Librería Portfolio

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.



Table of Contents

PREFACE xiii

0.1 About This Book xiv

0.2 Highlights xvi

0.2.1 Overview of Parallel and Distributed Embedded Systems xvi

0.2.2 Modeling of Parallel and Distributed Embedded Systems xvi

0.2.3 Optimization of Parallel and Distributed Embedded Systems xvii

0.3 Intended Audience xviii

0.4 Organization of the Book xviii

Part I Overview 1

1 Introduction 3

1.1 Embedded Systems Applications 6

1.1.1 Cyber-Physical Systems 6

1.1.2 Space 7

1.1.3 Medical 8

1.1.4 Automotive 9

1.2 Embedded Systems Applications Characteristics 10

1.2.1 Throughput-Intensive 10

1.2.2 Thermal-Constrained 11

1.2.3 Reliability-Constrained 11

1.2.4 Real-Time 11

1.2.5 Parallel and Distributed 12

1.3 Embedded Systems-Hardware and Software 12

1.3.1 Embedded Systems Hardware 12

1.3.2 Embedded Systems Software 15

1.4 Modeling-An Integral Part of the Embedded System Design Flow 16

1.4.1 Modeling Objectives 18

1.4.2 Modeling Paradigms 20

1.4.3 Strategies for Integration of Modeling Paradigms 22

1.5 Optimization in Embedded Systems 23

1.5.1 Optimization of Embedded Systems Design Metrics 25

1.5.2 Multi-Objective Optimization 28

1.6 Chapter Summary 29

2 Multicore-based EWSNs-An Example of Parallel and Distributed Embedded Systems 31

2.1 Multicore EmbeddedWireless Sensor Network Architecture 33

2.2 Multi-core Embedded Sensor Node Architecture 35

2.2.1 Sensing Unit 35

2.2.2 Processing Unit 35

2.2.3 Storage Unit 37

2.2.4 Communication Unit 37

2.2.5 Power Unit 37

2.2.6 Actuator Unit 38

2.2.7 Location Finding Unit 38

2.3 Compute-Intensive Tasks Motivating the Emergence of MCEWSNs 38

2.3.1 Information Fusion 39

2.3.2 Encryption 40

2.3.3 Network Coding 41

2.3.4 Software Defined Radio (SDR) 41

2.4 MCEWSN Application Domains 41

2.4.1 Wireless Video Sensor Networks (WVSNs) 41

2.4.2 Wireless Multimedia Sensor Networks (WMSNs) 42

2.4.3 Satellite-based Wireless Sensor Networks (SBWSN) 43

2.4.4 Space Shuttle Sensor Networks (3SN) 44

2.4.5 Aerial-Terrestrial Hybrid Sensor Networks (ATHSNs) 45

2.4.6 Fault-Tolerant (FT) Sensor Networks 46

2.5 Multi-core Embedded Sensor Nodes 46

2.5.1 InstraNode 47

2.5.2 Mars Rover Prototype Mote 47

2.5.3 Satellite-Based Sensor Node (SBSN) 47

2.5.4 Multi-CPU-based Sensor Node Prototype 48

2.5.5 Smart Camera Mote 48

2.6 Research Challenges and Future Research Directions 48

2.7 Chapter Summary 51

Part II Modeling 53

3 An Application Metrics Estimation Model for Embedded Wireless Sensor Networks 55

3.1 Application Metrics Estimation Model 56

3.1.1 Lifetime Estimation 57

3.1.2 Throughput Estimation 60

3.1.3 Reliability Estimation 61

3.1.4 Models Validation 62

3.2 Experimental Results 63

3.2.1 Experimental Setup 63

3.2.2 Results 64

3.3 Chapter Summary 66

4 Modeling and Analysis of Fault Detection and Fault Tolerance in Embedded Wireless Sensor Networks 67

4.1 Related Work 71

4.1.1 Fault Detection 71

4.1.2 Fault Tolerance 72

4.1.3 WSN Reliability Modeling 73

4.2 Fault Diagnosis in WSNs 74

4.2.1 Sensor Faults 74

4.2.2 Taxonomy for Fault Diagnosis Techniques 76

4.3 Distributed Fault Detection Algorithms 79

4.3.1 Fault Detection Algorithm 1: The Chen Algorithm 79

4.3.2 Fault Detection Algorithm 2: The Ding Algorithm 80

4.4 Fault-Tolerant Markov Models 81

4.4.1 Fault-Tolerance Parameters 82

4.4.2 Fault-Tolerant Sensor Node Model 84

4.4.3 Fault-Tolerant WSN Cluster Model 86

4.4.4 Fault-Tolerant WSN Model 88

4.5 Simulation of Distributed Fault Detection Algorithms 90

4.5.1 Using ns-2 to Simulate Faulty Sensors 90

4.5.2 Experimental Setup for Simulated Data 92

4.5.3 Experiments Using Real-World Data 92

4.6 Numerical Results 95

4.6.1 Experimental Setup 96

4.6.2 Reliability and MTTF for an NFT and an FT Sensor Node 97

4.6.3 Reliability and MTTF for an NFT and an FT WSN Cluster 101

4.6.4 Reliability and MTTF for an NFT and an FT WSN 106

4.7 Research Challenges and Future Research Directions 109

4.7.1 Accurate Fault Detection 109

4.7.2 Benchmarks for Comparing Fault Detection Algorithms 109

4.7.3 Energy-Efficient Fault Detection and Tolerance 109

4.7.4 Machine-Learning-Inspired Fault Detection 110

4.7.5 FT in Multimedia Sensor Networks 110

4.7.6 Security 110

4.7.7 WSN Design and Tuning for Reliability 112

4.7.8 Novel WSN Architectures 113

4.8 Chapter Summary 113

5 A Queueing Theoretic Approach for Performance Evaluation of Low-Power Multicore-based Parallel Embedded Systems 115

5.1 Related Work 118

5.2 Queueing Network Modeling of Multi-Core Embedded Architectures 121

5.2.1 Queueing Network Terminology 121

5.2.2 Modeling Approach 122

5.2.3 Assumptions 128

5.3 Queueing Network Model Validation 129

5.3.1 Theoretical Validation 130

5.3.2 Validation with a Multi-Core Simulator 130

5.3.3 Speedup 135

5.4 Queueing Theoretic Model Insights 136

5.4.1 Model Setup 137

5.4.2 The Effects of Cache Miss Rates on Performance 140

5.4.3 The Effects of Workloads on Performance 144

5.4.4 Performance per Watt and Performance per Unit Area Computations 146

5.5 Chapter Summary 152

Part III Optimization 153

6 Optimization Approaches in Distributed Embedded