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International Conference on Distributed Computing in Sensor Systems (DCOSS)

作者： Fotios Kalioras Gabriel Filios Stylianos Karatzas Sotiris Nikoletseas Department of Computer Engineering and Informatics University of Patras Greece Department of Computer Engineering and Informatics University of Patras Computer Technology Institute and Press “Diophantus” Greece Department of Civil Engineering University of Patras Greece

Indoor air quality (IAQ) is an important yet often overlooked aspect of public health, with poor IAQ contributing to a significant number of diverse health problems worldwide. Existing air quality standards have failed to fully address the problem, however emerging technologies in the field of IoT have demonstrated promise in effectively addressing this issue. This concept paper advocates a hybrid hierarchy system for IAQ monitoring that incorporates both offline and online connectivity. The system utilizes sensing elements that can perform a wide range of functions, including autocalibration, pollution event detection, and more, autonomously from the network. By combining a gateway device, the system's capabilities are enhanced, providing increased data granularity, additional calibration and sensing options as well as connection with Building Management Systems (BMS). Furthermore, by connecting the gateway to the cloud, the system can perform more advanced data analysis and machine learning, providing users with insights into not only air quality but also general environmental quality. Key features of the envisioned system include the integration of subjective perception of pollution via crowdsourcing, a TinyML technology at the edge, digital twins, and an optimised redundancy of sensors that can alleviate poor accuracy and drift as well as capture the spatial dispersion of pollution.

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Dynamically Reconfigurable Polarizer Implemented Using an Energy Harvesting Triggered Metasurface

Dynamically Reconfigurable Polarizer Implemented Using an En...

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Antennas and Propagation & USNC/URSI National Radio Science Meeting

作者： Adnan Nadeem Nosherwan Shoaib Photos Vryonides Symeon Nikolaou Frederick Research Center Nicosia Cyprus Electrical Engineering Department Frederick University Nicosia Cyprus School of Electrical Engineering and Computer Science (SEECS) National University of Sciences and Technology (NUST) Islamabad Pakistan

ISBN: (数字)9798350369908

ISBN: (纸本)9798350369915

The manuscript describes the implementation of a mm-wave dynamically reconfigurable polarizer, which is triggered by an energy harvester which controls a reconfigurable cells metasurface (MSF). The polarizer is implemented as a 5×5 MSF array operating in K-band (26 GHz) which is able to control the propagation of incident waves and selectively choose to filter out the y-polarization. The proposed MSF transmits both x and y-polarized components simultaneously when the diodes which are used in the reconfigurable geometry cells are in OFF state. However, upon detecting an incident y-polarized wave, the rectifier provides a trigger signal that activates the DC biasing circuit and switches all the diodes simultaneously to ON state. Under this biasing condition the transmitted wave is strictly polarized along the x direction and the incident y-polarized wave is reflected. In both states of the MSF, the simulation results demonstrate transmission efficiency exceeding 95%. As for the rectifier, it exhibits a maximum RF-DC power conversion efficiency of 56%.

关键词： Rectennas PIN photodiodes Simulation K-band Rectifiers Switches Metasurfaces

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Vehicle type recognition based on audio data 58

Vehicle type recognition based on audio data

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58th Hawaii International Conference on System Sciences, HICSS 2025

作者： Kobiela, Dariusz Hajdasz, Michal Erezman, Mateusz Nurzyńska, Karolina Zaporowski, Szymon Kurowski, Adam Weichbroth, Pawel Department of Software Engineering Faculty of Electronics Telecommunication and Informatics Gdańsk University of Technology Gabriela Narutowicza 11/12 Gdańsk80-233 Poland Department of Multimedia Systems Faculty of Electronics Telecommunication and Informatics Gdańsk University of Technology Gabriela Narutowicza 11/12 Gdańsk80-233 Poland Department of Algorithmics and Software Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice44-100 Poland

ISBN: (纸本)9780998133188

Identifying different vehicle types can help manage traffic more efficiently, reduce congestion, and improve public safety. This study aims to create a classification model that can recognize vehicle types based on the sound of passing vehicles. To achieve this, a database of raw audio files containing 1763 samples from two sources was assembled. The time-domain signals were converted to a time-frequency representation using the short-time Fourier transform to generate Mel Spectrograms. Mel-frequency Cepstral Coefficients (MFCCs) were also generated using the discrete cosine transform. In our experiments we compared these approaches. Since the data was imbalanced we applied online augmentation. Based on the literature review, we chose a Convolutional Neural Network (CNN) classifier because it is particularly well suited for analyzing large datasets due to its automatic feature extraction, parameter sharing and sparsity. The results showed that Mel Spectrograms were more effective for audio data preprocessing in this particular use case, achieving the highest accuracy of 0.875 and the highest f1-score of 0.877 compared to MFCCs. © 2025 IEEE computer Society. All rights reserved.

关键词： Image coding

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Bayesian Neural Networks for Direct Short-Term Net Load Forecasting in Solar-Integrated Residential Buildings

Bayesian Neural Networks for Direct Short-Term Net Load Fore...

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International Conference on Energy Transition in the Mediterranean Area (SyNERGY MED)

作者： Georgios Tziolis Andreas Livera Javier Lopez-Lorente Panagiotis Herodotou George Makrides George E. Georghiou Department of Electrical and Computer Engineering PV Technology Laboratory University of Cyprus Nicosia Cyprus PHAETHON Centre of Excellence for Intelligent Efficient and Sustainable Energy Solutions Nicosia Cyprus DNV Arnhem Netherlands

ISBN: (数字)9798350375923

ISBN: (纸本)9798350375930

Short-term net load forecasting (STNLF) is of utmost importance for the efficient operation and control of residential buildings, with integrated photovoltaic (PV) systems. The intermittent PV generation increases the uncertainty of STNLF and therefore the error. This study presents a machine learning model using Bayesian neural networks (BNNs) for direct STNLF in solar-integrated residential buildings. Historical data from PV-integrated households in cyprus, with self-sufficiency between 88.59% and 294.12%, were used for assessing the performance of the BNN model. The proposed model was applied to data from 68 individual and aggregated households in cyprus. The results indicated that the machine learning model exhibited normalized root mean square error of 7.20% for the aggregated data, rendering it suitable for STNLF in residential buildings. Finally, the model was compared to a naïve persistence model, achieving a skill score of 46.37% at the aggregated household level.

关键词： Accuracy Uncertainty Load forecasting Buildings Neural networks Machine learning Predictive models Data models Bayes methods Load modeling

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Cost Optimization for Serverless Edge Computing with Budget Constraints using Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Chen, Chen Guan, Peiyuan Chen, Ziru Taherkordi, Amir Hou, Fen Cai, Lin X. Department of Computer Science and Technology University of Cambridge United Kingdom Department of Informatics University of Oslo Norway Department of Electrical and Computer Engineering Illinois Institute of Technology United States Department of Electrical and Computer Engineering University of Macau China

Serverless computing adopts a pay-as-you-go billing model where applications are executed in stateless and short-lived containers triggered by events, resulting in a reduction of monetary costs and resource utilization. However, existing platforms do not provide an upper bound for the billing model which makes the overall cost unpredictable, precluding many organizations from managing their budgets. Due to the diverse ranges of serverless functions and the heterogeneous capacity of edge devices, it is challenging to receive near-optimal solutions for deployment cost in a polynomial time. In this paper, we investigated the function scheduling problem with a budget constraint for serverless computing in wireless networks. Users and IoT devices are sending requests to edge nodes, improving the latency perceived by users. We propose two online scheduling algorithms based on reinforcement learning, incorporating several important characteristics of serverless functions. Via extensive simulations, we justify the superiority of the proposed algorithm by comparing with an ILP solver (Midaco). Our results indicate that the proposed algorithms efficiently approximate the results of Midaco within a factor of 1.03 while our decision-making time is 5 orders of magnitude less than that of Midaco. © 2025, CC BY.

关键词： Budget control

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Improved Transportation Model with Internet of Things Using Artificial Intelligence Algorithm

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computers, Materials & Continua 2023年第8期76卷 2261-2279页

作者： Ayman Khallel Al-Ani Shams Ul Arfeen Laghari Hariprasath Manoharan Shitharth Selvarajan Mueen Uddin Cybersecurity Research Group Faculty of Computing and InformaticsUniversity Malaysia SabahJalan UMSKota KinabaluSabah88400Malaysia National Advanced IPv6 Centre(NAv6) Universiti Sains MalaysiaGelugorPenang11800Malaysia Department of Electronics and Communication Engineering Panimalar Engineering CollegePoonamalleeChennai600123TamilnaduIndia Department of Computer Science&Engineering Kebri Dehar UniversityKebri Dehar250Ethiopia College of Computing and Information Technology University of Doha for Science and TechnologyDoha24449Qatar

In this paper,the application of transportation systems in realtime traffic conditions is evaluated with data handling *** proposed method is designed in such a way as to detect the number of loads that are present in a vehicle where functionality tasks are computed in the *** to the existing approach,the design model in the proposed method is made by dividing the computing areas into several cluster regions,thereby reducing the complex monitoring system where control errors are ***,a route management technique is combined with Artificial Intelligence(AI)algorithm to transmit the data to appropriate central ***,the combined objective case studies are examined as minimization and maximization criteria,thus increasing the efficiency of the proposed ***,four scenarios are chosen to investigate the projected design’s *** all simulated metrics,the proposed approach provides better operational outcomes for an average percentage of 97,thereby reducing the amount of traffic in real-time conditions.

关键词： Transportation Artificial Intelligence(AI) data-driven Internet of Things(IoT)

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A Contend Node-Restricted Receiver-Initiated MAC Protocol With Consecutive Packet Transmission

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2025年

作者： Liu, Yuxin Ota, Kaoru Dong, Mianxiong Wang, Ruoyu Changsha University College of Computer Science and Engineering Changsha410022 China Muroran Institute of Technology Department of Sciences and Informatics Japan Central South University School of Computer Science and Engineering Changsha410083 China

The medium access control (MAC) protocol plays a crucial role in wireless communications, influencing key performance metrics, such as energy consumption, delay, collision, and throughput. This paper introduces the Contend Node Restricted Joint Consecutive Packet Transmissions Receiver-Initiated (CNR-CPT-RI) protocol, specifically designed to minimize collisions and reduce energy consumption in wake-up radio-enabled wireless sensor networks (WSNs). The CNR-CPT-RI protocol is an effective communication protocol that is mainly applied with nodes in the one-hop range of the sink. Since the sink has the ability to control each node, it becomes feasible to manage the nodes in a way that effectively reduces collisions and enhances network lifetime as well as throughput. Moreover, the CNR-CPT-RI protocol can adaptively adjust the number of nodes participating in channel contention, maintaining it within an optimal range to balance reduced collisions, decreased energy consumption, and sustained high throughput. Furthermore, the winning node in channel contention employs Consecutive Packet Transmissions (CPTs) method to further diminish competition. Comprehensive theoretical and experimental results demonstrate that the number of collisions of the CNR-CPT-RI protocol is only 42.4%, 35.9%, and 32.5% of the baseline protocol when the number of nodes is 10, 20, and 30, respectively. The energy consumption of the CNR-CPT-RI protocol is reduced by 5.7%, 15%, and 22.6%, and the delay is decreased by 6.7%, 16%, and 32%, respectively, compared with that of the existing protocol. © 2017 IEEE.

关键词： Internet protocols

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A Deep Reinforcement Learning Approach to Edge-based IDS Packets Sampling 5

A Deep Reinforcement Learning Approach to Edge-based IDS Pac...

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5th International Conference on Data Science and Information technology, DSIT 2022

作者： Wu, Guowen Li, Zhenwei Shen, Yizhou Zhang, Hong Shen, Shigen Yu, Shui Donghua University School of Computer Science and Technology Shanghai China School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Shaoxing University Department of Computer Science and Engineering Shaoxing China School of Computer Science University of Technology Sydney NSW Australia

ISBN: (数字)9781665498685

ISBN: (纸本)9781665498685

Edge computing expands the Internet of Things (IoT) by allowing partial computing tasks to be migrated to edge servers and alleviate the computing pressure of terminal devices. However, the edge servers will be attacked through malicious network traffic. A well-designed edge-based IDS plays a significant role in protecting from malicious attacks. In this paper, we employ a gated recurrent unit classifier to perform intrusion detection due to its characteristics on long-term memory of inputs and light structure. Furthermore, to reduce the cost of performing intrusion detection when facing a large volume of data, we propose an actor-critic network based on deep reinforcement learning for packets sampling to work on some packets by ignoring the others. The system we designed can achieve great classification performance through partial packets. We use dataset CIC-IDS-2017 to evaluate our model, and the accuracy of classification reaches 97% while the proportion of detection packets is 26% and below. Evaluations of our approach on UNSW-NB15 and CIC-IDS-2017 show that our model maintains under 26% and 18% of the selected packets, respectively. © 2022 IEEE.

关键词： Intrusion detection

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Ontological Model for Cohesive Smart Health Services Management

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computers, Materials & Continua 2023年第2期74卷 3679-3695页

作者： Muhammad Raza Naqvi Muhammad Waseem Iqbal Syed Khuram Shahzad M.Usman Ashraf Khalid Alsubhi Hani Moaiteq Aljahdali Le Laboratoire Génie de Production de l’École Nationale d’Ingénieurs de Tarbes(LGP-INP-ENIT) Universitéde ToulouseCEDEXTarbes65016France Department of Software Engineering Superior UniversityLahore53700Pakistan Department of Informatics&Systems University of Management&TechnologyLahore54000Pakistan Department of Computer Science University of Management and TechnologySialkot51310Pakistan Department of Computer Science King Abdul-Aziz UniversityJeddah21577Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21577Saudi Arabia

Health care has become an essential social-economic concern for all stakeholders(e.g.,patients,doctors,hospitals etc.),health needs,private care and the elderly class of *** massive increase in the usage of health care Internet of things(IoT)applications has great technological evolvement in human *** are various smart health care services like remote patient monitoring,diagnostic,disease-specific remote treatments and *** applications are available in a split fashion and provide solutions for variant diseases,medical resources and remote service *** main objective of this research is to provide a management platform where all these services work as a single unit to facilitate the *** ontological model of integrated healthcare services is proposed by getting requirements from various existing healthcare *** were 26 smart health care services and 26 smart health care services to classify the knowledge-based ontological *** proposed ontological model is derived from different classes,relationships,and constraints to integrate health care *** model is developed using Protégébased on each interrelated/correlated health care service having different *** querying SPARQL protocol and RDF query language(SPARQL)were used for knowledge *** Pellet Reasoner is used to check the validity and relations coherency of the proposed ontology *** to other smart health care services integration systems,the proposed ontological model provides more cohesiveness.

关键词： Ontology internet of things smart health services integration

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Dual-Band Planar Microstrip Monopole Antenna Design Using Multi-Objective Hybrid Optimization Algorithm

Dual-Band Planar Microstrip Monopole Antenna Design Using Mu...

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2023 Photonics and Electromagnetics Research Symposium, PIERS 2023

作者： Hosseini, V. Shapour, F. Pinho, P. Farhang, Y. Majidzadeh, K. Ghobadi, Ch. Nourinia, J. Barshandeh, S. Shokri, M. Amiri, Zh. Jalilirad, M. Kaboutari, K. Payame Noor University Department of Computer Engineering and Information Technology Tehran Iran Islamic Azad University Urmia Branch Computer Engineering Department Urmia Iran Instituto de Telecomunicações Aveiro Portugal University of Aveiro Department of Electronics Telecommunications and Informatics Aveiro Portugal Islamic Azad University Khoy Branch Department of Computer Engineering Khoy Iran Urmia University Department of Electrical Engineering Urmia Iran School of Engineering Department of Computer Science Afagh Higher Education Institute Urmia Iran

ISBN: (纸本)9798350312843

This research uses a new proposed multi-objective optimization algorithm that utilizes an untested combined chaotic map to integrate Customized Mutated PSO (CM-PSO) with a Modified Genetic Algorithm (MGA) to design an antenna with specific electromagnetic characteristics. The hybrid approach achieves preferred outcomes faster than PSO, CM-PSO, GA, and MGA by avoiding being trapped in local minimums. The proposed Metaheuristic Algorithm (MA) functionality has been authenticated successfully using Benchmark Functions (BFs) like Rastrigin Function (RaF), Ackley Function (AF), Rosenbrock Function (RoF), and Booth Function (BoF). Finally, the validity of the offered approach for electromagnetic applications is demonstrated by optimizing a dual-band planar microstrip monopole antenna with a simple structure, such that its optimized S11 be less than -10 dB at two frequency bands encompasses 2.4 to 2.484 and 5.15 to 5.825 GHz for Wireless Local Area Network (WLAN) with IEEE 802.11 standards. The proposed algorithm allows the optimization criteria to be customized. The optimization algorithm developed in MATLAB is used to determine the necessary parameter adjustments in order to achieve expected frequency bands using CM-PSO or an innovative MGA, while high-frequency and electromagnetic simulations are performed using computer Simulation technology (CST) Studio Suite. The dimensions of the proposed antenna's elements are critical input parameters of the algorithm, named decision variables. © 2023 IEEE.

关键词： Monopole antennas

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