000			14062nam a2200649 i 4500
001			7394661
003			IEEE
005			20230927112358.0
006			m o d
007			cr |n|||||||||
008			160302s2016 nju ob 001 eng d
010			_z 2015032994 (print)
020			_a9781119086383 _qelectronic
020			_z9781119086413 _qcloth
024	7		_a10.1002/9781119086383 _2doi
035			_a(CaBNVSL)mat07394661
035			_a(IDAMS)0b00006484bd8862
040			_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL
082	0	0	_a004/.35
100	1		_aMunir, Arslan, _eauthor.
245	1	0	_aModeling and optimization of parallel and distributed embedded systems / _cArslan Munir, Ann Gordon-Ross, Sanjay Ranka.
264		1	_aChichester, West Sussex, United Kingdom : _bJohn Wiley & Sons Inc., _c[2016]
264		2	_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[2016]
300			_a1 PDF (400 pages).
336			_atext _2rdacontent
337			_aelectronic _2isbdmedia
338			_aonline resource _2rdacarrier
490	1		_aWiley - IEEE
504			_aIncludes bibliographical references and index.
505	0		_a-- 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.
505	8		_a2.4.4 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.
505	8		_a4.7 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 Wireless Sensor Networks 155 -- 6.1 Architecture-Level Optimizations 157 -- 6.2 Sensor Node Component-Level Optimizations 158 -- 6.2.1 Sensing Unit 158 -- 6.2.2 Processing Unit 160 -- 6.2.3 Transceiver Unit 160 -- 6.2.4 Storage Unit 161 -- 6.2.5 Actuator Unit 161 -- 6.2.6 Location Finding Unit 161 -- 6.2.7 Power Unit 162 -- 6.3 Data Link-Level Medium Access Control Optimizations 162 -- 6.3.1 Load Balancing and Throughput Optimizations 162 -- 6.3.2 Power/Energy Optimizations 163 -- 6.4 Network-Level Data Dissemination and Routing Protocol Optimizations 165 -- 6.4.1 Query Dissemination Optimizations 165 -- 6.4.2 Real-Time Constrained Optimizations 167 -- 6.4.3 Network Topology Optimizations 167.
505	8		_a6.4.4 Resource Adaptive Optimizations 168 -- 6.5 Operating System-level Optimizations 168 -- 6.5.1 Event-Driven Optimizations 168 -- 6.5.2 Dynamic Power Management 169 -- 6.5.3 Fault Tolerance 169 -- 6.6 Dynamic Optimizations 169 -- 6.6.1 Dynamic Voltage and Frequency Scaling 170 -- 6.6.2 Software-Based Dynamic Optimizations 170 -- 6.6.3 Dynamic Network Reprogramming 170 -- 6.7 Chapter Summary 171 -- 7 High-Performance Energy-Efficient Multicore-based Parallel Embedded Computing 173 -- 7.1 Embedded Systems Applications Characteristics 177 -- 7.1.1 Throughput-Intensive 178 -- 7.1.2 Thermal-Constrained 180 -- 7.1.3 Reliability-Constrained 180 -- 7.1.4 Real-Time 180 -- 7.1.5 Parallel and Distributed 181 -- 7.2 Architectural Approaches 181 -- 7.2.1 Core Layout 182 -- 7.2.2 Memory Design 184 -- 7.2.3 Interconnection Network 185 -- 7.2.4 Reduction Techniques 188 -- 7.3 Hardware-Assisted Middleware Approaches 189 -- 7.3.1 Dynamic Voltage and Frequency Scaling 190 -- 7.3.2 Advanced Configuration and Power Interface 190 -- 7.3.3 Gating Techniques 191 -- 7.3.4 Threading Techniques 192 -- 7.3.5 Energy Monitoring and Management 193 -- 7.3.6 Dynamic Thermal Management 194 -- 7.3.7 Dependable Techniques 195 -- 7.4 Software Approaches 196 -- 7.4.1 Data Forwarding 196 -- 7.4.2 Load Distribution 197 -- 7.5 High-Performance Energy-Efficient Multicore Processors 199 -- 7.5.1 ARM11 MPCore 199 -- 7.5.2 ARM Cortex A-9 MPCore 201 -- 7.5.3 MPC8572E PowerQUICC III 201 -- 7.5.4 Tilera TILEPro64 and TILE-Gx 202 -- 7.5.5 AMD Opteron Processor 202 -- 7.5.6 Intel Xeon Processor 202 -- 7.5.7 Intel Sandy Bridge Processor 203 -- 7.5.8 Graphics Processing Units 203 -- 7.6 Challenges and Future Research Directions 204 -- 7.7 Chapter Summary 207 -- 8 An MDP-based Dynamic Optimization Methodology for Embedded Wireless Sensor Networks 209 -- 8.1 Related Work 211 -- 8.2 MDP-Based Tuning Overview 214 -- 8.2.1 MDP-Based Tuning Methodology for Embedded Wireless Sensor Networks 214 -- 8.2.2 MDP Overview with Respect to Embedded Wireless Sensor Networks 216.
505	8		_a8.3 Application Specific Embedded Sensor Node Tuning Formulation as an MDP 219 -- 8.3.1 State Space 219 -- 8.3.2 Decision Epochs and Actions 219 -- 8.3.3 State Dynamics 220 -- 8.3.4 Policy and Performance Criterion 220 -- 8.3.5 Reward Function 221 -- 8.3.6 Optimality Equation 224 -- 8.3.7 Policy Iteration Algorithm 224 -- 8.4 Implementation Guidelines and Complexity 225 -- 8.4.1 Implementation Guidelines 225 -- 8.4.2 Computational Complexity 226 -- 8.4.3 Data Memory Analysis 226 -- 8.5 Model Extensions 227 -- 8.6 Numerical Results 230 -- 8.6.1 Fixed Heuristic Policies for Performance Comparisons 230 -- 8.6.2 MDP Specifications 231 -- 8.6.3 Results for a Security/Defense System Application 234 -- 8.6.4 Results for a Health Care Application 238 -- 8.6.5 Results for an Ambient Conditions Monitoring Application 241 -- 8.6.6 Sensitivity Analysis 244 -- 8.6.7 Number of Iterations for Convergence 245 -- 8.7 Chapter Summary 245 -- 9 Online Algorithms for Dynamic Optimization of Embedded Wireless Sensor Networks 247 -- 9.1 Related Work 249 -- 9.2 Dynamic Optimization Methodology 250 -- 9.2.1 Methodology Overview 250 -- 9.2.2 State Space 251 -- 9.2.3 Objective Function 252 -- 9.2.4 Online Optimization Algorithms 253 -- 9.3 Experimental Results 256 -- 9.3.1 Experimental Setup 256 -- 9.3.2 Results 258 -- 9.4 Chapter Summary 262 -- 10 A Lightweight Dynamic Optimization Methodology for Embedded Wireless Sensor Networks 263 -- 10.1 Related Work 265 -- 10.2 Dynamic Optimization Methodology 267 -- 10.2.1 Overview 267 -- 10.2.2 State Space 269 -- 10.2.3 Optimization Objection Function 269 -- 10.3 Algorithms for Dynamic Optimization Methodology 271 -- 10.3.1 Initial Tunable Parameter Value Settings and Exploration Order 271 -- 10.3.2 Parameter Arrangement 272 -- 10.3.3 Online Optimization Algorithm 274 -- 10.3.4 Computational Complexity 276 -- 10.4 Experimental Results 276 -- 10.4.1 Experimental Setup 276 -- 10.4.2 Results 279 -- 10.5 Chapter Summary 291 -- 11 Parallelized Benchmark-Driven Performance Evaluation of Symmetric Multiprocessors and Tiled Multicore Architectures for Parallel Embedded Systems 293.
505	8		_a11.1 Related Work 295 -- 11.2 Multicore Architectures and Benchmarks 296 -- 11.2.1 Multi-Core Architectures 296 -- 11.2.2 Benchmark Applications and Kernels 298 -- 11.3 Parallel Computing Device Metrics 299 -- 11.4 Results 301 -- 11.4.1 Quantitative Comparison of SMPs and TMAs 302 -- 11.4.2 Benchmark-Driven Results for SMPs 304 -- 11.4.3 Benchmark-Driven Results for TMAs 304 -- 11.4.4 Comparison of SMPs and TMAs 313 -- 11.5 Chapter Summary 316 -- 12 High-Performance Optimizations on Tiled Manycore Embedded Systems: A Matrix Multiplication Case Study 317 -- 12.1 Related Work 320 -- 12.1.1 Performance Analysis and Optimization 320 -- 12.1.2 Parallelized MM Algorithms 321 -- 12.1.3 Cache Blocking 322 -- 12.1.4 Tiled Many-Core Architectures 322 -- 12.2 Tiled Many-Core Architecture (TMA) Overview 324 -- 12.2.1 Intel's TeraFLOPS Research Chip 324 -- 12.2.2 IBM's Cyclops-64 (C64) 326 -- 12.2.3 Tilera's TILEPro64 328 -- 12.2.4 Tilera's TILE64 332 -- 12.3 Parallel ComputingMetrics and Matrix Multiplication (MM) Case Study 332 -- 12.3.1 Parallel ComputingMetrics for TMAs 332 -- 12.3.2 Matrix Multiplication (MM) Case Study 334 -- 12.4 Performance Optimization on a Many-Core Architecture 335 -- 12.4.1 Performance Optimization on a Single Tile 335 -- 12.4.2 Parallel Performance Optimizations 336 -- 12.4.3 Compiler-Based Optimizations 341 -- 12.5 Results 344 -- 12.5.1 Data Allocation, Data Decomposition, Data Layout, and Communication 346 -- 12.5.2 Performance Optimizations on a Single Tile 349 -- 12.5.3 Parallel Performance Optimizations 356 -- 12.6 Chapter Summary 364 -- 13 Conclusions 369 -- Index 395.
506	1		_aRestricted to subscribers or individual electronic text purchasers.
530			_aAlso available in print.
538			_aMode of access: World Wide Web
588			_aDescription based on PDF viewed 03/02/2016.
650		0	_aParallel processing (Electronic computers)
650		0	_aElectronic data processing _xDistributed processing.
650		0	_aEmbedded computer systems.
655		0	_aElectronic books.
695			_aEpitaxial layers
695			_aExcitons
695			_aNitrogen
695			_aRadiative recombination
695			_aSilicon carbide
695			_aTemperature measurement
700	1		_aGordon-Ross, Ann, _eauthor.
700	1		_aRanka, Sanjay, _eauthor.
710	2		_aIEEE Xplore (Online Service), _edistributor.
710	2		_aWiley, _epublisher.
830		0	_aWiley - IEEE
856	4	2	_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=7394661
999			_c40700 _d40700