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Generative AI for Energy Harvesting Internet of Things Network: Fundamental, Applications, and Opportunities

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arXiv 2024年

作者： Xie, Wenwen Sun, Geng Li, Jiahui Wang, Jiacheng Du, Hongyang Niyato, Dusit Dobre, Octavia A. The College of Computer Science and Technology Jilin University Changchun130012 China The College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore The Faculty of Engineering and Applied Science Memorial University Canada

Internet of Things (IoT) devices are typically powered by small-sized batteries with limited energy storage capacity, requiring regular replacement or recharging. To reduce costs and maintain connectivity in IoT networks, energy harvesting technologies are regarded as a promising solution. Notably, due to its robust analytical and generative capabilities, generative artificial intelligence (GenAI) has demonstrated significant potential in optimizing energy harvesting networks. Therefore, we discuss key applications of GenAI in improving energy harvesting wireless networks for IoT in this article. Specifically, we first review the key technologies of GenAI and the architecture of energy harvesting wireless networks. Then, we show how GenAI can address different problems to improve the performance of the energy harvesting wireless networks. Subsequently, we present a case study of unmanned aerial vehicle (UAV)-enabled data collection and energy transfer. The case study shows distinctively the necessity of energy harvesting technology and verify the effectiveness of GenAI-based methods. Finally, we discuss some important open directions. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

Leveraging Digital Twins and AI for Enhanced Clinical Decision Support in Endometrial Cancer Treatment

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Leveraging Digital Twins and AI for Enhanced Clinical Decisi...

IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： Suresh Palarimath Pyingkodi Maran Wilfred Blessing N. R Cibi. A S. J. Kavitha Steffi S College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Sultanate of Oman Department of Computer Application Kongu Engineering College Erode India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Sultanate of Oman Data & Analytics Visualizaation – Delivery Virtusa Consulting Service Private Limited Chennai India Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Digital twins are a relatively new concept that have lately garnered interest in the industrial sector. Digital twins are virtual models that mirror actual products. In this research, we suggest using the technology of digital twins to the field of healthcare in order to enhance clinical decision support and to provide more individualized treatment for patients. When artificial intelligence (AI) is combined with digital twins, healthcare professionals are able to more effectively analyze large volumes of varied data and increase their ability to make diagnostic and therapeutic decisions. In this research, we provide a conceptual framework for leveraging digital twins and AI to solve existing limits in cancer care, notably endometrial cancer therapy. This research study mainly focuses on cancer care, and more specifically on endometrial cancer treatment. In addition, we analyze the possible challenges and opportunities that may arise throughout the process of integrating this technology in healthcare settings. Our overarching objective is to improve the standard of treatment as well as the clinical results for people who have cancer.

关键词： Accuracy Cancer treatment Medical services Machine learning Digital twins Mirrors Artificial intelligence Wearable sensors Standards Cancer

Controlling the False Discovery Rate in Subspace Selection

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arXiv 2024年

作者： Díaz, Mateo Chandrasekaran, Venkat Department of Applied Mathematics and Statistics Johns Hopkins University BaltimoreMD21218 United States Departments of Computing and Mathematical Sciences and of Electrical Engineering California Institute of Technology PasadenaCA91125 United States

Controlling the false discovery rate (FDR) is a popular approach to multiple testing, variable selection, and related problems of simultaneous inference. In many contemporary applications, models are not specified by discrete variables, which necessitates a broadening of the scope of the FDR control paradigm. Motivated by the ubiquity of low-rank models for high-dimensional matrices, we present methods for subspace selection in principal components analysis that provide control on a geometric analog of FDR that is adapted to subspace selection. Our methods crucially rely on recently-developed tools from random matrix theory, in particular on a characterization of the limiting behavior of eigenvectors and the gaps between successive eigenvalues of large random matrices. Our procedure is parameter-free, and we show that it provides FDR control in subspace selection for common noise models considered in the literature. We demonstrate the utility of our algorithm with numerical experiments on synthetic data and on problems arising in single-cell RNA sequencing and hyperspectral *** Codes 62H15, 62H25, 62H12, 62R07 Copyright © 2024, The Authors. All rights reserved.

关键词： Principal component analysis

Real-Time Seedless Post-Processing for Quantum Random Number Generators

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arXiv 2024年

作者： Li, Qian Zhou, Hongyi Shenzhen lnternational Center For Industrial And Applied Mathematics Shenzhen Research Institute of Big Data Shenzhen China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

Quantum-proof randomness extraction is essential for handling quantum side information possessed by a quantum adversary, which is widely applied in various quantum cryptography tasks. In this study, we introduce a real-time two-source quantum randomness extractor against quantum side information. Our extractor is tailored for forward block sources, a novel category of min-entropy sources introduced in this work. These sources retain the flexibility to accommodate a broad range of quantum random number generators. Our online algorithms demonstrate the extraction of a constant fraction of min-entropy from two infinitely long independent forward block sources. Moreover, our extractor is inherently block-wise parallelizable, presenting a practical and efficient solution for the timely extraction of high-quality randomness. Applying our extractors to the raw data of one of the most commonly used quantum random number generators, we achieve a simulated extraction speed as high as 64 Gbps. Copyright © 2024, The Authors. All rights reserved.

关键词： Extraction

Navigating Towards Fairness with Data Selection

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arXiv 2024年

作者： Zhang, Yixuan Li, Zhidong Wang, Yang Chen, Fang Fan, Xuhui Zhou, Feng School of Statistics and Data Science Southeast University China Data Science Institute University of Technology Sydney Australia School of Computing Macquarie University Australia Center for Applied Statistics School of Statistics Renmin University of China China Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing China

Machine learning algorithms often struggle to eliminate inherent data biases, particularly those arising from unreliable labels, which poses a significant challenge in ensuring fairness. Existing fairness techniques that address label bias typically involve modifying models and intervening in the training process, but these lack flexibility for large-scale datasets. To address this limitation, we introduce a data selection method designed to efficiently and flexibly mitigate label bias, tailored to more practical needs. Our approach utilizes a zero-shot predictor as a proxy model that simulates training on a clean holdout set. This strategy, supported by peer predictions, ensures the fairness of the proxy model and eliminates the need for an additional holdout set, which is a common requirement in previous methods. Without altering the classifier’s architecture, our modality-agnostic method effectively selects appropriate training data and has proven efficient and effective in handling label bias and improving fairness across diverse datasets in experimental evaluations. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

Advancing Post-growth HCI

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Advancing Post-growth HCI

2025 CHI Conference on Human Factors in computing Systems, CHI EA 2025

作者： Sharma, Vishal Lin, Hongjin Wani, Asra Sakeen Katzman, Jared Lee Tuli, Anupriya Karusala, Naveena Bardzell, Shaowen Becker, Christoph Tomitsch, Martin Kumar, Neha School of Interactive Computing Georgia Institute of Technology AtlantaGA United States School of Engineering and Applied Sciences Harvard University AllstonMA United States IIIT Delhi Delhi India School of Information University of Michigan Ann ArborMI United States KTH Royal Institute of Technology Stockholm Sweden Faculty of Information University of Toronto TorontoON Canada Transdisciplinary School University of Technology Sydney SydneyNSW Australia Georgia Tech AtlantaGA United States

ISBN: (纸本)9798400713958

The field of Human-Computer Interaction (HCI) both shapes and is shaped by the forces of economic growth. Extending the calls to move beyond the growth imperative in HCI, this workshop aims to bring HCI researchers, designers, and practitioners together in a critical dialogue on examining the often-hidden ways growth patterns manifest in HCI. We ask how the HCI community can use its research, design, and praxis to build a more emancipatory future. The workshop aims to nurture the Post-growth HCI Collective of scholars and practitioners to work together, in solidarity, to reflect and resist the commodification and drive for infinite capital accumulation in/through digital technologies for the betterment of HCI and the broader computing community. Through collective action, we aim to operationalize post-growth principles in HCI, contributing to more tangible pathways toward socio-ecologically just and sustainable technology-mediated futures for the planet and its residents. © 2025 Copyright held by the owner/author(s).

关键词： Green development

Data Trading and Monetization: Challenges and Open Research Directions

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arXiv 2024年

作者： Ramadan, Qusai Boukhers, Zeyd AlShaikh, Muath Lange, Christoph Jürjens, Jan Institute of Software Engineering University of Koblenz Germany Fraunhofer Institute for Applied Information Technology FIT Uniklinik Köln Germany College of Computing and Informatics Saudi Electronic University Saudi Arabia Fraunhofer Institute for Applied Information Technology FIT RWTH Aachen University Germany University of Koblenz Fraunhofer-Institute for Software and Systems Engineering ISST Germany

Traditional data monetization approaches face challenges related to data protection and logistics. In response, digital data marketplaces have emerged as intermediaries simplifying data transactions. Despite the growing establishment and acceptance of digital data marketplaces, significant challenges hinder efficient data trading. As a result, few companies can derive tangible value from their data, leading to missed opportunities in understanding customers, pricing decisions, and fraud prevention. In this paper, we explore both technical and organizational challenges affecting data monetization. Moreover, we identify areas in need of further research, aiming to expand the boundaries of current knowledge by emphasizing where research is currently limited or lacking. © 2024, CC BY.

关键词： Commerce

An Energy Efficient IPv6 Packet Delivery Outline for Industrial IoT Over G.9959 Protocol Based Wireless Sensor Network (WSN)

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SN Computer Science 2024年第7期5卷 813页

作者： Veena, R.S. Khambra, Geetanjli Sayal, Rishi Singh, Nagendra Dhandayuthapani, V. Bala Sar, Sumit Kumar Department of Information Science and Engineering Dayananda Sagar Academy of Technology and Management Kanakapura Road Karnataka Bengaluru India Department of Computer Science and Applications The Bhopal School of Social Sciences MP Bhopal India Department of Computer Science and Engineering Guru Nanak Instructions Technical Campus (Autonomous) Ibrahimpatnam Ranga Reddy District Telangana Hyderabad 501506 India Department of Electrical and Electronics Engineering Trinity College of Engineering and Technology Telangana Karimnagar 505001 India Department of IT College of Computing and Information Sciences University of Technology and Applied Sciences Shinas campus Shinas Oman Department of Computer Science BIT Durg Chattisgarh Durg India

The world is witnessing a significant paradigm shift in the delivery and management of industrial services, driven by the need for automated manufacturing in industries such as mining. This transformation is pivotal as it leverages the Internet to interconnect people and devices, giving rise to a new field known as the Industrial Internet of Things (IIoT). IIoT represents the fusion of IoT’s computational capabilities with the principles of the industrial revolution, facilitating extensive scalability through the transition from IPv4 to IPv6. A crucial challenge within IIoT is the battery life of sensor nodes, which is directly influenced by the efficiency of their operating modules and protocols. Various protocols have been tested within IIoT-based Wireless Sensor Networks (WSNs) to address this issue. However, these protocols often fall short in terms of data rate performance, which is essential for supporting the diverse range of IIoT applications and services. This manuscript investigates the IPv6 packet delivery rate, focusing on energy efficiency and delay. The research identifies the G.9959 protocol as superior to IEEE 802.15.4 for IIoT adoption. Additionally, it examines the impact of bandwidth variation on energy efficiency. Through comprehensive simulations, the proposed scheme demonstrates superior performance compared to existing protocols, highlighting its potential for enhancing IIoT applications. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： G.9959 IIoT IPv6 OPD WSN

TrIM: Transformed Iterative Mondrian Forests for Gradient-based Dimension Reduction and High-Dimensional Regression

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arXiv 2024年

作者： Baptista, Ricardo O’Reilly, Eliza Xie, Yangxinyu Computing and Mathematical Sciences Department California Institute of Technology United States Department of Applied Mathematics and Statistics Johns Hopkins University United States Department of Statistics and Data Science University of Pennsylvania United States

We propose a computationally efficient algorithm for gradient-based linear dimension reduction and high-dimensional regression. The algorithm initially computes a Mondrian forest and uses this estimator to identify a relevant feature subspace of the inputs from an estimate of the expected gradient outer product (EGOP) of the regression function. In addition, we introduce an iterative approach known as Transformed Iterative Mondrian (TrIM) forest to improve the Mondrian forest estimator by using the EGOP estimate to update the set of features and weights used by the Mondrian partitioning mechanism. We obtain consistency guarantees and convergence rates for the estimation of the EGOP matrix and the random forest estimator obtained from one iteration of the TrIM algorithm. Lastly, we demonstrate the effectiveness of our proposed algorithm for learning the relevant feature subspace across a variety of settings with both simulated and real data. Copyright © 2024, The Authors. All rights reserved.

关键词： Iterative methods