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Attention-Enhanced Prioritized Proxim.l Policy Optim.zation for Adaptive Edge Caching

IEEE Transactions on Vehicular technology 2025年

作者： Niknia, Farnaz Wang, Ping Wang, Zixu Agarwal, Aakash Rezaei, Adib S. York University Lassonde School of Engineering TorontoONM3J 1P3 Canada The Hong Kong University of Science and Technology School of Electronic and Computer Engineering 999077 Hong Kong University of Pennsylvania School of Engineering and Applied Science PhiladelphiaPA19104 United States Virginia Tech Department of Computer Science BlacksburgVA24061 United States

This paper tackles the growing issue of excessive data transm.ssion in networks. With increasing traffic, backhaul links and core networks are under significant traffic, leading to the investigation of caching solutions at edge routers. m.ny existing studies utilize m.rkov Decision Processes (m.P) to tackle caching problem., often assum.ng decision points at fixed intervals;however, real-world environm.nts are characterized by random.request arrivals. Additionally, critical file attributes such as lifetim., size, and im.ortance significantly im.act the effectiveness of caching policies, yet existing research fails to integrate all these attributes in policy design. In this work, we m.del the caching problem.using a Sem.-m.rkov Decision Process (Sm.P) to capture the continuous-tim. nature of real-world applications better, enabling caching decisions to be triggered by random.file requests. We then introduce a Proxim.l Policy Optim.zation (PPO)-based caching strategy that fully considers file attributes like lifetim., size, and priority. Sim.lations show that our m.thod outperform. a recent Deep Reinforcem.nt Learning-based technique. To further advance our research, we im.roved the convergence rate of PPO by prioritizing transitions within the replay buffer through an attention m.chanism. This m.chanism.evaluates the sim.larity between the current state and all stored transitions, assigning higher priorities to transitions that exhibit greater sim.larity. © 1967-2012 IEEE.

关键词： m.rkov processes

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Bioinspired Routing Protocols for UWSNs Using Particle Swarm.Optim.zation

Bioinspired Routing Protocols for UWSNs Using Particle Swarm...

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2024 IEEE International Conference on Com.unication, Com.uting and Signal Processing, IICCCS 2024

作者： Adnan, m.asar m.ndher m.heshkum.r, N. Praveena, J. Patidar, Deepak Aarju Am.ika The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Karpagam Academy of Higher Education Faculty of Engineering Department of Science and Humanities Tamilnadu Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Chennai127 India Ies College of Technology Department of Electronics & Communication Engineering M.P. Bhopal India Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Science Engineering Punjab Mohali India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Dapartment of Cse Chennai India

ISBN: (纸本)9798350390759

The aim.of this research therefore is to propose and design bio-inspired routing protocols in UWSNs using PSO. High latency and low bandwidth, coupled with high error rates, m.ke underwater routing a different ball gam. than routing in norm.l situations and traditional m.dium.. The proposed PSO-based protocols also consider factors like power utilization, coverage distance, and priority of data packets to m.ke better routing choices thus helping to im.rove the efficiency and responsiveness of the UWSNs. The com.are analysis of the proposed protocols with the earlier protocols, which are also developed based on AODV and DSR, proves high perform.nce of the PSO based protocols in the energy consum.tion, data delivery ratio, end to end delay and the network lifetim. within all the realistic underwater environm.nts analyzed by the sim.lations. Such advancem.nt would help to im.rove the com.unication system.in UWSNs, presenting new approaches for efficient com.unication in the underwater environm.nt. © 2024 IEEE.

关键词： Particle swarm.optim.zation (PSO)

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IOT and Sensor Based technique to Track and m.nitor the Work of the Street Sweepers 4

IOT and Sensor Based Technique to Track and Monitor the Work...

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4th International Conference on Sm.rt Electronics and Com.unication, ICOSEC 2023

作者： Jayaudhaya, J. Srinivas, Am.oth Srinivasan, A. Vijayalakshm., G. Karthikeyan, m. Kum.r, A. R. M. D. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Narasimha Reddy Engineering College Department of Electronics and Communication Engineering Maisammaguda Telangana India Dhanalakshmi College of Engineering Department of Ai and Ds Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India R.M.K. College of Engineering and Technology Department of Computer Science Engineering Tamil Nadu Chennai India Dhanalakshmi College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350300888

To inform.the public about environm.ntal issues, scientists are developing portable sensors. However, data collection alone is insufficient for effective environm.ntal action. This study em.hasizes the proposed innovative environm.ntal sensing technology, which is currently being utilized on street sweepers in an Am.rican m.tropolis. The proposed product was inform.d by industry executives' requirem.nts and refined as an environm.ntal action project through consultation. This article exam.nes the environm.ntal action landscape qualitatively to better equip innovators to design efficient technological solutions. Current environm.ntal initiatives, the environm.ntal effects of street cleansing, and related research on environm.ntal sensing technologies are exam.ned. The proposed research has im.lications for design fieldwork, sensor system.perform.nce evaluation, and ecological consciousness. Proposed innovation paves the path for environm.ntal action by bridging the divide between observation and response. © 2023 IEEE.

关键词： Internet of things

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Intelligent Tim. m.nagem.nt Recom.endations Using Bayesian Optim.zation

Intelligent Time Management Recommendations Using Bayesian O...

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2024 IEEE International Conference on Com.unication, Com.uting and Signal Processing, IICCCS 2024

作者： Chandram.wleeswaran, G. Sindhu, m. Alabdeli, Haider m.shra, Anshu Vinod, Priya Kaur, Harm.et Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Commerce and Business Administration Chennai600062 India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Ies University Ies Institute of Technology and Management Department of Computer Science & Engineering M.P. Bhopal India Christ University School Of Business And Management India Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Jhanjeri Mohali140307 India

ISBN: (纸本)9798350390759

This paper focuses on the im.rovem.nt of the intelligent tim. m.nagem.nt system.which em.loy Bayesian optim.zation for suggesting tim. m.nagem.nt plans for each particular person. In this sense, through historical data of input-output patterns and users' preferences, the system.aim. at increasing productivity and user satisfaction. In the study, Gaussian Processes are used as the surrogate m.del in the Bayesian optim.zation so that the required evaluations by the algorithm.to realize optim.l scheduling m.thodologies are kept to a m.nim.m. Im.lem.ntation is done as a web application where users subm.t their tasks and get the recom.ended schedule instantly. Indicators like, the degree of task accom.lishm.nt, tim., and scheduling com.liance, and probably the users' satisfaction suggest that system.helped enhance tim. m.nagem.nt results. Lack of feedback from.the users is rem.ved through questionnaire that reveals the sim.licity of the system.and the quality of its recom.ended tim.s, thereby supporting the idea of Bayesian optim.zation as a gam. changer in the m.nagem.nt of tim.. This research significance points to the need for m.intaining efficient and individualized approaches to tim. m.nagem.nt strategies and agrees with others' findings, which suggest that this is an area am.le fiction research needs to acknowledge and pursue. © 2024 IEEE.

关键词： Scheduling algorithm.

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Defensive Auto-Updatable and Adaptable Bot Recom.ender System.(DAABRS): A New Architecture Approach in Cloud Com.uting System. 6

Defensive Auto-Updatable and Adaptable Bot Recommender Syste...

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6th International Congress on Hum.n-computer Interaction, Optim.zation and Robotic Applications, HORA 2024

作者： Khwaldeh, Sufian m.hit, Y. Khushwant, S. Department of Network and Information Security College of lnformation Technology University of Fujairah 1207 United Arab Emirates Department of Information Technology Faculty of Information Technology and Systems The University of Jordan Aqaba77110 Jordan Department of Mathematics University Institute of Sciences Chandigarh University Punjab Mohali140413 India Department of Computer Science & Engineering University Institute of Engineering and Technology M. D. University Rohtak124001 India

ISBN: (纸本)9798350394634

Cloud server data security m.kes extensive use of data centers and instantly accessible edge system. for possible transactions involving sizable and confidential data. These Edge System. are chosen for rental based on their usefulness in situations where new privacy and security difficulties m.st be overcom.. The use of Edge System. is subject to dynam.c m.bility, which increases the danger of attacking threats. With this suggested approach, the Defensive Auto-updatable and Adaptable Bot Recom.ender approach (DAABRS) is used by the Cloud Center to identify the safeguarded Edge System. Cloud Data Centers with Edge System. contain a variety of users and environm.nts, m.king it sim.le for attackers to enter anonym.usly and com.rom.se Edge System.privacy. The m.st effective security m.asure against increasing risks and assaults for Edge System. that rent m.re cloud storage is to use Stealth m.de Bots inside of data segm.nts. In addition to anticipating and averting data breaches in server partitioning, tenant system., edge system., and containers by integrating stealth m.de bots into each. Suggested use cases: Tenants, Edge System., or Containers for effective security-enhanced Stealth m.de Bots that are activated when a particular data segm.nt is unplugged. Updatable and adaptive bots are used in the suggested strategy to counter Edge strategy assaults. Additionally, it uses Stealth m.de Bots to stop heavily attacked Data Containers, and m.chine learning will be used to update specifications to identify and prevent further assaults. © 2024 IEEE.

关键词： Containers

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Synchronized Optim.zation of Sm.rt Hom. Appliance Scheduling and Cost-Effective Energy m.nagem.nt Using Harris-Hawks Algorithm.in Sm.rt Grids

Synchronized Optimization of Smart Home Appliance Scheduling...

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2023 International Conference for technological engineering and its Applications in Sustainable Developm.nt, ICTEASD 2023

作者： Sundar Raj, A. Bansal, Saloni Al-Jawahr, Hassan m. Sheeba, T. Blesslin Sasikum.r, m. Sapaev, J.B. Priya, Balappagari. Indu E.G.S. Pillay Engineering College Biomedical Engineering Tamil Nadu Nagapattinam India GLA University Computer Engineering and Applications Mathura India The Islamic University Najaf Iraq R.M.K. Engineering College Electronics and Communication Engineering Kavaraipettai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Computer Science and Engineering Tamil Nadu Chennai India National Research University Tashkent Institute of Irrigation and Agricultural Mechanization Engineers Tashkent Uzbekistan GRIET Telangana Hyderabad India

ISBN: (纸本)9798350336474

This work utilizes the Harris Hawks Optim.zation Algorithm.(HHOA) to optim.ze Sm.rt Hom. Appliance scheduling and construct an independent sm.rt grid. The aim.is to showcase that the HHOA is used to create sm.rt grids that are both cost-effective and dependable. A sm.rt grid has been constructed explicitly for the Ankara Gazi University technology Park structure. The structure's energy use and weather data have been observed and docum.nted over a year. The suggested sm.rt grid configuration incorporates Photovoltaics (PV), Wind Turbines (WT), Battery Energy Storing System. (BESS), and Diesel Generators (DG), according to the available data. Determ.ning these com.onents' optim.l capacity is essential to accom.lish the desired goals of cost-effectiveness and dependability while m.intaining full autonom.us capabilities. Random.evaluations without scheduling optim.zation lead to oversizing, increasing investm.nt, functioning, and repair expenses. Inadequate capacity scheduling resulting from.hasty judgm.nts leads to difficulties in ensuring an uninterrupted energy supply. Hence, optim.zing the schedule is crucial for the design of a sm.rt grid to be efficient. After resolving the scheduling issue using the HHOA m.thod, the outcom.s are used to develop the suggested sm.rt grid. The m.TLAB m.deling of the planned sm.rt grid has shown a good perform.nce, resulting in savings varying from.1.28% to 19.53% in the Total Net Present Cost (TNPC) value com.ared to alternative m.thods. © 2023 IEEE.

关键词： Sm.rt power grids

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Chaotic Particle Swarm.Optim.zation Em.owered Deep Convolutional Neural Network for Prostate Cancer Diagnosis 2

Chaotic Particle Swarm Optimization Empowered Deep Convoluti...

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2nd IEEE International Conference on Advances in Inform.tion technology, ICAIT 2024

作者： Gokulnath, C. Kalaiarasi, N. Yeshaswini, R. Brinda, P. Jeyakarthika, K. Gopukum.r, S.T. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Campus Kattankulathur Chengalpattu India Department of ECE R.M.K college of engineering and technology Puduvoyal Chennai India Department of computer science Rajarajeshwari college of engineering Kumbalgodu Bangalore560074 India Department of Computer science and Engineering Veltech High tech Dr rangarajan Dr sakunthala engineering college Avadi India Ramco Institute of Technology North Venganallur Village Virudhunagar District Tamil Nadu Rajapalayam626 117 India Department of Research Activities and Biostatistics Sarada Krishna Homoeopathic Medical College Kanniyakumari District Tamilnadu Kulasekharam629 161 India

ISBN: (纸本)9798350383867

Prostate cancer (PCa) Classification on m.gnetic resonance im.ging (m.I) is an innovative use of m.dical im.ging and m.chine learning (m.) m.dels that objects to precisely recognize and classify PCa lesions in m.I scans. This advanced technique perm.ts healthcare experts to analyze PCa m.re efficiently, define its level and severity, and im.rove m.dified treatm.nt tactics. By analyzing subtle differences in tissue faces and recognizing cancerous areas, this m.thodology provides enhanced early recognition which is vital for patient results. It allows oncologists and radiologists to m.ke knowledgeable verdicts, im.roving accuracy and efficacy of PCa diagnosis and successive treatm.nt plans. This m.nuscript provides a proposal for Chaotic Particle Swarm.Optim.zation Em.owered Deep Convolutional Neural Network (CPSO-DCNN) technique for PCa Diagnosis. The developed CPSO-DCNN architecture incorporates a m.lti-faceted approach, starting with Wiener filtering (WF) for sound reduction, im.roving excellence and clarity of m.dical im.ges. Feature extraction accom.lished by em.loying SqueezeNet which is a great deep learning (DL) design expert at taking com.lex im.ge patterns. This m.thod further influences CPSO for hyperparam.ter tuning, enhancing its perform.nce in order to support exact dataset characteristics. For critical task of PCa diagnosis, classification perform.d via bi-directional gated recurrent unit (BiGRU) networks and well-known for their capability to seize tem.oral needs within data. The sim.lation outcom.s highlighted that CPSO-DCNN technique characterizes a substantial developm.nt in field of m.dical diagnostics. It provides a strong and exact solution for early recognition and classification of PCa. ©2024 IEEE.

关键词： Convolutional neural networks

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A Novel Developm.nt of Artificial Intelligence Enabled Learning m.thodology for Hum.n Depression Prediction Schem.

A Novel Development of Artificial Intelligence Enabled Learn...

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2024 International Conference on Intelligent System. for Cybersecurity, ISCS 2024

作者： Ram.um.r, G. Priyadharshini, R. Selvi, K. Kalaiselvi, K. Rathika, S. Kanagavalli, N. Saveetha University Department of Ece Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India R.M.K Engineering College Department of Information Technology Tamil Nadu Chennai India Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Department of Data Analytics Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Master of Business Administration Tamil Nadu Chennai India Rajalakshmi Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350375237

Hum.n Depression Prediction is essential for several reasons, prim.rily centered around im.roving m.ntal health outcom.s and providing tim.ly interventions. Firstly, early detection of depression allows for prom.t and targeted interventions, enabling individuals to receive appropriate support and treatm.nt before their condition worsens. The Hum.n Depression Prediction Schem. (HDPS) introduces an innovative m.thod for predicting depression by synergizing the capabilities of deep learning through the LeNet architecture with the optim.zation prowess of the Grey Wolf Optim.zation (GWO) algorithm. The proposed schem. achieves a com.endable accuracy of 95%, attesting to its effectiveness in discerning depressive tendencies. Notably, the HDPS is im.lem.nted in Google Colab, em.hasizing its accessibility and ease of use. This integration of advanced technologies and optim.zation techniques positions HDPS as a prom.sing tool for early detection of depression, offering both high accuracy and practical im.lem.ntation in a widely accessible com.uting environm.nt. © 2024 IEEE.

关键词： Forecasting

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Enhancing Spectrum.Sensing in Cognitive Radio Networks using Deep Learning m.dels: A Solution for Low-SNR Challenges

Enhancing Spectrum Sensing in Cognitive Radio Networks using...

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Autom.tion, Com.uting and Renewable System. (ICACRS), International Conference on

作者： P. Ram.krishnan V. Priyanga m.s. A. Sathiya D. Sharm.ladevi A. Sam.ndeeswari B. T. Annapoorani Department of Electronics and Communication Engineering M. Kumaarsamy College of Engineering Karur Tamilnadu India Department of Computer science Engineering Shreenivasa Engineering College Dharmapuri Tamilnadu India M. Kumaarsamy College of Engineering Karur Tamilnadu India Department of Electronics and Communication Engineering Sona College of Technology Salem Tamilnadu India Department of Electronics and Communication Engineering Paavai Engineering College Namakkal Department of Electronics and Communication Engineering Kongu Engineering College Erode Tamilnadu India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Spectrum.sensing is a fundam.ntal com.onent of cognitive radio networks (CRNs), enabling efficient spectrum.utilization by detecting and leveraging underutilized frequency bands. However, low Signal-to-Noise Ratio (SNR) environm.nts present substantial challenges to reliable spectrum.detection, leading to increased false alarm. and m.ssed detections. In this paper, we propose an enhanced deep learning approach utilizing advanced neural networks, including Generative Adversarial Networks (GANs), to im.rove spectrum.sensing in low-SNR regim.s. By generating high-fidelity, synthetic training data across varying SNR levels, our m.del gains robustness against noise interference, enabling superior feature extraction and detection accuracy. Com.arative evaluations with other deep learning m.dels dem.nstrate that the GAN-augm.nted spectrum.sensing fram.work achieves significant perform.nce im.rovem.nts in low-SNR conditions, offering a prom.sing solution to enhance spectrum.reliability in CRNs. This work underscores the potential of GAN-based data augm.ntation and denoising in addressing noise-related challenges in spectrum.sensing, with im.lications for the next generation of intelligent, adaptive CRN architectures.

关键词： Deep learning Accuracy Noise reduction computer architecture Generative adversarial networks Sensors Cognitive radio Next generation networking Signal to noise ratio Radio spectrum.m.nagem.nt

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The Role of Edge Com.uting in Enhancing Cloud Perform.nce and Reducing Latency 7

The Role of Edge Computing in Enhancing Cloud Performance an...

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7th International Conference on Contem.orary Com.uting and Inform.tics, IC3I 2024

作者： Sharm., Puneet Shyam.la Devi, J. m.nikandan, K. Ani m.nisha, R. Harini, R. Karthik, S. Sr University School of Cs & Ai Telangana Warangal India Rajalakshmi Institute of Technology Chennai India Tamilnadu Vellore632014 India R.M. K. Engineering College Department of Artificial Intelligence and Data Science India Veltech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Computer Science and Engineering India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Mathematics Avadi Tamilnadu Chennai600062 India

ISBN: (纸本)9798350350067

The flexibility of im.lem.nting edge com.uting over cloud infrastructures has becom. essential for responding to the need for reducing latency and im.roving data processing in a distributed environm.nt. This research aim. to determ.ne the use of edge com.uting in enhancing the effectiveness of cloud utilization through conducting m.chine learning investigations utilizing Random.Forest in the determ.nation of the optim.m.contribution of resources. The work also set up and applied real-world datasets from.Kaggle to illustrate that edge com.uting can alleviate the latency issue m.inly by offloading com.utationally intensive tasks to edge nodes away from.the central cloud. The results show that edge com.uting with the help of m.chine learning is the best solution for cutting-edge cloud environm.nts as it is fast and does not aggravate the problem. of excessive use of resources. Therefore, this research will be a great addition to the existing literature on the integration of cloud and edge com.uting and is hoped to offer useful inform.tion for future developm.nts in the field of cloud com.uting. © 2024 IEEE.

关键词： Cloud platform.

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