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State feedback control aware stochastic transmitting latency in cyber-physical power system

INTERNATIONAL JOURNAL OF distributed sensor networks 2018年第9期14卷 1-9页

作者： Ji, Hao Wang, Meng Yang, Ting Zhao, Junjie Tianjin Univ Minist Educ Key Lab Smart Grid Tianjin 300072 Peoples R China State Grid Tianjin Elect Power Co Tianjin Peoples R China

distributed sensor networks with a great number of sensors realize information gathering, transmitting, and controlling, which will greatly improve the reliability and efficiency of industrial infrastructure systems, such as cyber-physical power system. Cyber-physical power system is one of the heuristic systems tightly coupled by a continuous-time electrical power system and a discrete-time information system. The transmitting latency, packets disorder, or loss will fail system convergence, even unstable under disturbance. This article studies the effect of long latency on cyber-physical power system frequency stability based on network control theory and establishes a heuristic model to express the continuous-time distributed generation system and the discrete-time distributed sensor network. A new state feedback controller is designed based on the stochastic optimal control theory to solve the micro-grid's frequency stability problem with stochastic latency disturbance. Finally, we have conducted an extensive evaluation study using a real distributed generation micro-grid system. The simulation results show that the new controller reduces the influence of distributed sensor networks transmitting latency on the frequency stability, and the frequency dynamic process in the distributed generation micro-grid has smaller overshoot and obtains faster dynamic response speed.

关键词： distributed sensor networks cyber-physical power system network control theory transmitting latency distributed generation micro-grid system

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On the Robustness of Quantum Algorithms for Blockchain Consensus

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sensorS 2022年第7期22卷 2716-2716页

作者： Ullah, Muhammad Asad Setiawan, Jason William Rehman, Junaid Ur Shin, Hyundong Kyung Hee Univ Dept Elect & Informat Convergence Engn Yongin 17104 Gyeonggi Do South Korea

Blockchain has revolutionized many fields, such as distributed sensor networks, finance, and cryptocurrency. Consensus between distributed network nodes is at the core of such blockchain technologies. The three primary performance measures for any consensus algorithm are scalability, security, and decentralization. This paper evaluates the usefulness and practicality of quantum consensus algorithms for blockchain-enhanced sensor, and computing networks and evaluates them against the aforementioned performance measures. In particular, we investigate their noise robustness against quantum decoherence in quantum processors and over fiber-optic channels. We observe that the quantum noise generally increases the error rate in the list distribution. However, the effect is variable on different quantum consensus schemes. For example, the entanglement-free scheme is more affected than entanglement-based schemes for the local noise cases, while in the case of noisy optical fiber links, the effect is prominent on all quantum consensus schemes. We infer that the current quantum protocols with noisy intermediate-scale quantum devices and noisy quantum communication can only be employed for modular units in intraenterprise-level blockchain, such as Zilliqa, for sensor, and computing networks.

关键词： blockchain consensus quantum algorithms quantum noise distributed sensor networks modular computing units

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distributed Adaptive Estimation with Probabilistic Data Association

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IFAC Proceedings Volumes 1987年第5期20卷 215-220页

作者： K.C. Chang Y. Bar-shalom Advanced Decision Systems 201 San Antonio Circle 286 Mt. View CA 94040 USA U-157 ESE Department University of Connecticut Storrs CT 06268 USA

The probabilistic data association filter (PDAF) estimates the state of a target in a cluttered environment. This suboptimal Bayesian approach assumes that the exact target and measurement models are known. However, in most practical applications, there are difficulties in obtaining an exact mathematical model of the physical process. In this paper, the problem of estimating target states with uncertain measurement origins and uncertain system models in a distributed manner is considered. First, a scheme is described for local processing, then the fusion algorithm which combines the local processed results into a global one is derived. The algorithm can be applied for tracking a maneuvering target in cluttered and low detection environment with a distributed sensor network.

关键词： Estimation Target Tracking Data Association distributed sensor networks Multiple Model Estimation

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Learning Damage Event Discriminator Functions with distributed Multi-instance RNN/LSTM Machine Learning - Mastering the Challenge

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Procedia Manufacturing 2020年 52卷 193-202页

作者： Stefan Bosse University of Bremen Dept. of Mathematics & Computer Science 28359 Bremen Germany

Common Structural Health Monitoring systems are used to detect past damages occurred in structures with sensor networks and external sensor data processing. The time of the damage creation event is commonly unknown. Numerical methods and Machine Learning are used to extract relevant damage information from sensor signals that is characterised by a high data volume and dimension. In this work, distributed multi-instance learning applied to raw time-series of sensor data is deployed to predict the event of the occurrence of a hidden damage in a mechanical structure using typical vibrations of the structure. The sensor processing and learning is performed locally on sensor node level with a global fusion of prediction results to estimate the damage location and the time of the damage creation. Recurrent neural networks with a long-short-term memory architecture are considered implementing a damage discriminator function. The sensor data required for the evaluation of the proposed approach is generated by a multi-body physics simulation approximating material properties.

关键词： distributed Machine Learning Recurrent Neural networks Long-short-term Memory distributed sensor networks Time-series Forecasting

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A Virtual Machine Platform Providing Machine Learning as a Programmable and distributed Service for IoT and Edge On-Device Computing: Architecture, Transformation, and Evaluation of Integer Discretization

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ALGORITHMS 2024年第8期17卷 356页

作者： Bosse, Stefan Univ Bremen Dept Math & Comp Sci D-28359 Bremen Germany Univ Siegen Dept Mech Engn D-57072 Siegen Germany

Data-driven models used for predictive classification and regression tasks are commonly computed using floating-point arithmetic and powerful computers. We address constraints in distributed sensor networks like the IoT, edge, and material-integrated computing, providing only low-resource embedded computers with sensor data that are acquired and processed locally. sensor networks are characterized by strong heterogeneous systems. This work introduces and evaluates a virtual machine architecture that provides ML as a service layer (MLaaS) on the node level and addresses very low-resource distributed embedded computers (with less than 20 kB of RAM). The VM provides a unified ML instruction set architecture that can be programmed to implement decision trees, ANN, and CNN model architectures using scaled integer arithmetic only. Models are trained primarily offline using floating-point arithmetic, finally converted by an iterative scaling and transformation process, demonstrated in this work by two tests based on simulated and synthetic data. This paper is an extended version of the FedCSIS 2023 conference paper providing new algorithms and ML applications, including ANN/CNN-based regression and classification tasks studying the effects of discretization on classification and regression accuracy.

关键词： Tiny ML distributed sensor networks discretization integer arithmetic

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TOLERATING FAILURES OF CONTINUOUS-VALUED sensorS

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ACM TRANSACTIONS ON COMPUTER SYSTEMS 1990年第4期8卷 284-304页

作者： MARZULLO, K Cornell University

One aspect of fault-tolerance in process control programs is the ability to tolerate sensor failure. This paper presents a methodology for transforming a process control program that cannot tolerate sensor failures into one that can. Issues addressed include modifying specifications in order to accommodate uncertainty in sensor values and averaging sensor values in a fault-tolerant manner. In addition, a hierarchy of sensor failure models is identified, and both the attainable accuracy and the run-time complexity of sensor averaging with respect to this hierarchy is discussed.

关键词： distributed sensor networks FAULT-TOLERANCE N-MODULE REDUNDANCY

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Localizing Stationary Targets with Cooperative Unmanned Aerial Vehicles

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IFAC Proceedings Volumes 2009年第22期42卷 68-73页

作者： Dimitri V. Zarzhitsky Pedro DeLima Daniel J. Pack Department of Electrical and Computer Engineering U.S. Air Force Academy CO 80840

Unmanned aerial vehicles (UAVs) play an increasingly important role in a large number of military applications. Frequently deployed for intelligence collection (e.g., surveillance and reconnaissance), they are also useful for establishing long-range communication and providing tactical support. The focus of our research lies on the design and development of autonomous, cooperative UAVs using distributed control, sensing, and communication technologies. In this paper, we present several UAV technologies we developed that enable a team of UAVs to autonomously search, detect, and locate stationary ground targets using airborne sensors. These enabling technologies include: (1) distributed, cooperative control of UAVs, (2) a novel out-of-order sigma-point Kalman filtering method for local fusion of multi-view sensor data collected by different UAVs from varying perspectives, (3) a dynamic air-to-air and air-to-ground communication network, and (4) a UAV operating system to manage control, sensing, and communication activities. We illustrate the capabilities of this cooperative UAV system with test data acquired in flight demonstrations.

关键词： distributed sensor networks real-time sensor fusion cooperative control

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Intrusion Detection System using Honey Token based Encrypted Pointers to Mitigate Cyber Threats for Critical Infrastructure networks

Intrusion Detection System using Honey Token based Encrypted...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Muhammad Kamran Asif Yahya Subhi Al-Harthi Department of Electrical Engineering King Saud University

ISBN: (纸本)9781479938414

Recent advancements in cyberspace impose a greater threat to the security of critical infrastructure than ever before. The scale of damage that could be done on these infrastructures by well-planned cyber-attacks is enormous. Most of the research work done for the security of these critical infrastructures focuses on conventional security measures. In this paper, we designed an Intrusion Detection System (IDS) that is based on the novel approach of Honey Token based Encrypted Pointers to prevent critical infrastructure networks from cyber-attacks particularly from zero day cyber threats. These honey tokens inside the frame will serve as a trap for the attacker. All nodes operating within the working domain of critical infrastructure network are divided into four different pools. This division is based according to their computational power and level of vulnerability. These pools are provided with different levels of security measures within the network. IDS use different number of Honey Tokens (HT) per frame for every different pool. Moreover every pool uses different types of encryption schemes (AES-128,192,256) etc. We use critical infrastructure network of 64 nodes for our simulations. We analyzed the performance of IDS in terms of True Positive and False Negative Alarms. Finally we test this IDS through Network Penetration Testing (NPT). This NPT is accomplished by putting the critical infrastructure network of 64 nodes directly under the zero day cyber-attacks and then we analyze the behavior of the IDS under such realistic conditions. The IDS is designed in such a way that it not only detects the intrusions but also recovers the entire zero day attack using reverse engineering approach.

关键词： Intrusion Detection System Cyber Threats Zero Day Attacks Critical Infrastructure networks Information Security Honey Token Encrypted Pointers Industrial networks distributed sensor networks Industrial Communication Protocol DNP3 Cyber Securit Cyber Space SCADA Command and Control System Cyber Warfare Intelligence Infrastructure Information Infrastructure

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Nonlinear Diffusion Adaptation with Bounded Transmission over distributed networks

Nonlinear Diffusion Adaptation with Bounded Transmission ove...

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IEEE International Conference on Communications

作者： Jongmin Lee Cihan Tepedelenlioglu Mahesh K. Banavar Andreas Spanias Signal and Information Processing (SenSIP) Center School of Electrical Computer and Energy Engineering Arizona State University Department of Electrical and Computer Engineering Clarkson University

ISBN: (纸本)9781467364300

This paper introduces diffusion adaptation strategies over distributed networks with nonlinear transmissions, motivated by the necessity for bounded transmit power. Local information is exchanged in real-time with neighboring nodes in order to estimate a common parameter vector via constrained nonlinear transmissions, using an adaptive learning algorithm. We propose nonlinear diffusion strategies for such an adaptive estimation. We will study convergence properties of the proposed algorithm in the mean and the mean-square sense. Simulations support the performance analysis and show that the proposed algorithm performs close to the linear case with the added advantage of power savings.

关键词： Nonlinear diffusion Bounded transmission Adaptive networks Consensus distributed sensor networks

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Shape Reconstruction using Instruction Systolic Array

Shape Reconstruction using Instruction Systolic Array

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IEEE sensors Conference

作者： Partheepan Kandaswamy James A. Flint Vassilios A. Chouliaras Wolfson School of Mechanical Electrical and Manufacturing Engineering Loughborough University Loughborough LE11 3TU UK

ISBN: (纸本)9781509010134

This paper describes a novel, 2D mesh architecture prototype based on the Instruction Systolic Array (ISA) paradigm for distributed computing on fabrics. We discuss a real-time shape sensing and reconstruction application executing on this architecture and demonstrate a physical design for a wearable system based on the ISA concept constructed from off-the-shelf microcontrollers and sensors. Results demonstrate the application executes in 39 ms the prototype ISA implementation thus confirming the viability of the proposed architecture for fabric-resident computing devices.

关键词： Instruction Systolic Array distributed sensor networks System on fabrics wearable electronics fabric-resident computing device shape reconfiguration Systolic algorithms Wearable computers sensor networks instruction ARCHITECTURE Building construction distributed computing Textile fabrics fabric

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