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Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
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Searching... | 30000010159379 | TK7872.D48 D73 2003 | Open Access Book | Book | Searching... |
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Summary
Summary
Self-Organization in Sensor and Actor Networks explores self-organization mechanisms and methodologies concerning the efficient coordination between intercommunicating autonomous systems.Self-organization is often referred to as the multitude of algorithms and methods that organise the global behaviour of a system based on inter-system communication. Studies of self-organization in natural systems first took off in the 1960s. In technology, such approaches have become a hot research topic over the last 4-5 years with emphasis upon management and control in communication networks, and especially in resource-constrained sensor and actor networks. In the area of ad hoc networks new solutions have been discovered that imitate the properties of self-organization. Some algorithms for on-demand communication and coordination, including data-centric networking, are well-known examples.
Key features include:
Detailed treatment of self-organization, mobile sensor and actor networks, coordination between autonomous systems, and bio-inspired networking. Overview of the basic methodologies for self-organization, a comparison to central and hierarchical control, and classification of algorithms and techniques in sensor and actor networks. Explanation of medium access control, ad hoc routing, data-centric networking, synchronization, and task allocation issues. Introduction to swarm intelligence, artificial immune system, molecular information exchange. Numerous examples and application scenarios to illustrate the theory.Self-Organization in Sensor and Actor Networks will prove essential reading for students of computer science and related fields; researchers working in the area of massively distributed systems, sensor networks, self-organization, and bio-inspired networking will also find this reference useful.
Author Notes
Dr. Falko Dressler is Assistant Professor at the Department of Computer Sciences, University of Erlangen, Germany and, since 2004, Head of the Autonomic Networking Group. He has made many contributions in the area of quality of service in communication networks (IP, multicast, sensor networks), network security (intrusion detection, high-speed monitoring, IP traceback), ad hoc wireless sensor networks (communication paradigms, congestion control), and bio-inspired networking (lessons learnt from molecular biology to be adapted to communication networks). His areas of expertise include distributed systems and communication networks, self-organizing autonomous sensor/actuator networks and bio-inspired networking.
Table of Contents
Preface |
I Self-Organization |
1 Introduction to Self-Organization |
1.1 Understanding self-organization |
1.2 Application scenarios for self-organization |
2 System Management and Control - A Historical Overview |
2.1 System architecture |
2.2 Management and control |
2.2.1 Centralized control |
2.2.2 Distributed systems |
2.2.3 Self-organizing systems |
3 Self-Organization - Context and Capabilities |
3.1 Complex systems |
3.2 Self-organization and emergence |
3.3 Systems lacking self-organization |
3.3.1 External control |
3.3.2 Blueprints and templates |
3.4 Self-X capabilities |
3.5 Consequences of emergent properties |
3.6 Operating self-organizing systems |
3.6.1 Asimov's Laws of Robotics |
3.6.2 Attractors |
3.7 Limitations of self-organization |
4 Natural Self-Organization |
4.1 Development of understandings |
4.2 Examples in natural sciences |
4.2.1 Biology |
4.2.2 Chemistry |
4.3 Differentiation self-organization and bio-inspired |
4.3.1 Exploring bio-inspired |
4.3.2 Bio-inspired techniques |
4.3.3 Self-organization vs. bio-inspired |
5 Self-Organization in Technical Systems |
5.1 General applicability |
5.1.1 Autonomous systems |
5.1.2 Multi-robot systems |
5.1.3 Autonomic networking |
5.1.4 Mobile Ad Hoc Networks |
5.1.5 Sensor and Actor Networks |
5.2 Operating Sensor and Actor Networks |
6 Methods and Techniques |
6.1 Basic methods |
6.1.1 Positive and negative feedback |
6.1.2 Interactions among individuals and with the environment |
6.1.3 Probabilistic techniques |
6.2 Design paradigms for self-organization |
6.2.1 Design process |
6.2.2 Discussion of the design paradigms |
6.3 Developing nature-inspired self-organizing systems |
6.4 Modeling self-organizing systems |
6.4.1 Overview to modeling techniques |
6.4.2 Differential equation models |
6.4.3 Monte Carlo simulations |
6.4.4 Choosing the right modeling technique |
7 Self-Organization - Further Reading |
II Networking Aspects: Ad Hoc and Sensor Networks |
8 Mobile Ad Hoc and Sensor Networks |
8.1 Ad hoc networks |
8.1.1 Basic properties of ad hoc networks |
8.1.2 Mobile Ad Hoc Networks |
8.2 Wireless Sensor Networks |
8.2.1 Basic properties of sensor networks |
8.2.2 Composition of single sensor nodes |
8.2.3 Communication in sensor networks |
8.2.4 Energy aspects |
8.2.5 Coverage and deployment |
8.2.6 Comparison between MANETs and WSNs |
8.2.7 Application examples |
8.3 Challenges and research issues |
8.3.1 Required functionality and constraints |
8.3.2 Research objectives |
9 Self-Organization in Sensor Networks |
9.1 Properties and objectives |
9.2 Categorization in two dimensions |
9.2.1 Horizontal dimension |
9.2.2 Vertical dimension |
9.3 Methods and application examples |
9.3.1 Mapping with primary self-organization methods |
9.3.2 Global state |
9.3.3 Location information |
9.3.4 Neighborhood information |
9.3.5 Local state |
9.3.6 Probabilistic techniques |
10 Medium Access Control |
10.1 Contention-based protocols |
10.2 Sensor MAC |
10.2.1 Synchronized listen/sleep cycles |
10.2.2 Performance aspects |
10.2.3 Performance evaluation |
10.3 Power-Control MAC protocol |
10.4 Conclusion |
11 Ad Hoc Routing |
11.1 Overview and categorization |
11.1.1 Address-based routing vs. data-centric forwarding |
11.1.2 Classification of ad hoc routing protocols |
11.2 Principles of ad hoc routing protocols |
11.2.1 Destination Sequenced Distance Vector |
11.2.2 Dynamic Source Routing |
11.2.3 Ad Hoc on Demand Distance Vector |