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检索条件"任意字段=IEEE International Conference on Distributed Computing in Sensor Systems"

共 30906 条记录，以下是4201-4210 订阅

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A Middleware for Digital Twin-Enabled Flying Network Simulations Using UBSim and UB-ANC

A Middleware for Digital Twin-Enabled Flying Network Simulat...

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international conference on distributed computing in sensor systems (DCOSS)

作者： Sabarish Krishna Moorthy Ankush Harindranath Maxwell McManus Zhangyu Guan Nicholas Mastronarde Elizabeth Serena Bentley Michael Medley Department of Electrical Engineering University at Buffalo Buffalo NY USA Air Force Research Laboratory (AFRL) Rome NY USA

ISBN: (数字)9781665495127

ISBN: (纸本)9781665495134

Data-driven control based on AI/ML techniques has a great potential to enable zero-touch automated modeling, optimization and control of complex wireless systems. However, it is challenging to collect network traces in the real world because of high time and labor cost, weather limitations as well as safety concerns. In this work we attempt to tackle this challenge by designing a multi-fidelity simulator taking wireless Unmanned Aerial Vehicle (UAV) networks into consideration. We design the simulator by interfacing two Unmanned Aerial System (UAS) simulators we have developed in prior years: UBSim and UB-ANC. The former focuses on UAV network optimization and policy training by considering explicitly the network environments such as blockage dynamics, while the latter focuses more on high-fidelity UAV flight control. We first develop a coordination interface referred to as SimSocket for signaling exchanges between UBSim and UB-ANC in simulations, and then showcase coordinated simulations based on UBSim and UB-ANC. The new research that can be enabled by the integrated simulator is also discussed for digital twin-based UAS systems.

关键词： Wireless communication Training Wireless sensor networks Autonomous aerial vehicles Ad hoc networks sensor systems Safety

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Lifecycle Management Protocols for Batteryless, Intermittent sensor Nodes 39

Lifecycle Management Protocols for Batteryless, Intermittent...

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39th ieee international Performance computing and Communications conference (IPCCC)

作者： Wymore, Mathew L. Deep, Vishal Narayanan, Vishak Duwe, Henry Qiao, Daji Iowa State Univ Elect & Comp Engn Ames IA 50011 USA

ISBN: (纸本)9781728198293

Nodes in batteryless sensor networks operate intermittently, making tasks such as node-to-node communication and coordinated computation extremely challenging. Adding to this challenge, a node typically has little control over its intermittency. Therefore, in this paper, we introduce a new class of protocols, which we call lifecycle management protocols (LMPs), to better control and manage the intermittency of batteryless nodes. These protocols may be designed and optimized for a particular task;here, we propose and evaluate a set of LMPs designed to enable direct communication between intermittent batteryless sensor nodes with active radios.

关键词： sensor nodes

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Optimized Data Storage Algorithm of IoT Based on Cloud computing in distributed System

Optimized Data Storage Algorithm of IoT Based on Cloud Compu...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： K.Jaya Deepthi T.Suresh Balakrishnan Prabhakar Krishnan U.Samson Ebenezar Nageshwari Department of AIML Mohan Babu University Tirupati-2 AP India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India IEEE Research Scientist Center for Cyber security Systems and Networks Amrita Vishwa Vidyapeetham Amritapuri-campus India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesSaveetha University Chennai Tamil Nadu India Department of Computer Science and Engineering CARE College of Engineering Trichy Tamil Nadu

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

IoT and cloud technology are vital for large systems. An improved data storage technique for IoT applications and cloud distribution is described in this research. Scalability, reliability, latency, and resource usage are internal and external to the algorithm. The approach above scales well with work portion or dataset size. The first experiment shows the algorithm scaling perfectly even as IoT devices generate additional data. Experiment 2 proves linear scalability works for 100,000 IoT devices, while Experiment 3 efficiently organizes 1 million data points. It proved the algorithm by tolerating manufactured faults. To ensure data service availability, Experiment 1 tests the system’s operational dependability on node failures and network disturbances. With 0.1% downtime even under challenging conditions, experiment 2 showed the algorithm’s incredible robustness in real-world applications. To improve data organization, this experiment exhibits data consistency when nodes fail. Lower and constant time improves operation safety and reliability. Algorithm 1 shows how the algorithm can quickly answer data queries and keep up with the massive amount of data transfers, supporting low-latency IoT applications. Average data retrieval latency of experiment 2 is 10 ms, which is real-time for vital information. Trial 3 processes data online with a latency <20 ms, providing fast insights for speedy response and decision-making. The adaptive distributed workload uses little resources, making the approach efficient. Exp1 uses distributed nodes to share data and processing, improving task efficiency. In experiment 2, 60% of the CPU, 70% of the Memory tool, and 80% of the Storage system are used, demonstrating platform resource efficiency. Trial 3 provides optimal resource allocation with less than 5% waste, making it an effective system that passes all performance benchmarks.

关键词： Cloud computing Scalability Decision making Memory distributed databases Robustness Real-time systems

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2021 ieee/CIC international conference on Communications in China, ICCC 2021

2021 IEEE/CIC International Conference on Communications in ...

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2021 ieee/CIC international conference on Communications in China, ICCC 2021

ISBN: (纸本)9781665443852

The proceedings contain 207 papers. The topics discussed include: unsourced random access with a massive MIMO receiver: exploiting angular domain sparsity;BS-RIS-user association and beamforming designs for RIS-aided cellular networks;joint age of information and energy minimization in wireless sensor systems;a conflict resolution scheme in intent-driven network;a novel 3D geometry-based stochastic model for massive MIMO V2V communication in helical mountain road environments;secure network slicing deployment in edge computing;radio frequency fingerprint identification based on transfer learning;and mitigation of cell-to-cell interference in multi-level-cell (MLC) NAND flash memory using correction factors.

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ERAIA - Enabling Intelligence Data Pipelines for IoT-based Application systems 18

ERAIA - Enabling Intelligence Data Pipelines for IoT-based A...

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ieee international conference on Pervasive computing and Communications (PerCom)

作者： Hernandez, Aitor Xiao, Bin Tudor, Valentin Ericsson Res Stockholm Sweden

ISBN: (纸本)9781728146577

Establishing upon the connectivity layer provided by Internet of Things (IoT) platforms, modern industries are moving towards management and computation solutions which enable Artificial Intelligence (AI) services for data intensive applications. This raises two important challenges: first, the information carried by data should be refined and prepared for the various AI algorithms via data processing pipelines and;second, a distributed orchestration solution for data and AI computation resources featuring with migration capabilities is required to support the refining process. In order to address these challenges, this paper introduces ERAIA, an actor-based framework which provides a novel basis to build intelligence and data pipelines. ERAIA facilitates the deployment and migration of distributed AI computations for heterogeneous and dynamic IoT scenarios. An implementation description is accompanied by relevant performance evaluations to demonstrate the flexibility and scalability of the solution. ERAIA provides an interface to expand the scope of existing IoT systems as Application Enablement Platform (AEP), which hence accelerates the development of AI-based IoT solutions.

关键词： Internet of Things industrial IoT complex event processing distributed processing edge computing artificial intelligence machine learning IoT landscape reactive systems actor model

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distributed Scheduling Algorithm for Optimizing Age of Information in Wireless Networks 39

Distributed Scheduling Algorithm for Optimizing Age of Infor...

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39th ieee international Performance computing and Communications conference (IPCCC)

作者： Yu, Dongxiao Duan, Xinpeng Li, Feng Liang, Yi Yang, Huan Yu, Jiguo Shandong Unaivers Sch Comp Sci & Technol Qingdao Peoples R China Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA Qingdao Univ Coll Comp Sci & Technol Qingdao Peoples R China Qilu Univ Technol Sch Comp Sci & Technol Shandong Acad Sci Jinan Peoples R China Shandong Comp Sci Ctr Natl Supercomp Ctr Jinan Jinan Peoples R China

ISBN: (纸本)9781728198293

Age of Information (AoI) is an emerging concept to model information freshness from the perspective of destinations of information deliveries. Serving as a metric to characterize the data delivery timeliness, the peak age indicates the maximum value of the AoI prior to a data packet reception. In this paper, we present a distributed scheduling algorithm for peak age optimization in a wireless network where a number of sensor nodes attempt to deliver their sensed data to a data collector over a wireless channel. In particular, each sensor node accesses the channel for data delivery independently according to an adaptively tuned transmission probability. The beauty of our algorithm lies in that, even with neither centralized infrastructure nor coordinations among the sensor nodes, our algorithm asymptotically approximates the optimal solution by only a constant factor. We perform solid theoretical analysis and extensive simulations to verify the efficacy of our algorithm. To the best of our knowledge, it is the first fully distributed scheduling algorithm for AoI optimization in wireless networks.

关键词： distributed algorithms Age of Information Wireless Networks

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RunPHI: Enabling Mixed-criticality Containers via Partitioning Hypervisors in Industry 4.0

RunPHI: Enabling Mixed-criticality Containers via Partitioni...

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ieee international conference on Software Reliability Engineering Workshops (ISSRE Wksp)

作者： Marco Barletta Marcello Cinque Luigi De Simone Raffaele Della Corte Giorgio Farina Daniele Ottaviano DIETI - Universitaà degli Studi di Napoli Federico II Napoli Italy

ISBN: (数字)9781665476799

ISBN: (纸本)9781665476805

Orchestration systems are becoming a key component to automatically manage distributed computing resources in many fields with criticality requirements like Industry 4.0 (14.0). However, they are mainly linked to OS-level virtualization, which is known to suffer from reduced isolation. In this paper, we propose RunPHI with the aim of integrating partitioning hypervisors, as a solution for assuring strong isolation, with OS-level orchestration systems. The purpose is to enable container orchestration in mixed-criticality systems with isolation requirements through partitioned containers.

关键词： Virtual machine monitors conferences Containers Fourth Industrial Revolution Software reliability Virtualization distributed computing

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Acoustic sensor Networks: An Energy Efficient Grey Wolf Optimization Algorithm development for underwater networks 1

Acoustic Sensor Networks: An Energy Efficient Grey Wolf Opti...

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1st ieee international Interdisciplinary Humanitarian conference for Sustainability, IIHC 2022

作者： Vanitha, V. Barve, Amit Singh, Arjun Javanthi, R. Dwivedi, Rakesh Kumar Vishwath, N.C. Ajay India Department of CSE PIET Parul University Gujarat Vadodara India Dev Bhoomi Uttarakhand University Department of School of Engineering Uttarakhand Dehradun India Department of Computer Science & IT Bangalore India Faculty of Engineering Teer Thanker Mahaveer University Department of Computing Sciences & IT Uttar Pradesh Moradabad India Parul University Aeronautical Department Parul Institute of Engineering and Technology India

ISBN: (纸本)9781665456876

Underground wireless sensors (UWASNs) are intended to serve a range of needs for global research and marine management. For some of these uses, the network of several sensors is positioned underwater at various depths. These nodes need multi-hop transmission, which is made possible by efficient routing design, but the seafloor located IoT devices seem to be unable to connect without a hitch to any network that are near to the base level. The occurrence of uninhabited areas along the route may also have an impact on the overall efficacy of UWASNs. Therefore, the empty zone may be avoided by choosing the optimum introduction. The optimum routing node is chosen based on the strength of the connection, average energy, depth fluctuation, and length difference. An orientation is another factor that is taken into account while selecting the optimum forwarder node. Using the theory of the maned wolf optimization issue, the best course of action is determined. The suggested approach extends the life of the network and also balances network energy by avoiding the vacant zone. Utilizing the Matlab program, power and depths variability pragmatic void avoidance systems, and analyzing deep and moving area split knowledge routes, the suggested work is evaluated. In this study, a 180-unit setup with a configurable radio range of up to 1000 m yields a 96% total packet delivery ratio. This brings down the mean number of deceased nodes and the end-to-end time, respectively, to 53% and 145. This strategy also decreases latency and lengthens the overall network lifespan. Additionally, this strategy leads to the production of digital data books falling by 55%. In addition, the results section provides a variety of additional evaluation standards. © 2022 ieee.

关键词： MATLAB

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XGBoost and Deep Learning Based-Federated Learning for DDoS Attack Detection in 5G Core Network VNFs

XGBoost and Deep Learning Based-Federated Learning for DDoS ...

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ieee international conference on Computer Communication and the Internet (ICCCI)

作者： Abdoul-Aziz Maiga Edwin Ataro Stanley Githinji Pan African University Institute for Basic Sciences Technology and Innovation Nairobi Kenya Department of Electrical and Electronic Engineering Technical University of Kenya Nairobi Kenya Department of Computing United States International University-Africa (USIU-A) Nairobi Kenya

ISBN: (数字)9798350351767

ISBN: (纸本)9798350351774

distributed Denial-of-Service (DDoS) attacks can employ cloud network zombies to compromise the availability of 5G network services and hinder the ability of telecommunication service providers (TSPs) to deliver promised service level agreements (SLAs), which can lead to potential losses for both TSPs and their clients. In this study, a technique for mitigating DDoS attacks in 5G core network Virtual Network Functions (VNFs) was proposed. VNFs are cloud-based network functions that provide 5G services. First, eXtreme Gradient Boosting (XGBoost) is used to extract relevant features, and then the proposed hybrid deep neural network uses the XGBoost-extracted features for DDoS attack detection and mitigation. To address data privacy in heterogeneous 5G networks, where VNFs can be provided and hosted in different cloud computing systems, Federated Learning (FL) is used for the proposed model training. The anti-DDoS framework was evaluated through a simulation using the CICDDoS2019 dataset with 10 VNFs for FL. Through extensive experimentation and evaluation, the results demonstrated promising outcomes characterized by high accuracy, low false positive rate, and minimal detection time. The proposed solution offers a robust defense mechanism against evolving DDoS threats that can target 5G core networks, thereby ensuring the availability of critical network infrastructure.

关键词： Training Deep learning Cloud computing 5G mobile communication Federated learning Prevention and mitigation Denial-of-service attack

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Stealthy Backdoor Attack Towards Federated Automatic Speaker Verification

Stealthy Backdoor Attack Towards Federated Automatic Speaker...

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international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Longling Zhang Lyqi Liu Dan Meng Jun Wang Shengshan Hu School of Cyber Science and Engineering Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security OPPO Research Institute China

Automatic speech verification (ASV) authenticates individuals based on distinct vocal patterns, playing a pivotal role in many applications such as voice-based unlocking systems for devices. The ASV system comprises three stages: training, registration, and validation. The model refines using voice data in training, extracts vocal features in registration, and contrasts these with speech patterns in validation. Modern ASV models, primarily grounded in DNN architectures, require extensive data for training. Federated learning (FL) fosters model-sharing across multiple clients while ensuring data privacy. Due to its open architecture, FL is vulnerable to backdoor attacks. However, training a stealthy backdoor attack in FL presents challenges, including diminished attack generalization owing to data heterogeneity, and conspicuous triggers that render them easily detectable. In this paper, we propose a Federated Stealthy Backdoor Attack method ($FedSBA$). FedSBA aims to improve the attack model’s generalization, enhance its persistence, and elude anomaly detection under the heterogeneous data distribution. FedSBA constructs an attack model based on a personalized transformer and encompasses a stealthy trigger. Moreover, we also propose a defensive strategy that utilizes an adaptive weight aggregation scheme. The stealthiness and effectiveness of FedSBA are demonstrated by exhibiting superior performance in comparison to previous works.

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