Jagtap Abdalla Swikir School of Computer Science at Mohammed VI Polytechnical University Benguerir Morocco Centrale-Supelec University Paris-Saclay Gif-sur-Yvette France Robert Bosch Center for Cyber-Physical Systems Indian Institute of Science Bangalore Chair of Robotics and Systems Intelligence and MIRMI Technical University Munich (TUM) Munich Germany Department of Electrical and Electronic Engineering Omar Al-Mukhtar University (OMU) Albaida Libya Centre for Tactile Internet with Human-in-the-Loop (CeTI) Dresden Germany

In this paper, we present a compositional method-ology for constructing symbolic models of nonlinear interconnected impulsive systems. Our approach relies on the concept of “alternating simulation function” to establish a relation-ship between concrete subsystems and their symbolic models. Assuming some small-gain type conditions, we develop an alternating simulation function between the symbolic models of individual subsystems and those of the nonlinear interconnected impulsive systems. To construct symbolic models of nonlinear impulsive subsystems, we propose an approach that depends on incremental input-to-state stability and forward completeness properties. Finally, we demonstrate the advantages of our framework through a case study.

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Urban Digital Twins for intelligent Road Inspection

Urban Digital Twins for Intelligent Road Inspection

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IEEE International Conference on Big Data

作者： Rui Fan Yikang Zhang Sicen Guo Jiahang Li Yi Feng Shuai Su Yanting Zhang Wenshuo Wang Yu Jiang Mohammud Junaid Bocus Xingyi Zhu Qijun Chen the Department of Control Science & Engineering Frontiers Science Center for Intelligent Autonomous Systems and State Key Laboratory of Intelligent Autonomous Systems Robotics & Artificial Intelligence Laboratory (RAIL) Tongji University Shanghai P. R. China Department of Computer Science & Technology Donghua University Shanghai P. R. China Department of Civil Engineering McGill University Montreal Canada CTO office at ClearMotion Billerica MA USA Department of Electrical & Electronic Engineering the University of Bristol BS8 1UB U.K Key Laboratory of Road and Traffic Engineering of Ministry of Education Tongji University Shanghai P. R. China

ISBN: (纸本)9781665480468

Urban digital twin (UDT) technologies offer new opportunities for intelligent road inspection (IRI). This paper first reviews the state-of-the-art algorithms used in the two key components of UDT-based IRI systems: (1) multi-temporal, multi-dimension, multi-score, and heterogeneous road data acquisition, and (2) road distress detection. This paper then summarizes the UDTIRI competition, organized in conjunction with IEEE Bigdata 2022. More details on our competition are available at ***/view/udtiri-workshop/bigdata-2022.

关键词： Roads Data acquisition Inspection Big Data Rough surfaces Digital twins

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