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Enhancing WRSN Sustainability through On-demand Directional Wireless Charging with Multiple Green-powered Mobile Vehicles

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IEEE Internet of Things Journal 2025年第11期12卷 18019-18030页

作者： Ma, Xiongbo Liu, Xilong Ansari, Nirwan Ding, Jun The School of Information Science and Engineering Yunnan University Yunnan Kunming650500 China The Advanced Networking Laboratory Helen & John C. Hartmann Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

Current research on scheduling mobile charging vehicles (MCVs) generally focuses on periodic and omnidirectional charging of sensor nodes (SNs). However, this approach leads to significant energy wastage, especially when relying on fossil energy sources. In this work, we propose to schedule multiple green energy-powered MCVs equipped with directional antennas to efficiently charge SNs. We first develop an on-demand and directional charging model based on far-field wireless charging. Then, we formulate the SN survival maximization problem to efficiently prolong the lifetime of wireless rechargeable sensor networks (WRSNs). Given the NP-hard nature of this optimization problem, we develop the three-Step directIonal wireless charGiNg (SIGN) algorithm to efficiently solve this problem. SIGN strategically determines the MCVs’ anchor points (APs), traveling paths and wireless energy emitting directions. Finally, we conduct extensive simulation experiments to validate the superiority of our proposed algorithm in optimizing energy usage and prolonging network lifetime. © 2014 IEEE.

关键词： Wireless charging

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Edge-Driven Industrial Computing Power Networks: Digital Twin-Empowered Service Provisioning by Hybrid Soft Actor-Critic

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8095-8109页

作者： Zhang, Long Song, Deng-Ao Zhang, Hongliang Tian, Ni Zhuang, Zirui Niyato, Dusit Han, Zhu Hebei University of Engineering School of Information and Electrical Engineering Handan056038 China Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Nanyang Technological University College of Computing and Data Science 639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

With the proliferation of data-intensive industrial applications, the collaboration of computing powers among standalone edge servers is vital to provision such services for smart devices. In this paper, we propose an edge-driven industrial computing power network (CPN) by orchestrating the computing and network resources of edge servers through the centralized resource scheduling and decentralized task computing. However, efficient task offloading and collaborative processing is challenging, which requires higher degrees of network automation and intelligence. Therefore, we incorporate digital twins (DTs) into the edge-driven CPN architecture, where the DTs are created as the digital replicas to assist both the computation offloading and collaborative processing. A joint optimization problem of the computing power assignment, service association, task partition, and transmit power control is formulated for maximizing the system average weighted utility. Due to the temporal-spatial variability of tasks and the resulted dynamic environment, we transform the original problem as a Markov decision process aiming at maximizing the long-term average weighted utility. To efficiently handle the high-dimensional discrete-continuous action space, a hybrid soft actor-critic based deep reinforcement learning algorithm is developed for optimizing the joint design. Simulation results validate the superiority of our proposed algorithm over the benchmarks, showing the significant gains obtained by integrating DTs into the edge-driven CPN. © 1967-2012 IEEE.

关键词： Markov processes

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BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration 31

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration

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31st IEEE International Symposium on High Performance computer Architecture, HPCA 2025

作者： Chen, Yuzong Abouelhamayed, Ahmed F. Dai, Xilai Wang, Yang Andronic, Marta Constantinides, George A. Abdelfattah, Mohamed S. Cornell University Computer Systems Lab United States Systems and Networking Research Group Microsoft Research United States Imperial College London Department of Electrical and Electronic Engineering United Kingdom

ISBN: (纸本)9798331506476

Large language models (LLMs) have demonstrated remarkable performance across various machine learning tasks. Yet the substantial memory footprint of LLMs significantly hinders their deployment. In this paper, we improve the accessibility of LLMs through BitMoD1, an algorithm-hardware co-design solution that enables efficient LLM acceleration at low weight precision. On the algorithm side, BitMoD introduces fine-grained data type adaptation that uses a different numerical data type to quantize a group of (e.g., 128) weights. Through the careful design of these new data types, BitMoD is able to quantize LLM weights to very low precision (e.g., 4 bits and 3 bits) while maintaining high accuracy. On the hardware side, BitMoD employs a bitserial processing element to easily support multiple numerical precisions and data types;our hardware design includes two key innovations: First, it employs a unified representation to process different weight data types, thus reducing the hardware cost. Second, it adopts a bit-serial dequantization unit to rescale the per-group partial sum with minimal hardware overhead. Our evaluation on six representative LLMs demonstrates that BitMoD significantly outperforms state-of-the-art LLM quantization and acceleration methods. For discriminative tasks, BitMoD can quantize LLM weights to 4 -bit with © 2025 IEEE.

关键词： Integrated circuit design

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Enhanced Sensitivity of ISFET pH-Sensor Utilizing Reduced Graphene Oxide 6

Enhanced Sensitivity of ISFET pH-Sensor Utilizing Reduced Gr...

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6th Iranian International Conference on Microelectronics, IICM 2024

作者： Estakhroyeh, Hossein Rezaei Mehran, Mahdiyeh Rashedi, Esmat Shahid Bahonar University of Kerman RF MEMS and Bio-Nano Electronics Lab Department of Electrical Engineering Kerman Iran Graduate University of Advanced Technology Faculty of Electrical and Computer Engineering Kerman Iran

ISBN: (纸本)9798331511371

With an emphasis on the sensitivity of various gate-sensing layers, the current work sought to examine how well Ion-Sensitive Field-Effect Transistors (ISFETs) function as pH sensors. Reduced Graphene Oxide (rGO) has been investigated as a potential sensing material for ISFET sensors, however Metal Oxides have historically been the material of choice for the gate-sensing layer because of its stability and dependability. ISFET sensors using rGO as the sensing material and those with traditional Metal Oxide gate layers were thoroughly compared. The performance of the sensors was assessed under a range of operating situations by multiphysics modeling with finite element methods using COMSOL Multiphysics software. The results showed a considerable improvement in sensor performance when rGO was used as the gate-sensing layer. A lower threshold voltage needed to activate the sensor and a higher sensitivity to pH variations were two of these enhancements. In ISFET sensors for pH monitoring, this work emphasizes the benefits of adopting alternate gate materials, such rGO. For the purpose of creating more dependable and efficientpH-sensing instruments for a range of scientific and industrial applications, it is helpful to analyze the fundamental mechanics and performance characteristics of sensors. © 2024 IEEE.

关键词： Threshold voltage

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Power Allocation for Frequency-Modulated OFDM Wireless Systems 100

Power Allocation for Frequency-Modulated OFDM Wireless Syste...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Son, Joohyun Choi, Sooyong Roberts, Ian P. Hong, Daesik Yonsei University Information and Telecommunication Lab School of Electrical and Electronic Engineering Seoul Korea Republic of Yonsei University Advanced Communications Lab School of Electrical and Electronic Engineering Seoul Korea Republic of Ucla Wireless Lab Department of Electrical and Computer Engineering Los AngelesCA United States

ISBN: (纸本)9798331517786

Frequency-modulated orthogonal frequency division multiplexing (FM-OFDM) is a recently proposed waveform which overcomes the notoriously high peak-to-average power ratio (PAPR) of traditional OFDM wireless systems. Employing this new FM-OFDM waveform, however, will demand a redesign of certain signal processing mechanisms, since those tailored to traditional OFDM waveforms may no longer be optimal or even applicable. This paper proposes two novel power allocation schemes for FM-OFDM systems, both of which take into account this new waveform's unique interactions with noise and with the propagation channel. We analytically derive and optimize closed-form expressions for both of the proposed power allocation schemes, the first of which improves average bit error rate (BER) while the second maximizes spectral efficiency. Our simulation results illustrate the improvements in BER and in data rate when employing our power allocation schemes over conventional benchmarks but also underscore what regimes they should be employed to maximize these gains. © 2024 IEEE.

关键词： Orthogonal frequency division multiplexing

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ChromaSense-Empowering Health, Empowering You

ChromaSense-Empowering Health, Empowering You

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2023 IEEE Biomedical Circuits and Systems Conference, BioCAS 2023

作者： Durbha, Ravi Koomson, Valencia Tufts University Advanced Integrated Circuits & Systems Lab Department of Electrical & Computer Engineering MedfordMA United States

ISBN: (纸本)9798350300260

Wearable devices have transformed from novelties into indispensable companions for millions, finding applications in health and wellness, empowering individuals to proactively manage their well-being. Among these, pulse oximeters play a vital role in detecting health issues like hypoxemia. Yet, traditional oximeters struggle with accuracy in individuals with darker skin tones and lack multi-vital sign monitoring using Photoplethysmogram (PPG) signals. Addressing these challenges, we introduce ChromaSense, a Skintone Compensated Pulse Oximeter (SCPO) equipped with advanced sensors and intelligent algorithms. It aims to provide precise oxygen saturation readings for all skin tones, ensuring equitable access to quality healthcare. By addressing pulse oximetry disparities, ChromaSense seeks to enhance patient outcomes, promote inclusivity in healthcare, revolutionize medical diagnostics and treatment, and drive a patient-centered healthcare transformation. © 2023 IEEE.

关键词： Accessible healthcare Business Canvas model Chronic health patient-centric diagnosis Photoplethysmogram Pulse Oximeter Purpose Launchpad radar Tele-medicine Value Preposition

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Site-Specific Beam Alignment without Explicit Channel Knowledge via Deep Learning 58

Site-Specific Beam Alignment without Explicit Channel Knowle...

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58th Asilomar Conference on Signals, Systems and computers, ACSSC 2024

作者： Kwak, Jong Woo Yoo, Hanju Roberts, Ian P. Chae, Chan-Byoung School of Integrated Technology Yonsei University Intelligence Networking Lab. Seoul03722 Korea Republic of Ucla Wireless Lab Department of Electrical and Computer Engineering Los AngelesCA90095 United States

ISBN: (纸本)9798350354058

Quickly and accurately steering the highly directional beams of a millimeter-wave (mmWave) base station (BS) toward the intended user(s) is a challenging task known as the beam alignment problem. Prior work has identified machine learning (ML) as a promising tool for developing fast, effective beam alignment solutions through site-specific optimization. However, these approaches typically rely on training datasets containing the propagation channel information between the BS and virtually every possible user within the site. Acquiring such datasets is impractical through channel estimation and time-consuming when using ray-tracing tools. In this paper, we propose a novel ML-driven framework that relies solely on measurements of received signal power, collected by users during standard 5G beam sweeping procedures, for training. By leveraging these beam sweeping measurements, our model implicitly learns features of the surrounding environment, enabling the construction of a sparse probing beam codebook and a corresponding beam selector for rapid beam alignment. Remarkably, our approach matches the speed and effectiveness of state-of-the-art methods trained on perfect explicit channel data, even when the beam sweeping measurements used for training are noisy. © 2024 IEEE.

关键词： Channel estimation

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Robust Wireless Power Transfer by Self-Oscillating Controlled Inverter and Circular Pads 3

Robust Wireless Power Transfer by Self-Oscillating Controlle...

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3rd International Conference on electrical Machines and Drives, ICEMD 2023

作者： Eikani, Alireza Amirkhani, Mohammad Jafari, Hossein Vaez-Zadeh, Sadegh Mirsalim, Mojtaba Khaburi, Davood Arab University of Tehran Advanced Motion Systems Research Lab School of Electrical and Computer Engineering College of Engineering Tehran Iran Department of Electrical Engineering Tehran Iran Iran University of Science and Technology Department of Electrical Engineering Tehran Iran

ISBN: (纸本)9798350393743

Inductive wireless power transfer (WPT) faces significant challenges in achieving high efficiency and stable output power for charging mobile devices like EVs. Using circular pads (CP) as the transmitter (Tx) and receiver (Rx) and a self-oscillating controlled inverter, this paper demonstrates a robust WPT system that maintains high constant transfer efficiency and output power in a defined region regardless of coupling coefficient variations without communication between Tx and Rx or control circuits for the receiver side. In contrast to couplers with non-identical Tx and Rx pads and multiple decoupled coils in the Rx pad, the compact CP-CP magnetic coupler with a single identical coil on the Tx and Rx is more robust at a wider transfer distance (air gap) and lateral and rotational misalignment between the Tx and Rx. The proposed 1-kW robust WPT system with up to a 20 cm air gap between the Tx and Rx and up to 12 cm lateral misalignment in all directions at a 15 cm air gap and all degrees of rotational misalignment can tolerate coupling variations and achieve constant output power with 93% constant transfer efficiency. © 2023 IEEE.

关键词： Energy transfer

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Theoretical analysis and design of a dual-wavelength and selectable all-optical broadband QDs semiconductor optical amplifier (QDs-SOA) with inhomogeneous broadening

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Optical and Quantum Electronics 2025年第2期57卷 1-26页

作者： Eyvazi, Mahdiyeh Yadipour, Reza Rostami, Ali Rostami, Parisa Mirtagioglu, Hamid Faculty of Electrical and Computer Engineering University of Tabriz Tabriz*** Iran Faculty of Electrical and Computer Engineering University of Tabriz Tabriz*** Iran SP-EPT Lab ASEPE Company Industrial Park of Advanced Technologies Tabriz Iran Faculty of Medicine Tabriz University of Medical Science Tabriz Iran Department of Statistics Faculty of Science and Literature University of Bitlis Eren Bitlis Turkey

This study delves into the significant role played by Quantum Dot Semiconductor Optical Amplifiers (QD-SOAs) in meeting the ever-growing bandwidth demands. QD-SOAs offer a unique blend of cost-effectiveness, integration capabilities, wide bandwidth, rapid responsiveness, robust power output, stability, and spectral adaptability, driving notable advancements in optical communication systems. In this work, we introduce a novel two-wavelength amplifier structure based on Quantum Dot Semiconductor Optical Amplifiers (QD-SOAs) that utilizes quantum dots of different sizes to achieve efficient amplification at specific mid-infrared wavelengths. This innovative approach, which incorporates quantum dots with varying sizes, enables enhanced performance by optimizing the amplification process for each specific wavelength. Furthermore, this work demonstrates the use of tailored optical pumping mechanisms that enhance the carrier recovery process and reduce carrier relaxation times in the active region. This novel optical pumping technique leads to a significant increase in the efficiency and speed of the amplifier, distinguishing this study from others in the field. Simulation results provide detailed insights into the complex interplay between carrier interactions and gain spectra, offering a comprehensive understanding of QD-SOA operational dynamics. The proposed Quantum Dot Semiconductor Optical Amplifier (QD-SOA) achieves significant advancements in optical communication, offering maximum amplification rates of 26.9 and 19.9 times for QD1 and QD2, respectively, and bandwidths of 12 THz and 15 THz. The study highlights the role of quantum dot size, homogeneous and inhomogeneous broadening, and optical pumping in enhancing gain and performance, demonstrating the potential of QD-SOAs for high-gain, wide-bandwidth applications in photonic systems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Semiconductor quantum dots

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Spatial Feature Topology-Based Heterogeneous Knowledge Transfer Framework for Long-Term Fingerprint Positioning

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IEEE Transactions on Vehicular Technology 2025年

作者： Si, Haonan Guo, Xiansheng Boateng, Gordon Owusu Yang, Yin Ansari, Nirwan University of Electronic Science and Technology of China Department of Electronic Engineering Chengdu611731 China New Jersey Institute of Technology Advanced Networking Lab. Department of Electrical and Computer Engineering NewarkNJ07102 United States

Transfer learning (TL) is effective for addressing distribution discrepancies in fingerprint positioning. However, existing TL frameworks cannot react well to the heterogeneous feature dimensions of fingerprints caused by the topology variations of base stations (BSs) in evolving long-term environments. To address this issue, we propose a spatial feature topology-based heterogeneous knowledge transfer framework tailored for long-term fingerprint positioning (SFTP). Firstly, we partition the heterogeneous features into common and domain-specific (including source and target-specific) features from static and dynamic environmental components perspectives, respectively. Notably, we observe that the features captured from BSs with significant spatial distances differ across all samples, while those from BSs with close spatial distances show higher similarity. Based on these observations, we approximate the cross-domain mapping for each domain-specific feature by integrating the mappings of similar common features, which are easy to achieve using deep neural networks (DNNs). Subsequently, the heterogeneous feature spaces are effectively transformed into homogeneous counterparts, and a deep adaptation network (DAN) is utilized to further predict the positions for testing samples. Hence, SFTP is capable of capturing evolutionary environmental information for long-term positioning. Finally, real-world experimental results demonstrate the superiority and robustness of the proposed framework. © 1967-2012 IEEE.

关键词： Deep neural networks

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