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Cartpole Problem with PDL and GP Using Multi-objective Fitness Functions Differing in a Priori Knowledge 1

14th International Conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2021

作者： Shannon, Peter David Nehaniv, Chrystopher L. Phon-Amnuaisuk, Somnuk Adaptive Systems Research Group Centre for Computer Science & Informatics Research University of Hertfordshire Hatfield United Kingdom Laksmana College of Business Bandar Seri Begawan Brunei Department of Systems Design Engineering University of Waterloo WaterlooON Canada Department of Electrical & Computer Engineering University of Waterloo WaterlooON Canada School of Computing and Informatics Universiti of Teknologi Brunei Bandar Seri Begawan Brunei

ISBN: (数字)9783030802530

ISBN: (纸本)9783030802523

We present a study looking at the effect of a priori domain knowledge on an EA fitness function. Our experiment has two aims: (1) applying an existing NSGA-II framework for GP with PDL to the cartpole problem—applying GP & PDL to cartpole and a purely behavioral problem for the first time—and (2) contrasting two multi-objective fitness functions: one with high and the other with low a priori domain knowledge. In our experiment we created two populations with an EA, varying in the number of objectives use for the fitness function, 2 objective criteria to represent low a priori knowledge and 3 to represent high. With fitness functions tailored to find specifically prescribed solutions we expect greater discriminating power and more feedback to an evolutionary process. This comes at the cost of excluding some unexpected solutions from the evolutionary process and placing a greater burden on the designer. We address the question: how large is the disadvantage for the low a priori fitness function in a worst-case scenario, where innovative solutions will not enhance performance. This question is interesting because we would prefer to guide EA with simple, easy to create and understand, objective criteria rather than complex and highly specific criteria. Understanding any associated penalty for using simple, easy to create fitness functions, is crucial in assessing how much effort and should be put into designing objective criteria. © 2021, Springer Nature Switzerland AG.

关键词： Domain Knowledge

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DRL-based STAR-RIS-Assisted ISAC Secure Communications

DRL-based STAR-RIS-Assisted ISAC Secure Communications

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Ubiquitous Communication (Ucom), International Conference on

作者： Zhengyu Zhu Mengfei Gong Zheng Chu Pei Xiao Gangcan Sun De Mi Ziming He Fei Tong School of Electrical and Information Engineering Zhengzhou University China National Mobile Communications Research Laboratory Southeast University China Institute for Communication Systems University of Surrey UK School of Computing and Digital Technology Birmingham City University UK Samsung Cambridge Solution Centre Samsung Electronics UK

In this paper, we explore a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted integrated sensing and communication (ISAC) secure communication system. The average long-term security rate of the legitimate user (LU) is maximized by jointly designing the receive filters and transmit beamforming of the base station (BS), and the transmitting and reflecting coefficients of STAR-RIS, and in the meantime, guaranteeing the lower bound of echo signal-to-noise ratio (SNR) and the achievable rate of LU constraint. We propose to apply two deep reinforcement learning (DRL) algorithms to solve the complex non-convex problem and maximize the long-term benefits of the system by optimizing the BS beamforming and STAR-RIS phase shifts. The simulation results thoroughly evaluate the performance of two DRL algorithms and demonstrate that STAR-RIS outperforms the conventional reconfigurable intelligent surface (RIS) in comparison with two benchmarks.

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Joint Path planning and Power Allocation of a Cellular-Connected UAV using Apprenticeship Learning via Deep Inverse Reinforcement Learning

arXiv

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arXiv 2023年

作者： Shamsoshoara, Alireza Lotfi, Fatemeh Mousavi, Sajad Afghah, Fatemeh Güvenç, Ismail School of Informatics Computing and Cyber Systems Northern Arizona University FlagstaffAZ United States Department of Electrical and Computer Engineering Clemson University ClemsonSC United States Harvard Medical School BostonMA United States North Carolina State University RaleighNC United States

This paper investigates an interference-aware joint path planning and power allocation mechanism for a cellular-connected unmanned aerial vehicle (UAV) in a sparse suburban environment. The UAV’s goal is to fly from an initial point and reach a destination point by moving along the cells to guarantee the required quality of service (QoS). In particular, the UAV aims to maximize its uplink throughput and minimize the level of interference to the ground user equipment (UEs) connected to the neighbor cellular BSs, considering the shortest path and flight resource limitation. Expert knowledge is used to experience the scenario and define the desired behavior for the sake of the agent (i.e., UAV) training. To solve the problem, an apprenticeship learning method is utilized via inverse reinforcement learning (IRL) based on both Q-learning and deep reinforcement learning (DRL). The performance of this method is compared to learning from a demonstration technique called behavioral cloning (BC) using a supervised learning approach. Simulation and numerical results show that the proposed approach can achieve expert-level performance. We also demonstrate that, unlike the BC technique, the performance of our proposed approach does not degrade in unseen situations. © 2023, CC BY.

关键词： Drones

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STARS-Enabled Full-Duplex Two-Way mMIMO System Under Spatially-Correlated Channels

arXiv

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arXiv 2025年

作者： Papazafeiropoulos, Anastasios Kourtessis, Pandelis Chatzinotas, Symeon Kaklamani, Dimitra I. Venieris, Iakovos S. The Communications and Intelligent Systems Research Group University of Hertfordshire HatfieldAL10 9AB United Kingdom SnT The University of Luxembourg Luxembourg The Microwave and Fiber Optics Laboratory Greece The Intelligent Communications and Broadband Networks Laboratory School of Electrical and Computer Engineering National Technical University of Athens Athens Zografou15780 Greece

Low-cost reconfigurable intelligent surfaces (RISs) are being considered as promising physical-layer technology for next-generation wireless networks due to their ability to re-engineer the propagation environment by tuning their elements. Although RIS-aided systems have many advantages, one of their major bottlenecks is that they can provide coverage only in front of the surface. Fortunately, simultaneous transmitting and reflecting surface (STARS)-assisted systems have emerged to fill this gap by providing 360◦ wireless coverage. In parallel, full-duplex (FD) communication offers a higher achievable rate through efficient spectrum utilization compared to the half-duplex (HD) counterpart. Moreover, two-way/bi-directional communications in an FD system can further enhance the system’s spectral efficiency. Hence, in this paper, we propose a STARS-enabled massive MIMO deployment in an FD two-way communication network for highly efficient spectrum utilization, while covering the dead zones around the STARS. This model enables simultaneous information exchange between multiple nodes, while potentially doubling the spectral efficiency (SE). By invoking the use-and-then-forget (UaTF) combining scheme, we derive a closed-form expression for an achievable SE at each user of the system considering both uplink and downlink communications based on statistical channel state information (CSI), while also accounting for imperfect CSI and correlated fading conditions. Moreover, we formulate an optimization problem to obtain an optimal passive beamforming matrix design at the STARS that maximizes the sum achievable SE. The considered problem is non-convex and we propose a provably-convergent low-complexity algorithm, termed as projected gradient ascent method (ProGrAM), to obtain a stationary solution. Extensive numerical results are provided to establish the performance superiority of the FD STARS-enabled system over the HD STARS-enabled and FD conventional RIS (cRIS)-enabled coun

关键词： Digital television

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Enabling Real-time computing and Transmission Services in Large-Scale LEO Satellite Networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Guo, Binquan Zhang, Zhou Atapattu, Saman Pan, Miao Yan, Ye Xiong, Zehui Li, Hongyan Xidian University State Key Laboratory of Integrated Services Networks Xi'an710071 China Tianjin300457 China Singapore University of Technology and Design Pillar of Information Systems Technology and Design 487372 Singapore Hunan University College of Computer Science and Electronic Engineering Changsha410012 China University School of Engineering MelbourneVIC3000 Australia University of Houston Department of Electrical and Computer Engineering HoustonTX77204 United States Tianjin Artificial Intelligence Innovation Center Tianjin300387 China

Enhanced by onboard computing resources and inter-satellite links (ISLs), large-scale satellite networks (SNs) can supplement terrestrial networks, facilitating real-time edge computing and transmission in remote areas at low latency. This capability benefits applications such as real-time remote object tracking, vehicular edge computing, and environmental monitoring. However, different from terrestrial vehicular networks, satellite mobility and the intermittent nature of ISLs often render many computing satellites inaccessible, necessitating efficient scheduling scheme for joint computing and communication resource allocation. To meet this requirement, we investigate joint computing and transmission strategy for real-time computing applications over SNs to reduce end-to-end delays. We formulate the real-time computing and transmission problem over time-varying SNs as an integer linear programming problem. The mathematical optimization method has an exponential time complexity of O(2|L⫟| . (4 |V⫟| + 4∣ L⫟|)2|L∣) in the worst case, which grows rapidly with the growth of network sizes. Here, |V⫟| and |L⫟| denote the number of nodes and links, respectively, within the time window {\tau }. Resorting to the designed k-shortest path-based method yields a worst-case complexity of O(|V⫟|! . |V⫟|3). While efficient under common scenarios, its running time is still relatively unstable and may become tens of times longer when computing resources become scarce. To enhance efficiency, we further design a graph-based algorithm that leverages the solution space's unique structure, thereby achieving optimal solutions with polynomial time complexity O(3|L⫟| + (2\log |V⫟|+1) |V⫟|). Extensive evaluations conducted on the world's largest Starlink system validate the efficacy of the designed methods and highlight their potential synergy. © 1967-2012 IEEE.

关键词： Satellite links

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Optimal AoI-based Block Propagation and Incentive Mechanism for Blockchain Networks in Web 3.0

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IEEE Transactions on Cognitive Communications and Networking 2024年

作者： Wen, Jinbo Kang, Jiawen Xiong, Zehui Du, Hongyang Yang, Zhaohui Niyato, Dusit Shen, Meng Jiao, Yutao Zhang, Yang Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology China Guangdong University of Technology School of Automation China Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore Hong Kong University Department of Electrical and Electronic Engineering Hong Kong Zhejiang University College of Information Science and Electronic Engineering China Nanyang Technological University College of Computing and Data Science Singapore Beijing Institute of Technology School of Cyberspace Science and Technology China Army Engineering University of PLA College of Communications Engineering China

Web 3.0 is regarded as a revolutionary paradigm that enables users to securely manage data without a centralized authority. Blockchains, which enable data to be managed in a decentralized and transparent manner, are key technologies for achieving Web 3.0 goals. However, blockchain-based Web 3.0 is still in its infancy, such as ensuring block freshness and optimizing block propagation to improve blockchain performance. In this paper, we develop a freshness-aware block propagation optimization framework for Web 3.0. We first propose a novel metric called Age of Block Information (AoBI) based on the concept of age of information to quantify block freshness. AoBI measures the time elapsed from the freshest transaction generation to the completion of block consensus. To make block propagation optimization tractable, we classify miners into five different states and propose a block propagation model for public blockchains inspired by epidemic models. Moreover, considering that the miners are bounded rational, we propose an incentive mechanism based on the evolutionary game for block propagation to improve block propagation efficiency. Numerical results demonstrate that compared with other block propagation mechanisms in public blockchains, the proposed scheme has a higher block forwarding probability, which improves block propagation efficiency and decreases the minimum value of average AoBI. © 2015 IEEE.

关键词： age of information block propagation evolutionary game Web 3.0 wireless networks

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Enabling Protection for Critical Infrastructure through Security and Privacy in the Industrial Internet of Things

Enabling Protection for Critical Infrastructure through Secu...

引用

International Conference on Communication and Electronics systems (ICCES)

作者： B. Yasotha T. Vijayanandh P Thirumoorthy M.Sangeetha Priya Sasidaran S S.B. Prakalya Department of Data Science and Business Systems SRMIST Kattankulathur Department of Computer Science & Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Dhanalakshmi College of Engineering Chennai India Department of Computer Science and Engineering (Cyber Security) Saveetha Engineering College Chennai Department of Information Technology Panimalar Engineering College Chennai Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

Industrial Internet of Things can improve critical infrastructure in energy, transportation, and manufacturing. However, IIoT device and system integration opened security weaknesses that bad actors may exploit, causing huge disruptions and financial losses. IoT network security and privacy must be prioritized to protect key infrastructure. IIoT security issues and solutions are examined in this research. First, we take a look at the state of security and the unique risks that industrial IoT devices are up against, including hacking, illegal access, and cyber-physical assaults. Then, to lessen the impact of these dangers, we investigate cutting-edge security solutions, such as authentication, encryption, and access control systems. Building a robust security architecture specifically for IIoT applications is one of the main goals of the project. To improve safety and anonymity, this system uses cutting-edge innovation like blockchain, AI, and edge computing. We also talk about how privacy-preserving methods like differential privacy and anonymization are important for keeping sensitive information safe while allowing data sharing for analysis and operations. We assess the efficacy of the suggested security protocols and procedures using a battery of case studies and simulations. Reducing the attack surface and improving the resilience of IIoT systems can be achieved by a multi-layered security approach that includes effective privacy protections. We conclude by outlining our research's larger implications, which include the importance of standardization, regulatory compliance, and stakeholder collaboration in ensuring the safe and privacy-protecting deployment of IIoT technologies in vital infrastructure. Protecting vital infrastructure from cyber threats is our ultimate goal, and our work helps build IIoT systems that are more secure and dependable.

关键词： Privacy Collaboration Standardization Critical infrastructure Encryption Security Stakeholders Protection computer crime Industrial Internet of Things

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Differentially Private Energy Sharing Among Smart Grid-Powered Base Stations

Differentially Private Energy Sharing Among Smart Grid-Power...

引用

IEEE Conference on Vehicular Technology (VTC)

作者： Liwan Qi Bochun Wu Fei Wang Kai Li Wei Ni Abbas Jamalipour State Key Laboratory of Integrated Chips and Systems Fudan University Shanghai China College of Physics and Electromechanical Engineering Hexi University Zhangye China University of Cambridge Cambridge U.K. Real-Time and Embedded Computing Systems Research Centre (CISTER) Porto Portugal Commonwealth Scientific and Industrial Research Organisation (CSIRO) Sydney NSW Australia School of Electrical and Information Engineering University of Sydney NSW Australia

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

Allowing for energy sharing among base stations (BSs), we investigate a distributed BS system equipped with renewable power units and energy storage batteries. While offering significant benefits, this raises concerns about privacy protection. By leveraging the Laplace mechanism, our differential privacy (DP)-based method safeguards energy consumption data related to processing data tasks from different BSs. To address a long-term average cost minimization problem, we propose a distributed online algorithm for efficient energy sharing. We demonstrate that the boundary constraints of energy storage batteries can still be satisfied by choosing parameters appropriately. We provide a theoretical bound for the optimality gap and validate the effectiveness of our theoretical results. Numerical results indicate our algorithm can potentially reduce the average costs of BSs by up to 34%.

关键词： Renewable energy sources Privacy Energy consumption Base stations Vehicular and wireless technologies Costs Batteries Smart grids Protection Optimization

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Optimizing Transmission Infrastructure Investments to Support Line De-energization for Mitigating Wildfire Ignition Risk

arXiv

引用

arXiv 2022年

作者： Kody, Alyssa Piansky, Ryan Molzahn, Daniel K. Energy Systems Division Argonne National Laboratory LemontIL United States School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States

Wildfires pose a growing risk to public safety in regions like the western United States, and, historically, electric power systems have ignited some of the most destructive wildfires. To reduce wildfire ignition risks, power system operators preemptively de-energize high-risk power lines during extreme wildfire conditions as part of "Public Safety Power Shutoff" (PSPS) events. While capable of substantially reducing acute wildfire risks, PSPS events can also result in significant amounts of load shedding as the partially de-energized system may not be able to supply all customer demands. In this work, we investigate the extent to which infrastructure investments can support system operations during PSPS events by enabling reduced load shedding and wildfire ignition risk. We consider the installation of grid-scale batteries, solar PV, and line hardening or maintenance measures (e.g., undergrounding or increased vegetation management). Optimally selecting the locations, types, and sizes of these infrastructure investments requires considering the line de-energizations associated with PSPS events. Accordingly, this paper proposes a multi-period optimization formulation that locates and sizes infrastructure investments while simultaneously choosing line de-energizations to minimize wildfire ignition risk and load shedding. The proposed formulation is evaluated using two geolocated test cases along with realistic infrastructure investment parameters and actual wildfire risk data from the United States Geological Survey. We evaluate the performance of investment choices by simulating de-energization decisions for the entire 2021 wildfire season with optimized infrastructure placements. With investment decisions varying significantly for different test cases, budgets, and operator priorities, the numerical results demonstrate the proposed formulation's value in tailoring investment choices to different settings. Copyright © 2022, The Authors. All rights reserved.

关键词： Investments

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Dynamic AI-Driven Network Slicing with O-RAN for Continuous Connectivity in Connected Vehicles and Onboard Consumer Electronics

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IEEE Transactions on Consumer Electronics 2025年

作者： Shah, Syed Danial Ali Bashir, Ali Kashif Al-Otaibi, Yasser D. Dabel, Maryam M. Al Ali, Farman University of Leeds School of Electronic and Electrical Engineering LeedsLS2 9JT United Kingdom Manchester Metropolitan University Department of Computing and Mathematics M15 6BH United Kingdom Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Punjab Rajpura140401 India King Abdulaziz University Faculty of Computing and Information Technology in Rabigh Department of Information Systems Jeddah21589 Saudi Arabia University of Hafr Al Department of Computer Science and Engineering Batin Saudi Arabia Sungkyunkwan University School of Convergence Seoul Korea Republic of

The rise of connected and autonomous vehicles signifies an era of intelligent transportation systems, where robust and continued network connectivity is essential for critical applications and enhanced in-vehicle Consumer Electronics (CE) experiences. Slicing at the network's edge offers tailored and dedicated logical networks for diverse and low-latency vehicular demands, including Advanced Driver Assistance systems (ADAS) and in-car infotainment. However, seamless migration of network slices as vehicles traverse coverage areas of different network operators presents formidable challenges, such as ensuring continuous connectivity and uninterrupted service for both safety-critical systems and consumer-oriented services. In this paper, we introduced dynamic network slicing for continuous connectivity in connected vehicles and onboard CE using the Open Radio Access Network (O-RAN) framework in a highly dynamic and mobile environment. We implemented an xAPP within O-RAN that enables Deep Reinforcement Learning (DRL) agent to learn optimal policies through interaction with the network, guiding intelligent decisions on slice migration, resource allocation, and handover optimization. We conducted simulations and evaluations to demonstrate the effectiveness of the proposed xAPP in maintaining optimal Quality of Service (QoS), ensuring efficient RAN resource utilization, minimizing service interruptions, and prioritizing safety-critical slices, all while supporting seamless operation of CE within vehicles during mobility. © 1975-2011 IEEE.

关键词： Advanced driver assistance systems

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