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Consumer Communications and Networking Conference, CCNC IEEE

作者： Leonardo Montecchiari Angelo Trotta Luciano Bononi Marco Di Felice Enrico Natalizio Department of Computer Science and Engineering University of Bologna Italy Technology Innovation Institute (TII) United Arab Emirates Advanced Research Center on Electronic Systems “Ercole De Castro” University of Bologna Italy

Several use-cases of the Internet of Things (IoT) rely on the development of large-scale Wireless Sensor Networks (WSNs) in harsh environments characterized by limited Internet connectivity and battery-powered operations. In such scenarios, the failure of a single node due to energy depletion or hardware issues may cause network partitions and disrupt partially or completely the system operations until the intervention of a human operator. In this paper, we investigate the usage of Unmanned Aerial Networks (UAVs) to enable sensory data collection and support resilient communications in presence of faulty sensor nodes. More specifically, we study the possibility of replacing the ground devices with UAVs which are able to temporarily restore the multi-hop communication towards the WSN sink. To this aim, we extended the Uhura framework, a platform for robotic networking, with novel features for automatic network partition detection and UAV-sink coordination. Then, we created a small test-bed composed of a Bluetooth Mesh WSN and one drone, and characterized the performance of the UAV-assisted WSN system in terms of packet delivery ratio of the end-to-end data flows.

关键词： Wireless sensor networks Systems operation Robot kinematics Spread spectrum communication Data collection Robot sensing systems Feature extraction

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Unleashing the Power of Machine Learning: Advancing Early Prediction and Analysis of Diabetes Mellitus

Unleashing the Power of Machine Learning: Advancing Early Pr...

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2023 IEEE World Conference on Communication and Computing, WCONF 2023

作者： Nancy Sil, Sagarika Rani, Ritu Bisla, Nidhi Jaiswal, Garima Sharma, Arun Indira Gandhi Delhi Technical University of Women Department of Information Technology Delhi India Indira Gandhi Delhi Technical University of Women Department of Electronics & Communication Engineering Delhi India University for Women Center of Excellence-Artificial Intelligence Indira Gandhi Delhi Technical Delhi India OP Jindal Global University Jindal Global Business School Sonipat India Bennet University Computer Science Deptt. Noida India

ISBN: (纸本)9798350311204

Diabetes is a condition which makes it difficult to regulate blood sugar/glucose. Diabetes is a leading cause of heart failure, stroke, renal failure, disability, blindness, and kidney failure. As per the figures given by International Diabetes Federation, there are 382 million diabetics worldwide. By 2035, there is a possibility that this will increase to 592 million. There are multiple forms of diabetes, and each one is treated differently. The most crucial step, however, is a precise diagnosis of the level of diabetes. Diabetes risk and severity can be significantly reduced if reliable early prognosis is possible. Here, we are proposing a model to predict diabetes using variables like glucose, body mass index (BMI), age, insulin, etc. using Johndasilva dataset. Several machine learning algorithms, including Random Forest(RF), Support Vector Machine (SVM), and Decision Tree (DT), are employed. The model is then chosen among those with the maximum accuracy. Random Forest gave the greatest accuracy of 99.5%. © 2023 IEEE.

关键词： Forecasting

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Gigabit-rate Quantum Key Distribution on Integrated Photonic Chips

arXiv

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arXiv 2025年

作者： Ng, Si Qi Kanitschar, Florian Zhang, Gong Wang, Chao Centre for Quantum Technologies National University of Singapore Singapore Atominstitut Technische Universität Wien Stadionallee 2 Vienna1020 Austria AIT Austrian Institute of Technology Center for Digital Safety&Security Giefinggasse 4 Vienna1210 Austria Department of Electrical & Computer Engineering National University of Singapore Singapore

Quantum key distribution (QKD) provides information-theoretic security guaranteed by the laws of quantum mechanics, making it resistant to future computational threats, including quantum computers. While QKD technology shows great promise, its widespread adoption depends heavily on its usability and viability, with key rate performance and cost-effectiveness serving as critical evaluation metrics. In this work, we report an integrated silicon photonics-based QKD system that achieves a secret key rate of 1.213 Gbit s−1 over a metropolitan distance of 10 km with polarization multiplexing. Our contributions are twofold. First, in the quantum optical layer, we developed an on-chip quantum transmitter and an efficient quantum receiver that operate at 40 Gbaud/s at room temperature. Second, we designed a discrete-modulated continuous variable (DM CV) QKD implementation with efficient information reconciliation based on polar codes, enabling potentially high-throughput real-time data processing. Our results demonstrate a practical QKD solution that combines high performance with cost efficiency. We anticipate this research will pave the way for large-scale quantum secure networks. Copyright © 2025, The Authors. All rights reserved.

关键词： Qubits

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Distributed Service Discovery over Heterogeneous Robotic Systems-of-Systems

Distributed Service Discovery over Heterogeneous Robotic Sys...

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International Conference on Distributed Computing in Sensor Systems (DCOSS)

作者： Leonardo Montecchiari Angelo Trotta Lorenzo Gigli Lorenzo Bellone Marco Di Felice Enrico Natalizio Department of Computer Science and Engineering University of Bologna Italy Advanced Research Center on Electronic Systems “Ercole De Castro” University of Bologna Italy Technology Innovation Institute (TII) United Arab Emirates

In robotic swarms, nodes must cooperate in order to accomplish complex tasks. Robot interactions may occur at run-time based on the dynamic network topology and on mission-specific goals. For this reason, service discovery mechanisms are fundamental in order to inform each robot about the functionalities offered by the other peers. Although several network discovery protocols have been proposed so far, none of them fits the unique characteristics of robotic swarms in terms of dynamic topology, communication heterogeneity, and latency requirements. In this paper, we fill such gap by designing, implementing and evaluating a novel service discovery mechanisms for generic Robotic Systems-of-Systems (RSoS). The study proposes algorithmic and practical contributions. Regarding the first, we describe a novel distributed service discovery algorithm for RSoS which adapts to highly mobile robotic environments while limiting the network overhead and latency. Regarding the latter, we implemented the proposed algorithm within the Uhura swarm robotics framework; as a result, our solution is able to support multi-radio scenarios where robots are provided with heterogeneous Machine-to-Machine (M2M) communication technologies. In addition, we validate our solution through large-scale simulations and a test-bed in which ground robots are able to discover a Federated Learning (FL) task and join it at run-time to improve the accuracy.

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EEiF: Efficient Isolated Forest with e Branches for Anomaly Detection

EEiF: Efficient Isolated Forest with e Branches for Anomaly ...

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IEEE International Conference on Data Mining (ICDM)

作者： Yifan Zhang Haolong Xiang Xuyun Zhang Xiaolong Xu Wei Fan Qin Zhang Lianyong Qi School of software Nanjing University of Information Science and Technology China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services China School of Computing Macquarie University Australia Uffield Department of Women's & Reproductive Health University of Oxford England College of Computer Science and Software Engineering Shenzhen University China Faculty of Science and Engineering University of Petroleum (East China) China

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

Anomaly detection is a popular research topic in Artificial Intelligence and has been widely applied in network security, financial fraud detection, and industrial equipment failure detection. Isolation forest based methods are the base algorithms to detect anomalies in these scenarios for their simplicity and efficiency, which has been further exploited with multi-folk trees and learning mechanisms to realize the optimal isolation forest for high detection accuracy. However, the optimal isolation forest is time-consuming with the learning mechanisms, resulting in the task failing of time-constrained applications. Moreover, the original optimal isolation forest fails to construct the optimal tree structure restricted by the time complexity. To address the above challenges, we propose an efficient anomaly detection method called EEIF, which realizes the real e-folk structure of the optimal isolation forest in our practical algorithm design. Specifically, we design a distribution that perfectly matches the e-branch theory to construct the optimal isolation forest. Then, we design an FR clustering scheme to achieve fast training of the isolation forest with learning to hash and provide related proofs of accuracy and efficiency. Besides, a parallel algorithm is integrated into our method to reduce prediction time. Finally, extensive experiments are conducted on a large amount of real-world datasets and the results demonstrate that our method significantly improves efficiency while ensuring effectiveness, compared with the state-of-the-art methods.

关键词： Learning systems Training Accuracy Clustering algorithms Forestry Network security Fraud Time complexity Parallel algorithms Anomaly detection

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Maximizing Energy Efficiency of HSRC: A Cache-Aided Time-Domain Power Allocation

Maximizing Energy Efficiency of HSRC: A Cache-Aided Time-Dom...

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International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Yeshen Li Ke Xiong Han Li Bo Ai Pingyi Fan Khaled Ben Letaief Engineering Research Center of Network Management Technology for High Speed Railway of Ministry of Education School of Computer and Information Technology Beijing Jiaotong University Beijing China Collaborative Innovation Center of Railway Traffic Safety Beijing Jiaotong University Beijing China State Key Laboratory of Electronic and Information Engineering School Beijing Jiaotong University Beijing China Department of Electronic Engineering Tsinghua University Beijing China Department of Electrical and Computer Engineering Hong Kong University of Science and Technology (HKUST) Hong Kong

This paper presents a cache-aided time-domain power allocation (CaTDPA) for 5G high-speed railway communications (HSRC), where two kinds of users, online interactive application users (OIUs) and online non-interactive application users (ONIUs), are jointly taken into account. For OIUs, their minimum ground-train data rate demand is guaranteed. For ONIUs, their minimum cache-releasing data rate demand is guaranteed. To achieve the green HSRC design, an optimization problem is formulated to maximize the system energy efficiency (EE) by jointly optimizing the power allocation (PA) and cache releasing start time (CRST) subject to the constraints of total available energy, maximum transmit power, ONIUs’ ground-train data rate threshold, ONIUs’ cache-releasing data rate threshold, and causality of CRST. To address such a non-convex and fractional problem, a two-step solution is designed. First, with a given CRST, a Dinkelbach-based method is applied to obtain the correspondingly optimal PA. Second, the one-dimensional traversal method is used to find the optimal CRST. Experimental results show that our proposed CaTDPA achieves the maximum EE among multiple compared methods while meeting the requirements of OIUs and ONIUs. Moreover, EE will decrease as the OIUs’ ground-train data rate threshold and the train’s moving speed increase, while it is observed that the ONIUs’ cache-releasing data rate threshold has a relatively small effect on the EE of HSRC.

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A BLE Mesh Edge Framework for QoS-Aware IoT Monitoring Systems

A BLE Mesh Edge Framework for QoS-Aware IoT Monitoring Syste...

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IEEE World Forum on Internet of Things (WF-IoT)

作者： Leonardo Montecchiari Angelo Trotta Ivan D. Zyrianoff Luciano Bononi Enrico Natalizio Marco Di Felice Technology Innovation Institute (TII) United Arab Emirates Department of Computer Science and Engineering University of Bologna Italy Advanced Research Center on Electronic Systems “Ercole De Castro” University of Bologna Italy

ISBN: (数字)9798350311617

ISBN: (纸本)9798350311624

Internet of Things (IoT) monitoring applications of-ten require a flexible network design to continuously adjust device configurations and meet Quality of Service (QoS) requirements effectively. In this paper, we provide evidence of this concept in the context of the Bluetooth Low Energy (BLE) Mesh network, where performance heavily relies on tuning various network parameters. Our contributions in this study are threefold. First, we present a software framework designed for BLE Mesh monitoring, QoS metrics computation, and QoS-aware parameter selection. This framework operates on an edge device connected to the BLE gateway, enabling efficient data gathering and decision-making. Second, we propose a Reinforcement Learning (RL) algorithm to determine the optimal BLE Mesh parameters, allowing for the simultaneous fulfillment of multiple QoS requirements. Through the RL technique, we can dynamically adapt network configurations to varying conditions and demands. Third, we conduct thorough validation of our framework on a real-world BLE Mesh test-bed, demonstrating the effectiveness of the proposed technique in network performance enhancement. The results show the ability to maximize Packet Delivery Ratio (PDR), minimize delay, or determine a suitable trade-off between these two metrics, depending on the specific user's needs.

关键词： Performance evaluation Mesh networks Software algorithms Quality of service Reinforcement learning Logic gates Software

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Modifying the Complex Refractive Index of Thin-Films for Nanooptical Applications

Modifying the Complex Refractive Index of Thin-Films for Nan...

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IEEE/LEOS International Conference on Optical MEMS

作者： Marco Künne Thomas Kusserow Bernd Witzigmann Hartmut Hillmer Measurement Technology Group Electrical Engineering and Computer Science University of Kassel Germany Department EEI Institute of Optoelectronics Friedrich-Alexander Universität Erlangen-Nürnberg Germany Institute of Nanostructure Technologies and Analytics (INA) Technological Electronics University of Kassel Germany Center for Interdisciplinary Nanostructure Science and Technology (CINSaT) University of Kassel Germany

ISBN: (数字)9798350384925

ISBN: (纸本)9798350384932

We present a method to generate 2D refractive index patterns in thin-films for nanooptical applications, like metasurfaces and plasmonics. The method is based on standard deposition, lithography and manipulation processes and can be applied to numerous material systems. Examples for modification of indium tin oxide and transition metal nitride layers are given.

关键词： Nanophotonics Indium tin oxide Refractive index Tin Metasurfaces Plasmons Optical variables control

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The Development of GPS Trajectory Filter for Agricultural Machinery Application

The Development of GPS Trajectory Filter for Agricultural Ma...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Yung-Hsing Peng Ying-Hsu Chen Po-Cheng Huang Ho-Chun Lin Szu-Hsuan Wang Kuo-Chuan Wu Digital Transformation Research Institute Institute for Information Industry Kaohsiung City Taiwan Chang Shen Tea Factory Taoyuan City Taiwan Myshine Technology Co. Ltd. Kaohsiung City Taiwan Department of Computer Science and Information Engineering National Pingtung University Pingtung City Taiwan

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

In Taiwan, the number of licensed agricultural machinery has exceeded 200,000 according to the latest statistics from the Ministry of Agriculture (MOA). Therefore, intelligent management of these machinery becomes both interesting and important. In most research studies, a GPS tracker is installed on some target machine, so that the working efficiency of such machine can be analyzed with its movement trajectory. However, we have observed that the pre-processing stage of agricultural trajectories is rarely discussed. By referring to the concept of image processing, in this paper we propose a filter for trajectories from agricultural machinery. The motivation of this filter is to generate a new trajectory, which keeps the shape of the raw trajectory but achieves better data presentation. Experimental results show that our approach can benefit the application of GPS on agricultural machinery, which offers a potential opportunity for intelligent services.

关键词： Electric potential Target tracking Shape Image processing Agricultural machinery Agriculture Trajectory

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The comparison of EEG features in specific Mandarin words’ speech

The comparison of EEG features in specific Mandarin words’ ...

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International Conference on Fuzzy Theory and it's Applications (iFUZZY)

作者： Wen-Shan Tsai Syue-Guan Huang Kuan-Chih Huang Jung-Chih Chen Chin-Teng Lin Institute of Biomedical Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Electronics and Electrical Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Brain Science and Technology Center College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Human-Centric AI Centre (HAI) Faculty of Engineering and Information Technology University of Technology Sydney Ultimo NSW Australia

Due to advancements in brain signal application technology, there has been a growing focus on the human speech Brain-computer Interface (BCI) in recent years. An essential initial phase in crafting speech recognition systems is comprehending the inherent features of nature speech processing. To ensure the decoding capability of human speech BCI, researchers have to investigate the differences between overt speech and imagined speech, and they also need to find the best paradigm to build a reliable BCI. Thus, this study compared the differences in brain dynamics between overt speech and imagined speech while using the four Mandarin words "上" (up), "下" (down), "左" (left), and "右" (right). We first calculated the EEG alpha and beta band power for multiple channels situated in the regions of Broca's and Wernicke's areas (specifically F5, F7, FC3, FC5, and C5), to analyze the brain activity changes while speaking or imagining speech. Second, we used Daubechies 4 (db4) wavelet to do a 7-level decomposition to the EEG signal. The corresponding discrete wavelet transform (DWT) coefficients were further calculated with six statistical features for support vector machine (SVM) classification. We found that the "speak (overt speech)" task and the "imagine (imagined speech)" task resulted in significantly different alpha band power changes, and the beta band shows the same phenomenon. The result of average classification accuracy is 71.97% for "speak" and "imagine" distinction. In conclusion, we analyzed the brain dynamics and find that overt speech and imagined speech could be differentiate through DWT coefficients features. This may pave the way to establish a Mandarin imagined speech BCI.

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