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检索条件"任意字段=4th IEEE International Conference on Distributed Computing in Sensor Systems"

共 6248 条记录，以下是11-20 订阅

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A fog and blockchain-based distributed Virtual Private Networks (VPN) 20

A fog and blockchain-based distributed Virtual Private Netwo...

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20th Annual international conference on distributed computing in Smart sensor systems and the Internet of things (DCOSS-IoT)

作者： Trabelsi, Rahma Fersi, Ghofrane Jmaiel, Mohamed Natl Sch Engineers Sfax ReDCAD Lab Sfax Tunisia

ISBN: (纸本)9798350369458;9798350369441

the Internet of things (IoT) is a breakthrough technology that interconnects and empowers numerous smart devices allowing them to communicate, collect, and exchange data. the most challenging issue in IoT networks is securing exchanged data. Virtual Private Networks (VPNs) have a significant impact on ensuring security within IoT systems. So implementing a VPN-based solution can bring multiple security values to IoT systems, especially for exchanged data. However, most VPN-based solutions rely on a single central server responsible for managing all VPN connections. this presents a major problem when the number of managed VPNs increases because it will lead to the performance deterioration of the VPN server and can even lead to a single point of failure problem. We propose in this paper a distributed VPN-based solution that relies on distributed fog nodes playing the role of VPN servers. To determine the VPN server responsible for a specific communication, a fully decentralized and efficient search is guaranteed within the fog nodes through the utilization of the DHT-based Chord protocol. To enforce security and immutability, blockchain is used to verify the different criteria of a new requester to join a specific VPN. the performance evaluations have shown that our solution is efficient in terms of cost, time, and complexity.

关键词： IoT Security Blockchain distributed Virtual Private Network Fog computing

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A Bi-Layer Joint Training Reinforcement Learning Framework for Post-Disaster Rescue 20

A Bi-Layer Joint Training Reinforcement Learning Framework f...

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20th Annual international conference on distributed computing in Smart sensor systems and the Internet of things (DCOSS-IoT)

作者： Huang, Songjun Sun, Chuanneng Gong, Jie Pompili, Dario Rutgers Univ New Brunswick Dept Elect & Comp Engn New Brunswick NJ 08901 USA Rutgers Univ New Brunswick Dept Civil & Environm Engn New Brunswick NJ USA

ISBN: (纸本)9798350369458;9798350369441

In modern post-disaster rescue missions, the deployment of Multi-Robot systems (MRSs) plays a key role in minimizing injuries and deaths among rescue personnel and civilians involved in the disaster. Achieving optimal MRS efficiency requires the implementation of a well-suited task allocation mechanism and a highly efficient pathfinding algorithm. However, due to inconsistent communication and low bandwidth, traditional frameworks in the mentioned domains may be impractical or may not work well. To address these problems, a novel Bi-Layer Joint Training Reinforcement (BJoT-RL) framework is proposed where, in the first layer, a Multi-Head Deep Q-Learning (MHDQN) is designed to perform task allocation;whereas, in the second layer, a Condition-Constrained Q-Learning (CCQ) is proposed to perform pathfinding. Noticeably, the output of each layer is used in the training of the other layer to realize a tight coupling, hence the innovative joint training. thorough simulations show that the BJoT-RL framework performs better than state-of-the-art solutions in such applications.

关键词： Multi-Agent Reinforcement Learning distributed Learning and computing Task Allocation Pathfinding

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sensorBFT: Fault-Tolerant Target Localization using Voronoi diagrams and Approximate Agreement 44

SensorBFT: Fault-Tolerant Target Localization using Voronoi ...

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44th ieee international conference on distributed computing systems (ICDCS)

作者： Bandarupalli, Akhil Bhat, Adithya Chaterji, Somali Reiter, Michael K. Kate, Aniket Bagchi, Saurabh Purdue Univ W Lafayette IN 47907 USA Visa Res Foster City CA 94404 USA Duke Univ Durham NC USA Supra Res Kelowna BC Canada Chainlink Labs New York NY 10001 USA

ISBN: (纸本)9798350386066;9798350386059

the target localization primitive is used for detecting and locating an adverse event called a target in a geographic area. this versatile primitive is applicable in the physical security domain (e.g., detecting intruders in an area) or for disaster preemption, such as detecting ignition events of forest fires. Prior systems implemented this primitive over large areas by deploying a network of sensor devices, which detect changes in a specific physical parameter like pressure or temperature induced by a target. However, these systems are not designed for use in adverse environments where one or more sensors can behave in a faulty manner. While many algorithms in the distributed systems literature can be naively used to implement target localization in a fault-tolerant manner, these approaches are energy-intensive as they use computationally expensive cryptographic operations not appropriate for resource-constrained sensors. We present sensorBFT, an energy-efficient, fault-tolerant approach for target localization. sensorBFT uses a novel asynchronous approximate agreement protocol that enables correct sensors to achieve an approximate consensus in the presence of faulty sensors. sensors fulfill their energy budgets by tuning the precision and accuracy of localization, where precision is the difference between honest sensors' outputs and accuracy is the difference between an honest sensor's output and the target's true location. In optimal scenarios, this protocol reduces communication from O(n(3)) to O(n(2)) messages per round, where n is the number of sensors sharing coverage over a piece of area. In a sensor testbed with n = 19 sensors, sensorBFT consumes 2/5 th the energy consumed by existing solutions for a minor 2% loss in accuracy, significantly enhancing efficiency and coverage.

关键词： Approximate Agreement Byzantine Agreement Byzantine Fault Tolerance Energy Efficiency sensor Networks Target Localization Voronoi diagrams

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IAQ Monitoring System Optimizing Data-Driven sensor Placement 20

IAQ Monitoring System Optimizing Data-Driven Sensor Placemen...

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20th Annual international conference on distributed computing in Smart sensor systems and the Internet of things (DCOSS-IoT)

作者： Filios, Gabriel Nikoletseas, Sotiris Stivaros, Ioannis Univ Patras Dept Comp Engn & Informat Patras Greece Comp Technol Inst & Press Diophantus Patras Greece Univ Patras Dept Elect & Comp Engn Patras Greece

ISBN: (纸本)9798350369458;9798350369441

Indoor Air Quality (IAQ) significantly impacts people's health and comfort in buildings. Although IAQ research spans two decades, a comprehensive assessment of factors affecting indoor air pollution remains elusive. Recent efforts focus on real-time monitoring using virtual sensing, a computational technique in engineering and data science. this paper presents a novel IAQ monitoring system emphasizing dynamic sensor placement for enhanced efficiency. the system employs random sensor positions and calculates measurement predictability, allowing identification and removal of less useful sensors, reducing data volume, and saving energy. Multiple reduction strategies are available, depending on the target number of edge devices or the desired maximum prediction error. Importantly, the system operates locally, without relying on internet connectivity. It consists of edge devices using air quality sensors, a gateway for data gathering and algorithm initiation, by training and evaluating multiple different machine learning techniques to determine point combination predictability. Deployed in two indoor settings, one with HVAC and the other naturally ventilated, the system's effectiveness is assessed, shortcomings identified, and conclusions drawn for future work.

关键词： Data Driven sensor Placement Indoor Air Quality Monitoring System Machine Learning Virtual Sensing sensor Reduction

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Human Activity Recognition Using Spectrograms of Binary Motion sensor Data 20

Human Activity Recognition Using Spectrograms of Binary Moti...

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20th Annual international conference on distributed computing in Smart sensor systems and the Internet of things (DCOSS-IoT)

作者： Seyedtalebi, Nima Silvestri, Simone Univ Kentucky Dept Comp Sci Lexington KY 40506 USA

ISBN: (纸本)9798350369458;9798350369441

Human activity recognition is at the basis of several applications in the smart living domain, such as energy management, elder care, and health management. Human activity recognition research can be divided into two categories, depending on the type of sensors used: wearable sensors, such as those found in mobile phones and smart watches, and ambient sensors, such as motion sensors or cameras placed in the environment. Among ambient sensors, binary sensors are often perceived as less invasive than sensors that collect video, audio, or biometric data. However, the performance of classifiers trained on binary sensor data is often lower since the data inherently contains less information. In this paper, we propose a non-intrusive human activity recognition framework that only exploits binary sensor data and results in high classification accuracy. Our approach is inspired by audio and image processing applied to binary sensors. Specifically, we exploit the Short-Time Fourier Transform (STFT) to extract features from binary data. these features are used to train a hybrid machine learning model which pairs Convolutional Neural Network (CNN) with a Long-Short-Term Memory (LTSM) architecture. We use a real dataset of human activities monitored through binary sensor data for evaluating the impact of the features on classifier performance. Results show that the proposed method significantly outperforms state-of-the-art solutions, requiring minimal training data needed to achieve a given level of accuracy.

关键词： Internet of things Smart Home Human Activity Recognition Spectrogram Short-Time Fourier Transform

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Coded distributed computing with Wireless Shared Heterogeneous Workers 4

Coded Distributed Computing with Wireless Shared Heterogeneo...

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4th ieee international conference on Electronic Technology, Communication and Information, ICETCI 2024

作者： Lei, Yu Li, Mingming Zhao, Guangquan Peng, Xiyuan Harbin Institute of Technology School of Electronics and Information Engineering Harbin China Norinco Group Air Ammunition Research Institute Co. LTD Test Technology Department Harbin China

ISBN: (纸本)9798350361643

distributed computing can parallelize large-scale computing tasks. However, due to the straggler effect, the performance of distributed systems may be diminished. Coded distributed computing mitigates the straggler effect by introducing redundant loads. In this research, we investigate a wireless network comprising multiple masters and heterogeneous workers, with each master allocated a distinct task. We divide computing tasks into two types: long-term computing tasks and short-term computing tasks. For long-term computing tasks, we propose the Static Worker Assignment algorithm (SWA), which balances the assignment of workers and minimizes the single iteration time of all tasks. Comparing SWA with two reference benchmarks reveals significant speed improvements of up to 54.03% and 51.14% over the respective benchmarks. For short-term tasks, we propose the Dynamic Worker Assignment algorithm (DWA), which reduces task reloading time without significantly increasing single iteration time. Compared to reloading by SWA, the DWA method may result in a 0.89% increase in single iteration time, while the task reloading delay decreases by 52.70%. © 2024 ieee.

关键词： distributed computer systems

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An Adaptive Drone-based Multigenerational sensor System for Monitoring Large Ecosystems 20

An Adaptive Drone-based Multigenerational Sensor System for ...

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20th Annual international conference on distributed computing in Smart sensor systems and the Internet of things (DCOSS-IoT)

作者： Mukkamala, Ravi Olariu, Stephan Aljohani, Meshari Sunkara, Mohan Aldabagh, Hind Old Dominion Univ Dept Comp Sci Norfolk VA 23529 USA

ISBN: (纸本)9798350369458;9798350369441

With the increase in the use of sensors and drones to monitor soil and crop conditions in vast agricultural lands as well as for situational-monitoring of natural disasters such as forest fires and increasing sea levels, there is a need for systems that can support long-term monitoring with minimal cost. In this paper, we propose a novel architecture that combines sensor, drone, blockchain, and machine learning technologies to support a long-term monitoring system for vast areas of land. Long-term monitoring is achieved by employing multigenerational sensors that have been deployed at the start but are woken up in stages as and when needed to replace or reinforce existing sensors. A swarm of drones periodically scans the landscape to identify areas that need immediate attention (e.g., dry soil, pests or insects, humidity, temperature, etc.). Drones store and process the data using onboard units and make real-time decisions regarding changing path plans or changing scanning frequencies. Optionally, data is sent to the base station that fuses the information from multiple drones in the swarm, and inputs the fused data to a machine-learning model that recommends the needed actions. the actions may include obtaining additional data, awakening more sensors, actions to handle any situation by signaling for external actions such as cloud seeding, controlled chemical spraying from a drone, etc., in agricultural applications. Using simulated scenarios, we have employed three prediction algorithms-the Prophet model, CNN, and LSTM-to determine the effectiveness of these algorithms in predicting sensors values in the simulated scenarios. the proposed architecture is scalable, supports long-term monitoring, and is effective in predicting future conditions to enable preemptive actions.

关键词： Agriculture sensors Drones UAVs Machine Learning Smart contract Blockchain Blackbox

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ISFD: Efficient and Fault-tolerant In-System-Failure-Detection for LP FPGA-based Smart-sensors in Space Expeditions 20

ISFD: Efficient and Fault-tolerant In-System-Failure-Detecti...

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20th Annual international conference on distributed computing in Smart sensor systems and the Internet of things (DCOSS-IoT)

作者： Rahman, Kazi Mohammad Abidur Dirkes, Timo Delfs, Bjoern Wyrwoll, Vanessa Kulau, Ulf Hamburg Univ Technol Smart Sensors Grp Hamburg Germany DSI Aerosp GmbH Dept Engn Bremen Germany Carl von Ossietzky Univ Oldenburg Dept Med Radiat Phys Oldenburg Germany

ISBN: (纸本)9798350369458;9798350369441

the health of astronauts is a top priority in manned space missions, and space conditions can have a major impact on the physiology of the human body. this is why extensive health monitoring is essential, especially for future missions beyond low Earth orbit (LEO), where astronauts will be increasingly exposed to higher radiation doses. To address these challenges, the AuRelia project has developed space-capable smart sensors that are designed to be failure-tolerant and autonomous. these sensors employ an innovative In-System-Failure-Detection (ISFD) technique that caters to the increasing need for Single Event Effect (SEE)-induced failure tolerance in resource-constrained and ultra-low power SRAM-based FPGAs used in miniaturized smart sensors. the ISFD technique integrates a hardware-based radiation-tolerant Test Core (using only 77 LUTs) with a software-based Test Controller, resulting in a lightweight yet efficient solution for failure detection. What's more, the adaptability of ISFD facilitates seamless integration into existing firmware and software, offering developers a swift implementation process. ISFD complements traditional blind scrubbing methods, enhancing sensor data reliability, and has been validated with radiation up to 70Krad.

关键词： Smart sensor Space Electronics LP FPGA Fault Detection Radiation Test ISFD

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Irregular Honeycomb Network: Revolutionizing k-Coverage in Spatial Wireless sensor Networks 20

Irregular Honeycomb Network: Revolutionizing k-Coverage in S...

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20th Annual international conference on distributed computing in Smart sensor systems and the Internet of things (DCOSS-IoT)

作者： Maddala, Dakshanya Ammari, Habib M. Texas A&M Univ Kingsville Frank H Dotterweich Coll Engn Dept Elect Engn & Comp Sci Wireless Sensor & Mobile Autonomous Networks WiSe Kingsville TX 78363 USA

ISBN: (纸本)9798350369458;9798350369441

sensor deployment, and in particular coverage, is a fundamental component in the design and development of wireless sensor networks. Achieving coverage (or 1-coverage) in three-dimensional (or spatial) wireless sensor networks (WSNs), which requires that every point in a spatial field of interest (FoI), is an open challenging problem. In particular, the problem of k-coverage in spatial WSNs, where every point in a spatial FoI is k-covered (or covered by at least k sensors at the same time), is even more challenging. In this paper, we investigate the problem of connected k-coverage in spatial WSNs, where a spatial FoI is k-covered while all the sensors participating in this k-coverage process are connected to each other. Our study aims at producing an energy-efficient and optimized protocol, which helps achieve connected k-coverage in spatial WSNs with a minimum number of sensors, thus, extending the network lifespan. Precisely, this study is based on convex polyhedral space fillers that help establish an irregular hexagonal prism tessellation referred to as Irregular Honeycomb Network (IHN), which serves as a medium to achieve k-coverage in spatial WSNs. First, we propose a randomly distributed sensor placement strategy, which is designed to guarantee full k-coverage of a spatial FoI. To this end, we tile a spatial FoI with irregular hexagonal prisms, known as space fillers, which are adjacent to each other without any gaps or overlaps. Second, we exploit this tiling strategy to guarantee k-coverage of a spatial FoI. then, we compute the spatial sensor density (i.e., number of sensors per unit volume) to k-cover a spatial FoI. third, we determine the necessary relationship between the sensors' communication and sensing ranges to ensure network connectivity. We corroborate our analysis with various simulation results.

关键词： Spatial wireless sensor networks convex polyhedral space-fillers quality of coverage k-coverage connectivity

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Optimized Live 4K Video Multicast Streaming on Commodity WiGig Devices 44

Optimized Live 4K Video Multicast Streaming on Commodity WiG...

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44th ieee international conference on distributed computing systems (ICDCS)

作者： He, Zhaoyuan Ge, Changhan Li, Wangyang Qiu, Lili Li, Peijie Baig, Ghufran Univ Texas Austin Austin TX 78712 USA Microsoft Res Asia Shanghai Peoples R China Google Austin TX USA

ISBN: (纸本)9798350386066;9798350386059

the popularity of 4K videos is on the rise. However, streaming such high-quality videos over mmWave to several users presents significant challenges due to directional communication, fluctuating channels, and high bandwidth demands. To address these challenges, this paper introduces an innovative 4K layered video multicast streaming system. We (i) develop a video quality model tailored for layered video coding, (ii) optimize resource allocation, scheduling, and beamforming based on the channel conditions of different users, and (iii) design a streaming strategy that integrates fountain code to eliminate redundancy in multicast groups, coupled with a Leaky-Bucket approach for congestion control. We implement our system on Commodity-Off-the-Shelf (COTS) WiGig devices and demonstrate its effectiveness through comprehensive testbed and emulation experiments.

关键词： Video streaming Millimeter-wave Multicast Layered coding

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