Sniatala / Amini / Boroojeni Fundamentals of Brooks–Iyengar Distributed Sensing Algorithm

Trends, Advances, and Future Prospects
1. Auflage 2020
ISBN: 978-3-030-33132-0
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: Wasserzeichen (»Systemvoraussetzungen)

E-Book, Englisch, 202 Seiten, eBook

This book provides a comprehensive analysis of Brooks-Iyengar Distributed Sensing Algorithm, which brings together the power of Byzantine Agreement and sensor fusion in building a fault-tolerant distributed sensor network. The authors analyze its long-term impacts, advances, and future prospects. The book starts by discussing the Brooks-Iyengar algorithm, which has made significant impact since its initial publication in 1996. The authors show how the technique has been applied in many domains such as software reliability, distributed systems and OS development, etc. The book exemplifies how the algorithm has enhanced new real-time features by adding fault-tolerant capabilities for many applications. The authors posit that the Brooks-Iyengar Algorithm will to continue to be used where fault-tolerant solutions are needed in redundancy system scenarios.This book celebrates S.S. Iyengar's accomplishments that led to his 2019 Institute of Electrical and Electronics Engineers' (IEEE) Cybermatics Congress "Test of Time Award" for his work on creating Brooks-Iyengar Algorithm and its impact in advancing modern computing.

Zielgruppe

Research

Autoren/Hrsg.

Sniatala, Pawel

Amini, M. Hadi

Boroojeni, Kianoosh G.

Inhaltsverzeichnis

Part I Introduction1 Introduction to Sensor Networks1.1 General Description1.2 Wireless Sensor Networks1.3 Distributed Sensor Networks1.4 Sensor Networks Applications1.5 Distributed Systems1.6 Sensor Fusion1.6.1 Bayesian Filter1.6.2 Kalman Filter1.6.3 Particle Filter1.7 Byzantine’s Fault Tolerance and Brooks-Iyengar Hybrid Algorithm1.7.1 Byzantine’s Fault Tolerance1.7.2 Brooks-Iyengar Hybrid Algorithm1.8 Summary and Outlook2 Introduction to Algorithms for Wireless Sensor Networks2.1 Sensor Deployment and Coverage2.1.1 Deterministic Deployment2.1.2 Maximizing Coverage Lifetime2.1.3 Deployment Quality2.2 Routing2.2.1 Unicast2.2.2 Multicast and Broadcast2.2.3 Data Collection and Distribution2.3 Sensor Fusion2.4 Conclusion3 Fault Tolerant Distributed Sensor Networks3.1 Introduction3.2 Byzantine Generals Problem3.3 Fault Tolerant Sensor Fusion3.3.1 Precision and Accuracy3.3.2 Brooks-Iyengar Algorithm3.3.3 Where Does Brooks-Iyengar Algorithm Stand?3.3.4 Comparing the Performance of Different Algorithms3.4 Theoretical Analysis of Distributed Agreement Algorithms3.4.1 Background3.4.2 Naive Averaging3.4.3 Approximate Byzantine Agreement3.4.4 Inexact Agreement - Fast Convergence Algorithm (FCA)3.4.5 Byzantine Vector Consensus (BVC)3.4.6 Marzullo’s Algorithm3.4.7 Brooks-Iyengar Algorithm3.5 Multi-dimensional Sensor Fusion3.5.1 Faulty Sensor Averaging Problem3.5.2 Interval Trees3.5.3 Algorithm to Find the Optimal Region3.5.4 Algorithm Complexity3.5.5 Comparison with Known Methods3.6 ConclusionReferencesPart II Advances of Sensor Fusion Algorithm4 Theoretical Analysis of Brooks-Iyengar Algorithm: Accuracy and Precision Bound4.1 Introduction4.2 Background4.3 Precision Bounds4.3.1 Naive Averaging4.3.2 Approximate Byzantine Agreement4.3.3 Inexact Agreement - Fast Convergence Algorithm (FCA)4.3.4 Byzantine Vector Consensus (BVC)4.3.5 Marzullo’s Algorithm4.3.6 Brooks-Iyengar Algorithm4.4 Comparison4.4.1 Approximate Byzantine Agreement vs. Approximate BVC4.4.2 Approximate Byzantine Agreement vs. Brooks-Iyengar algorithm4.4.3 Approximate BVC vs. Brooks-Iyengar algorithm4.5 Precision bound of the Brooks-Iyengar Algorithm4.5.1 Analysis and Proof of Precision Bound4.6 Conclusion5 The Profound Impact of the Brooks-Iyengar Algorithm5.1 Business, Media and Academic References5.2 Industrial Projects Incorporating the Algorithm5.3 Impacts of Brooks-Iyengar Algorithms on Academic Dissertations5.3.1 The SDSN-Aggregation5.3.2 Sensor Fusion5.4 Algorithm Potential for Future Market Growth5.5 Related Contribution to Sensor Networks by S. S. Iyengar5.6 Conclusion and OutlookPart III Trends of Brooks-Iyengar Algorithm6 Robust Fault Tolerant Rail Door State Monitoring Systems6.1 Introduction6.2 Safety-Critical Transportation Applications6.3 Theory6.4 Implementation6.5 ConclusionPart IV Applications of Brooks-Iyengar Algorithm for The Next 10 Years7 Decentralization of Data-Source using Blockchain-based Brooks-Iyengar Fusion7.1 Introduction7.1.1 Consensus in Blockchain7.2 Blockchain Structures7.2.1 Transaction Procedure in Blockchain7.3 Transaction Source of Blockchain7.3.1 Brooks–Iyengar Algorithm7.3.2 Combining the Brooks–Iyengar Algorithm with Blockchains7.3.3 Example7.4 Accuracy and Precision of the Brooks–Iyengar Algorithm7.4.1 Fault Tolerance7.5 Blockchain Architecture7.6 Modeling of Blockchain Assignment Based on Byzantine Consensus7.6.1 Byzantine Consensus Problem Based on Monte Carlo7.7 A Fuzzy Byzentine Consensus7.8 Simulation7.9 Conclusions8 A Novel Fault-Tolerant Random Forest Model using Brooks-Iyengar Fusion8.1 Introduction8.2 Random Forest Classifiers8.3 Enhanced Random Forest Regressors Utilizing Brooks-Iyengar Fusion Method8.4 Applications in Autonomous Car8.5 Conclusion9 Designing a Deep-Learning Neural Network chip to detect Hardware Errors using Brooks-Iyengar Algorithm9.1 Motivation9.2 Design Vision9.3 Introduction9.4 System Design and Architecture9.5 Design for Brooks-Iyengar Algorithm9.6 Similar Attempts9.6.1 Google: first Tensor Processing Unit (TPU)9.7 Conclusion10 Ubiquitous Brooks-Iyengars Robust Distributed Real-time Sensing Algorithm: Past, Present and Future10.1 Introduction BROOK IYENGAR ALGORITHM10.2 REAL TIME MINIX OPERATING SYSTEM10.3 INFLUENCE OF BROOKS-IYENGAR ALGORITHM10.3.1 Brooks-Iyengars Algorithm on MINIX Operating System10.3.2 CASE STUDY Open MPI + Virtualization10.4 Brook-Iyengar Use cases10.5 Conclusion10.6 AcknowledgementIndex

Über Autor(innen)

Pawel Sniatala received the M.S. degree in Telecommunication, M.S. degree in Computer Science and Ph.D. degree in Microelectronics all from Poznan University of Technology, Poland. He received the habilitation degree (D.Sc.) in electronics in 2016. From 1998 to 2002 he was with the Department of Computer Engineering at the Rochester Institute of Technology (USA). He returned to Poznan University of Technology to take a position in the Faculty of Computing, where currently, he is the Vice Dean for Industrial Cooperation. He also graduated the international MBA study in join programme George State University and Pozna University of Economics. His area of interests focuses on VLSI circuits for digital and mixed analog-digital signal processing systems: UltraLow Power ASIC design, Implantable IC, Signal/Image processing hardware-software codesign, Hardware accelerators. However, following his computing science background, he is also involved in research projects related to eHealth area. He was involved in several industrial projects e.g.: control and monitoring systems for gas mine systems, control systems for water treatment plant and teletechniques systems for airport. He has served in several international Ph.D. and M.S. committees (Portugal, USA). He was giving invited courses/lectures in several universities outside Poland: USA, Portugal, Peru, Taiwan. He is an author and coauthor of 90 papers including a monograph: P. Sniatala, CMOS Current Mode Modulators, Poznan Monographs in Computing and Its Applications, Poznan 2016.M. Hadi Amini M. Hadi Amini received his Ph.D. and M.Sc. from Carnegie Mellon University in 2019 and 2015 respectively. He also holds a doctoral degree in Computer Science and Technology. Prior to that, he received M.Sc. from Tarbiat Modares University in 2013, and the B.Sc. from Sharif University of Technology in 2011. He is currently an Assistant Professor at School of Computing and Information Sciences, College of Engineering and Computing at Florida International University (FIU). He is also the founding director of Sustainability, Optimization, and Learning for InterDependent networks laboratory (solid lab). His research interests include distributed machine learning and optimization algorithms, distributed intelligence, sensor networks, interdependent networks, and cyberphysical resilience. Application domains include energy systems, healthcare, device-free human sensing, and transportation networks. Prof. Amini is a life member of IEEE-Eta Kappa Nu (IEEE-HKN), the honor society of IEEE. He organized a panel on distributed learning and novel artificial intelligence algorithms, and their application to healthcare, robotics, energy cybersecurity, distributed sensing, and policy issues in 2019 workshop on artificial intelligence at FIU. He also served as President of Carnegie Mellon University Energy Science and Innovation Club; as technical program committee of several IEEE and ACM conferences; and as the lead editor for a book series on ‘‘Sustainable Interdependent Networks’’ since 2017, as well as . He has published more than 60 refereed journal and conference papers, and book chapters. He has co-authored two books, and edited three books on various aspects of optimization and machine learning for interdependent networks. He is the recipient of the best reviewer award from four IEEE Transactions, the best journal paper award in “Journal of Modern Power Systems and Clean Energy”, and the dean’s honorary award from the President of Sharif University of Technology. (homepage: www.hadiamini.com; lab website: www.solidlab.network)Kianoosh G. Boroojeni received his PhD in computer science from Florida International University. He received his B.Sc degree from University of Tehran in 2012. His research interests include smart grids and cybersecurity. He is the author/co-author of a number of books published by MIT Press and Springer and various peer-reviewed journal publications and conference proceedings. He is currently a faculty at Florida International University.