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检索条件"任意字段=6th IEEE International Conference on Distributed Computing in Sensor Systems"

共 6263 条记录，以下是1-10 订阅

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An O(1)-rounds Deterministic distributed Approximation Algorithm for the Traveling Salesman Problem in Congested Clique 20

An O(1)-rounds Deterministic Distributed Approximation Algor...

引用

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

作者： Saikia, Parikshit Natl Inst Technol Silchar Assam India

ISBN: (纸本)9798350369458;9798350369441

We study the Traveling Salesman Problem (TSP) in the Congested Clique Model (CCM) of distributed computing. We present a deterministic distributed algorithm that computes a tour for the TSP using O(1) rounds and O(m) messages for a given undirected weighted complete graph of n nodes and m edges with an approximation factor 2 of the optimal. the TSP has wide applications in logistics, planning, manufacturing and testing microchips, DNA sequencing etc., and we claim that our proposed O(1)-rounds approximation algorithm to the TSP, which is fast and efficient, can also be used to minimize the energy consumption in Wireless sensor Networks.

关键词： Traveling Salesman Problem distributed computing Approximation Congested Clique

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Adaptive Dynamic Adversarial Training with Real-Time Feedback in 6G 20

Adaptive Dynamic Adversarial Training with Real-Time Feedbac...

引用

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

作者： Iakami, Ahmed I. Pissinou, Niki Rajhi, Mohammed Florida Int Univ Sch Comp & Informat Sci Miami FL 33199 USA Jazan Univ Fac Comp Sci & Informat Technol Jazan Saudi Arabia

ISBN: (纸本)9798350369458;9798350369441

the emergence of 6G communications brings forth both unprecedented opportunities and security challenges. this work introduces a novel method, dynamic adversarial training incorporating real-time feedback (DAT-RTF), to bolster the resilience of machine learning models in 6G mmWave beamforming prediction against adversarial attacks. By enabling models to adapt to new threats, DAT-RTF maintains their robustness. this dynamic approach, leveraging continuous feedback from the network, adjusts and refines defense mechanisms to remain effective against a range of adversarial strategies. We tackle the issues of resource demand and overfitting, offering strategies to enhance performance without sacrificing security. Our method represents a significant advancement in protecting 6G networks from evolving adversarial threats.

关键词： 6G communications machine learning adversarial training real-time feedback mmWave beamforming network security

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Engineering distributed Collective Intelligence in Cyber-Physical Swarms 20

Engineering Distributed Collective Intelligence in Cyber-Phy...

引用

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

作者： Aguzzi, Gianluca Savaglio, Claudio Univ Bologna Cesena Italy Univ Calabria Calabria Italy

ISBN: (纸本)9798350369458;9798350369441

Cyber-physical swarms represent a paradigm shift in distributed systems, mirroring characteristics akin to natural swarms, such as self-organization, scalability, and fault tolerance. this paper delves into these complex systems, characterized by vast networks of cyber-physical entities with limited environmental awareness, yet capable of exhibiting emergent collective behaviors. these systems encompass a diverse array of scenarios, ranging from swarm robotics to the interconnectivity in smart cities, as well as the collaboration among augmented humans. the engineering of such systems presents unique challenges, primarily due to their intricate complexity and the spontaneous nature of their collective behaviors. this paper aims to dissect these challenges, offering a clear delineation of potential approaches. We present a comprehensive analysis, shedding light on the intricacies of engineering cyberphysical swarms and discussing modern solutions in engineering collective applications for such systems.

关键词： distributed collective intelligence large-scale IoT collective computing aggregate computing

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A distributed AI-based Disease Classification Approach 20

A Distributed AI-based Disease Classification Approach

引用

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

作者： Comito, Carmela Forestiero, Agostino Fazzinga, Bettina CNR ICAR Arcavacata Di Rende CS Italy Unical DICES Arcavacata Di Rende CS Italy

ISBN: (纸本)9798350369458;9798350369441

this paper presents a distributed learning approach designed to assist physicians in diagnosing diseases based on a patient's current health status, clinical history, and test results. the methodology relies on neural network models and supervised classifiers to identify illnesses. It utilizes word embedding to capture semantic relationships among hospital admissions, symptoms, and diagnoses. Additionally, it introduces a method to evaluate the connection between different diagnoses based on symptom similarity, aiding in prediction tasks. Experimental results on a real-world Electronic Health Records (EHR) dataset showcase the effectiveness and accuracy of the proposed technique, providing clinically relevant interpretations. these findings suggest promising avenues for future enhancements of the framework as a valuable diagnostic tool.

关键词： Clinical Decision Support System Disease Classification Word Embeddings

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Detecting multiple jammers using Fuzzy-Logic Intrusion Detection System (FLIDS) 20

Detecting multiple jammers using Fuzzy-Logic Intrusion Detec...

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

作者： Savva, Michael Ioannou, Iacovos Vassiliou, Vasos Univ Cyprus Dept Comp Sci Nicosia Cyprus CYENS Ctr Excellence Nicosia Cyprus

ISBN: (纸本)9798350369458;9798350369441

this research paper aims to evaluate the efficiency and effectiveness of FLIDS in detecting multiple jamming attacks. the study conducted experiments by placing two jammers at sixteen different positions to examine the system's performance. the experiments were carried out in various scenarios of Contiki 3.1 OS and the Cooja Simulator tool using the Routing Protocol for Low-Power and Lossy Networks (RPL). Furthermore, the optimal time frame for the solution to detect real-time jamming attacks was determined after analyzing the data. the simulation results demonstrate that Fuzzy Logic is an effective technique for recognizing multiple jamming attacks with high accuracy, precision, and recall rate in different scenarios.

关键词： Multiple jamming attacks Intrusion Detection Fuzzy Logic Internet of things Wireless sensor Networks

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(POSTER) Navigating the Unknown: Anomaly Detection in sensor Nodes Based on Event Traces 20

(POSTER) Navigating the Unknown: Anomaly Detection in Sensor...

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

作者： Band, Saurabh Foerster, Anna Univ Bremen Dept Sustainable Commun Networks Bremen Germany

ISBN: (纸本)9798350369458;9798350369441

In wireless sensor networks, post-deployment issues persist despite extensive testing, primarily due to unpredictable environmental factors and limited debugging tools for resource-constrained end nodes. this challenge is particularly pronounced in remote applications such as extraterrestrial habitats. To address this, we propose a runtime anomaly detection and diagnosis method for resource-constrained sensor nodes. A key advantage of our approach is its ability to learn expected behavior from historical data, eliminating the need for explicit behavior modeling, unlike other runtime fault detection methods. Our method comprises three main components: logging, detection, and diagnosis. We log event traces on the sensor nodes, enabling activity tracking down to the variable level. For anomaly detection, we explore various methods, including state transition, execution interval analysis, and clustering. Subsequently, diagnosis is performed using the logged event traces.

关键词： fault detection fault diagnosis IoT reliability machine learning pattern recognition anomaly detection

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Evaluation of Occupancy Detection with distributed Environmental sensors for IoT Applications 20

Evaluation of Occupancy Detection with Distributed Environme...

引用

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

作者： Bovo, Matteo Agyeman, Rockson Arif, Muhammad Rinner, Bernhard Univ Udine Dipartimento Matemat Informat & Fis Udine Italy Univ Klagenfurt Inst Networked & Embedded Syst Klagenfurt Austria AAU SAL USE Lab Silicon Austria Labs Klagenfurt Austria

ISBN: (纸本)9798350369458;9798350369441

Occupancy refers to the presence of people in rooms and buildings. It is an essential input for IoT applications, including controlling lighting, heating, access, and monitoring space limitation policies. Occupancy information can also be used to improve users' comfort and to reduce energy waste in buildings. this paper evaluates the performance and resource consumption of recent machine learning techniques for occupancy detection and measurement by exploiting data from distributed environmental sensors. this evaluation is founded on a dataset captured by our dedicated sensor network for indoor monitoring, comprising temperature, humidity, and carbon dioxide (CO2) sensors. Using different sensor modalities and spatio-temporal data selections, we compare eight classification algorithms based on the accuracy achieved and the required runtimes. Binary classification for occupancy detection (OD) achieves accuracies over 90% for individual modalities and close to 100% for modality combinations. Multi-class classification for occupancy measurements (OM) shows as clear ranking of the sensor modalities, and gradient boosting algorithms are superior when combining sensor modalities and fusing data from multiple sensors.

关键词： sensor network indoor environments multiclass classification sensor fusion smart rooms

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SAGIN-CAIN: A 3D Routing Protocol for Post-Disaster SAGIN 6G Network 20

SAGIN-CAIN: A 3D Routing Protocol for Post-Disaster SAGIN 6G...

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

作者： Marinho, Ricardo Pagoto Vieira, Luiz F. M. Vieira, Marcos A. M. Loureiro, Antonio A. F. Univ Fed Minas Gerais Dept Comp Sci Belo Horizonte MG Brazil

ISBN: (纸本)9798350369458;9798350369441

the 6G wireless networks are already being studied and their main novelties are beginning to emerge. the 6G network promises 100% network coverage in every area, including wilderness areas such as the ocean, desert, or remote areas, as it will be a fully 3D network, i.e., it will integrate space, air, and terrestrial networks. Also, with all these types of devices, the complexity of 6G networks increases, and Artificial Intelligence (AI) techniques will be mandatory for all the 6G's potential data and parameters. Hence, a Space-Air-Ground Integrated Network (SAGIN) is a potential alternative to enable all the network coverage as drones and satellites can cover large areas and fill possible network coverage gaps. the 6G network coverage also includes post-disaster regions with no network infrastructure and no network coverage, making SAGIN very suitable for them. thus, in this work, we expand CAIN, a post-disaster 6G routing protocol, so it can also be used in SAGIN scenarios, increasing even more network coverage. With drones as relays, SAGIN-CAIN routes the CAIN's Cluster Head's messages until it reaches a satellite.

关键词： 6G SAGIN RL Network coverage area

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(POSTER) Wireless Multi-sensor Hardware Platform to Enhance Smart Lighting Solutions 20

(POSTER) Wireless Multi-Sensor Hardware Platform to Enhance ...

引用

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

作者： Gomes, Joao Correia, Diogo Resende, Carlos Moreira, Waldir Filipe, Jorge Silva, Carlos Sousa, Antonio Fraunhofer Portugal AICOS Porto Portugal Tridon Portugal Vila Nova De Gaia Portugal

ISBN: (纸本)9798350369458;9798350369441

Smart lighting solutions bring benefits in terms of energy usage and users' well-being. However, their use is not widespread due to high installation and maintenance costs. the current manual commissioning procedure is one of the main human and time-consuming tasks, so its automation is expected to reduce the overall installation cost. Considering this, we propose the Lite4More device, a Bluetooth Low Energy (BLE) based sensor platform that supports the commissioning procedure and is backwards compatible with Digital Addressable Lighting Interface (DALI) devices. the Lite4More device produces sensor data that can feed algorithms to automate the commissioning of lighting infrastructures, namely luminaire localisation and groups and scene creation. the hardware platform was validated in both lab and real office environments. the preliminary results show that Lite4More device sensors are suitable for feeding luminaire localisation and group creation algorithms, and the considered communication technology is viable for smart lighting solutions.

关键词： Lighting Infrastructure Commissioning BLE Multi-sensor Device Smart Lighting

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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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