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Realizing Digital Product Passports with Crowdsourcing Principles: The Case of Sustainable Smart Grids 19

Realizing Digital Product Passports with Crowdsourcing Princ...

19th Annual International Conference on Distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2023

作者： Voulgaridis, Konstantinos Lagkas, Thomas Karampatzakis, Dimitris Argyriou, Vasileios Sarigiannidis, Panagiotis International Hellenic University Department of Computer Science Kavala Campus Greece Kingston University Department of Networks and Digital Media Kingston upon Thames United Kingdom University of Western Macedonia Department of Electrical and Computer Engineering Kozani Greece

ISBN: (纸本)9798350346497

Due to constant technological advancements, Smart Grids have the potential to significantly reduce energy consumption, while providing efficient energy management. However, their implementation requires a thorough collection of data related to procedures of energy generation and distribution, as well as the inclusion of Circular Economy (CE) principles to ensure sustainability. Considering the impact of Industry 5.0, Digital Product Passports (DPPs) are a sufficient solution for the recording, presentation, and circularity of the respective data, combined with suitable crowdsourcing strategies for the integration of real-time data provided by consumers. This paper explores the potential implementation of DPPs, infused with crowdsourcing principles, for smart grid integration, through the design of a fundamental CE framework, which is further supported by a systematic literature review and a reference use- case scenario of its operational flow. © 2023 IEEE.

关键词： Smart power grids

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Performance Optimization Across the Edge-Cloud Continuum: A Multi-agent Rollout Approach for Cloud-Native Application Workload Placement

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SN computer Science 2024年第3期5卷 318页

作者： Soumplis, Polyzois Kontos, Georgios Kokkinos, Panagiotis Kretsis, Aristotelis Barrachina-Muñoz, Sergio Nikbakht, Rasoul Baranda, Jorge Payaró, Miquel Mangues-Bafalluy, Josep Varvarigos, Emmanuel School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Institute of Communication and Computer Systems Athens Greece Department of Digital Systems University of Peloponnese Sparta Greece Services as Networks (SaS) Centre Tecnològic Telecomunicacions CTTC/CERCA) Catalunya Spain

The advancements in virtualization technologies and distributed computing infrastructures have sparked the development of cloud-native applications. This is grounded in the breakdown of a monolithic application into smaller, loosely connected components, often referred to as microservices, enabling enhancements in the application’s performance, flexibility, and resilience, along with better resource utilization. When optimizing the performance of cloud-native applications, specific demands arise in terms of application latency and communication delays between microservices that are not taken into consideration by generic orchestration algorithms. In this work, we propose mechanisms for automating the allocation of computing resources to optimize the service delivery of cloud-native applications over the edge-cloud continuum. We initially introduce the problem’s Mixed Integer Linear Programming (MILP) formulation. Given the potentially overwhelming execution time for real-sized problems, we propose a greedy algorithm, which allocates resources sequentially in a best-fit manner. To further improve the performance, we introduce a multi-agent rollout mechanism that evaluates the immediate effect of decisions but also leverages the underlying greedy heuristic to simulate the decisions anticipated from other agents, encapsulating this in a Reinforcement Learning framework. This approach allows us to effectively manage the performance–execution time trade-off and enhance performance by controlling the exploration of the Rollout mechanism. This flexibility ensures that the system remains adaptive to varied scenarios, making the most of the available computational resources while still ensuring high-quality decisions. © The Author(s) 2024.

关键词： Cloud edge continuum Cloud native applications Multi-agent rollout Reinforcement learning Resource allocation

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A Stable Task Assignment Mechanism for Multi-Platform Mobile Crowdsensing

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8079-8094页

作者： Peng, Shuo Zhang, Guo Zhang, Baoxian Yao, Zheng Liu, Chen Li, Cheng University of Chinese Academy of Sciences Research Center of Ubiquitous Sensor Networks Beijing100049 China University of British Department of Electrical and Computer Engineering BCV6T 1Z4 Canada Simon Fraser University School of Engineering Science BurnabyBCV5A 1S6 Canada

Mobile crowdsensing (MCS) is a new paradigm for Internet of Things. It can fully utilize the smart devices carried by mobile users for accomplishing various sensing tasks. Most existing task assignment mechanisms have assumed the availability of just a single service platform and without considering the preference of users and platforms. In this paper, we focus on studying how to design efficient task assignment mechanism when there are multiple service platforms in the MCS system and further the preferences of both users and platforms are considered while the sensing quality of each user is unknown in advance. The design objective is to maximize the overall sensing qualities of all completed tasks while respecting the budget constraint of each platform. We build a multi-platform oriented task assignment framework and formulate the problem under study as a 0-1 integer linear programming (ILP) problem. We propose a Multi-platform Stable Task Assignment mechanism (MSTA). MSTA works in a round by round manner. In each round, MSTA first performs budget splitting among different task locations for each platform, then makes stable matching between users and platforms and performs online learning of users' sensing qualities by using the multi-armed bandit (MAB) model. We deduce the time complexity of MSTA and prove that MSTA has the properties of stability, individual rationality, budget feasibility, and truthfulness. Simulation results demonstrate the high performance of the proposed MSTA mechanism. © 1967-2012 IEEE.

关键词： Budget control

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Learning on Bandwidth Constrained Multi-Source Data With MIMO-Inspired DPP MAP Inference

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2024年 2卷 1341-1356页

作者： Chen, Xiwen Li, Huayu Amin, Rahul Razi, Abolfazl Clemson University School of Computing ClemsonSC29634 United States The University of Arizona Department of Electrical and Computer Engineering TucsonAZ85721 United States MIT Lincoln Laboratory Tactical Networks Group LexingtonMA02421 United States

Determinantal Point Process (DPP) is a powerful technique to enhance data diversity by promoting the repulsion of similar elements in the selected samples. Particularly, DPP-based Maximum A Posteriori (MAP) inference is used to identify subsets with the highest diversity. However, a commonly adopted presumption of all data samples being available at one point hinders its applicability to real-world scenarios where data samples are distributed across distinct sources with intermittent and bandwidth-limited connections. This paper proposes a distributed version of DPP inference to enhance multi-source data diversification under limited communication budgets. First, we convert the lower bound of the diversity-maximized distributed sample selection from matrix determinant optimization to a simpler form of the sum of individual terms. Next, a determinant-preserved sparse representation of selected samples is formed by the sink as a surrogate for collected samples and sent back to sources as lightweight messages to eliminate the need for raw data exchange. Our approach is inspired by the channel orthogonalization process of Multiple-Input Multiple-Output (MIMO) systems based on the Channel State Information (CSI). Extensive experiments verify the superiority of our scalable method over the most commonly used data selection methods, including GreeDi, Greedymax, random selection, and stratified sampling by a substantial gain of at least 12% reduction in Relative Diversity Error (RDE). This enhanced diversity translates to a substantial improvement in the performance of various downstream learning tasks, including multi-level classification (2%-4% gain in accuracy), object detection (2% gain in mAP), and multiple-instance learning (1.3% gain in AUC). © 2023 CCBY.

关键词： Channel state information

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Reliable and Efficient Data Collection in UAV based IoT networks

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IEEE Communications Surveys and Tutorials 2025年

作者： Poorvi, Joshi Kalita, Alakesh Gurusamy, Mohan National University of Singapore Communications and Networks Lab Department of Electrical and Computer Engineering Singapore Dhanbad Mathematics and Computing Department India

IoT involves sensors for monitoring and wireless networks for efficient communication. However, resource-constrained IoT devices and limitations in existing wireless technologies hinder its full potential. Integrating UAVs into IoT networks can address some challenges by expanding its coverage, providing security, and bringing computing closer to IoT devices. Nevertheless, effective data collection in UAV-assisted IoT networks is hampered by factors, including dynamic UAV behaviour, environmental variables, connectivity instability, and security considerations. In this survey, we first explore various communication technologies for UAV-IoT networks, performance metrics, and simulation tools, followed by a categorization of the UAV-IoT system based on the service model of UAV, mode of operation of UAV and UE during data collection, and network topology. As this article primarily emphasizes reliable and efficient data collection in UAV-assisted IoT networks, we briefly discuss existing research on data accuracy and consistency, network connectivity, and data security and privacy to provide insights into reliable data collection. Additionally, we discuss efficient data collection strategies in UAV-based IoT networks, covering trajectory planning and constraint management, sensor network clustering, data aggregation, and UAV swarm formations. Further, advanced AI techniques are discussed, which enable efficient and reliable data transfer in UAV-IoT systems. Finally, we discuss future challenges and their possible solutions using different approaches such as Decentralized Reinforcement and Federated Learning, Generative AI, and hybrid optical-RF communication links with integration of blockchain for reliable, efficient, and secure data collection in UAV-assisted IoT networks. © 2025 IEEE. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Using IoT for Patient Health Monitoring System(PHMS) to Measure Temperature, Blood Pressure and ECG Based on Arduino IDE 2.x

Using IoT for Patient Health Monitoring System(PHMS) to Meas...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Esraa Abrahim Elkhidr Abdelsalam Saleh Elrashdi Salem El Khiralla Nada Ali Altawerghi Fatma Mohammed Hasan Department of Computer Networks College of Computer Technology Benghazi Libya Department of Software Engineering College of Computer Technology Benghazi Libya

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

By 2030, there will be 500 billion devices with sensors and an Internet connection. The Internet of Things is the name of the network that links these gadgets together for data transport (IoT). In order to rapidly exchange medical information to connect people, resources, and organizations we have proposed paper using an IoT for Patient Health Monitoring (PHMS). For almost medical equipment having an internet connection and the capacity to track at least one patient's health parameter, including blood pressure, body temperature, heart rate, ECG, steps taken, etc. We made a prototype that carries out different procedures for example, if the patient's temperature rises above 38 degrees Celsius, it will send messages to the doctor that there is a patient who needs special care. The Arduino IDE 2.x processes the data produced by the sensors. The feedback was extremely favorable and supportive of the efficacy of monitoring patient health by the use of Arduino IDE 2.x.

关键词： Temperature measurement Temperature sensors Medical services Blood pressure Pressure measurement Internet of Things Biomedical monitoring

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An Optimal Scheme for WSN Based on Compressed Sensing

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computers, Materials & Continua 2022年第7期72卷 1053-1069页

作者： Firas Ibrahim AlZobi Ahmad Ali AlZubi Kulakov Yurii Abdullah Alharbi Jazem Mutared Alanazi Sami Smadi Department of Information Systems and Networks Faculty of Information TechnologyThe World Islamic Sciences&Education UniversityJordan Computer Science Department Community CollegeKing Saud UniversitySaudi Arabia Department of Computer Engineering National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic InstituteUkraine

Wireless sensor networks(WSNs)is one of the renowned ad hoc network technology that has vast varieties of applications such as in computer networks,bio-medical engineering,agriculture,industry and many *** has been used in the internet-of-things(IoTs)applications.A method for data collecting utilizing hybrid compressive sensing(CS)is developed in order to reduce the quantity of data transmission in the clustered sensor network and balance the network *** cluster head nodes are chosen first from each temporary cluster that is closest to the cluster centroid of the nodes,and then the cluster heads are selected in order based on the distance between the determined cluster head node and the undetermined candidate cluster head ***,each ordinary node joins the cluster that is nearest to *** greedy CS is used to compress data transmission for nodes whose data transmission volume is greater than the threshold in a data transmission tree with the Sink node as the root node and linking all cluster head *** simulation results demonstrate that when the compression ratio is set to ten,the data transfer volume is reduced by a factor of *** compared to clustering and SPT without CS,it is reduced by 75%and 65%,*** compared to SPT with Hybrid CS and Clustering with hybrid CS,it is reduced by 35%and 20%,*** and SPT without CS are compared in terms of node data transfer volume standard *** with Hybrid CS and clustering with Hybrid CS were both reduced by 62%and 80%,*** compared to SPT with hybrid CS and clustering with hybrid CS,the latter two were reduced by 41%and 19%,respectively.

关键词： Compressed sensing computer networks sensor networks ad hoc networks

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Optimizing Healthcare Delivery through CloudBased Clinical Decision Support Systems

Optimizing Healthcare Delivery through CloudBased Clinical D...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Ebenezar, U. Samson Jenila Vennila, G. Balakrishnan, T. Suresh Krishnan, Prabhakar Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy India Mohan Babu University School of Computing Department of Ai & Ml Andhra Pradesh Tirupati India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Amrita Vishwa Vidyapeetham Center for Cyber Security Systems and Networks Amritapuri-campus India

ISBN: (纸本)9798350373783

The experiment aims at evaluating how four machine learning algorithms work: Decision Tree, Random Forest, SVM, and Neural Network, in a task, of binary classification. Models were evaluated using comprehensive metrics such as accuracy, precision, recall, F-score and AUC that revealed a good diagnostic performance. Models selected for the experiment and parameters tested on real datasets show steady changes in performance. The Neural Network model remained on top, achieving accuracy, precision, recall, F-score and AUC-ROC of 91.8%, 92.1%, 91.3%, 91.7% and 0.93 respectively. Random Forest displayed a competitive accordance along with the accuracy of 88.6%, precision of 89.2%, recall of 88.7%, and the F-score of 89.0%, and the AUC of 0.91. From the results, Support Vector Machine and Decision Tree models posted lower accuracy, recall, and fscores compared to the Neural Network and Random Forest models in most cases. These reveal the need for the improvement of the suitable machine learning model that comes from specific task needs as well as the consideration of each one of factors including accuracy, interpretability, and computational efficiency. Extensive and development of the model may result in more comprehensive understanding of how the model leads classifying of real-world applications such as medical diagnoses, fraud detection and target marketing. © 2024 IEEE.

关键词： Random forests

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Novel Path Counting-Based Method for Fractal Dimension Estimation of the Ultra-Dense networks

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Intelligent Automation & Soft Computing 2023年第4期36卷 561-572页

作者： Farid Nahli Alexander Paramonov Naglaa F.Soliman Hussah Nasser AlEisa Reem Alkanhel Ammar Muthanna Abdelhamied A.Ateya Department of Telecommunication Networks and Data Transmission The Bonch-Bruevich Saint-Petersburg State University of TelecommunicationsSaint Petersburg193232Russia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Electronics and Communications Engineering Zagazig UniversityZagazig44519SharqiaEgypt

Next-generation networks,including the Internet of Things(IoT),ﬁfth-generation cellular systems(5G),and sixth-generation cellular systems(6G),suf-fer from the dramatic increase of the number of deployed *** puts high constraints and challenges on the design of such *** changing of the network is one of such challenges that affect the network performance,includ-ing the required quality of service(QoS).The fractal dimension(FD)is consid-ered one of the main indicators used to represent the structure of the communication *** this end,this work analyzes the FD of the network and its use for telecommunication networks investigation and *** clus-ter growing method for assessing the FD is introduced and *** article proposes a novel method for estimating the FD of a communication network,based on assessing the network’s connectivity,by searching for the shortest *** the cluster growing method,the proposed method does not require multiple iterations,which reduces the number of calculations,and increases the stability of the results ***,the proposed method requires less compu-tational cost than the cluster growing method and achieves higher *** method is quite simple to implement and can be used in the tasks of research and planning of modern and promising communication *** developed method is evaluated for two different network structures and compared with the cluster growing *** validate the developed method.

关键词： Cluster growing connectivity dense networks fractal dimension network structure shortest route quality of service

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Using Equivalent Gold Sequences for Parallel Data Transmission with Non-orthogonal Code Division 8

Using Equivalent Gold Sequences for Parallel Data Transmissi...

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8th International Conference on Information, Control, and Communication Technologies, ICCT 2024

作者： Kukunin, Dmitry Berezkin, Alexander Kirichek, Ruslan Bonch-Bruevich Saint-Petersburg University of Telecommunications Communication Networks and Data Transmission Department Saint-Petersburg Russia Bonch-Bruevich Saint-Petersburg University of Telecommunications Software Engineering and Computer Science Department Saint-Petersburg Russia

ISBN: (纸本)9798331517564

This article describes a new technology for the formation of compacted code structures based on maximum length sequences. The most important distinguishing feature of the code division method presented here is the absence of requirements for the orthogonality of sequences that can be used as address combinations when organizing parallel data transmission. Thus, the identification of information recipients is carried out at the physical level using special signal-code structures. At the same time, it is possible to implement a system with anonymous recipients, when any of the recipient nodes does not have information about what data was transferred to other nodes. The communication system model built in accordance with such principles allows the use of the most noise-resistant type of modulation, for example, binary phase-shift keying. This, in turn, ensures the minimum peak factor of the signal. It is also worth noting that the main tool for processing the resulting compacted code structures are dual bases of Galois fields. © 2024 IEEE.

关键词： Code division multiple access

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