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

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

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

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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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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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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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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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