Self-Organization in Sensor and Actor Networks
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 behavi...
Saved in:
| Main Author | |
|---|---|
| Format | eBook Book |
| Language | English |
| Published |
Chichester
Wiley
2007
John Wiley & Sons, Incorporated Wiley-Blackwell |
| Edition | 1 |
| Subjects | |
| Online Access | Get full text |
| ISBN | 9780470028209 0470028203 9780470724460 0470724463 |
| DOI | 10.1002/9780470724460 |
Cover
| Abstract | 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 Networkswill 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. |
|---|---|
| AbstractList | 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 Networkswill 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. 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 | Dressler, Falko |
| Author_xml | – sequence: 1 fullname: Dressler, Falko |
| BackLink | https://cir.nii.ac.jp/crid/1130282273207416704$$DView record in CiNii |
| BookMark | eNpVkDtPwzAQx42giLZ0ZGDLgJAYUs6P2PFYqvKQKjoUsUZOYoNpZEMcqMSnxyUIwQ139z_9dK8ROnDeaYROMEwxALmUIgcmQBDGOOyhyR-d0f1fDSQnIAdoRABEFNEO0ZCDzJlkGT5CkxBeIBrNmGB8iKZr3Zh01T4pZz9VZ71LrEvW2gXfJsrVyazqYnavu61vN-EYDYxqgp78xDF6vF48zG_T5ermbj5bporHNXiqDGc1BSbzWjKustqw0piyNkYrwgFLY6iJBaG0EhQUVpyyXEJd5lzTqqJjdNE3VmGjt-HZN10oPhpder8Jxb_rI3ves6-tf3vXoSu-sUq7rlVNsbiaRzbDO_CsB521RWV3HmO6-xkRlIBgmAtgETvtMau1LvqJOVApCNAvTVptvA |
| ContentType | eBook Book |
| Copyright | 2008 Wiley |
| Copyright_xml | – notice: 2008 Wiley |
| DBID | RYH |
| DEWEY | 681/.2 |
| DOI | 10.1002/9780470724460 |
| DatabaseName | CiNii Complete |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISBN | 9780470724453 0470724455 9780470724460 0470724463 |
| Edition | 1 |
| ExternalDocumentID | 9780470724453 EBC470513 BA85721026 8039720 |
| GroupedDBID | 089 20A 38. 3W9 3X9 3XM 4ST 5VX 6IK A4I A4J AABBV AARDG ABARN ABBFG ABIAV ABQPQ ABQPW ACGYG ACIQC ACLGV ACNUM ACYFF ADVEM AERYV AFOJC AJFER AKHYG AKQZE ALMA_UNASSIGNED_HOLDINGS ALTAS AZZ BBABE BEFXN BFFAM BGNUA BKEBE BPEOZ CZZ ECNEQ GEOUK HF4 IETMW IPJKO IVK IVR JFSCD JJU KJBCJ MYL OCL PLCCB PQEST PQQKQ PQUKI W1A WIIVT YPLAZ ZEEST ABAZT AHWGJ RYH |
| ID | FETCH-LOGICAL-a60476-af64d30498d946a5df4bffbdffea26019ff3fffb7aea730a1a634890db86e3cc3 |
| ISBN | 9780470028209 0470028203 9780470724460 0470724463 |
| IngestDate | Fri Nov 08 01:52:57 EST 2024 Wed Oct 01 01:17:04 EDT 2025 Thu Jun 26 22:34:02 EDT 2025 Mon Aug 28 20:39:30 EDT 2023 |
| IsPeerReviewed | false |
| IsScholarly | false |
| LCCN | 2007028888 |
| LCCallNum_Ident | TK7872.D48D74 2007 |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-a60476-af64d30498d946a5df4bffbdffea26019ff3fffb7aea730a1a634890db86e3cc3 |
| Notes | Includes bibliographical references (p. [335]-353) and index |
| OCLC | 609849451 |
| PQID | EBC470513 |
| PageCount | 386 |
| ParticipantIDs | askewsholts_vlebooks_9780470724453 proquest_ebookcentral_EBC470513 nii_cinii_1130282273207416704 ieee_books_8039720 |
| PublicationCentury | 2000 |
| PublicationDate | c2007. |
| PublicationDateYYYYMMDD | 2007-01-01 |
| PublicationDate_xml | – year: 2007 text: c2007. |
| PublicationDecade | 2000 |
| PublicationPlace | Chichester |
| PublicationPlace_xml | – name: Chichester – name: Newark |
| PublicationYear | 2007 |
| Publisher | Wiley John Wiley & Sons, Incorporated Wiley-Blackwell |
| Publisher_xml | – name: Wiley – name: John Wiley & Sons, Incorporated – name: Wiley-Blackwell |
| SSID | ssj0000354746 ssib010067427 |
| Score | 2.3023815 |
| Snippet | Self-Organization in Sensor and Actor Networks explores self-organization mechanisms and methodologies concerning the efficient coordination between... |
| SourceID | askewsholts proquest nii ieee |
| SourceType | Aggregation Database Publisher |
| SubjectTerms | Biologically-inspired computing Communication, Networking and Broadcast Technologies Components, Circuits, Devices and Systems Computer networks Computer networks -- Management -- Data processing Computing and Processing Data processing Management Self-organizing systems Sensor networks |
| TableOfContents | Foreword.  Preface. About the Author. List of Abbreviations.  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. Appendix I Self-Organization – Further Reading. II Networking Aspects: Ad Hoc and Sensor Networks. 7 Mobile Ad Hoc and Sensor Networks. 7.1 Ad hoc networks. 7.1.1 Basic properties of ad hoc networks. 7.1.2 Mobile Ad Hoc Networks. 7.2 Wireless Sensor Networks. 7.2.1 Basic properties of sensor networks. 7.2.2 Composition of single sensor nodes. 7.2.3 Communication in sensor networks. 7.2.4 Energy aspects. 7.2.5 Coverage and deployment. 7.2.6 Comparison between MANETs and WSNs. 7.2.7 Application examples. 7.3 Challenges and research issues. 7.3.1 Required functionality and constraints. 7.3.2 Research objectives. 8 Self-Organization in Sensor Networks.  8.1 Properties and objectives. 8.2 Categorization in two dimensions. 8.2.1 Horizontal dimension. 8.2.2 Vertical dimension. 8.3 Methods and application examples. 8.3.1 Mapping with primary self-organization methods. 8.3.2 Global state. 8.3.3 Location information. 8.3.4 Neighborhood information. 8.3.5 Local state. 8.3.6 Probabilistic techniques. 9 Medium Access Control. 9.1 Contention-based protocols. 9.2 Sensor MAC. 9.2.1 Synchronized listen/sleep cycles. 9.2.2 Performance aspects. 9.2.3 Performance evaluation. 9.3 Power-Control MAC protocol. 9.4 Conclusion. 10 Ad Hoc Routing. 10.1 Overview and categorization. 10.1.1 Address-based routing vs. data-centric forwarding. 10.1.2 Classification of ad hoc routing protocols. 10.2 Principles of ad hoc routing protocols. 10.2.1 Destination Sequenced Distance Vector. 10.2.2 Dynamic Source Routing. 10.2.3 Ad Hoc on Demand Distance Vector. 10.2.4 Dynamic MANET on Demand. 10.3 Optimized route stability. 10.4 Dynamic address assignment. 10.4.1 Overview and centralized assignment. 10.4.2 Passive Duplicate Address Detection. 10.4.3 Dynamic Address Allocation. 10.5 Conclusion. 11 Data-Centric Networking. 11.1 Overview and classification. 11.1.1 Data dissemination. 11.1.2 Network-centric operation. 11.1.3 Related approaches. 11.2 Flooding, gossiping, and optimizations.  11.2.1 Flooding. 11.2.2 Pure gossiping. 11.2.3 Optimized gossiping. 11.3 Agent-based techniques. 11.4 Directed diffusion. 11.4.1 Basic algorithm. 11.4.2 Mobility support. 11.4.3 Energy efficiency. 11.5 Data aggregation. 11.5.1 Principles and objectives. 11.5.2 Aggregation topologies. 11.6 Conclusion. 12 Clustering. 12.1 Principles of clustering. 12.1.1 Requirements and classification. 12.1.2 k-means. 12.1.3 Hierarchical clustering. 12.2 Clustering for efficient routing. 12.2.1 Low-Energy Adaptive Clustering Hierarchy. 12.2.2 Hybrid Energy-Efficient Distributed Clustering Approach. 12.3 Conclusion.  Appendix II Networking Aspects – Further reading. III Coordination and Control: Sensor and Actor Networks. 13 Sensor and Actor Networks. 13.1 Introduction. 13.1.1 Composition of SANETs – an example. 13.1.2 Properties and capabilities. 13.1.3 Components of SANET nodes. 13.1.4 Application examples. 13.2 Challenges and research objectives. 13.2.1 Communication and coordination. 13.2.2 Collaboration and task allocation. 13.3 Limitations. 14 Communication and Coordination. 14.1 Synchronization vs. coordination. 14.1.1 Problem statement. 14.1.2 Logical time. 14.1.3 Coordination. 14.2 Time synchronization in WSNs and SANETs. 14.2.1 Requirements and objectives. 14.2.2 Conventional approaches. 14.2.3 Algorithms for WSNs. 14.3 Distributed coordination. 14.3.1 Scalable coordination. 14.3.2 Selected algorithms. 14.3.3 Integrated sensor-actor and actor-actor coordination. 14.3.4 Problems with selfish nodes. 14.4 In-network operation and control.  14.5 Conclusion. 15 Collaboration and Task Allocation. 15.1 Introduction to MRTA. 15.1.1 Primary objectives. 15.1.2 Classification and taxonomy. 15.2 Intentional cooperation – auction-based task allocation. 15.2.1 Open Agent Architecture. 15.2.2 MURDOCH. 15.2.3 Dynamic negotiation algorithm. 15.3 Emergent cooperation. 15.3.2 Stimulation by state. 15.4 Conclusion. Appendix III Coordination and Control – Further reading. IV Self-Organization Methods in Sensor and Actor Networks. 16 Self-Organization Methods – Revisited.  16.1 Self-organization methods in SANETs. 16.2 Positive and negative feedback. 16.3 Interactions among individuals and with the environment . 16.4 Probabilistic techniques. 17 Evaluation Criteria. 17.1 Scalability. 17.2 Energy considerations. 17.2.1 Energy management. 17.2.2 Transmission power management. 17.3 Network lifetime. 17.3.1 Definition of network lifetime. 17.3.2 Scenario-based comparisons of network lifetime. V Bio-inspired Networking. 18 Bio-inspired Systems. 18.1 Introduction and overview.  18.1.1 Ideas and concepts. 18.1.2 Bio-inspired research fields. 18.2 Swarm Intelligence. 18.2.1 Principles of ant foraging. 18.2.2 Ant-based routing. 18.2.3 Ant-based task allocation. 18.3 Artificial Immune System. 18.3.1 Principles of the immune system. 18.3.2 Application examples. 18.4 Cellular signaling pathways. 18.4.1 Introduction to signaling pathways. 18.4.2 Applicability in SANETs. 18.5 Conclusion. Appendix IV Bio-inspired Networking – Further reading. Bibliography. Index. 14.3.1 Scalable coordination -- 14.3.2 Selected algorithms -- 14.3.3 Integrated sensor-actor and actor-actor coordination -- 14.3.4 Problems with selfish nodes -- 14.4 In-network operation and control -- 14.5 Conclusion -- 15 Collaboration and Task Allocation -- 15.1 Introduction to MRTA -- 15.1.1 Primary objectives -- 15.1.2 Classification and taxonomy -- 15.2 Intentional cooperation - auction-based task allocation -- 15.2.1 Open Agent Architecture -- 15.2.2 Murdoch -- 15.2.3 Dynamic negotiation algorithm -- 15.3 Emergent cooperation -- 15.3.1 Stimulation by work -- 15.3.2 Stimulation by state -- 15.4 Conclusion -- Appendix III Coordination and Control - Further Reading -- IV Self-Organization Methods in Sensor and Actor Networks -- 16 Self-Organization Methods - Revisited -- 16.1 Self-organization methods in SANETs -- 16.2 Positive and negative feedback -- 16.3 Interactions among individuals and with the environment -- 16.4 Probabilistic techniques -- 17 Evaluation Criteria -- 17.1 Scalability -- 17.2 Energy considerations -- 17.2.1 Energy management -- 17.2.2 Transmission power management -- 17.3 Network lifetime -- 17.3.1 Definition of 'network lifetime' -- 17.3.2 Scenario-based comparisons of network lifetime -- V Bio-inspired Networking -- 18 Bio-inspired Systems -- 18.1 Introduction and overview -- 18.1.1 Ideas and concepts -- 18.1.2 Bio-inspired research fields -- 18.2 Swarm Intelligence -- 18.2.1 Principles of ant foraging -- 18.2.2 Ant-based routing -- 18.2.3 Ant-based task allocation -- 18.3 Artificial Immune System -- 18.3.1 Principles of the immune system -- 18.3.2 Application examples -- 18.4 Cellular signaling pathways -- 18.4.1 Introduction to signaling pathways -- 18.4.2 Applicability in SANETs -- 18.5 Conclusion -- Appendix IV Bio-inspired Networking - Further Reading -- Bibliography -- Index Intro -- Self-Organization in Sensor and Actor Networks -- Contents -- Foreword -- Preface -- About the Author -- List of Abbreviations -- 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 10.3 Optimized route stability -- 10.4 Dynamic address assignment -- 10.4.1 Overview and centralized assignment -- 10.4.2 Passive Duplicate Address Detection -- 10.4.3 Dynamic Address Allocation -- 10.5 Conclusion -- 11 Data-Centric Networking -- 11.1 Overview and classification -- 11.1.1 Data dissemination -- 11.1.2 Network-centric operation -- 11.1.3 Related approaches -- 11.2 Flooding, gossiping and optimizations -- 11.2.1 Flooding -- 11.2.2 Pure gossiping -- 11.2.3 Optimized gossiping -- 11.3 Agent-based techniques -- 11.4 Directed diffusion -- 11.4.1 Basic algorithm -- 11.4.2 Mobility support -- 11.4.3 Energy efficiency -- 11.5 Data aggregation -- 11.5.1 Principles and objectives -- 11.5.2 Aggregation topologies -- 11.6 Conclusion -- 12 Clustering -- 12.1 Principles of clustering -- 12.1.1 Requirements and classification -- 12.1.2 k-means -- 12.1.3 Hierarchical clustering -- 12.2 Clustering for efficient routing -- 12.2.1 Low-Energy Adaptive Clustering Hierarchy -- 12.2.2 Hybrid Energy-Efficient Distributed Clustering Approach -- 12.3 Conclusion -- Appendix II Networking Aspects - Further Reading -- III Coordination and Control: Sensor and Actor Networks -- 13 Sensor and Actor Networks -- 13.1 Introduction -- 13.1.1 Composition of SANETs - an example -- 13.1.2 Properties and capabilities -- 13.1.3 Components of SANET nodes -- 13.1.4 Application examples -- 13.2 Challenges and research objectives -- 13.2.1 Communication and coordination -- 13.2.2 Collaboration and task allocation -- 13.3 Limitations -- 14 Communication and Coordination -- 14.1 Synchronization vs coordination -- 14.1.1 Problem statement -- 14.1.2 Logical time -- 14.1.3 Coordination -- 14.2 Time synchronization in WSNs and SANETs -- 14.2.1 Requirements and objectives -- 14.2.2 Conventional approaches -- 14.2.3 Algorithms for WSNs -- 14.3 Distributed coordination 6.4 Modeling self-organizing systems -- 6.4.1 Overview of modeling techniques -- 6.4.2 Differential equation models -- 6.4.3 Monte Carlo simulations -- 6.4.4 Choosing the right modeling technique -- Appendix I Self-Organization - Further Reading -- II Networking Aspects: Ad Hoc and Sensor Networks -- 7 Mobile Ad Hoc and Sensor Networks -- 7.1 Ad hoc networks -- 7.1.1 Basic properties of ad hoc networks -- 7.1.2 Mobile Ad Hoc Networks -- 7.2 Wireless Sensor Networks -- 7.2.1 Basic properties of sensor networks -- 7.2.2 Composition of single-sensor nodes -- 7.2.3 Communication in sensor networks -- 7.2.4 Energy aspects -- 7.2.5 Coverage and deployment -- 7.2.6 Comparison between MANETs and WSNs -- 7.2.7 Application examples -- 7.3 Challenges and research issues -- 7.3.1 Required functionality and constraints -- 7.3.2 Research objectives -- 8 Self-Organization in Sensor Networks -- 8.1 Properties and objectives -- 8.2 Categorization in two dimensions -- 8.2.1 Horizontal dimension -- 8.2.2 Vertical dimension -- 8.3 Methods and application examples -- 8.3.1 Mapping with primary self-organization methods -- 8.3.2 Global state -- 8.3.3 Location information -- 8.3.4 Neighborhood information -- 8.3.5 Local state -- 8.3.6 Probabilistic techniques -- 9 Medium Access Control -- 9.1 Contention-based protocols -- 9.2 Sensor MAC -- 9.2.1 Synchronized listen/sleep cycles -- 9.2.2 Performance aspects -- 9.2.3 Performance evaluation -- 9.3 Power-Control MAC protocol -- 9.4 Conclusion -- 10 Ad Hoc Routing -- 10.1 Overview and categorization -- 10.1.1 Address-based routing vs data-centric forwarding -- 10.1.2 Classification of ad hoc routing protocols -- 10.2 Principles of ad hoc routing protocols -- 10.2.1 Destination Sequenced Distance Vector -- 10.2.2 Dynamic Source Routing -- 10.2.3 Ad Hoc on Demand Distance Vector -- 10.2.4 Dynamic MANET on Demand |
| Title | Self-Organization in Sensor and Actor Networks |
| URI | https://ieeexplore.ieee.org/servlet/opac?bknumber=8039720 https://cir.nii.ac.jp/crid/1130282273207416704 https://ebookcentral.proquest.com/lib/[SITE_ID]/detail.action?docID=470513 https://www.vlebooks.com/vleweb/product/openreader?id=none&isbn=9780470724453&uid=none |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3NT8IwFH8RvMhJ_Iio6GK8meFGv7ajEAgxwRMabkv30YRARiLowb_e167AABOjl2brlnb7vfV9rH2_AtxLHAWhx5jLFBEuJVThkJIYrKBCJmgQw7bSycnDFz54pc9jNt5QVZjskmXcSr5-zCv5j1SxDuWqs2T_INl1o1iBxyhfLFHCWO44v-tTm7-RzZQ7L6VR6t8WC4xI7ZJIs4nOQ14s8d5sHa9Da5v515ez6Xwr5Bc7Ib_RF1txoEeFCZ4M08C-VixYVu19Ag16QeG_QzTdeQqY0BEgr0BFCK8Kh2gae8P1LyuPMCooN3n9tj-y4jBa9W9pTPHscau_GtTkYop6G3X6cmG3skF7nk8me1bQmPbRMVR1ukcdDrL8BGolesZTaO3B7Exyp4DZQZgdA7OzgvkM3vq9UXfg2v0lXMnxybgrFaepnmcM0pByyVJFY6XiVKlMaqq1UCmisELITKImlL7khAahl8YBz0iSkHOo5vM8uwBHxnhXW0dflNNQ4tAIMiKYCH2Wog_qN-Cu9P7R58zMhS-iEkiMNKCuYYmKS4GHvmLba0ATQYqSiS59Pc-MPp0gbeNLC4824HYFX2Qatat6o16niy0zn1z-0sIVHG2-smuoLt8_sia6Wsv4xn4C31-YHKw |
| linkProvider | ProQuest Ebooks |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=book&rft.title=Self-organization+in+sensor+and+actor+networks&rft.au=Dressler%2C+Falko&rft.date=2007-01-01&rft.pub=Wiley&rft.isbn=9780470028209&rft_id=info:doi/10.1002%2F9780470724460&rft.externalDocID=BA85721026 |
| thumbnail_m | http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=https%3A%2F%2Fvle.dmmserver.com%2Fmedia%2F640%2F97804707%2F9780470724453.jpg |