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

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Enhancing Autonomy with Blockchain and Multi-Access Edge computing in distributed Robotic systems 5

Enhancing Autonomy with Blockchain and Multi-Access Edge Com...

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5th international conference on Fog and Mobile Edge computing (FMEC)

作者： Queralta, Jorge Pena Li Qingqing Zou, Zhuo Westerlund, Tomi Univ Turku Fac Sciece & Engn Turku Intelligent Embedded & Robot Syst Lab Turku Finland Fudan Univ Sch Informat Sci & Technol Shanghai Peoples R China

ISBN: (纸本)9781728172163

This conceptual paper discusses how different aspects involving the autonomous operation of robots and vehicles will change when they have access to next-generation mobile networks. 5G and beyond connectivity is bringing together a myriad of technologies and industries under its umbrella. High-bandwidth, low-latency edge computing services through network slicing have the potential to support novel application scenarios in different domains including robotics, autonomous vehicles, and the Internet of Things. In particular, multi-tenant applications at the edge of the network will boost the development of autonomous robots and vehicles offering computational resources and intelligence through reliable offloading services. The integration of more distributed network architectures with distributed robotic systems can increase the degree of intelligence and level of autonomy of connected units. We argue that the last piece to put together a services framework with third-party integration will be next-generation low-latency blockchain networks. Blockchains will enable a transparent and secure way of providing services and managing resources at the Multi-Access Edge computing (MEC) layer. We overview the state-of-the-art in MEC slicing, distributed robotic systems and blockchain technology to define a framework for services the MEC layer that will enhance the autonomous operations of connected robots and vehicles.

关键词： Edge computing Robotics 5G Computational Offloading Multi-Access Edge computing Autonomous Robots Network Slicing Mapping and localization Edge AI

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A hybridization of Mobile Crowdsensing, Twitter Analytics, and sensor Data for the holistic approach of pollen onsets detection 15

A hybridization of Mobile Crowdsensing, Twitter Analytics, a...

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15th Annual international conference on distributed computing in sensor systems (DCOSS)

作者： Nikoletseas, Sotiris Tzamalis, Pantelis Vikatos, Pantelis Univ Patras Patras Greece CTI Patras Greece

ISBN: (纸本)9781728105703

In this paper, we focus on designing a holistic approach to gather, monitor and analyze heterogeneous sources on allergies and allergens into one system. The dimensions of mobile crowdsensing, the sensor-based measurements, and micro-blogging raw texts are combined to address all different situations and needs. We envision that our system can be used for public health monitoring in order to early predict allergy/allergens or allergy-related irritants outbreaks. To the best of our knowledge, our system is the first holistic approach of a surveillance system for allergy in the US region.

关键词： allergy allergens mobile crowdsensing twitter analytics sensors participatory sensing irritants mhealth

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Adaptive Cooperative distributed Compressed Sensing for Edge Devices: A Multiagent Deep Reinforcement Learning Approach

Adaptive Cooperative Distributed Compressed Sensing for Edge...

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ieee Annual conference on Pervasive computing and Communications Workshops (PerCom)

作者： Masatoshi Sekine Satoshi Ikada Innovation Promotion Center Oki Electric Industry Co. Ltd. Saitama Japan

ISBN: (纸本)9781665447249

We propose a lightweight and adaptive distributed compressed sensing (DCS) with multi-sensor collaboration based on multiagent deep reinforcement learning (LADICS-MARL). To efficiently acquire data generated by sensor nodes deployed over a wide area and for long periods, we previously proposed a lightweight and adaptive compressed sensing method based on deep learning for edge devices, called LACSLE that changes the compression ratio in real-time according to the data between one sender and one receiver using pre-trained deep learning model. LADICS-MARL is an extension for multiple senders and one receiver and supports DCS through which multiple compressed data are simultaneously reconstructed. Multiple sensor nodes cooperate based on multiagent reinforcement learning to estimate the optimal compression ratio for all senders according to each corresponding data, as well as the transmitted compressed data from other sensor nodes. In addition, a gateway optimizes the combination of groups where some compressed data are reconstructed simultaneously. A performance evaluation using acceleration data from multiple sensor terminals acquired on a bridge suggests that the multiagent-based LADICS-MARL can reconstruct original data from less compressed data compared to the single-agent-based LACSLE under the threshold of reconstruct error.

关键词： Performance evaluation Deep learning Three-dimensional displays conferences distributed databases Reinforcement learning Receivers

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Smart Irrigation System for Agriculture using Wireless sensor Network 7

Smart Irrigation System for Agriculture using Wireless Senso...

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7th international conference on Advanced computing and Communication systems, ICACCS 2021

作者： Santhakumar, G. Vadivelu, R. Harshini, K. Krishanth, N. Gokul Haritha, G. Sri Krishna College of Technology Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9781665405201

In India, development foresees a fundamental half for food production movement. Company enterprise in our country is relying on a destructive storm that is not a pleasant source of water. As a consequence, the water structure is used in the commercial world. Bush gets aquatic irrigation in Irrigation structure, which is based on world type. The nursery built mostly current husbandry adventures area unit the new would like taking all things along components of business enterprise in Bharat. during this advancement, the wetness and leaf soundness of plants area unit all controlled. as a result of the variable measuring system conditions these conditions each therefore typically might move by and huge in mammoth house, that makes implausibly arduous to stay up the consistency in any respect the spots within the house very. it's seen that startlingly Associate in Nursing humanoid phonephone management the Irrigation structure, that might offer the operating environments of maintaining uniform natural conditions area unit projected. the elemental goal of this paper is to make up a pointy distant device affiliation (WSN) for Associate in Nursing farming climate. Checking developing climate for various parts, as an example, soil wetness, Leaf Health and diligence close totally different parts are often of importance. A customary technique to manage live these parts during a common climate projected individual very taking evaluations and checking them at totally different occasions. This paper researches a faraway seeing framework utilizing RF part. These focuses conduct information remotely to a focal employee, what accumulates the knowledge, vittles it and can agree to it to be examined by then showed unsteady and may besides be enraptured off the punter versatile. © 2021 ieee.

关键词： Horticulture

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BEI-TAB: Enabling Secure and distributed Airport Baggage Tracking with Hybrid Blockchain-Edge System 21

BEI-TAB: Enabling Secure and Distributed Airport Baggage Tra...

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21st ieee international conference on Communication Technology, ICCT 2021

作者： Wang, Fei Si, Pengbo Sun, Enchang Su, Yuzhao Beijing University of Technology Faculty of Information Technology Beijing China

ISBN: (纸本)9781665432061

Global air transport carries about 7.3 billion pieces of luggage each year and up to 56 percent of travelers prefer obtaining real-time baggage tracking information throughout their trip. However, the traditional baggage tracking scheme is generally based on optical scanning and centralized storage systems, which suffers from low efficiency and information leakage. In this paper, a blockchain and edge computing based IOT system for tracking of airport baggage (BEI-TAB) is proposed. Through the combination of radio frequency identification technology (RFID) and blockchain, real-time baggage processing information is automatically stored in blockchain. In addition, we deploy Interplanetary File System (IPFS) at edge nodes with ciphertext policy attribute-based encryption (CP-ABE) to store basic baggage information. Only hash values returned by the IPFS network are kept in blockchain, enhancing the scalability of the system. Furthermore, a multi-channel scheme is designed to realize the physical isolation of data, and to rapidly process multiple types of data and business requirements in parallel. To the best of our knowledge, it is the first architecture that integrates RFID, IPFS, CP-ABE with blockchain technologies to facilitate secure, decentralized, and real-time characteristics for storing and sharing data for baggage tracking. We have deployed a testbed with both software and hardware to evaluate the proposed system, considering the performances of transaction processing time and speed. © 2021 ieee.

关键词： Blockchain

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Federated Variational Learning for Anomaly Detection in Multivariate Time Series

Federated Variational Learning for Anomaly Detection in Mult...

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ieee international conference on Performance, computing and Communications (IPCCC)

作者： Kai Zhang Yushan Jiang Lee Seversky Chengtao Xu Dahai Liu Houbing Song Embry-Riddle Aeronautical University Daytona Beach FL Air Force Research Laboratory Rome NY

ISBN: (纸本)9781665443326

Anomaly detection has been a challenging task given high-dimensional multivariate time series data generated by networked sensors and actuators in Cyber-Physical systems (CPS). Besides the highly nonlinear, complex, and dynamic nature of such time series, the lack of labeled data impedes data exploitation in a supervised manner and thus prevents an accurate detection of abnormal phenomenons. On the other hand, the collected data at the edge of the network is often privacy sensitive and large in quantity, which may hinder the centralized training at the main server. To tackle these issues, we propose an unsupervised time series anomaly detection framework in a federated fashion to continuously monitor the behaviors of interconnected devices within a network and alert for abnormal incidents so that countermeasures can be taken before undesired consequences occur. To be specific, we leave the training data distributed at the edge to learn a shared Variational Autoencoder (VAE) based on Convolutional Gated Recurrent Unit (ConvGRU) model, which jointly captures feature and temporal dependencies in the multivariate time series data for representation learning and downstream anomaly detection tasks. Experiments on three real-world networked sensor datasets illustrate the advantage of our approach over other state-of-the-art models. We also conduct extensive experiments to demonstrate the effectiveness of our detection framework under non-federated and federated settings in terms of overall performance and detection latency.

关键词： Training Representation learning Image edge detection Time series analysis Training data sensor phenomena and characterization sensor systems

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Unity makes strength: Coalition Formation-based Group-buying for Timely UAV Data Collection

Unity makes strength: Coalition Formation-based Group-buying...

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GLOBECOM 2022 - 2022 ieee Global Communications conference

作者： Nan Qi Yeting Huang Wen Sun Shi Jin Theodoros A. Tsiftsis Qihui Wu Xiang Su National Mobile Communications Research Laboratory Southeast University Nanjing China College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing China School of Cyber Science and Engineering Southeast University Nanjing China School of Intelligent Systems Science & Engineering Jinan University Zhuhai China Department of Computer Science Norwegian University of Science and Technology Gjøvik Norway Center for Ubiquitous Computing University of Oulu Oulu Finland

ISBN: (纸本)9781665435413

With their high mobility, unmanned aerial vehicles (UAVs) become appealing data collectors in hard-to-reach wide-area distributed sensor networks. Different from existing works focusing on the perspective of UAVs for service order optimization and UAV utility maximization, we consider the utilities of both sensors and UAVs, and innovatively model the competition among sensors (buyers) for the service of UAVs (sellers) as an auction game. A “unity makes strength” strategy is exploited. That is, to strengthen the bidding competitiveness, a group-buying coalition auction method that encourages sensors to form coalitions to bid for UAV service is proposed. Besides, we propose a parallel variable neighborhood ascent search algorithm, we can quickly determine the approximately optimal group-buying coalition structure. Numerical results show that the proposed method outperforms the joint trajectory design-task scheduling (TDTS) UAV-to-community method and the single coalition formation game (CFG) method.

关键词： Wireless sensor networks Games Data collection Autonomous aerial vehicles Search problems Information age Trajectory

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A Cython Bound Tracklet- Tracklet Correlation for Resident Space Objects

A Cython Bound Tracklet- Tracklet Correlation for Resident S...

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ieee conference on Aerospace

作者： Kathrin Rack Simon Schmitz Martin Stoffers Benjamin Hofmann Michael Meinel Hauke Fiedler Thomas Schildknecht Department High-Performance Computing Institute for Software Technology German Aerospace Center Linder Hohe Koln Germany Department Space Flight Technology Institute for Space Operations and Astronaut Training German Aerospace Center Weßling Germany Department Intelligent and Distributed Systems Institute for Software Technology German Aerospace Center Berlin Germany Astronomical Institute University of Bern Bern Switzerland

Almost all humankind depends on satellite technology orbiting the Earth. However, space operations are increasingly endangered by the rapidly growing number of orbiting objects and debris. To avoid collisions with debris particles and to keep the space infrastructure alive, it is mandatory to monitor cooperative and uncooperative resident space objects. For this purpose, we are developing a software system called Backbone Catalogue of Relational Debris Information (BACARDI). It provides a database with orbit information of resident space objects and related products like ephemerides, which enables us to derive close approaches and provide collision warnings. The challenge is to detect, identify and track new resident space objects that were recently launched, created by fragmentations and collisions or recently observed due to improved sensor tech-nologies. Typically, passive optical telescopes observe objects for a relatively short time. A short time series of observation is called a tracklet. To derive the full orbit of an object, at least a pair of tracklets is needed. In case the tracklets cannot be associated to an already known object, it should therefore be identified which tracklets belong to the same object. The algorithm performing this task is called the Tracklet- Tracklet Correlation (TTC). Our aim is to integrate the TTC into BACARDI. Therefore, we have to consider that the individual algorithm meets different requirements than the overall software. On the one hand, BAC- ARDI is a software system that needs to be easily maintainable, extendable and usable and is thus written in Python. On the other hand, the TTC has to be computationally fast, which is why it is written in C++. To fulfil these opposing requirements, we present a successful linkage of a fast C++ based TTC to a Python-based BACARDI. To perform this task, we used the pro-gramming language Cython. The Cython syntax is a superset of the Python syntax and provides additional rules to address

关键词： Space vehicles Codes Correlation Software algorithms C++ languages Object recognition Task analysis

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IoT-CAD: Context-Aware Adaptive Anomaly Detection in IoT systems Through sensor Association 20

IoT-CAD: Context-Aware Adaptive Anomaly Detection in IoT Sys...

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39th ieee/ACM international conference On Computer Aided Design (ICCAD)

作者： Yasaei, Rozhin Hernandez, Felix Al Faruque, Mohammad Abdullah Univ Calif Irvine Dept Elect Engn & Comp Sci Irvine CA 92697 USA

ISBN: (纸本)9781665423243

The deployment of Internet of Things (IoT) devices in cyber-physical applications has introduced a new set of vulnerabilities. The new security and reliability challenges require a holistic solution due to the cross-domain, cross-layer, and interdisciplinary nature of IoT systems. However, the majority of works presented in the literature primarily focus on the cyber aspect, including the network and application layers, and the physical layer is often overlooked. In this paper, we utilize IoT sensors that capture the physical properties of the system to ensure the integrity of IoT sensors data and identify anomalous incidents in the environment. We propose an adaptive context-aware anomaly detection method that is optimized to run on a fog computing platform. In this approach, we devise a novel sensor association algorithm that generates fingerprints of sensors, clusters them, and extracts the context of the system. Based on the contextual information, our predictor model, which comprises an Long-Short Term Memory (LSTM) neural network and Gaussian estimator, detects anomalies, and a consensus algorithm identifies the source of the anomaly. Furthermore, our model updates itself to adapt to the variation in the environment and system. The results demonstrate that our model detects the anomaly with 92.0% precision in 532ms, which meets the real-time constraint of the system under test.

关键词： Internet of Things context sensor association anomaly detection recurrent neural networks LSTM encoder-decoder

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Parallel Iteration Algorithm of Roll Angle Based On High-Performance ARM Processor 3

Parallel Iteration Algorithm of Roll Angle Based On High-Per...

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3rd international conference on Unmanned systems (ICUS)

作者： Wang, Shiyao Li, Dongguang Xiao, Siyu Beijing Inst Technol Sch Mechatron Engn Beijing Peoples R China

ISBN: (纸本)9781728180250

The method for calculating the attitude of the projectile requires high real-time performance and a large amount of calculation. The parallel calculation method of the missile-borne signal is studied. For the linear iterative equations in the attitude solution algorithm, a parallel calculation based on Newton method is proposed. This method decomposes linear iterative equations in parallel to form a distributed multi-core parallel solution process, which can effectively optimize the algorithm solution time. Experiments were conducted through four sets of data with different data lengths and frame rates to verify the execution efficiency of multi-core parallel computing.

关键词： Rolling Angle Solution Iterative Algorithms Parallel computing Geomagnetic sensor

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