Buch, Englisch, 202 Seiten, HC runder Rücken kaschiert, Format (B × H): 160 mm x 241 mm, Gewicht: 512 g
Trends, Advances, and Future Prospects
Buch, Englisch, 202 Seiten, HC runder Rücken kaschiert, Format (B × H): 160 mm x 241 mm, Gewicht: 512 g
ISBN: 978-3-030-33131-3
Verlag: Springer International Publishing
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.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Technische Informatik Computersicherheit
- Mathematik | Informatik EDV | Informatik Angewandte Informatik
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
- Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik
Weitere Infos & Material
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