Wireless Sensor Networks: An Information Processing Approach(亞洲平裝版ISBN-9812592695)

Feng Zhao, Leonidas Guibas

  • 出版商: Morgan Kaufmann
  • 出版日期: 2004-06-01
  • 售價: $3,710
  • 貴賓價: 9.5$3,525
  • 語言: 英文
  • 頁數: 376
  • 裝訂: Hardcover
  • ISBN: 1558609148
  • ISBN-13: 9781558609143
  • 相關分類: 感測器 Sensor
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Designing, implementing, and operating a wireless sensor network involves a wide range of disciplines and many application-specific constraints. To make sense of and take advantage of these systems, a holistic approach is needed—and this is precisely what Wireless Sensor Networks delivers.

Inside, two eminent researchers review the diverse technologies and techniques that interact in today’s wireless sensor networks. At every step, they are guided by the high-level information-processing tasks that determine how these networks are architected and administered. Zhao and Guibas begin with the canonical problem of localizing and tracking moving objects, then systematically examine the many fundamental sensor network issues that spring from it, including network discovery, service establishment, data routing and aggregation, query processing, programming models, and system organization. The understanding gained as a result—how different layers support the needs of different applications, and how a wireless sensor network should be built to optimize performance and economy—is sure to endure as individual component technologies come and go.


Table of Contents:

1 Introduction
1.1 Unique Constraints and Challenges
1.2 Advantages of Sensor Networks
1.2.1 Energy advantage
1.2.2 Detection advantage
1.3 Sensor Network Applications
1.3.1 Habitat monitoring: wildlife conservation through autonomous, non-intrusive sensing
1.3.2 Tracking chemical plumes: ad hoc, just-in-time deployment mitigating disasters
1.3.3 Smart transportation: networked sensors making roads safer and less congested
1.4 Collaborative Processing
1.5 Key Definitions of Sensor Networks
1.6 The Rest of the Book

2 Canonical Problem: Localization and Tracking
2.1 A Tracking Scenario
2.2 Problem Formulation
2.2.1 Sensing model
2.2.2 Collaborative localization
2.2.3 Bayesian state estimation
2.3 Distributed Representation and Inference of States
2.3.1 Impact of choice of representation
2.3.2 Design desiderata in distributed tracking
2.4 Tracking Multiple Objects
2.4.1 State-space decomposition
2.4.2 Data association
2.5 Sensor Models
2.6 Performance Comparison and Metrics
2.7 Summary
2.8 Appendix A: Optimal Estimator Design
2.9 Appendix B: Particle Filter

3 Networking Sensors
3.1 Key Assumptions
3.2 Medium Access Control
3.2.1 The S-MAC Protocol
3.2.2 IEEE 802.15.4 Standard and ZigBee
3.3 General Issues
3.4 Geographic, Energy-Aware Routing
3.4.1 Unicast Geographic Routing
3.4.2 Routing on a Curve
3.4.3 Energy-Minimizing Broadcast
3.4.4 Energy-Aware Routing to a Region
3.5 Attribute-Based Routing
3.5.1 Directed Diffusion
3.5.2 Rumor Routing
3.5.3 Geographic Hash Tables
3.6 Summary

4 Infrastructure Establishment
4.1 Topology Control
4.2 Clustering
4.3 Time Synchronization
4.3.1 Clocks and Communication Delays
4.3.2 Interval Methods
4.3.3 Reference Broadcasts
4.4 Localization and Localization Services
4.4.1 Ranging Techniques
4.4.2 Range-Based Localization Algorithms
4.4.3 Other Localization Algorithms
4.4.4 Location Services
4.5 Summary

5 Sensor Tasking and Control
5.1 Task-Driven Sensing
5.2 Roles of Sensor Nodes and Utilities
5.3 Information-Based Sensor Tasking
5.3.1 Sensor selection
5.3.2 IDSQ: Information-driven sensor querying
5.3.3 Cluster leader based protocol
5.3.4 Sensor tasking in tracking relations
5.4 Joint Routing and Information Aggregation
5.4.1 Moving center of aggregation
5.4.2 Multi-step information-directed routing
5.4.3 Sensor group management
5.4.4 Case study: sensing global phenomena
5.5 Summary
5.6 Appendix A: Information Utility Measures
5.7 Appendix B: Sample Sensor Selection Criteria

6 Sensor Network Databases
6.1 Sensor Database Challenges
6.2 Querying The Physical Environment
6.3 Query Interfaces
6.3.1 Cougar sensor database and abstract data types
6.3.2 Probabilistic queries
6.4 High-level Database Organization
6.5 In-Network Aggregation
6.5.1 Query propagation and aggregation
6.5.2 TinyDB query processing
6.5.3 Query processing scheduling and optimization
6.6 Data-Centric Storage
6.7 Data Indices and Range Queries
6.7.1 One-dimensional indices
6.7.2 Multi-dimensional indices for orthogonal range searching
6.7.3 Non-orthogonal range searching
6.8 Distributed Hierarchical Aggregation
6.8.1 Multi-resolution summarization
6.8.2 Partitioning the summaries
6.8.3 Fractional cascading
6.8.4 Locality preserving hashing
6.9 Temporal Data
6.9.1 Data aging
6.9.2 Indexing motion data
6.10 Summary

7 Sensor Network Platforms and Tools
7.1 Sensor Network Hardware
7.1.1 Berkeley motes
7.2 Sensor Network Programming Challenges
7.3 Node-Level Software Platforms
7.3.1 Operating system: TinyOS
7.3.2 Imperative language: nesC
7.3.3 Dataflow style language: TinyGALS
7.4 Node-Level Simulators
7.4.1 ns-2 and its sensor network extensions
7.4.2 TOSSIM
7.5 Programming Beyond Individual Nodes: State-centric programming
7.5.1 Collaboration groups
7.5.2 PIECES: A state-centric design framework
7.5.3 Multi-target tracking problem revisited
7.6 Summary

8 Applications and Future Directions
8.1 A Summary of the Book
8.2 Emerging Applications
8.3 Future Research Directions
8.3.1 Secure embedded systems
8.3.2 Programming models and embedded operating systems
8.3.3 Management of collaborative groups
8.3.4 Light-weight signal processing
8.3.5 Networks of high-data-rate sensors
8.3.6 Google for the physical world
8.3.7 Closing the loop with actuators
8.3.8 Distributed information architecture
8.4 Conclusion