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检索条件"任意字段=18th Annual International Conference on Distributed Computing in Sensor Systems, DCOSS 2022"

共 79 条记录，以下是1-10 订阅

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Proceedings - 18th annual international conference on distributed computing in sensor systems, dcoss 2022

Proceedings - 18th Annual International Conference on Distri...

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18th annual international conference on distributed computing in sensor systems, dcoss 2022

ISBN: (纸本)9781665495127

the proceedings contain 67 papers. the topics discussed include: end-to-end gesture recognition framework for the identification of allergic rhinitis symptoms;semi-supervised multi-source domain adaptation in wearable activity recognition;real-time human pose estimation at the edge for gait analysis at a distance;SELF-CARE: selective fusion with context-aware low-power edge computing for stress detection;publishing asynchronous event times with pufferfish privacy;tradeoff between accuracy and message complexity for approximate data aggregation;low-power distinct sum for wireless sensor networks;a software-defined underwater visible light communication testbed;a differential BCG sensor system for long term health monitoring experiment on the ISS;network economics-based crowdsourcing in UAV-assisted smart cities environments;and cost-aware inference of bovine respiratory disease in calves using precision livestock technology.

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Low-Power Distinct Sum for Wireless sensor Networks 18

Low-Power Distinct Sum for Wireless Sensor Networks

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18th annual international conference on distributed computing in sensor systems (dcoss)

作者： William, Ebram Kamal Chan, Mun Choon Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9781665495127

Continuous monitoring is a major component of many applications in wireless sensor network (WSN). In these applications, to reduce the communication overhead, some form of data summary or aggregation can be performed. However, performing non-trivial in-network data processing such as finding frequent items, Top-K monitoring, and clustering efficiently are challenging in practice. In this paper, we present Low-Power Distinct Sum (LDS), a distributed in-network data aggregation primitive that performs the sum of unique items in WSN. LDS serves as the underlying primitive that can be used to implement many distributed data processing efficiently. To demonstrate LDS's capabilities, we design and implement a distributed data streaming application with LDS running on Contiki OS. Compared to the baseline algorithm, LDS can reduce the completion time by up to 66%.

关键词： wireless sensor networks synchronous transmission distributed edge processing data aggregation

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RSS-Based Localization using A Single Robot in Complex Environments 18

RSS-Based Localization using A Single Robot in Complex Envir...

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18th annual international conference on distributed computing in sensor systems (dcoss)

作者： Guo, Hongzhi Quartey, Irvin Green, Cameron Norfolk State Univ Engn Dept Norfolk VA 23504 USA

ISBN: (纸本)9781665495127

this paper considers the problem of localizing a static transmitter using a robot with a single receiving antenna and a single communication chamiel in unknown complex environments. Existing solutions using Time -of-Arrival (TOA) and Angle -of-Arrival (AOA) rely on complex wireless communication systems with multiple receive antennas or multiple communication channels, which are not available for robots with off-the -shelf low-cost radios. this paper develops a localization framework using Received Signal Strength (RSS) to estimate unknown channel model parameters considering mullipath fading and spatial -correlated shadowing effects. the robot moves along a predefined trajectory to collect RSS data. AOA information is also estimated and integrated with the robot SLAM (Simultaneous Localization and Mapping) results to improve the localization accuracy. Numerical simulations and experiments in an indoor environment are conducted. Results show that 90% of the estimation error is smaller than 2 m to localize a randomly placed transmitter in a 10 x 10 m(2) area.

关键词： Complex environment estimation localization received signal strength robot sensor data processing wireless channel model

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Analysis of Pandemic Atmosphere Pollution Data Using Virtual sensors in Sao Paulo City 18

Analysis of Pandemic Atmosphere Pollution Data Using Virtual...

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18th annual international conference on distributed computing in sensor systems (dcoss)

作者： Campos, Gabriel Oliveira Villas, Leandro Aparecido da Cunha, Felipe Domingos Univ Campinas UNICAMP Inst Comp Campinas SP Brazil Pontificial Catholic Univ Minas Gerais PUC MG Dept Comp Sci Belo Horizonte MG Brazil

ISBN: (纸本)9781665495127

Virtual sensing models have been used to generate synthetic data and provide complementary information. However, with the increase in cases related to COVID-19 and the lockdown, a problematic factor is that virtual sensing models may produce different results than they should since the environment has become better with the decrease in traffic conditions and industrial production. therefore, this article will evaluate virtual sensing models for the city of Sao Paulo, using pre-pandemic and pandemic data in a lockdown scenario. As a result, we analyzed that even with these behavioral changes in the city, the pre-pandemic model produced similar results to the lockdown period model.

关键词： Virtual sensor Urban computing Internet of things Air Pollution Machine Learning Deep Learning COVID-19

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Demo: BCG Measurement by differential Sensing in Real-Time 18

Demo: BCG Measurement by differential Sensing in Real-Time

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18th annual international conference on distributed computing in sensor systems (dcoss)

作者： Kulau, Ulf Rust, Jochen Albrecht, Urs-Vito Hamburg Univ Technol Smart Sensors Hamburg Germany DSI Aerosp Technol GmbH Bremen Germany Bielefeld Univ Med Fac OWL Dept Digital Med Bielefeld Germany

ISBN: (纸本)9781665495127

We propose a comprehensive health monitoring of astronauts which is based on Ballistocardiography (BCG). BCG uses high-precision accelerometers to measure the motion of the heart at the body surface. In contrast to conventional methods (e.g. Electrocardiography (ECG)), BCG enables the derivation of heart-related parameters that can otherwise only be recorded by imaging methods such as echocardiography. A huge issue is the signal quality of the acceleration measurement, however, by means of differential arrangement of digital acceleration sensors, the signal quality and also the susceptibility to interference can be reduced when measuring the smallest accelerations. In preparation for a BCG experiment on the international Space Station (ISS), a pre-flight measurement system was developed whose architectural design is similar to the flight hardware, but extended by a reference ECG. To simplify measurement campaigns, the simultaneously recorded BCG and ECG data can be visualized in real time, which will be shown in this demo.

关键词： sensor System Ballistocardiography Differential Sensing Accelerometers Health Monitoring Human Spaceflight

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Trade off between Accuracy and Message Complexity for Approximate Data Aggregation 18

Trade off between Accuracy and Message Complexity for Approx...

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18th annual international conference on distributed computing in sensor systems (dcoss)

作者： Nugroho, Saptadi Weinmann, Alexander Schindelhauer, Christian Albert Ludwigs Univ Freiburg Freiburg Germany

ISBN: (纸本)9781665495127

We consider large scale Peer-to-Peer sensor Networks, which try to calculate and distribute the mean value of all sensor inputs. For this we design, simulate and evaluate distributed approximation algorithms which reduce the number of messages. the main difference of these algorithms is the underlying communication protocol which all use the random call model, where in discrete round model each node can call a random sensor node with uniform probability. the amount of data exchanged between sensor nodes and used in the calculation process affects the accuracy of the aggregation results leading to a trade-off situation. the key idea of our algorithms is to limit the sample size using the Finite Population Correction (FPC) method and collect the data using a distribution aggregation using Push-Pull Sampling, Pull Sampling, and Push Sampling communication protocols. It turns out that all methods show exponential improvement of Mean Squared Error (MSE) with the number of messages and rounds.

关键词： data aggregation sensor network message complexity approximation random call model

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SELF-CARE: Selective Fusion with Context-Aware Low-Power Edge computing for Stress Detection 18

SELF-CARE: Selective Fusion with Context-Aware Low-Power Edg...

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18th annual international conference on distributed computing in sensor systems (dcoss)

作者： Rashid, Nafiul Mortlock, Trier Al Faruque, Mohammad Abdullah Univ Calif Irvine Dept Elect Engn & Comp Sci Irvine CA 92697 USA Univ Calif Irvine Dept Mech & Aerosp Engn Irvine CA USA

ISBN: (纸本)9781665495127

Detecting human stress levels and emotional states with physiological body-worn sensors is a complex task, but one with many health-related benefits. Robustness to sensor measurement noise and energy efficiency of low-power devices remain key challenges in stress detection. We propose SELFCARE, a fully wrist-based method for stress detection that employs context-aware selective sensor fusion that dynamically adapts based on data from the sensors. Our method uses motion to determine the context of the system and learns to adjust the fused sensors accordingly, improving performance while maintaining energy efficiency. SELF-CARE obtains state-of-theart performance across the publicly available WESAD dataset, achieving 86.34% and 94.12% accuracy for the 3-class and 2-class classification problems, respectively. Evaluation on real hardware shows that our approach achieves up to 2.2x (3-class) and 2.7x (2-class) energy efficiency compared to traditional sensor fusion.

关键词： Stress detection edge computing energy efficiency sensor fusion

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Utilizing Carriers for the Energy Node Placement Algorithm in WSNs and IoT Networks 18

Utilizing Carriers for the Energy Node Placement Algorithm i...

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18th annual international conference on distributed computing in sensor systems (dcoss)

作者： Temene, Natalie Sergiou, Charalampos Georgiou, Chryssis Vassiliou, Vasos Univ Cyprus Dept Comp Sci Nicosia Cyprus CYENS Ctr Excellence Nicosia Cyprus

ISBN: (纸本)9781665495127

the limitations of the networks of Internet of things (IoTs) and Wireless sensor Networks (WSNs) in terms of computational power, memory and connectivity give rise to several issues that need to be tackled, mostly dynamically, to achieve their tasks. Definitively, a critical factor for the proper operation of these networks is to maintain the connectivity between the nodes, especially in a wireless mesh setting, where communication is performed in hop-by-hop fashion. A method that gains significant research interest for tackling the aforementioned issues is the employment of mobile nodes or as they are frequently called, mobile elements. In this work, we propose a scheme that utilizes carriers to transport mobile nodes to the required points in the network. We provide both a high level description of the concept and also a detailed algorithmic solution. the proposed solution is evaluated through a case study, where hot-spots are created due to congestion in the network and mobile elements are being used to resolve the problem. the experimental results demonstrate that the proposed algorithm can effectively restore the network operation. We believe that our proposed approach can be used to solve similar types of problems.

关键词： Internet of things Wireless sensor Networks Energy Efficiency Mobile Nodes Mobile Robots Drones

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(POSTER) SmartTwins: Secure and Auditable DLT-based Digital Twins for the WoT 18

(POSTER) SmartTwins: Secure and Auditable DLT-based Digital ...

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18th annual international conference on distributed computing in sensor systems (dcoss)

作者： Pittaras, Iakovos Polyzos, George C. Athens Univ Econ & Business Sch Informat Sci & Technol Dept Informat Mobile Multimedia Lab Athens Greece

ISBN: (纸本)9781665495127

Digital Twins and the Internet of things (IoT) are two of the most prominent recent concepts and technologies. the IoT supports many applications that merge the physical with the cyber world. this highlights the need for improved security. Here, we argue that digital twins can help in securing and strengthening the IoT by using them for interacting with the actual IoT devices. While most digital twins implementations are centralized, we propose to integrate distributed Ledger Technologies (DLTs) and digital twins into the IoT to realize decentralized, secure, available, flexible, and auditable blockchain-based IoT services for IoT devices that follow the W3C Web of things (WoT) standards. In this work, we present the design of SmartTwin, a blockchain-based digital twin framework for which we provide two different implementations using two different blockchains, we present the design trade-offs, and we discuss future research and development directions.

关键词： Digital Twins distributed Ledger Technologies Internet of things Sensing Actuation Blockchain Ethereum Hyperledger Fabric Auditability Availability

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Modelling Virtual sensors for Indoor Environments with Machine Learning 18

Modelling Virtual Sensors for Indoor Environments with Machi...

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18th annual international conference on distributed computing in sensor systems (dcoss)

作者： Polanski, Dawid Marek Angelopoulos, Constantinos Marios Bournemouth Univ Dept Comp & Informat Poole Dorset England

ISBN: (纸本)9781665495127

Virtual sensors model the sensing operation of physical sensors deployed in an area of interest by generating sensory data with accuracy and precision close to those collected by physical sensors. their use in applications such as augmenting the infrastructure of IoT facilities and test beds, monitoring and calibrating the operation of physical sensors, and developing Digital Twins of physical systems have led virtual sensors to attract research attention. Machine learning provides methods for modelling patterns in complex and big data generated by IoT sensing devices, allowing to model the behaviour of these devices. In this work, we investigate ML methods as means of implementation for virtual sensors. In particular, we evaluate the performance of six ML methods in terms of their effectiveness, accuracy and precision in generating sensory data based on data from physical sensors. In our study, we use a multi-modal dataset comprising IoT sensory data for temperature, humidity and illumination collected over a period of two years in an office space at University of Geneva. Our results show that the best performing model at predicting an output of a missing sensor is the Random Forest method, achieving MAPE error below 3%, 5% and 18% respectively for temperature, humidity and illuminance. the worst performing models were the linear radial basis function neural network and linear regression. In future research, we plan to deploy the best performing models natively on IoT devices, making use of tinyML and extreme edge computing methods.

关键词： Internet of things sensor Virtual sensor Machine Learning

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