Modeling and Optimization of Parallel and Distributed Embedded Systems

This book introduces the state-of-the-art in research in parallel and distributed embedded systems, which have been enabled by developments in silicon technology, micro-electro-mechanical systems (MEMS), wireless communications, computer networking, and digital electronics. These systems have diverse applications in domains including military and defense, medical, automotive, and unmanned autonomous vehicles.

The emphasis of the book is on the modeling and optimization of emerging parallel and distributed embedded systems in relation to the three key design metrics of performance, power and dependability.

Key features:

• Includes an embedded wireless sensor networks case study to help illustrate the modeling and optimization of distributed embedded systems.

• Provides an analysis of multi-core/many-core based embedded systems to explain the modeling and optimization of parallel embedded systems.

• Features an application metrics estimation model; Markov modeling for fault tolerance and analysis; and queueing theoretic modeling for performance evaluation.

• Discusses optimization approaches for distributed wireless sensor networks; high-performance and energy-efficient techniques at the architecture, middleware and software levels for parallel multicore-based embedded systems; and dynamic optimization methodologies.

• Highlights research challenges and future research directions.

The book is primarily aimed at researchers in embedded systems; however, it will also serve as an invaluable reference to senior undergraduate and graduate students with an interest in embedded systems research.

Auteur

Arslan Munir, University of Nevada, Reno (UNR), USA
Arslan Munir is currently an Assistant Professor in the Department of Computer Science and Engineering (CSE) at the UNR. Before then he was a postdoctoral research associate in the Electrical and Computer Engineering (ECE) department at Rice University (Houston, Texas) between May 2012 and June 2014. He received his M.A.Sc. in ECE from the University of British Columbia (Vancouver, Canada) in 2007 and his Ph.D. in ECE from the University of Florida (Gainesville, Florida) USA in 2012. Between 2007 and 2008, he worked as a software development engineer at Mentor Graphics in the Embedded Systems Division. His current research interests include embedded and cyber-physical systems, computer architecture, parallel computing, fault-tolerance, and computer security.

Ann Gordon-Ross, University of Florida, USA
Ann Gordon-Ross is currently an Associate Professor of Electrical and Computer Engineering at the University of Florida and is a member of the NSF Center for High Performance Reconfigurable Computing (CHREC) at the University of Florida. She is also the faculty advisor for the Women in Electrical and Computer Engineering (WECE) and the Phi Sigma Rho National Society for Women in Engineering and Engineering Technology. Her research interests include embedded systems, computer architecture, low-power design, reconfigurable computing, dynamic optimizations, hardware design, real-time systems, and multi-core platforms.

Sanjay Ranka, University of Florida, USA
Sanjay Ranka researches energy efficient computing, high performance computing, data mining and informatics at the University of Florida's Department of Computer Science. He has coauthored two books, 75 journal articles and 125 refereed conference articles. He is a fellow of the IEEE and AAAS, and a member of IFIP Committee on System Modeling and Optimization.

Contenu

Preface xv

Acknowledgment xxi

Part I OVERVIEW

1 Introduction 3

1.1 Embedded Systems Applications 6

1.1.1 Cyber-Physical Systems 6

1.1.2 Space 6

1.1.3 Medical 7

1.1.4 Automotive 8

1.2 Characteristics of Embedded Systems Applications 9

1.2.1 Throughput-Intensive 9

1.2.2 Thermal-Constrained 9

1.2.3 Reliability-Constrained 10

1.2.4 Real-Time 10

1.2.5 Parallel and Distributed 10

1.3 Embedded SystemsHardware and Software 11

1.3.1 Embedded Systems Hardware 11

1.3.2 Embedded Systems Software 14

1.4 ModelingAn Integral Part of the Embedded Systems Design Flow 15

1.4.1 Modeling Objectives 16

1.4.2 Modeling Paradigms 18

1.4.3 Strategies for Integration of Modeling Paradigms 20

1.5 Optimization in Embedded Systems 21

1.5.1 Optimization of Embedded Systems Design Metrics 23

1.5.2 Multiobjective Optimization 26

1.6 Chapter Summary 27

2 Multicore-Based EWSNsAn Example of Parallel and Distributed Embedded Systems 29

2.1 Multicore Embedded Wireless Sensor Network Architecture 31

2.2 Multicore Embedded Sensor Node Architecture 33

2.2.1 Sensing Unit 34

2.2.2 Processing Unit 34

2.2.3 Storage Unit 34

2.2.4 Communication Unit 35

2.2.5 Power Unit 35

2.2.6 Actuator Unit 35

2.2.7 Location Finding Unit 36

2.3 Compute-Intensive Tasks Motivating the Emergence of MCEWSNs 36

2.3.1 Information Fusion 36

2.3.2 Encryption 38

2.3.3 Network Coding 38

2.3.4 Software-Defined Radio (SDR) 38

2.4 MCEWSN Application Domains 38

2.4.1 Wireless Video Sensor Networks (WVSNs) 39

2.4.2 Wireless Multimedia Sensor Networks (WMSNs) 39

2.4.3 Satellite-Based Wireless Sensor Networks (SBWSN) 40

2.4.4 Space Shuttle Sensor Networks (3SN) 41

2.4.5 AerialTerrestrial Hybrid Sensor Networks (ATHSNs) 42

2.4.6 Fault-Tolerant (FT) Sensor Networks 43

2.5 Multicore Embedded Sensor Nodes 43

2.5.1 InstraNode 43

2.5.2 Mars Rover Prototype Mote 43

2.5.3 Satellite-Based Sensor Node (SBSN) 44

2.5.4 Multi-CPU-Based Sensor Node Prototype 44

2.5.5 Smart Camera Mote 44

2.6 Research Challenges and Future Research Directions 45

2.7 Chapter Summary 47

Part II MODELING

3 An Application Metrics Estimation Model for Embedded Wireless Sensor Networks 51

3.1 Application Metrics Estimation Model 52

3.1.1 Lifetime Estimation 53

3.1.2 Throughput Estimation 56

3.1.3 Reliability Estimation 57

3.1.4 Models Validation 57

3.2 Experimental Results 58

3.2.1 Experimental Setup 58

3.2.2 Results 59

3.3 Chapter Summary 61

4 Modeling and Analysis of Fault Detection and Fault Tolerance in Embedded Wireless Sensor Networks 63

4.1 Related Work 67

4.1.1 Fault Detection 67

4.1.2 Fault Tolerance 68

4.1.3 WSN Reliability Modeling 69

4.2 Fault Diagnosis in WSNs 70

4.2.1 Sensor Faults 70

4.2.2 Taxonomy for Fault Diagnosis Techniques 72

4.3 Distributed Fault Detection Algorithms 74

4.3.1 Fault Detection Algorithm 1: The Chen Algorithm 74

4.3.2 Fault Detection Algorithm 2: The Ding Algorithm 76

4.4 Fault-Tolerant Markov Models 77

4.4.1 Fault-Tolerance Parameters 77

4.4.2 Fault-Tolerant Sensor Node Model 79

4.4.3 Fault-Tolerant WSN Cluster Model 81

4.4.4 Fault-Tolerant WSN Model 83

4.5 Simulation of Distributed Fault Detection Algorithms 85

4.5.1 Using ns2 to Simulate Faulty Sensors 85

4.5.2 Experimental Setup for Simulated Data 86

4.5.3 Experiments Using Real-World Data 86 ...