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检索条件"任意字段=First IEEE International Conference on Distributed Computing in Sensor Systems, DCOSS 2005"

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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...

引用

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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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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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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Optimizing RF-Sensing for Drone Detection: The Synergy of Ensemble Learning and sensor Fusion 20

Optimizing RF-Sensing for Drone Detection: The Synergy of En...

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

作者： Tanveer, Laiba Alam, Muhammad Zeshan Misbah, Maham Orakzai, Farooq Alam Alkhayyat, Ahmed Kaleem, Zeeshan COMSATS Univ Islamabad Elect & Comp Engn Wah Campus Wah Pakistan Brandon Univ Dept Comp Sci Brandon MB Canada Islamic Univ Coll Engn Dept Comp Tech Engn Najaf Iraq

ISBN: (纸本)9798350369458;9798350369441

Unmanned Aerial Vehicles (UAVs) find extensive applications across various industries, surveillance, and communication services. However, concerns regarding their potential misuse have prompted the development of counter-drone measures. In this paper, we propose a counter-UAV approach centered on radio frequency (RF) signal sensing. Upon the detection of an RF signal, our system employs a Short-Time Fourier Transform (STFT)-based spectrogram (SP) generation process. This SP is further refined through adaptive windowing and logarithmic tuning to extract multi-intensity features. To classify the complex RF time-domain signals and STFT spectrograms, we utilize two deep learning classifiers: RF-Network and SP-Network, facilitating a multi-class classification process by using deep neural networks (DNN). To enhance the overall accuracy of our model, we leverage an ensemble neural network (EN-Net) by combining predictions from the RF-Network and SP-Network classifiers. Fusing data from a single sensor in both time and frequency domains enhances DNN accuracy by providing complementary information, improving robustness, and reducing overfitting, resulting in increased model performance and a deep understanding of the data. Our results demonstrate a notable improvement in accuracy-specifically, a 36% increase for multi-class models when compared to single-class models. This proves the effectiveness of our EN-Net model in addressing security threats posed by UAVs through advanced RF signal analysis and classification.

关键词： Ensemble Neural Network Radio Frequency RF Spectrogram UAVs Detection

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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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A Virtual Sink-based Strategy for Reducing the Funneling Effect in ieee 802.15.4 DSME Networks 18

A Virtual Sink-based Strategy for Reducing the Funneling Eff...

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

作者： Gonzalez, Ivonne Andrea Mantilla Turau, Volker Hamburg Univ Technol Inst Telemat Hamburg Germany

ISBN: (纸本)9781665495127

The MAC protocol ieee 802.15.4 DSME has features for WSNs to support exigent requirements such as high reliability and adaptability to dynamic traffic. This work introduces the concept of a virtual sink, which comprises the sink and its 1-hop neighbors, a.k.a. satellites, as the core of a strategy to alleviate the burden caused by the funneling effect in data collection scenarios. Our strategy enables the coexistence of a centralized scheduling algorithm at the virtual sink and a decentralized scheduling algorithm for the remaining nodes of the network. Through a simulative assessment, we compare the performance of the virtual sink-based strategy with the status quo of DSME via a decentralized slot scheduler TPS. Results show an improvement of the network throughput of up to 38% and a reduction of the energy consumption of about 30% at satellites.

关键词： Funneling effect ieee 802.15.4 DSME virtual sink heuristic scheduling algorithm.

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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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