 |
Wireless Sensor Networks View Larger Image | Feng Zhao, Leonidas Guibas
Morgan Kaufmann, Hardcover, Published July 2004, 358 pages, ISBN 1558609148 | List Price: $73.95
Our Price: $63.95
You Save: $10.00 (14% Off)
| | | Availability: Out-Of-Stock |
Be the First to Write a Review and tell the world about this title!Books on similar topics, in best-seller order: Books from the same publisher, in best-seller order:
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.
Features & Benefits
•Written for practitioners, researchers, and students and relevant
to all application areas, including environmental monitoring, industrial sensing
and diagnostics, automotive and transportation, security and surveillance,
military and battlefield uses, and large-scale infrastructural maintenance.
•Skillfully integrates the many disciplines at work in wireless sensor
network design: signal processing and estimation, communication theory and
protocols, distributed algorithms and databases, probabilistic reasoning,
energy-aware computing, design methodologies, evaluation metrics, and more.
•Demonstrates how querying, data routing, and network self-organization
can support high-level information-processing tasks.
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
About the Authors
Feng Zhao is a senior researcher at Microsoft, where he manages the
Networked Embedded Computing Group. He received his Ph.D. in Electrical Engineering
and Computer Science from MIT and has taught at at Stanford University and Ohio
State University. Dr. Zhao was a principal scientist at Xerox PARC and directed
PARC’s sensor network research effort. He is serving as the Editor-In-Chief
of ACM Transactions on Sensor Networks.
Professor Leonidas Guibas heads the Geometric Computation group in the
Computer Science Department of Stanford University, where he works on algorithms
for sensing, modeling, reasoning about, rendering, and acting on the physical
world. He is well-known for his work in computational geometry, computer graphics,
and discrete algorithms. Professor Guibas obtained his Ph.D. from Stanford,
has worked at PARC, MIT, and DEC/SRC, and was recently elected an ACM Fellow.
|
|
