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(POSTER) Enhancing Structural Health Monitoring with On-device AI 20

(POSTER) Enhancing Structural Health Monitoring with On-devi...

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

作者： Lee, Junhak Hwang, Chungwhan Park, Jaehyun Park, Tae Rim Inha Univ Dept Info & Comm Engn Incheon South Korea Inha Univ Dept Elect & Comp Engn Incheon South Korea Ino On Inc Seoul South Korea

ISBN: (纸本)9798350369458;9798350369441

This paper presents a service model for structural health monitoring (SHM) by leveraging on-device AI techniques. Unlike conventional SHM systems relying on server-based diagnostics, our approach incorporates recent advancements in lightweight AI platforms to enable real-time diagnostics directly on the sensor nodes. This integration enhances both real-time performance and accuracy while significantly reducing network load by minimizing the need for data transfer to centralized servers. Through the experimentation using real data collected from a pedestrian bridge, we demonstrate the feasibility and efficacy of the on-device AI approach for SHM. By seamlessly integrating on-device AI into IoT infrastructure, our approach not only offers improved real-time performance and accuracy but also showcases the potential of AI in optimizing SHM systems.

关键词： Structure health monitoring internet of things (IoT) on-device AI wireless sensor network

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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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Simulation of distributed systems in Constrained Environments Using ESDS: the Arctic Tundra Case 16

Simulation of Distributed Systems in Constrained Environment...

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international conferences on Internet of Things (iThings) / Green computing and Communications (GreenCom) / Cyber, Physical and Social computing (CPSCom) / Smart Data, SmartData and Congress on Cybermatics (Cybermatics)

作者： Guegan, Loic Rais, Issam UiT Arctic Univ Norway Dept Comp Sci Tromso Norway

ISBN: (纸本)9798350309461

Studying distributed systems (DS) is important. They are used in many computer systems such as Internet of Things (IoT), Wireless sensor Networks (WSN), Cyber-Physical systems (CPS). Conducting research studies on DS is possible with simulation. However, depending on the research context, having access to the proper simulator is challenging. Existing simulation frameworks can be complex and not suitable to study DS outside of commonly seen contexts, such as systems deployed in constrained environments. Often, authors from state of the art rely on building their own simulator. This is a time consuming, delicate and dangerous approach, especially when such simulators are not validated. This paper presents the Extensible Simulator for distributed systems (ESDS) in the context of systems deployed in constrained environments. In our case, the Arctic Tundra. This simulation framework is simple to use and suitable for the study of DS. The architecture of the simulation framework is detailed at a fine grain level. Results show that, ESDS can be used to conduct energy consumption and network performance studies of distributed systems such as CPS.

关键词： Simulation distributed systems networking energy consumption Cyber-Physical System arctic tundra Internet Of Things Wireless sensor Networks

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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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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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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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Demo: SenShaMart - A sensor Sharing Marketplace for IoT 43

Demo: SenShaMart - A Sensor Sharing Marketplace for IoT

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43rd ieee international conference on distributed computing systems (ICDCS)

作者： Dawod, Anas Georgakopoulos, Dimitrios Jayaraman, Prem Prakash Milovac, Josip Karabotic Liao, Kewen Chrysanthis, Panos K. Swinburne Univ Technol Melbourne Australia Australian Catholic Univ Peter Faber Business Sch HilstLab Sydney Australia Univ Pittsburgh Pittsburgh PA USA

ISBN: (纸本)9798350339864

The sensor Sharing Marketplace (SenShaMart) enables IoT applications to find IoT sensors, which are owned and managed by other parties, integrate them, and pay for using their data. To provide corresponding services that implement that FAIR (Findable, Accessible, Interoperable, Reusable) principles of IoT, SenShaMart incorporates a specialized blockchain that manages all the information its services need to allow different parties in IoT to describe, query, integrate, pay for, and use IoT sensors and their data. The paper presents the SenShaMart's architecture, implementation, evaluation, and demonstration.

关键词： Blockchain Internet of Things sensor Discovery sensor Sharing Data Marketplace

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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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A Lightweight Three Dimensional Redeployment Algorithm for distributed Mobile Wireless sensor Networks

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WIRELESS PERSONAL COMMUNICATIONS 2024年第2期135卷 835-873页

作者： Boufares, Nadia Ben Saied, Yosra Azouz Saidane, Leila Univ Manouba Natl Sch Comp Sci Manouba Tunisia Univ Carthage Tunis Tunisia

In recent years, there has been significant growth in mobile wireless sensor networks (WSNs), yet prevailing research has primarily focused on 2D planar deployments, overlooking the importance of three-dimensional (3D) coverage in various applications. This oversight leads to ineffective data gathering due to incomplete area coverage and network connectivity. In previous researches (Boufares et al. in: 2018 31 ieee/ACS 15th international conference on computer systems and applications (AICCSA), Aqaba, Jordan, pp 1-8, 2018, in: 13th international wireless communications and mobile computing conference (IWCMC), Valencia, pp 1628-1633, 2017, in: ieee wireless communications and mobile computing conference (IWCMC), Dubrovnik, Croatia, pp 563-568, 2015a, in: the 4th international conference on performance evaluation and modeling in wired and wireless networks (PEMWN), Hammamet, Tunisia, pp 103-108, 2015b), we proposed 3D mobile autonomous redeployment strategies based on the Virtual Forces Algorithm, tailored for diverse configurations: 3D volume applications such as smart homes or agriculture, 3D flat surfaces like snow monitoring, and 3D terrain surfaces like volcano monitoring. Our approach ensured complete coverage and connectivity in these scenarios. Moreover, energy efficiency emerges as a critical concern, given the autonomous and mobile nature of sensor nodes operating on finite battery power. Hence, in this paper, we provide an overview of our previous results, highlighting the efficacy of our 3D mobile autonomous redeployment strategies across various configurations. Subsequently, we delve into an in-depth analysis of the energy consumption associated with the different proposed contributions. Building upon these insights, we propose an energy harvesting approach aimed at extending the operational lifespan of mobile 3D WSNs, thus ensuring sustained functionality in diverse real-world *** these contributions, we address critical challenges and p

关键词： 3D distributed autonomous mobile wireless sensor networks Coverage and connectivity Virtual forces Energy consumption Energy harvesting RF power splitting

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