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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Thangavel, Gunasekaran Malarvizhi, C. Banu, M. Ameena Anthonisamy, Arun Hemalatha, M. Manikandan, Raja University of Technology and Applied Sciences Department of Electronics and Communication Engineering Muscat Oman Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Chembarambakkam India NPR College of Engineering and Technology Department of Electronics and Communication Engineering Natham India Panimalar Engineering College Department of Computer Science and Business System Tamil Nadu Chennai600123 India Nandha Arts and Science College Department of Information Technology Tamil Nadu Erode638052 India K. Ramakrishnan College of Technology Department of ECE Tamil Nadu Trichy India

ISBN: (纸本)9798331534950

Earlier generations of mobile technology experienced exponential growth in mobile customers, disruptive ecosystems like Internet of Things applications, and massive channel capacity needed to connect trillions of devices. 5G networks are a vital solution. 5G aims to provide 10 Gbps to a single user, as well as quality of service, increased spectrum efficiency, ultra-reliable communication, extended device battery life, massive machine-type communication (mMTC), and a 1000-fold capacity gain. Due to the heavy traffic volume and high resource consumption of 5G applications like augmented reality (AR), virtual reality (VR), and wireless body area networks, there are major barriers to channel capacity and potential service disruptions for other users. New strategies are needed to increase spectral efficiency and channel capacity. This paper explores many major new wireless technologies that aim to increase spectral efficiency and channel capacity to meet growing user demands and improve network performance. This paper also examines these technologies' benefits and research challenges. Massive Multi-Input Multi-Output (mMIMO) technology's effectiveness and potential distinguish it for 5G and Beyond 5G (B5G) networks, offering a variety of benefits and characteristics. This article can also assist researchers who are conducting in-depth studies, as well as wireless network operators who aim to streamline the development of 5G and B5G networks. © 2024 IEEE.

关键词： 5G mobile communication systems

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Metric ideals and its Structures in AL-monoids

arXiv

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arXiv 2024年

作者： Regasa Ashale, Tekalign Aklilu Abebe, Girum Venkateswarlu, Kolluru Department of Mathematics College of Natural and Computational Science College Addis Ababa University Addis Ababa Ethiopia Department of Computer Science and System Engineering College of Engineering Andhra University AP Visakhapatanam India

This paper introduces the concept of metric ideals in AL-monoids. We also examine the structure of AL-monoids and describe some of the properties of homomorphism and fundamentalisomorphism *** we introduce and examine... 详细信息

关键词： Algebra

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Research on Autonomous Driving Decision-making Strategies based Deep Reinforcement Learning

arXiv

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arXiv 2024年

作者： Wang, Zixiang Yan, Hao Wei, Changsong Wang, Junyu Xiao, Minheng College of Engineering and Computer Science Syracuse University SyracuseNY United States College of Engineering and Computer Science Syracuse Sichuan Chengdu China UCLA Computer Science Department University of California Los AngelesCA United States Department of Integrated System Engineering Ohio State University ColumbusOH United States

The behavior decision-making subsystem is a key component of the autonomous driving system, which reflects the decision-making ability of the vehicle and the driver, and is an important symbol of the high-level intelligence of the vehicle. However, the existing rule-based decision-making schemes are limited by the prior knowledge of designers, and it is difficult to cope with complex and changeable traffic scenarios. In this work, an advanced deep reinforcement learning model is adopted, which can autonomously learn and optimize driving strategies in a complex and changeable traffic environment by modeling the driving decision-making process as a reinforcement learning problem. Specifically, we used Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) for comparative experiments. DQN guides the agent to choose the best action by approximating the state-action value function, while PPO improves the decision-making quality by optimizing the policy function. We also introduce improvements in the design of the reward function to promote the robustness and adaptability of the model in real-world driving situations. Experimental results show that the decision-making strategy based on deep reinforcement learning has better performance than the traditional rule-based method in a variety of driving tasks. © 2024, CC BY.

关键词： Deep reinforcement learning

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Optimal energy efficient path planning of UAV using hybrid MACO-MEA* algorithm: theoretical and experimental approach

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Journal of Ambient Intelligence and Humanized Computing 2023年第10期14卷 13847-13867页

作者： Balasubramanian, E. Elangovan, E. Tamilarasan, P. Kanagachidambaresan, G.R. Chutia, Dibyajyoti Department of Mechanical Engineering Head-Centre for Autonomous System Research Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology Avadi Tamilnadu Chennai600062 India Department of Electronics and Communication Engineering Centre for Autonomous System Research Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology Avadi Tamilnadu Chennai600062 India Department of Aeronautical Engineering Centre for Autonomous System Research Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology Avadi Tamilnadu Chennai600062 India Department of Computer Science and Engineering and Associate Editor Wireless Networks Springer Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology Avadi Tamilnadu Chennai600062 India Department of Space North Eastern Space Applications Centre ISRO Meghalaya Umiam793103 India

Autonomous mission capabilities with optimal path are stringent requirements for Unmanned Aerial Vehicle (UAV) navigation in diverse applications. The proposed research framework is to identify an energy-efficient optimal path to achieve the designated missions for the navigation of UAVs in various constrained and denser obstacle prone regions. Hence, the present work is aimed to develop an optimal energy-efficient path planning algorithm through combining well known modified ant colony optimization algorithm (MACO) and a variant of A*, namely the memory-efficient A* algorithm (MEA*) for avoiding the obstacles in three dimensional (3D) environment and arrive at an optimal path with minimal energy consumption. The novelty of the proposed method relies on integrating the above two efficient algorithms to optimize the UAV path planning task. The basic design of this study is, that by utilizing an improved version of the pheromone strategy in MACO, the local trap and premature convergence are minimized, and also an optimal path is found by means of reward and penalty mechanism. The sole notion of integrating the MEA* algorithm arises from the fact that it is essential to overcome the stringent memory requirement of conventional A* algorithm and to resolve the issue of tracking only the edges of the grids. Combining the competencies of MACO and MEA*, a hybrid algorithm is proposed to avoid obstacles and find an efficient path. Simulation studies are performed by varying the number of obstacles in a 3D domain. The real-time flight trials are conducted experimentally using a UAV by implementing the attained optimal path. A comparison of the total energy consumption of UAV with theoretical analysis is accomplished. The significant finding of this study is that, the MACO-MEA* algorithm achieved 21% less energy consumption and 55% shorter execution time than the MACO-A*. moreover, the path traversed in both simulation and experimental methods is 99% coherent with each other.

关键词： Energy utilization

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Occlusion-Aware Secure Device-to-Device Communication in Social Internet of Things networks

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IEEE Transactions on Consumer Electronics 2025年

作者： Chandra, Saurabh Prateek Arya, Rajeev Sharma, Rohit Almansour, Shahad Gandor, Michal National Institute of Technology Patna Wireless Sensor Networks Lab Department of Electronics and Communication Engineering Bihar Patna800005 India ABES Engineering College Department of Electronics and Communication Engineering UP Ghaziabad India University of Hail Applied College Saudi Arabia Cracow University of Technology Faculty of Computer Science and Telecommunications Department of Computer Science Warszawska Krakow Poland

The Social Internet of Things (SIoT) framework facilitates the development of various consumer electronic devices such as smartphones, smart wearables, and smart wireless devices, enabling social relationships between electronic devices and humans. Human-centric intelligent Device-to-Device (D2D)-enabled SIoT networks provide essential services like traffic offloading and secure data rate, but face challenges from urban interference caused by obstacles;to mitigate this, we propose a user-driven approach that prioritizes social relationships to enhance network performance. We address occlusion challenges in urban areas by formulating a problem aimed at maximizing throughput and secrecy rate while minimizing power consumption, employing an intelligent resource allocation and spline-based power transmission scheme for efficient utilization. Field tests using Universal Software Radio Peripheral (USRP) and LabVIEW have been performed to validate the proposed method. The results show a throughput improvement of 10.93%, and a 27.86% enhancement in secrecy rate compared to other benchmark schemes. The comprehensive adoption of this algorithm can enable secure D2D communication for consumer electronics products driven by social IoT networks. © 1975-2011 IEEE.

关键词： Radio equipment

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Uplink Control Information Based Clustered Access and Discontinuous Reception Management for mMTC

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IEEE Communications Letters 2025年第6期29卷 1380-1384页

作者： Wang, Shao-Di Wang, Chang-Long Wang, Hui-Ming Zhou, Feng Leung, Victor C. M. Xidian University Key Laboratory of Electronic Information Countermeasure and Simulation Technology of Ministry of Education Xi’an710071 China Xi’an Jiaotong University School of Information and Communication Engineering Ministry of Education Key Lab for Intelligent Networks and Network Security Xi’an710049 China Shenzhen MSU BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Uplink control information (UCI) and discontinuous reception (DRX) play important roles for massive machine type communication (mMTC). Despite their standalone significance, a conspicuous gap exists in comprehensively exploring the synergy between UCI and DRX concerning both energy conservation and latency reduction for mMTC. In this letter, we propose a UCI-based clustered access and DRX management (UCADM) scheme that strikes a balance between these two critical performance metrics, reducing energy usage while preserving low latency in uplink-downlink interaction for mMTC. Specifically, we present a strictly feasible solution for the proposed UCADM scheme by using the spectral radius and the constraint projection, which can effectively solve DRX switching operations as indicated by scheduling information from UCI. Simulation results confirm the effectiveness of our scheme. © 1997-2012 IEEE.

关键词： Information management

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Blockchain Interoperability for A Reputation-Based Drug Supply Chain Management

Blockchain Interoperability for A Reputation-Based Drug Supp...

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International Conference on Information systems and computer networks (ISCON)

作者： Deluwar Hussen Tanvir Ruhul Amin Ashraful Islam Mohammad Shamsul Islam Muhammed Mamunur Rashid Department of Computer Science and Engineering Sylhet Engineering College Sylhet Bangladesh Department of Computer Science and Engineering Faridpur Engineering College Faridpur Bangladesh System Analyst Investment Corporation of Bangladesh Dhaka Bangladesh

The pharmaceutical drug industry faces considerable challenges in supply chain management, particularly in regard to tracking and tracing medications. Counterfeit drugs pose a significant global threat, and existing supply chain management practices often prove insufficient to prevent adulteration. Blockchain technology offers a potential solution to these issues by establishing a decentralized and distributed ecosystem where stakeholders can foster a trustworthy relationship. To address these limitations, we propose a blockchain-based system for tracking drugs in the supply chain, incorporating a reputation mechanism to ensure transparency and accountability among entities. To mitigate the interoperability challenges between blockchain networks, we implement inter-blockchain communication using the burn-to-claim protocol. Hyperledger Fabric is used as the primary blockchain because it ensures confidentiality between multiple organizations, and Quorum is the other blockchain. Our solution prioritizes trust, traceability, transparency, and accountability to deliver safe, authentic medication to consumers.

关键词： Drugs Industries Supply chain management Protocols Supply chains Organizations Blockchains

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Blockchain Driven Metaheuristic Route Planning in Secure Vehicular Adhoc networks

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computers, Materials & Continua 2022年第12期73卷 6461-6477页

作者： Siwar Ben Haj Hassine Saud SAlotaibi Hadeel Alsolai Reem Alshahrani Lilia Kechiche Mrim M.Alnfiai Amira Sayed A.Aziz Manar Ahmed Hamza Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Sciences and Technology Taif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Nowadays,vehicular ad hoc networks(VANET)turn out to be a core portion of intelligent transportation systems(ITSs),that mainly focus on achieving continual Internet connectivity amongst vehicles on the *** VANET was utilized to enhance driving safety and build an ITS in modern *** safety is a main portion of VANET,the privacy and security of these messages should be *** this aspect,this article presents a blockchain with sunflower optimization enabled route planning scheme(BCSFO-RPS)for secure *** presented BCSFO-RPSmodel focuses on the identification of routes in such a way that vehicular communication is *** addition,the BCSFO-RPS model employs SFO algorithm with a fitness function for effectual identification of ***,the proposed BCSFO-RPS model derives an intrusion detection system(IDS)encompassing two processes namely feature selection and *** detect intrusions,correlation based feature selection(CFS)and kernel extreme machine learning(KELM)classifier is *** performance of the BCSFO-RPS model is tested using a series of experiments and the results reported the enhancements of the BCSFO-RPS model over other approaches with maximum accuracy of 98.70%.

关键词： VANET sunflower optimization machine learning blockchain intrusion detection

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Deep Complex-valued Convolutional Learning for Waveform OFDM Receiver Design

Deep Complex-valued Convolutional Learning for Waveform OFDM...

引用

IEEE Conference on Wireless Communications and Networking

作者： Jiequ Ji Nam Phuong Tran Zehui Xiong Kun Zhu Tony Q. S. Quek Department of Information System and Design Singapore University of Technology Design Singapore College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Orthogonal frequency division multiplexing (OFD-M) has been widely used in modern communication networks. Notice that OFDM typically relies on (inverse) Discrete Fourier Transform (DFT/IDFT) for processing its waveforms. In this context, we propose a deep learning-based OFDM receiver that uses a deep complex-valued convolutional neural network (DC-CNN) to recover the information bit stream from synchronized time-domain signals without relying on DFT/IDFT. Specifically, a learned linear transform is designed to utilize the cyclic prefix (CP) of OFDM waveforms instead of DFT/IDFT, which presents the ability of DCCNN for complex communication waveforms. To improve the convergence of the training model for the DCCNN-based receiver, a novel transfer learning scheme is developed to train channel equalization and demodulation in two phases. In addition, both the DCCNN equalizer and DCCNN demodulator are trained and tested at different SNRs for Rayleigh fading and noise, and a mixed multiple fading channel model with various delay spreads is utilized to smooth the training loss. Simulation results suggest that our developed DCCNN channel estimator outperforms conventional estimators such as least square (LS), linear minimum mean square error (LMMSE) and low-rank approximation of LMMSE (ALMMSE) in multipath Rayleigh fading models with varying Doppler spreads and delay spreads.

关键词： Training OFDM Transfer learning Channel estimation Receivers Rayleigh channels Delays Synchronization Time-domain analysis Demodulation

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HRSE: Heterogeneous Reliable-Aware SFC Embedding by DRL with Greedy Rules in SAGIN

HRSE: Heterogeneous Reliable-Aware SFC Embedding by DRL with...

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International Conference on computer Supported Cooperative Work in Design

作者： Kaixin Qin Songtao Guo Guiyan Liu Yue Zeng College of Computer Science Chongqing University Key Laboratory of Embedded System and Service Computing (Tongji University) Ministry of Education Shanghai Department of Computer Science and Technology The State Key Laboratory for Novel Software Technology Nanjing University

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Ensuring the quality of service (QoS) in the Space-Air-Ground Integrated Network (SAGIN) requires implementing appropriate service function chain (SFC) embedding strategies to provide reliable general services and AI services. However, the complicated topology, limited infrastructure resources, and the heterogeneity in hardware and software in SAGIN hinder existing reliable embedding solutions from delivering high QoS. To address the challenges, this paper takes into account the heterogeneous reliable-aware SFC embedding (HRSE) problem in SAGIN. Specifically, we formulate the HRSE problem as a nonlinear integer programming problem that is NP-hard. Subsequently, an algorithm called HRSFCEA, which consists of deep reinforcement learning (DRL) and greedy rules, is proposed to tackle the challenges. Initially, leveraging the autonomous decision-making capability of DRL, SFC embedding is executed to select backups efficiently. The proposed algorithm reduces frequent trial and error by modifying its actions. Next, the paper gives the algorithm analysis including the complexity and unreliable environment. Finally, simulation results based on real-world datasets demonstrate that the DRL scheme converges to high rewards with minimal differences across varying numbers of hidden layer neurons. The proposed algorithm can improve the acceptance rate by up to 82% while incurring no more than 5% additional cost compared to the state-of-art algorithms.

关键词： Service function chaining computer network reliability Software algorithms Quality of service Space-air-ground integrated networks Reliability engineering Deep reinforcement learning Software Software reliability Topology

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