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Decentralized Spatial Computing: Foundations of Geosensor Networks, by Matt Duckham
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Computing increasingly happens somewhere, with that geographic location important to the computational process itself. Many new and evolving spatial technologies, such as geosensor networks and smartphones, embody this trend. Conventional approaches to spatial computing are centralized, and do not account for the inherently decentralized nature of "computing somewhere": the limited, local knowledge of individual system components, and the interaction between those components at different locations. On the other hand, despite being an established topic in distributed systems, decentralized computing is not concerned with geographical constraints to the generation and movement of information. In this context, of (centralized) spatial computing and decentralized (non-spatial) computing, the key question becomes: "What makes decentralized spatial computing special?"
In Part I of the book the author covers the foundational concepts, structures, and design techniques for decentralized computing with spatial and spatiotemporal information. In Part II he applies those concepts and techniques to the development of algorithms for decentralized spatial computing, stepping through a suite of increasingly sophisticated algorithms: from algorithms with minimal spatial information about their neighborhoods; to algorithms with access to more detailed spatial information, such as direction, distance, or coordinate location; to truly spatiotemporal algorithms that monitor environments that are dynamic, even using networks that are mobile or volatile. Finally, in Part III the author shows how decentralized spatial and spatiotemporal algorithms designed using the techniques explored in Part II can be simulated and tested. In particular, he investigates empirically the important properties of a decentralized spatial algorithm: its computational efficiency and its robustness to unavoidable uncertainty. Part III concludes with a survey of the opportunities for connecting decentralized spatial computing to ongoing research and emerging hot topics in related fields, such as biologically inspired computing, geovisualization, and stream computing.
The book is written for students and researchers of computer science and geographic information science. Throughout the book the author's style is characterized by a focus on the broader message, explaining the process of decentralized spatial algorithm design rather than the technical details. Each chapter ends with review questions designed to test the reader's understanding of the material and to point to further work or research. The book includes short appendices on discrete mathematics and SQL. Simulation models written in NetLogo and associated source code for all the algorithms presented in the book can be found on the author's accompanying website.
- Sales Rank: #3340385 in Books
- Brand: Brand: Springer
- Published on: 2012-07-27
- Original language: English
- Number of items: 1
- Dimensions: 9.20" h x .90" w x 6.10" l, 1.35 pounds
- Binding: Hardcover
- 322 pages
- Used Book in Good Condition
Review
“This book is an excellent summary on the topic of distributed spatial planning. … This book provides valuable insight in terms of working with sensor networks which are now arising around the world. … the concepts and discussions are useful and valuable to a wide ranging auidence. For this reason, anyone working with geosensors today will find this book well worth the read.” (Jeff Thurston, 3dvisworld.com, December, 2014)
From the Back Cover
Computing increasingly happens somewhere, with that geographic location important to the computational process itself. Many new and evolving spatial technologies, such as geosensor networks and smartphones, embody this trend. Conventional approaches to spatial computing are centralized, and do not account for the inherently decentralized nature of "computing somewhere": the limited, local knowledge of individual system components, and the interaction between those components at different locations. On the other hand, despite being an established topic in distributed systems, decentralized computing is not concerned with geographical constraints to the generation and movement of information. In this context, of (centralized) spatial computing and decentralized (non-spatial) computing, the key question becomes: "What makes decentralized spatial computing special?"
In Part I of the book the author covers the foundational concepts, structures, and design techniques for decentralized computing with spatial and spatiotemporal information. In Part II he applies those concepts and techniques to the development of algorithms for decentralized spatial computing, stepping through a suite of increasingly sophisticated algorithms: from algorithms with minimal spatial information about their neighborhoods; to algorithms with access to more detailed spatial information, such as direction, distance, or coordinate location; to truly spatiotemporal algorithms that monitor environments that are dynamic, even using networks that are mobile or volatile. Finally, in Part III the author shows how decentralized spatial and spatiotemporal algorithms designed using the techniques explored in Part II can be simulated and tested. In particular, he investigates empirically the important properties of a decentralized spatial algorithm: its computational efficiency and its robustness to unavoidable uncertainty. Part III concludes with a survey of the opportunities for connecting decentralized spatial computing to ongoing research and emerging hot topics in related fields, such as biologically inspired computing, geovisualization, and stream computing.
The book is written for students and researchers of computer science and geographic information science. Throughout the book the author's style is characterized by a focus on the broader message, explaining the process of decentralized spatial algorithm design rather than the technical details. Each chapter ends with review questions designed to test the reader's understanding of the material and to point to further work or research. The book includes short appendices on discrete mathematics and SQL. Simulation models written in NetLogo and associated source code for all the algorithms presented in the book can be found on the author's accompanying website.
About the Author
Dr. Duckham is a Senior Lecturer at the Department of Geomatics, University of Melbourne, Australia. He also currently (2012) holds a prestigious Australian Research Council research fellowship, an ARC Future Fellowship, for research into ambient spatial intelligence. Previously he worked at the National Center for Geographic Information and Analysis (NCGIA) at the University of Maine, USA, as well as in the UK (Universities of Edinburgh, Leicester, Glasgow, and Keele). He is a coauthor with Prof. Mike Worboys of the significant advanced undergraduate/postgraduate textbook “GIS: A Computing Perspective" (2nd ed., CRC Press). He has many further related journal and book publications, and he is the Editor‐in-Chief of the Journal of Spatial Information Science (JOSIS).
Most helpful customer reviews
0 of 1 people found the following review helpful.
Condensing the most relevant topics in distributed algorithms, discrete math and networking to understand spatial computing
By Arthur Sanders
"Decentralized Spatial Computing" provides a unique compilation of algorithms and analysis tools for large-scale computing systems with a focus on sensor networks. After formally anchoring such systems in graph theory, the book describes a series of standard distributed algorithms that are relevant for this class of systems. Here, algorithms are grouped by their information requirements, that is neighborhood-based algorithms, location-based algorithms, and time-series/memory-based algorithms. All algorithms are accompanied by Netlogo code and rich online resources, making this a perfect resource for practitioners and as a graduate textbook.
What is great about this book is that there simply has not been a single resource to get started on spatial computing, combining the essence of the relevant distributed algorithms, discrete mathematics and systems knowledge, that are needed to understand large-scale distributed sensing/computation networks.
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