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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Bhushan Shaharkar Tanusha Mittal Krishna Chaitanya Sunkara Vishal Shukla Ishan Desai Ujal Mookherjee Department of Information Technology Walchand Institute of Technology Solapur India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Data Center Design & Modeling AL/ML Expert Oracle Cloud Engineering Raleigh USA Department of Mechanical Engineering Ramdeobaba University Nagpur India Department of Electrical Engineering Faculty of Engineering and Technology Parul institute of Engineering and Technology Parul University Post Limda India Faculty of Law Symbiosis Law School Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

The rise of Internet of Things (IoT) networks has introduced new opportunities for innovation but also increased vulnerability to cyberattacks, particularly Distributed Denial-of-Service (DDoS) attacks. Traditional detection and mitigation mechanisms often struggle to handle the dynamic and distributed nature of IoT systems. In this study, we propose a Multi-Agent Deep Reinforcement Learning (MADRL) framework designed to detect and mitigate DDoS attacks in IoT networks in real-time. The framework consists of multiple collaborative agents, each deployed at different network layers or nodes, trained using deep reinforcement learning to identify attack patterns and take immediate mitigation actions. Through cooperative learning, these agents adapt to evolving attack strategies and optimize defense policies. Experimental evaluations demonstrate that the proposed MADRL framework outperforms traditional techniques by achieving higher detection accuracy, faster response times, and reduced false positive rates. Our results indicate that this framework can effectively secure IoT networks against complex DDoS attacks, maintaining network stability and availability.

关键词： Wireless communication Technological innovation Accuracy Prevention and mitigation Collaboration Denial-of-service attack Deep reinforcement learning Stability analysis Time factors Internet of Things

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Cooperative Urban Air Mobility Trajectory Design for Power and AoI Optimization: A Multi-agent Reinforcement Learning Approach

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IEEE Transactions on Vehicular Technology 2025年

作者： Kim, Hyeonsu Aung, Pyae Sone Munir, Md. Shirajum Saad, Walid Hong, Choong Seon Kyung Hee University Department of Artificial Intelligence Gyeonggi-do Yongin-si17104 Korea Republic of Kyung Hee University Department of Computer Science and Engineering Gyeonggi-do Yongin-si17104 Korea Republic of University of West Georgia School of Computing Analytics and Modeling CarrolltonGA30118 United States Virginia Tech Bradley Department of Electrical and Computer Engineering VA24061 United States

Urban air mobility (UAM) is a revolutionary urban transportation paradigm that aims to transport passengers, emphasizing safety, power efficiency, and autonomous operation. Also, UAM aircraft needs to regularly transmit status and environmental conditions to the base stations (BS) to maintain updated information in the system. Hence, the age of information (AoI) can be used as a status measurement indicator to evaluate the degree to which the current status information of UAM aircraft is outdated. Optimizing each UAM aircraft's flight trajectory for both power efficiency and safety, while also minimizing the AoI for timely data transmission forms a computationally challenging NP-hard problem. In this paper, a multi-agent deep reinforcement learning based solution is proposed to address this problem by exploiting a sophisticated method to handle multidimensional decision spaces for power efficiency, safety in trajectory planning, and AoI. Simulation results demonstrate that our method produce a power efficiency improvement of 8.8% compared to the PPO method and 13.8% compare to the detour method. Furthermore, the proposed approach significantly improve the data freshness by up to 31.5%, 31.7%, and 12.9% in terms of AoI compared to the greedy, detour, and single PPO methods, respectively. © 1967-2012 IEEE.

关键词： Urban transportation

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Dynamic Neural Potential Field: Online Trajectory Optimization in Presence of Moving Obstacles

arXiv

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arXiv 2024年

作者： Staroverov, Aleksey Alhaddad, Muhammad Narendra, Aditya Mironov, Konstantin Panov, Aleksandr The Artificial Intelligence Research Institute Moscow105064 Russia Federal Research Center "Computer Science and Control Moscow117312 Russia Center of Cognitive Modeling Moscow Institute of Physics and Technology Dolgoprudny141701 Russia Faculty of Electrical and Electronic Engineering University of Aleppo Syria Ufa University of Science and Technology Ufa450000 Russia

We address a task of local trajectory planning for the mobile robot in the presence of static and dynamic obstacles. Local trajectory is obtained as a numerical solution of the Model Predictive Control (MPC) problem. Collision avoidance may be provided by adding repulsive potential of the obstacles to the cost function of MPC. We develop an approach, where repulsive potential is estimated by the neural model. We propose and explore three possible strategies of handling dynamic obstacles. First, environment with dynamic obstacles is considered as a sequence of static environments. Second, the neural model predict a sequence of repulsive potential at once. Third, the neural model predict future repulsive potential step by step in autoregressive mode. We implement these strategies and compare it with CIAO* and MPPI using BenchMR framework. First two strategies showed higher performance than CIAO* and MPPI while preserving safety constraints. The third strategy was a bit slower, however it still satisfy time limits. We deploy our approach on Husky UGV mobile platform, which move through the office corridors under proposed MPC local trajectory planner. The code and trained models are available at https://***/CognitiveAISystems/ Dynamic-Neural-Potential-Field. © 2024, CC BY.

关键词： Cost functions

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Neuro-symbolic Explainable Artificial Intelligence Twin for Zero-touch IoE in Wireless Network

arXiv

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arXiv 2022年

作者： Munir, Shirajum Kim, Ki Tae Adhikary, Apurba Saad, Walid Shetty, Sachin Park, Seong-Bae Hong, Choong Seon Virginia Modeling Analysis and Simulation Center Department of Computational Modeling and Simulation Engineering Old Dominion University SuffolkVA23435 United States Department of Computer Science and Engineering Kyung Hee University Yongin-si17104 Korea Republic of Department of Computer Science and Engineering School of Computing Kyung Hee University Kyung Hee University Yongin-si17104 Korea Republic of Wireless@VT Group Bradley Department of Electrical and Computer Engineering Virginia Tech ArlingtonVA22203 United States

Explainable artificial intelligence (XAI) twin systems will be a fundamental enabler of zero-touch network and service management (ZSM) for sixth-generation (6G) wireless networks. A reliable XAI twin system for ZSM requires two composites: an extreme analytical ability for discretizing the physical behavior of the Internet of Everything (IoE) and rigorous methods for characterizing the reasoning of such behavior. In this paper, a novel neuro-symbolic explainable artificial intelligence twin framework is proposed to enable trustworthy ZSM for a wireless IoE. The physical space of the XAI twin executes a neural-network driven multivariate regression to capture the time-dependent wireless IoE environment while determining unconscious decisions of IoE service aggregation. Subsequently, the virtual space of the XAI twin constructs a directed acyclic graph (DAG)-based Bayesian network that can infer a symbolic reasoning score over unconscious decisions through a first-order probabilistic language model. Furthermore, a Bayesian multi-arm bandits-based learning problem is proposed for reducing the gap between the expected explained score and current obtained score of the proposed neuro-symbolic XAI twin. To address the challenges of extensible, modular, and stateless management functions in ZSM, the proposed neuro-symbolic XAI twin framework consists of two learning systems: 1) implicit learner that acts as an unconscious learner in physical space, and 2) explicit leaner that can exploit symbolic reasoning based on implicit learner decisions and prior evidence. Experimental results show that the proposed neuro-symbolic XAI twin can achieve around 96.26% accuracy while guaranteeing from 18% to 44% more trust score in terms of reasoning and closed-loop automation. © 2022, CC BY-NC-ND.

关键词： Internet of Everything

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Evaluation of the efficiency of the use of electricity storage systems in the balancing group and the small distribution system 2

Evaluation of the efficiency of the use of electricity stora...

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2nd IEEE KhPI Week on Advanced Technology, KhPI Week 2021

作者： Blinov, Ihor Trach, Ihor Parus, Yevhen Khomenko, Viktor Kuchanskyy, Vladislav Shkarupylo, Vadym Institute of Electrodynamics of NAS of Ukraine Department of Electrical Energy Objects and Systems Modeling Kyiv Ukraine VAR Energy Project Department Kyiv Ukraine Institute of Electrodynamics of NAS of Ukraine Department of Optimization Electrical Power Systems Kiev Ukraine Institute for Modelling in Energy Engineering of NAS of Ukraine Department of Mathematical and Computer Modelling Kiev Ukraine

ISBN: (纸本)9781665412056

Mathematical models for evaluating the efficiency of the energy storage system (ESSs) use in regulating the modes of a developing small distribution system are proposed. An analysis of the operation of a small distribution system (SDSs) including a formed group of four ESSs in the case of increasing the SDS load is carried out. A standard test IEEE 33-node 12.6 kV network has been used as a base model. The development of an electrical network is modeled by a 1.4-fold increase in the load in periods of maximum loads compared to the rated load capacity. A target cost function has been formed based on the cost of purchasing electricity by the balancing group and investment costs for the installation of the ESSs, provided that the argument is the capacities of the ESSs and an electricity storage period. The optimization is performed for a given real daily electricity price schedule and a daily load capacity schedule. The rated lower and upper voltage values in the network nodes are defined as constraints. © 2021 IEEE.

关键词： Cost functions

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MCMC calibration of spot-prices models in electricity markets

MCMC calibration of spot-prices models in electricity market...

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作者： Guerini, Alice Marziali, Andrea De Nicolao, Giuseppe Modeling and Pricing A2A S.p.A. Milano Italy Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy

The calibration of some stochastic differential equation used to model spot prices in electricity markets is investigated. As an alternative to relying on standard likelihood maximization, the adoption of a fully Bayesian paradigm is explored, that relies on Markov chain Monte Carlo (MCMC) stochastic simulation and provides the posterior distributions of the model parameters. The proposed method is applied to one- and two-factor stochastic models, using both simulated and real data. The results demonstrate good agreement between the maximum likelihood and MCMC point estimates. The latter approach, however, provides a more complete characterization of the model uncertainty, an information that can be exploited to obtain a more realistic assessment of the forecasting error. In order to further validate the MCMC approach, the posterior distribution of the Italian electricity price volatility is explored for different maturities and compared with the corresponding maximum likelihood estimates. © 2019 John Wiley & Sons, Ltd.

关键词： Power markets

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A Multi-Carrier Energy Method for Self-Consumption Enhancement in Smart Residential Buildings 11

A Multi-Carrier Energy Method for Self-Consumption Enhanceme...

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11th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2022

作者： Zhanbolatov, Arshyn Zhakiyeva, Svetlana Azibek, Balzhan Zhakiyev, Nurkhat Kayisli, Korhan Tursyngul, Dushayeva Astana It University Dept. of Science and Innovation Astana Kazakhstan L.N. Gumilyov Eurasian National University Mathematical and Computer Modeling Astana Kazakhstan Astana It University Department of Intellectual Systems and Cybersecurity Astana Kazakhstan Gazi University Electrical-Electronic Engineering Ankara Turkey Zhangir Khan West Kazakhstan Agrarian Technical University Dept. of Information Technology Uralsk Kazakhstan

ISBN: (数字)9781665471404

ISBN: (纸本)9781665471404

For the most time nowadays people stay indoors, at their homes, in buildings, which leads to increased energy demand and resource consumption in the residential sector. In this context, PV deployment has gained traction among residences as there is no cost for the energy and no emissions when the energy is produced. With the growing deployment of PV, the cost per PV panel has comparably decreased and the feed-in tariffs are starting to be gradually lowered and, in some cases, eliminated entirely. This created a new market incentive, known as self-consumption. The problem with self-consumption is a misalignment between supply and demand scheduling due to the intermittent nature of renewable energy sources. The goal of this paper is to apply a comprehensive multi-carrier energy approach for numerical assessment of PV self-consumption enhancement ways in residential buildings. The obtained results can be used for different cases of renewable integration for Smart Homes. © 2022 IEEE.

关键词： Renewable energy resources

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Spiking neural network for nonlinear regression

arXiv

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arXiv 2022年

作者： Henkes, Alexander Eshraghian, Jason K. Wessels, Henning Institute for Computational Modeling in Civil Engineering Technical University of Braunschweig Pockelsstr. 3 Braunschweig38106 Germany Department of Electrical and Computer Engineering University of California Santa Cruz 1156 High street Santa Cruz95064 United States

Spiking neural networks, also often referred to as the third generation of neural networks, carry the potential for a massive reduction in memory and energy consumption over traditional, second-generation neural networks. Inspired by the undisputed efficiency of the human brain, they introduce temporal and neuronal sparsity, which can be exploited by next-generation neuromorphic hardware. To open the pathway toward engineering applications, we introduce this exciting technology in the context of continuum mechanics. However, the nature of spiking neural networks poses a challenge for regression problems, which frequently arise in the modeling of engineering sciences. To overcome this problem, a framework for regression using spiking neural networks is proposed. In particular, a network topology for decoding binary spike trains to real numbers is introduced, utilizing the membrane potential of spiking neurons. As the aim of this contribution is a concise introduction to this new methodology, several different spiking neural architectures, ranging from simple spiking feedforward to complex spiking long short-term memory neural networks, are derived. Several numerical experiments directed towards regression of linear and nonlinear, history-dependent material models are carried out. A direct comparison with counterparts of traditional neural networks shows that the proposed framework is much more efficient while retaining precision and generalizability. All code has been made publicly available in the interest of reproducibility and to promote continued enhancement in this new domain. © 2022, CC BY.

关键词： Continuum mechanics

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Correction: LPM-Net: A Data-Driven Resource-Efficient Predictive Motion Planner for Mobile Robots

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Neural Processing Letters 2025年第1期57卷 1-1页

作者： Amirhosseini, Fakhreddin Nilforoushan, Zahra Mirtaheri, Seyedeh Leili Electrical and Computer Engineering Department Kharazmi University Tehran Iran Department of Informatics Modeling Electronics and System Engineering (DIMES) University of Calabria Arcavacata Italy

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Semi-Quantitative Group Testing for Efficient and Accurate qPCR Screening of Pathogens with a Wide Range of Loads

arXiv

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arXiv 2023年

作者： Nambiar, Ananthan Pan, Chao Rana, Vishal Cheraghchi, Mahdi Ribeiro, João Maslov, Sergei Milenkovic, Olgica Department of Bioengineering University of Illinois Urbana-Champaign UrbanaIL United States Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign UrbanaIL United States Center for Artificial Intelligence and Modeling Carl R. Woese Institute for Genomic Biology University of Illinois Urbana-Champaign UrbanaIL United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States NOVA LINCS and NOVA School of Science and Technology Caparica Portugal

Pathogenic infections pose a significant threat to global health, affecting millions of people every year and presenting substantial challenges to healthcare systems worldwide. Efficient and timely testing plays a critical role in disease control and transmission prevention. Group testing is a well-established method for reducing the number of tests needed to screen large populations when the disease prevalence is low. However, it does not fully utilize the quantitative information provided by qPCR methods, nor is it able to accommodate a wide range of pathogen loads. To address these issues, we introduce a novel adaptive semi-quantitative group testing (SQGT) scheme to efficiently screen populations via two-stage qPCR testing. The SQGT method quantizes cycle threshold (Ct) values into multiple bins, leveraging the information from the first stage of screening to improve the detection sensitivity. Dynamic Ct threshold adjustments mitigate dilution effects and enhance test accuracy. Comparisons with traditional binary outcome GT methods show that SQGT reduces the number of tests by 24% while maintaining a negligible false negative rate. © 2023, CC BY.

关键词： Disease control

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