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检索条件"丛书名=Computer Communications and Networks"

共 4934 条记录，以下是71-80 订阅

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Enabling Reliable Environmental Sensing with LoRa, Energy Harvesting, and Domain Adaptation 33

Enabling Reliable Environmental Sensing with LoRa, Energy Ha...

引用

33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Ma, Aitian Rodriguez, Jean C. Tonday Sha, Mo Florida Int Univ Knight Fdn Sch Comp & Informat Sci 11200 SW 8th St Miami FL 33199 USA

ISBN: (纸本)9798350348439;9798350384611

Environmental sensing is essential for many applications. Many existing efforts rely on the readings provided by the weather stations maintained by federal, regional, or local government agencies. While the accuracy of the readings provided by those weather stations is high, the ability of such data to reflect the temperature variability experienced by urban populations is generally low. Therefore, recent studies have proposed to deploy new infrastructures with low-power communication and energy harvesting capabilities to provide fine-scale measurements. Recently, there has been an increasing interest in deploying environmental sensing systems with LoRa radios and solar panels. However, there have been very few studies looking into the reliability of solar power in the LoRa-based environmental sensing settings. In this paper, we present an empirical study that investigates how well solar energy powers an environmental sensing platform. Our study shows that solar energy generation forecasting plays an important role in the performance of the sensing platform. To address the challenges, we develop a novel solution that leverages LoRa, energy harvesting, and domain adaptation to enable reliable environmental sensing. Experimental results show that our solution outperforms the baselines and effectively supports end devices to perform environmental sensing operations without interruptions.

关键词： Environmental sensing LoRa energy-harvesting domain adaptation

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Securing Federated Learning: Enhancing Defense Mechanisms against Poisoning Attacks 33

Securing Federated Learning: Enhancing Defense Mechanisms ag...

引用

33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Birdman, Benjamin Thamilarasu, Geethapriya Univ Washington Bothell Bothell WA 98011 USA

ISBN: (纸本)9798350348439;9798350384611

In recent years, Federated Learning (FL) has emerged as a powerful paradigm, revolutionizing machine learning by harnessing distributed computational resources. FL enables collaborative model training across multiple devices or servers without centralizing data, thereby enhancing model inference quality. However, these distributed systems are not immune to security threats, particularly poisoning attacks leading to compromised model performance and potential privacy breaches. In this paper, we develop defense strategies to mitigate poisoning attacks in federated learning systems. Specifically, we address the challenge posed by distributed backdoor poisoning attacks against existing poisoning defenses in federated learning systems. We propose a defense solution, known as Area Similarity FoolsGold (ASF), that builds upon the existing cosine similarity solution by integrating Triangle Area Similarity and Sector Area Similarity algorithms. Our solution aims to enhance the identification of maliciously compromised clients and mitigate the impact of distributed backdoor attacks in federated learning systems.

关键词： Federated learning

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iCrop: Enabling High-Precision Crop Disease Detection via LoRa Technology 33

iCrop: Enabling High-Precision Crop Disease Detection via Lo...

引用

33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Tao, Xu Butcher, Jackson Silvestri, Simone Shot, Flavin Espo Univ Kentucky Dept Comp Sci Lexington KY 40506 USA St Louis Univ Dept Comp Sci St Louis MO 63103 USA

ISBN: (纸本)9798350348439;9798350384611

Crop disease recognition is a fundamental keystone in enabling disease control, limiting disease spread, and mitigating farmers' losses. Recently, advanced image processing techniques for crop disease detection, based on deep learning, have gained significant popularity. However, the practical deployment of these models in real farms remains challenging. This is mostly due to the lack of Internet connectivity which prevents the transmission of the acquired images to sufficiently powerful edge/cloud servers to execute such complex models. LoRa has emerged as a promising network solution for rural areas, thanks to its extensive communication range and cost-efficient deployment. However, the low data rate of this technology prevents its effective application for the transmission of large images for crop disease detection. In this paper, we propose a LoRa-based framework called iCrop. iCrop enables high disease classification accuracy while exploiting the cost-effectiveness of LoRa transmission technologies. Specifically, iCrop is based on a LoRa Node, which captures crop leaf images and preprocesses them through image segmentation. The node selects and transmits the most informative segments over LoRa to the LoRa Edge Server. The server, in turn, runs the disease classification using a Convolutional Nerual Network (CNN) deep learning model empowered with majority voting among segments. To prevent data losses, typical of LoRa transmission, we develop a reliable transmission protocol on top of LoRa, which takes care of retransmissions and efficient communication. Extensive experiments on a real LoRa testbed show the advantages over two comparison approaches with respect to several performance metrics.

关键词： LoRa Crop Disease Monitoring Smart Farming Precision Agriculture

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Uncertainty-Aware Forecasting of Computational Load in MECs Using Distributed Machine Learning: A Tokyo Case Study 33

Uncertainty-Aware Forecasting of Computational Load in MECs ...

引用

33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Aupke, Phil Nakao, Akihiro Kassler, Andreas Karlstad Univ Dept Math & Comp Sci Karlstad Sweden Univ Tokyo Tokyo Japan Deggendorf Inst Technol Dept Comp Sci Deggendorf Germany

ISBN: (纸本)9798350348439;9798350384611

Mobile Edge Clouds (MECs) address the critical needs of bandwidth-intensive, latency-sensitive mobile applications by positioning computing and storage resources at the network's edge in Edge Data Centers (EDCs). However, the diverse, dynamic nature of EDCs' resource capacities and user mobility poses significant challenges for resource allocation and management. Efficient EDC operation requires accurate forecasting of computational load to ensure optimal scaling, service placement, and migration within the MEC infrastructure. This task is complicated by the temporal and spatial fluctuations of computational load. We develop a novel MEC computational demand forecasting method using Federated Learning (FL). Our approach leverages FL's distributed processing to enhance data security and prediction accuracy within MEC infrastructure. By incorporating uncertainty bounds, we improve load scheduling robustness. Evaluations on a Tokyo dataset show significant improvements in forecast accuracy compared to traditional methods, with a 42.04% reduction in Mean Absolute Error (MAE) using LightGBM and a 34.93% improvement with CatBoost, while maintaining minimal networking overhead for model transmission.

关键词： Mobile Edge Cloud Edge Data Center Resource Management CPU-Load Prediction Federated Learning Machine Learning

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ECG-grained Cardiac Monitoring Using RFID 33

ECG-grained Cardiac Monitoring Using RFID

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33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Wang, Ningning Zhao, Tianya Mao, Shiwen Bai, Harrison X. Jiao, Zhicheng Wang, Xuyu Florida Int Univ Knight Fdn Sch Comp & Informat Sci Miami FL 33199 USA Auburn Univ Dept Elect & Comp Engn Auburn AL 36849 USA Johns Hopkins Univ Dept Radiol & Radiol Sci Sch Med Baltimore MD 21287 USA Rhode Isl Hosp Dept Diagnost Imaging Providence RI 02903 USA

ISBN: (纸本)9798350348439;9798350384611

Heartbeat signals are useful to disease prediction, sub-health diagnosis, fatigue warning, and even emotion estimation. There is a compelling need for contactless, easy-to-deploy, and long-term heartbeat monitoring. This paper presents a contactless Radio Frequency Identification (RFID) based system for heartbeat monitoring that leverages the insight that RFID signal fluctuations induced by chest motion are synchronous with both respiration and heartbeat. The proposed system collects the temporal phase information from the tag pair on the body to extract heartbeat signals using a sequence of signal processing techniques. We propose a signal separation method based on empirical mode decomposition (EMD) to obtain heart rate after preprocessing. Furthermore, the estimated signal is input to an enhanced variational autoencoder (VAE) model to recover the heartbeat waveform. Implemented with commercial off-the-shelf (COTS) RFID devices, the system achieves accurate heart rate monitoring with less than 3% relative errors. The detected waveform exhibits a median cosine similarity of 0.83 as compared with the ground truth, which validate the system's wide applicability and high reliability for fine-grained, contactless heartbeat monitoring.

关键词： Radio Frequency Identification (RFID) vital sign monitoring contact-free sensing variational autoencoder

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Privacy-Preserving Edge Assistance for Solving Matrix Eigenvalue Problem 33

Privacy-Preserving Edge Assistance for Solving Matrix Eigenv...

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33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Zhao, Xiaotong Zhang, Hanlin Lin, Jie Kong, Fanyu Yu, Leyun Qingdao Univ Coll Comp Sci & Technol Qingdao Peoples R China Xi An Jiao Tong Univ Sch Comp Sci & Technol Xian Peoples R China Shandong Univ Sch Comp Sci & Technol Jinan Peoples R China JIC IOT CO LTD Nanchang Peoples R China

ISBN: (纸本)9798350348439;9798350384611

The large-scale matrix eigenvalue computation, as a basic mathematical tool, has been widely used in many fields such as face recognition and data analysis. However, local terminal devices lack sufficient resources to undertake heavy computational tasks, which poses a challenge to the applications of eigenvalue computation. In this paper, we propose the first privacy-preserving edge-assisted computation scheme for solving the largest eigenvalue and corresponding eigenvector. We propose a privacy-preserving transformation method to protect data privacy and prevent edge servers from retrieving sensitive information. Meanwhile, we design a verification scheme to ensure the correctness of the results returned by the edge servers. In addition, we design a distributed parallel computing scheme to ensure the efficiency of edge computation. Through theoretical analysis and simulation experiments, we verify the feasibility and efficiency of our proposed scheme.

关键词： Eigenvalue problem edge computing privacypreserving parallel computing

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On the Potential of an Independent Avatar to Augment Metaverse Social networks 33

On the Potential of an Independent Avatar to Augment Metaver...

引用

33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Raptis, Theofanis P. Boldtini, Chiara Conti, Marco Passarella, Andrea CNR Inst Informat & Telemat Rome Italy

ISBN: (纸本)9798350348439;9798350384611

We present a computational modelling approach which targets capturing the specifics on how to virtually augment a Metaverse user's available social time capacity via using an independent and autonomous version of her digital representation in the Metaverse. We motivate why this is a fundamental building block to model large-scale social networks in the Metaverse, and emerging properties herein. We envision a Metaverse-focused extension of the traditional avatar concept: An avatar can be as well programmed to operate independently when its user is not controlling it directly, thus turning it into an agent-based digital human representation. This way, we highlight how such an independent avatar could help its user to better navigate their social relationships and optimize their socializing time in the Metaverse by (partly) offloading some interactions to the avatar. We model the setting and identify the characteristic variables by using selected concepts from social sciences: ego networks, social presence, and social cues. Then, we formulate the problem of maximizing the user's non-avatar-mediated spare time as a linear optimization. Finally, we analyze the feasible region of the problem and we present some initial insights on the spare time that can be achieved for different parameter values of the avatar-mediated interactions.

关键词： Agent Avatar Ego network Metaverse Social cues Social presence

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Towards Protecting On-Device Machine Learning with RISC-V based Multi-Enclave TEE 33

Towards Protecting On-Device Machine Learning with RISC-V ba...

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33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Wang, Yongzhi Boggaram, Venkata Sai Ramya Padmasri Texas A&M Univ Corpus Christi Dept Comp Sci Corpus Christi TX 78412 USA

ISBN: (纸本)9798350348439;9798350384611

On-device machine learning is a trending paradigm that empowers the artificial intelligence of various smart devices, including IoT, mobile, and robotics, etc. On the other hand, this emerging paradigm has brought new security challenges that traditional system security techniques cannot harness. In this paper, we explored the possibility of using multi-enclave Trusted Execution Environments to address these security challenges. We first identified the challenges and threats that on-device machine learning systems are facing. Then, we presented our experimental results of using RISC-V-based multi-enclave TEE to secure on-device machine learning system, demonstrating a promising performance advantage. Finally, we discussed the technical directions for the threats that cannot be completely addressed by the TEE.

关键词： On-Device Machine Learning Trusted Execution Environments RISC-V Machine Learning Security IoT

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Neural Network-based Blocking Prediction for Dynamic Network Slicing 33

Neural Network-based Blocking Prediction for Dynamic Network...

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33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Movva, Nitin Datta Ishigaki, Genya San Jose State Univ Dept Comp Sci San Jose CA 95192 USA

ISBN: (纸本)9798350348439;9798350384611

Network slicing in Optical Transport networks (OTNs) is a promising technology to provide hard resource isolation and performance assurance of services with diverse requirements. When network slices are allowed to request their scaling based on their service traffic trends (i.e., dynamic network slicing), it is difficult for a slice provider to maintain resource isolation and high resource utilization simultaneously. We formulate a blocking prediction problem for the slice provider to assess the possibility of future blocking events for a given set of slice requests. We use a Multi-Layer Perceptron classifier to identify a set of slice requests that would cause blocking. It is demonstrated by the simulations with different request patterns that our proposal outperforms other learning-based approaches. The results indicate that a slice provider could assess the risk of experiencing a violation of service assurance when accepting a set of requests and take an appropriate countermeasure such as admission control, using our prediction module.

关键词： Dynamic network slicing Optical Transport Network (OTN) Request blocking

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Super-resolution Localization and Tracking in WiFi Sensing 33

Super-resolution Localization and Tracking in WiFi Sensing

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33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Wang, Jian Chuang, Jack Sempert, Sebastian Golmie, Nada NIST Wireless Networks Div Gaithersburg MD 20899 USA Tech Univ Ilmenau Inst Informat Technol Ilmenau Germany

ISBN: (纸本)9798350348439;9798350384611

Integrated sensing and communication (ISAC) systems have been investigated by the research and standardization communities in the recent past. Accurately localizing the target and tracking the target's movement are critical for numerous smart Internet of Things (IoT) systems (smart manufacturing, smart transportation, etc.). This paper aims to realize super-resolution localization and tracking in WiFi sensing by leveraging the IEEE 802.11ad beamforming training procedure. We leverage the CLEAN-Space-Alternating Generalized Expectation-maximization (CLEAN-SAGE) algorithm on a single beam sweeping cycle for target localization and investigate the targets' delays and angle estimation. For tracking moving targets, we design mechanisms to estimate the target's motion, including the target's velocity and motion pattern, such as estimating the target's spatial positions over time to obtain the Doppler shift or tracking its trajectory using a Kalman filter. In order to prove that our approach works effectively, we conduct a thorough performance evaluation study. Our evaluation results confirm that the CLEAN-SAGE algorithm can achieve estimation performance beyond the ISAC system's inherent bandwidth and beamwidth constraints. Furthermore, we provide insights into how system configurations, including antenna size, beam overlap, and the number of iterations in the SAGE algorithm, influence its performance.

关键词： Edge processing IoT applications mmWave CLEAN and SAGE WiFi sensing Super-resolution algorithm Localization and tracking

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