113,99 €
Mehr erfahren.
- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: Wiley Series on Cooperative Communications
- Sprache: Englisch
Autonomic Intelligence Evolved Cooperative Networking offers a comprehensive advancement of the state-of-the art technological developments in the fields of Cooperative Networking and Autonomic Computing. Based on his track record in industrial standardisation, as well as academic and applied research, the author presents a fully-fledged Autonomic Cooperative Networking Architectural Model that encompasses the relevant workings of both the Layers of the Open Systems Interconnection Reference Model and the Levels of the Generic Autonomic Network Architecture.
Sie lesen das E-Book in den Legimi-Apps auf:
Seitenzahl: 448
Veröffentlichungsjahr: 2018
Ähnliche
Table of Contents
Cover
Title Page
Copyright
About the Author
Preface
Acknowledgements
Acronyms
Notation
Chapter 1: Introduction
Chapter 2: Autonomically Driven Cooperative Design
2.1 Introduction
2.2 Biologically Inspired Autonomics
2.3 Emergent Autonomic Networking
2.4 Synergetic Cooperative Approach
2.5 Conclusion
References
Chapter 3: Protocol Level Spatio-Temporal Processing
3.1 Introduction
3.2 Multiple-Input Multiple-Output Channel
3.3 Space-Time Coding Techniques
3.4 Protocol Level Overlay Logic
3.5 Conclusion
References
Chapter 4: Function Level Relaying Techniques
4.1 Introduction
4.2 Conventional and Cooperative Relaying
4.3 Fixed Relay Deployment Concepts
4.4 Function Level Overlay Logic
4.5 Conclusion
References
Chapter 5: Node Level Routing Mechanisms
5.1 Introduction
5.2 Optimised Link State Routing Protocol
5.3 Routing Information Enhanced Cooperation
5.4 Node Level Overlay Logic
5.5 Conclusion
References
Chapter 6: Network Level System Orchestration
6.1 Introduction
6.2 Standardisation Driven Design
6.3 Cooperative Emergency Networking
6.4 Network Level Overlay Logic
6.5 Conclusion
References
Chapter 7: Conclusion
Appendix A: Appendix
Index
End User License Agreement
Pages
ix
xi
xiii
xv
xvi
xvii
xviii
xix
xx
xxi
xxiii
1
2
3
4
5
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
223
224
225
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
253
254
255
256
257
258
259
Guide
Cover
Table of Contents
Preface
Begin Reading
List of Illustrations
Chapter 2: Autonomically Driven Cooperative Design
Figure 2.1 Roots of autonomics. Adapted from Paulson (2002).
Figure 2.2 Position of awareness. Adapted from Vassev and Hinchey (2010).
Figure 2.3 Notions of being autonomic, autonomous, and cognitive.
Figure 2.4 Constituents of self-management.
Figure 2.5 Placement levels of autonomic features.
Figure 2.6 High-level view of the Generic Autonomic Network Architecture. Adapted from ETSI-GS-AFI-002 (2013).
Figure 2.7 Reference points. Adapted from ETSI-GS-AFI-002 (2013).
Figure 2.8 Autonomic element. Adapted from Kephart and Chess (2003).
Figure 2.9 Simplified view of an autonomic node.
Figure 2.10 Protocol level hierarchical autonomic control loop. Adapted from ETSI-GS-AFI-002 (2013).
Figure 2.11 Function level hierarchical autonomic control loop. Adapted from ETSI-GS-AFI-002 (2013).
Figure 2.12 Node level hierarchical autonomic control loop. Adapted from ETSI-GS-AFI-002 (2013).
Figure 2.13 Network level hierarchical autonomic control loop. Adapted from ETSI-GS-AFI-002 (2013).
Figure 2.14 Vertical Technological Pillars.
Figure 2.15 Horizontal Architectural Extensions.
Figure 2.16 Extension to the Autonomic Cooperative Node.
Figure 2.17 Extension to the Autonomic Cooperative Behaviour.
Figure 2.18 Extension to the Autonomic Cooperative Networking Protocol.
Figure 2.19 Hierarchical relations among autonomic entities.
Figure 2.20 Correspondence between vertical layers and horizontal levels.
Figure 2.21 Overview of the Autonomic Cooperative Networking Architectural Model.
Chapter 3: Protocol Level Spatio-Temporal Processing
Figure 3.1 Selection combining. Adapted from Vucetic and Yuan (2003).
Figure 3.2 Maximal ratio combining. Adapted from Vucetic and Yuan (2003).
Figure 3.3 Delay transmit diversity. Adapted from Larsson and Stoica (2003b).
Figure 3.4 Single-input single-output and single-input multiple-output systems.
Figure 3.5 Multiple-input single-output and multiple-input multiple-output systems.
Figure 3.6 EVMIMO channel. Adapted from Vucetic and Yuan (2003).
Figure 3.7 Capacity of VMIMO channel for .
Figure 3.8 Probability density function for five sinusoids.
Figure 3.9 Diagram of space-time coded system.
Figure 3.10 Generic space-time coded system. Adapted from Alamouti (1998).
Figure 3.11 Base code for 4-PSK modulation. Adapted from Tarokh et al. (1998).
Figure 3.12 Example of STTC decoding process.
Figure 3.13 Performance of STTC in AWGN channel.
Figure 3.14 Example of STTC with 4-PSK modulation. Adapted from Tarokh et al. (1998).
Figure 3.15 Example of STTC with 8-PSK modulation. Adapted from Tarokh et al. (1998).
Figure 3.16 Concatenated STBC with TCM. Adapted from Gong and Letaief (2002).
Figure 3.17 ACNs from a level-driven orthogonal perspective.
Figure 3.18 Interaction over protocol stack.
Figure 3.19 CTDE versus system for .
Figure 3.20 CTDE in relation to and .
Figure 3.21 usage percentage.
Figure 3.22 Dependencies between the physical layer and link layer routines.
Figure 3.23 Architectural relations stemming from the physical layer and protocol level.
Figure 3.24 ACNs from the layer-driven orthogonal perspective.
Chapter 4: Function Level Relaying Techniques
Figure 4.1 Conventional relaying.
Figure 4.2 Cooperative relaying. Adapted from Zimmermann et al. (2005).
Figure 4.3 Classification of cooperation methods.
Figure 4.4 Projection of collaborative protocol.
Figure 4.5 Projection of supportive protocol.
Figure 4.6 General relay cooperation scheme. Adapted from Herhold et al. (2004b).
Figure 4.7 Distributed space-time block coding system. Adapted from Laneman and Wornell (2003).
Figure 4.8 Virtual antenna arrays. Adapted from Dohler et al. (2004).
Figure 4.9 Manhattan fixed deployment concept. Adapted from Esseling et al. (2005).
Figure 4.10 Multi-frame structure.
Figure 4.11 Process-based interactions.
Figure 4.12 Total number of packets sent for buffer length of 30.
Figure 4.13 Total number of packets sent for buffer length of 60.
Figure 4.14 Total number of packets lost for buffer length of 30.
Figure 4.15 Packet loss ratio for buffer length of 30.
Figure 4.16 Total number of packets lost for buffer length of 60.
Figure 4.17 Packet loss ratio for buffer length of 60.
Figure 4.18 Average deviation of time slot length for adaptive system with buffer size of 30.
Figure 4.19 Average deviation of time slot length for adaptive system with buffer size of 60.
Figure 4.20 Baseline relay deployment indoor scenario. Adapted from Dottling et al. (2009).
Figure 4.21 Super-frame structure.
Figure 4.22 (a) Deployment of FRNs. (b) Relative throughput for direct transmission. (c) Single-path relaying via FRN3. (d) FRN2–FRN3 cooperation.
Figure 4.23 equivalent distributed space-time block encoder in a Rayleigh channel.
Figure 4.26 equivalent distributed space-time block encoder in a Rayleigh channel.
Figure 4.27 equivalent distributed space-time block encoder in a Rayleigh channel.
Figure 4.28 Legacy fast re-routing.
Figure 4.29 Cooperative re-routing.
Figure 4.30 CRDE at relative throughput threshold of 0.99.
Figure 4.32 CRDE at relative throughput threshold of 0.93.
Figure 4.33 CRDE at relative throughput threshold of 0.90.
Figure 4.34 Dependencies among the routines of the physical layer, link layer, and network layer.
Figure 4.35 Architectural relations stemming from the link layer and function level.
Figure 4.36 Extended version of an Autonomic Cooperative Node.
Chapter 5: Node Level Routing Mechanisms
Figure 5.1 OLSR packet. Adapted from Clausen and Jacquet (2003).
Figure 5.2 Hello message. Adapted from Clausen and Jacquet (2003).
Figure 5.3 UDP datagram. Adapted from Postel (1980).
Figure 5.4 Multi-point relay station selection heuristics. Adapted from Qayyum et al. (2002).
Figure 5.5 OLSR protocol repositories.
Figure 5.6 Common aspect of MPRs and VAAs.
Figure 5.7 REACT scenario.
Figure 5.8 Performance of REACT.
Figure 5.9 Link Code. Adapted from Clausen and Jacquet (2003).
Figure 5.10 Modified Hello message.
Figure 5.11 Extended Link Code.
Figure 5.12 Extended Link Mask.
Figure 5.13 Generalised Hello message.
Figure 5.14 Address auto-configuration.
Figure 5.15 Address duplication scenario.
Figure 5.16 Integration of MPRs and ACNs.
Figure 5.17 Performance for first-hop 10 dB or 20 dB SNR.
Figure 5.18 ACNP routing table.
Figure 5.19 Evaluation scenario.
Figure 5.20 Performance comparison for ACNP.
Figure 5.21 Overhead of Modified Hello message and Generalised Hello message.
Figure 5.22 Roots of the ACNP.
Figure 5.23 Conceptual transitions.
Figure 5.24 Dependencies among the physical layer, link layer, and network layer routines.
Figure 5.25 Architectural relations stemming from the link layer and function level.
Chapter 6: Network Level System Orchestration
Figure 6.1 Standardisation cycle. Adapted from Tanenbaum and Wetherall (2011).
Figure 6.2 Approach to standardisation. Adapted from Wódczak et al. (2011).
Figure 6.3 Work Items. Adapted from Wódczak et al. (2011).
Figure 6.4 Standardisation ecosystem. Adapted from Wódczak et al. (2011).
Figure 6.5 First stage of GANA instantiation. Adapted from ETSI-GS-AFI-002 (2013).
Figure 6.6 Second stage of GANA instantiation. Adapted from ETSI-GS-AFI-002 (2013).
Figure 6.7 Third stage of GANA instantiation. Adapted from ETSI-GS-AFI-002 (2013).
Figure 6.8 Fourth stage of GANA instantiation. Adapted from ETSI-GS-AFI-002 (2013).
Figure 6.9 Possible standardisation synergies.
Figure 6.10 Baseline star topology.
Figure 6.11 Cooperative multi-hop configuration.
Figure 6.12 Supportive communication between CFRs.
Figure 6.13 Supportive omission of affected CFR.
Figure 6.14 Cooperative mode of operation.
Figure 6.15 Instantiation of cooperation by MEOC.
Figure 6.16 Proactive and reactive resiliency process.
Figure 6.17 Integration into Autonomic Cooperative Networking Architectural Model.
Figure 6.18 (a) Deployment of CFRs. (b) Relative throughput for CFR2–CFR3 cooperation. (c) Single-path relaying via CFR2. (d) Single-path relaying via CFR3.
Figure 6.19 (a) Deployment of CFRs. (b) Relative throughput for CFR2–CFR3 cooperation. (c) Single-path relaying via CFR2. (d) Single-path relaying via CFR3.
Figure 6.20 (a) Deployment of CFRs. (b) Relative throughput for CFR2–CFR3 cooperation. (c) Single-path relaying via CFR2. (d) Single-path relaying via CFR3.
Figure 6.21 (a) Deployment of CFRs. (b) Relative throughput for CFR2–CFR3 cooperation. (c) Single-path relaying via CFR2. (d) Single-path relaying via CFR3.
Figure 6.22 Consolidated view of the Autonomic Cooperative Networking Architectural Model.
Figure 6.23 Autonomic Cooperative Networking Protocol and Autonomic Cooperative Behaviour.
Figure 6.24 Relative position of Autonomic Cooperative Networking Protocol.
Figure 6.30 Layout A.
Figure 6.25 Layout B.
Figure 6.26 Layout C.
Figure 6.27 Cumulative distribution functions for layouts A1–A4.
Figure 6.28 Cumulative distribution functions for layouts B1–B4.
Figure 6.29 Cumulative distribution functions for layouts C1–C4.
Figure 6.31 Structure of the CTDE.
Figure 6.32 Structure of the CRDE.
Figure 6.33 Structure of the CMDE.
Figure 6.34 Structure of the CODE.
List of Tables
Chapter 4: Function Level Relaying Techniques
Table 4.1 Forwarding strategy
Table 4.2 Protocol nature
Table 4.3 Average delay for buffer length of 30
Table 4.4 Average delay for buffer length of 60
Table 4.5 System parameters
Chapter 5: Node Level Routing Mechanisms
Table 5.1 Neighbourhood type characteristics
Table 5.2 Interface association tuple. Adapted from Clausen and Jacquet (2003)
Table 5.3 Link tuple. Adapted from Clausen and Jacquet (2003)
Table 5.4 Neighbour tuple. Adapted from Clausen and Jacquet (2003)
Table 5.5 Two-hop neighbour tuple. Adapted from Clausen and Jacquet (2003)
Table 5.6 MPR selector tuple. Adapted from Clausen and Jacquet (2003)
Table 5.7 VAA selector tuple. Adapted from Wódczak (2014)
Table 5.8 Topology tuple. Adapted from Clausen and Jacquet (2003)
Table 5.9 Link types. Adapted from Clausen and Jacquet (2003)
Table 5.10 Neighbour types. Adapted from Clausen and Jacquet (2003), as well as from Wódczak (2014)
Table 5.11 Duplicate address detection for the OLSR protocol
Autonomic Intelligence Evolved Cooperative Networking
Wiley Series on Cooperative Communications
Michał Wódczak
This edition first published 2018
© 2018 John Wiley & Sons Ltd
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.
The right of Michał Wódczak to be identified as the author of this work has been asserted in accordance with law.
Registered Office(s)
John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA
John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK
Editorial Office
The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK
For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com.
Wiley also publishes its books in a variety of electronic formats and by print-on-demand. Some content that appears in standard print versions of this book may not be available in other formats.
Limit of Liability/Disclaimer of Warranty
While the publisher and author have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.
Library of Congress Cataloging-in-Publication Data:
Names: Wódczak, Michał, author.
Title: Autonomic intelligence evolved cooperative networking / by Michał Wódczak.
Description: Hoboken, NJ : John Wiley {&} Sons, 2018. | Series: Wiley series on cooperative communications | Includes bibliographical references and index.
Identifiers: LCCN 2017050777 (print) | LCCN 2018000675 (ebook) | ISBN 9781119215981 (pdf) | ISBN 9781119215998 (epub) | ISBN 9781118325414 (cloth)
Subjects: LCSH: Wireless communication systems. | Autonomic computing.
Classification: LCC TK5103.2 (ebook) | LCC TK5103.2 .W583 2018 (print) | DDC 004.6-dc23
LC record available at https://lccn.loc.gov/2017050777
Cover Design: Wiley
Cover Image: © RBFried/iStockphoto
About the Author
Michał Wódczak holds a PhD in Telecommunications from Poznań University of Technology, obtained under the umbrella of the European Union Sixth Framework Programme, as well as an Executive MBA from Aalto University School of Business, distinguished by the Triple Crown of AACSB, AMBA, and EQUIS accreditations. Currently, he is with Samsung Electronics, while prior to that he was also with Telcordia Technologies, formerly known as Bellcore or Bell Communications Research, and with Ericsson, following the Telcordia merger. He served as an Editorial Board Member of IEEE ComSoc Technology News, as well as ran standardisation activities as Vice Chairman and Rapporteur of ETSI ISG AFI, the Industry Specification Group on Autonomic network engineering for the self-managing Future Internet, established under the auspices of European Telecommunications Standards Institute. He is also a Senior Member of the IEEE Communications Society and, apart from this book, he has published two single-authored scientific books with Springer, co-authored and co-edited two industrial standardisation specifications with ETSI, and, overall, authored or co-authored more than 60 peer-reviewed journal, magazine, and conference papers, as well as book chapters. He has also contributed to over 40 scientific reports within European Union FP6 and FP7 projects IST-2003-507581 WINNER I, IST-4-027756 WINNER II, IST-2004-507325 NEWCOM, INFSO-ICT-215549 EFIPSANS, and SEC-242411 E-SPONDER. In addition, he holds Postgraduate Diplomas in Managerial Studies and Psychology of Management, both from Poznań School of Banking, as well as a BA in English Philology and a Postgraduate Diploma in Translation and Interpreting, both from Adam Mickiewicz University. In this capacity, he also acted as an Executive Board Member of the Association of Polish Translators and Interpreters.
Preface
In this book the concept of Autonomic Intelligence Evolved Cooperative Networking is proposed, building on top of both the previous books by the author, where the technological developments in the form of autonomic cooperative networking and autonomic computing enabled cooperative networked design were outlined. In fact, while the former emphasised the aspects of the Open Systems Interconnection Reference Model and the latter elevated the perspective of the Generic Autonomic Network Architecture, not only does the idea of Autonomic Intelligence Evolved Cooperative Networking provide a substantially expanded, but also the most comprehensive and consolidated account in this respect. In other words, a fully-fledged Autonomic Cooperative Networking Architectural Model is presented encompassing the relevant workings both of the layers of the Open Systems Interconnection Reference Model and the levels of the Generic Autonomic Network Architecture. Given the lack of direct correspondence between these two dimensions, as such classified to form the Vertical Technological Pillars and Horizontal Architectural Extensions, the mechanisms of the Autonomic Cooperative Node, Autonomic Cooperative Behaviour, and Autonomic Cooperative Networking Protocol are deployed along with all the pertinent architectural extensions thereto. What is more, during the entire endeavour, the notion of autonomic computing becomes naturally elevated and transposed into autonomic intelligence, as explained later in the book, on the one hand, to better reflect the value it brings to cooperative networking in general, and, on the other hand, to prepare the ground for possible further conceptual advancements.
Acknowledgements
The ultimate design outlined in this book was inspired by the prior involvement of the author in the European Union Sixth Framework Programme IST-2003-507581 Integrated Project: Wireless World Initiative New Radio I (WINNER I), the European Union Sixth Framework Programme IST-4-027756 Integrated Project: Wireless World Initiative New Radio II (WINNER II), the European Union Sixth Framework Programme IST-2004-507325 Network of Excellence in Wireless COMmunications (NEWCOM), the European Union Seventh Framework Programme INFSO-ICT-215549 Integrated Project: Exposing the Features in IP version Six protocols that can be exploited/extended for the purposes of designing/building Autonomic Networks and Services (EFIPSANS), and the European Union Seventh Framework Programme SEC-242411 Integrated Project: A Holistic Approach Towards the Development of the First Responder of the Future (E-SPONDER), as well as the Industry Specification Group (ISG) on Autonomic network engineering for the self-managing Future Internet (AFI) established under the auspices of the European Telecommunications Standards Institute (ETSI). In the light of the above, for the sake of transparency, the utmost attention was paid to clearly highlight the author's unique contribution to the state-of-the-art advancement in the major theme of this book, as well as to ensure its proper separation from any externally referenced background and context-setting information. At the same time, it is crucial to note that all the evaluation results presented by the author were obtained with the use of a dedicated simulation environment designed exclusively for the needs of the preparation of this book, while all the views presented in this book are of the author's.
Acronyms
3GPP
3rd Generation Partnership Project
AAC
Address Auto-Configuration
AAC-OLSR
Address Auto-Configuration OLSR
AB
Autonomic Behaviour
AC
Autonomic Computing
ACB
Autonomic Cooperative Behaviour
ACL
Autonomic Control Loop
ACN
Autonomic Cooperative Node
ACNAM
Autonomic Cooperative Networking Architectural Model
ACNP
Autonomic Cooperative Networking Protocol
ACRR
Autonomic Cooperative Re-Routing
ACS
Autonomic Cooperative Set
ACSAM
Autonomic Cooperative System Architectural Model
ACT
Autonomic Cooperative Transmission
ADME
Autonomic Decision-Making Element
AE
Autonomic Element
AF
Amplify-and-Forward
AFI
Autonomic network engineering for the self-managing Future Internet
AI
Artificial Intelligence
AIECN
Autonomic Intelligence Evolved Cooperative Networking
ALD
Angular Diversity
AM
Autonomic Manager
AN
Autonomic Networking
ANO
Autonomic Node
ANCS
Autonomic Networked Computing System
ANS
Autonomic Nervous System
AO
Autonomic Overlay
AP
Access Point
AR
Autonomic Routine
ARP
Address Resolution Protocol
AS
Autonomic System
ASS
Autonomous System
ATS
Agent System
AUF
Autonomic Function
AWGN
Additive White Gaussian Noise
B2B
Business-to-Business
B2C
Business-to-Customer
BBF
Broadband Forum
BER
Bit Error Rate
BS
Base Station
CA
Coding Advantage
CAdDF
Complex Adaptive Decode-and-Forward
CAS
Computer-Assisted Simulation
CB
Coherence Bandwidth
CBR
Constant Bit Rate
CCG
Channel Capacity Gain
CCI
Co-Channel Interference
CD
Coherence Distance
CDF
Cumulative Distribution Function
CDR
Code Rate
CFR
Chief First Responder
CG
Coding Gain
CHB
Channel Bandwidth
CHG
Channel Gain
CI
Characteristic Information
CLI
Command Line Interface
CM
Channel Matrix
CMDE
Cooperation Management Decision Element
CNR
Conventional Relaying
COD
Complex Orthogonal Design
CODE
Cooperation Orchestration Decision Element
COR
Cooperative Relaying
COT
Cooperative Transmission
CPR
Computing Process
CPU
Central Processing Unit
CPX
Cyclic Prefix
CRDE
Cooperative Re-Routing Decision Element
CRO
Cooperative Routing
CRR
Cooperative Re-Routing
CSI
Channel State Information
CT
Coherence Time
CTP
Control Plane
CTDE
Cooperative Transmission Decision Element
D2D
Device-to-Device
DAA
Duplicate Address Avoidance
DAD
Duplicate Address Detection
DCP
Decision Plane
DMP
Dissemination Plane
DSP
Discovery Plane
DE
Decision Element
DF
Decode-and-Forward
DG
Diversity Gain
DHCP
Dynamic Host Configuration Protocol
DME
Decision-Making Element
DMN
Decision-Making Entity
DN
Destination Node
DO
Diversity Order
DTP
Data Plane
DR
Decode-and-Reencode
DSTBC
Distributed Space-Time Block Coding
DTD
Delayed Transmission Diversity
DVB-T
Digital Terrestrial Video Broadcasting
ECMP
Equal Cost Multipath Protocol
ECN
Emergency Communications Network
EDSTBE
Equivalent Distributed Space-Time Block Encoder
EFIPSANS
Exposing the Features in IP version Six protocols that can be exploited/extended for the purposes of designing/building Autonomic Networks and Services
EGC
Equal Gain Combining
ELC
Extended Link Code
ELM
Extended Link Mask
EMS
Evolved Messaging Structure
EOC
Emergency Operations Centre
EREACT
Extended Routing information Enhanced Algorithm for Cooperative Transmission
E-SPONDER
A Holistic Approach Towards the Development of the First Responder of the Future
ES
Economic Science
ETSI
European Telecommunications Standards Institute
EU
European Union
EVMIMO
Equivalent Virtual Multiple-Input Multiple-Output
EVMISO
Equivalent Virtual Multiple-Input Single-Output
FB
Functional Block
FI
Future Internet
FLD
Frequential Diversity
FNL
Function Level
FMDE
Fault Management Decision Element
FMPR
Flooding Multi-Point Relay
FP
Framework Programme
FP6
Sixth Framework Programme
FP7
Seventh Framework Programme
FR
First Responder
FRN
Fixed Relay Node
FRR
Fast Re-Routing
GANA
Generic Autonomic Network Architecture
GCOD
Generalised Complex Orthogonal Design
GR
Generic Receiver
GS
Group Specification
GT
Generic Transmitter
GVAA
Generalised Virtual Antenna Array
HACL
Hierarchical Autonomic Control Loop
HAE
Horizontal Architectural Extension
HANS
Human Autonomic Nervous System
HRP
Horizontal Reference Point
IANA
Internet Assigned Number Authority
ICI
Inter-Channel Interference
IETF
Internet Engineering Task Force
IoT
Internet of Things
IP
Internet Protocol
IPv6
Internet Protocol version 6
ISG
Industry Specification Group
ITS
Intelligent Transport System
ITU-T
International Telecommunication Union – Telecommunications
KNP
Knowledge Plane
L3DF
Layer-3 Decode-and-Forward
LNK
Link Layer
LoA
Level of Abstraction
LOS
Line-of-Sight
LSB
Least Significant Bit
LSRP
Link-State Routing Protocol
LSTC
Layered Space-Time Coding
LTE
Long Term Evolution
LV
Link Verification
M2M
Machine-to-Machine
MAC
Medium Access Control
MAD
Multiple Address Declaration
MANET
Mobile Ad hoc Network
MAS
Multi-Agent System
MCS
Modulation and Coding Scheme
ME
Managed Element
MEA
Multi-Element Array
MEN
Managed Entity
MEOC
Mobile Emergency Operations Centre
MIMO
Multiple-Input Multiple-Output
MISO
Multiple-Input Single-Output
MLD
Maximum Likelihood Detection
MLSE
Maximum Likelihood Sequence Estimator
MMIMO
Massive Multiple-Input Multiple-Output
MMSE
Minimum Mean Square Error
MN
Mobile Node
MNP
Management Plane
MPR
Multi-Point Relay
MRC
Maximal Ratio Combining
MRN
Mobile Relay Node
MRRC
Maximal Ratio Receive Combining
MSB
Most Significant Bit
NC
Network Coding
ND
Neighbour Discovery
NDL
Node Level
NE
Network Element
NET
Network Layer
NEWCOM
Network of Excellence in Wireless COMmunications
NFV
Network Function Virtualisation
NGMN
Next Generation Mobile Networks
NGN
Next Generation Network
NLOS
Non-Line-of-Sight
NOA-OLSR
No Overhead Auto-Configuration OLSR
NTL
Network Level
OBU
On-Board Unit
OFDM
Orthogonal Frequency-Division Multiplexing
OFDMA
Orthogonal Frequency-Division Multiple Access
OLSR
Optimised Link State Routing
OSI
Open Systems Interconnection
OSPF
Open Shortest Path First
PDAD-OLSR
Passive Duplicate Address Detection OLSR
Probability Density Function
PHY
Physical Layer
PI
Process Interaction
PLD
Polar Diversity
PSK
Phase-Shift Keying
PSN
Public Safety Network
PTL
Protocol Level
QO
Quasi-Orthogonal
QoS
Quality of Service
QPSK
Quadrature Phase-Shift Keying
RA
Reference Architecture
RAP
Radio Access Point
RBCD
Repetition-Based Cooperative Diversity
REACT
Routing information Enhanced Algorithm for Cooperative Transmission
REC
Relay-Enhanced Cell
RF
Radio Frequency
RFP
Reference Point
RM
Reference Model
RME
Routing Mechanism
RMPR
Routing Multi-Point Relay
RN
Relay Node
RND
Reception Diversity
RPA
Reference Point Architecture
RSDE
Resilience and Survivability Decision Element
RTB
Routing Table
SA
Software Agent
SAdDF
Simple Adaptive Decode-and-Forward
SAS
Single-Agent System
SBA
Service Based Architecture
SC
Selection Combining
SCD
Scanning Diversity
SDE
Sub-Decision Element
SDN
Software-Defined Networking
SDO
Standards Development Organisation
SDR
Software-Defined Radio
SFAAC
Stateful Address Auto-Configuration
SIMO
Single-Input Multiple-Output
SINR
Signal-to-Interference-plus-Noise Ratio
SISO
Single-Input Single-Output
SLAAC
Stateless Address Auto-Configuration
SLD
Spatial Diversity
SN
Source Node
SNR
Signal-to-Noise Ratio
SOA
Service-Oriented Architecture
SON
Self-Organising Network
SPR
Single-Path Relaying
STBC
Space-Time Block Coding
STBD
Space-Time Block Decoder
STBE
Space-Time Block Encoder
STC
Space-Time Coding
STCCD
Space-Time-Coded Cooperative Diversity
STP
Spatio-Temporal Processing
STTC
Space-Time Trellis Coding
SVD
Singular-Value Decomposition
SWC
Switched Combining
TC
Topology Control
TCM
Trellis-Coded Modulation
TCO
Total Cost of Ownership
TCP
Transmission Control Protocol
TCP/IP
Transmission Control Protocol/Internet Protocol
TDD
Time Division Duplex
TLD
Temporal Diversity
TMF
Telemanagement Forum
TND
Transmission Diversity
TTL
Time To Live
TS
Technical Specification
UDP
User Datagram Protocol
UF
Utility Function
UT
User Terminal
VAA
Virtual Antenna Array
VANET
Vehicular Ad hoc NETwork
VCS
Virtual Cooperative Set
VMIMO
Virtual Multiple-Input Multiple-Output
VRP
Vertical Reference Point
VTP
Vertical Technological Pillar
WI
Work Item
WINNER I
Wireless World Initiative New Radio I
WINNER II
Wireless World Initiative New Radio II
WRR
Weighted Round-Robin
Notation
Set of Autonomic Cooperative Nodes providing the capability of cooperative transmission between the source node
and the destination node
.
Set of channel coefficients between the members of
and
itself.
Equivalent distributed space-time block encoder, where
denotes a specific space-time block coding scheme, e.g.
,
,
,
, or
.
Generic transmitter
, where
.
Generic receiver
, where
.
Channel coefficient between the
th transmitting antenna and
th receiving antenna, where
and
.
Channel gain.
Channel matrix for
transmitting antennae and
receiving antennae.
identity matrix.
Path loss defining a given radio propagation model.
Buffer load in the previous cycle
, where
denotes a slot, while
corresponds either to the base station, indicated by the
, or a fixed relay node, assigned a value of 1 through 4.
Set of multi-point relays of a given source node
.
Redundant, i.e. secondary, ternary, and so on, set of multi-point relays of a given source node
, where
;
is equivalent to
.
Chapter 1Introduction
The opening, context-setting, chapter introduces the background behind the concept of autonomic computing, and accounts for its convergence with modern networked systems. A conceptual analysis is then carried out in order to draft a fully-fledged framework depicting the scientific advancement in this respect. In essence, the general vision and the state of the art in the field of autonomic computing are approached from the viewpoint of the related mechanisms inherent in the functioning of the human autonomic nervous system. Given its importance, this consists in the analysis of the key dimensions of self-configuration, self-optimisation, self-healing, and self-protection, altogether known to constitute the notion of self-management, and is extended to cover the pertinent architectural assumptions and variations complemented with insight into the overlapping nature of autonomic computing and agent systems. Then, the ultimate question of convergence between autonomic computing and autonomic networking is addressed, and, thus, the ground for the discussion of the role of self-awareness is settled, with the eventual goal of introducing the target Autonomic Cooperative Networking Architectural Model. In order to make this possible, first the investigation of the most recent incarnation of the Generic Autonomic Network Architecture is characterised with special attention paid to the explanation of the role of decision elements and hierarchical autonomic control loops, along with their respective levels of abstraction, presented in an incremental order, starting from the lowest protocol level, through the function level and node level, up to the top network level.
Once the related ground has been settled, the scope of the Autonomic Cooperative Networking Architectural Model is examined in more detail through the introduction of the Vertical Technological Pillars and the Horizontal Architectural Extensions. In fact, the layers of the Open Systems Interconnection Reference Model are made perpendicular to the levels of the Generic Autonomic Network Architecture in order to identify the key architectural challenges to be addressed by the ultimate Autonomic Cooperative Networking Architectural Model. For this reason, an incremental conceptual outline is presented involving the key architectural components in the form of the Autonomic Cooperative Node, Autonomic Cooperative Behaviour, and Autonomic Cooperative Networking Protocol, as well as the major decision elements of relevance. In particular, first, the protocol level cooperative transmission decision element is presented with its responsibility for virtual multiple input multiple output channel based and distributed space-time block coding enabled cooperative relaying. Next, the function level cooperative re-routing decision element is deployed, with its role of being a trigger for transmission resiliency driven cooperative re-routing. Moving forward, the node level cooperation management decision element is introduced in order to facilitate the integration between cooperative relaying and routing mechanisms. Last, but not least, the network level cooperation orchestration decision element is presented as being accountable for comprehensive oversight of the overall system. All in all, a high-level blueprint of the Autonomic Cooperative Networking Architectural Model is drafted to be further advanced in the chapters to follow.
The third chapter follows on with specific architectural considerations. In particular, the presentation is started with the foundations of the protocol level spatio-temporal processing, where the initial emphasis is laid on developments related to the multiple-input multiple-output channel to provide a good understanding of its workings. Then, the pertinent diversity-rooted origins of spatio-temporal processing are discussed, so that it becomes possible to clearly justify its role and the necessity for its later deployment. Moreover, the question of radio channel virtualisation is visited, where the singular-value decomposition theorem is explained in order to introduce the notion of an equivalent virtual multiple-input multiple-output radio channel to be deployable among Autonomic Cooperative Nodes. The related radio channel capacity is incorporated into the bigger picture of the opening analysis to account for its linear scaling with the number of so-called generic transmitters or generic receivers. Finally, a specific model for radio channel coefficient calculation, to be referenced throughout this book, is described, and the difference between coding gain and diversity gain is addressed for the sake of clarity. Given such a context, the focus moves towards space-time coding techniques, to account for their superiority over the above-mentioned diversity techniques and to pave the way for their later use in networked configurations, where the concept of distributed space-time block coding is expected to prevail.
In particular, the most baseline approach to space-time coding is presented with special attention paid to space-time block coding, where the question of its being perceived more as a modulation rather than a coding technique is visited. Then, the derivation process of the decoding metrics for a selected set of space-time block coding matrices is outlined with the aim, among others, of clarifying certain inconsistencies the author came across in the referenced source materials. Based on this, an extension towards space-time trellis coding is also presented, where additional coding gain becomes clearly visible. Eventually, after all the aforementioned technological aspects have been analysed, their relation to the protocol level control logic is discussed in the light of the prospective architectural integration aspects. To this end, the notion of an Autonomic Cooperative Node is introduced as one of the major building blocks of the proposed concept. Not only is the relation between autonomics and cooperation discussed further, but the internal structure of the Autonomic Cooperative Node is scrutinised. Next, the cooperative transmission decision element is brought into the global picture as belonging to the protocol level, while being mostly responsible for the interaction with the routines of the physical layer. Given such a context, not only is the role and notion of the concept of a protocol addressed, but a pertinent adaptive logic is presented, where the relevant code matrices are switched on the basis of the radio channel parameters. Finally, all the architectural integration aspects of relevance are outlined and the way is prepared for further extensions.
In the fourth chapter, the topics of both conventional and cooperative relaying are addressed from the classificatory perspective; the two approaches are characterised, and the forwarding strategy and protocol nature of the latter are further investigated. Following this, the focus is redirected towards the question of supportive and collaborative protocols, introduced as subcategories of a generic cooperative protocol. Such an approach means that the former shall be considered as a preparatory phase for the latter, making the interaction between the two highly correlated. Going further, the concept of virtual antenna arrays is outlined on the basis of its most versatile multi-tier incarnation, where, assuming a generalised cooperative transmission scheme, its special operation mode of distributed space-time block coding is discussed as being clearly intended to play a crucial role for all the further developments to be discussed in this book. Given such a context, attention is directed towards a fixed deployment concept, where both the conventional and cooperative relaying techniques could become equally applicable, yet the plot is advanced on the assumption that the subject of subsequent analyses will be the mobile deployment concept. In particular, the grid-based Manhattan scenario is initially outlined to underline that as much as the pattern formed by the buildings could become critically important for the suppression of interference among the fixed relay nodes, it would make it literally impossible to exercise any cooperative relaying based on virtual antenna arrays.
In essence, the evaluation effort is carried out to highlight that, despite limitations related to cooperative relaying, certain link layer and network layer performance optimisations would still be possible. To this end a specific adaptation strategy is proposed with regard to the framing structure and the buffer memory, so that, using the process interaction simulation method, it becomes possible to observe improved packet throughput at the network layer. Similarly, a cooperation-enabled relay-enhanced cell indoor scenario is analysed, where the major emphasis is put on the link layer aspects, keeping in mind its applicability to any later mobile deployment concept considerations. Eventually, the focus is shifted towards the function level overlay logic, where, first of all, the roots of Autonomic Cooperative Behaviour are outlined to account for its role and complexity, including its enablers – the equivalent distributed space-time block encoder in particular. Then, the rationale behind the cooperative re-routing decision element is presented, including its transition from the node level to the function level and the logic behind cooperative re-routing involving the role of the fault management decision element and the place of the resilience and survivability decision element. Last, but not least, the architectural integration aspects are discussed to account for the general dependencies between the routines of all three layers of interest, as well as to provide a more detailed insight into the architectural relations driven by the pairing of the link layer and the function level, complemented by the introduction of a specifically extended version of the Autonomic Cooperative Node.
In the fifth chapter, first of all the workings of the experimentation-related version of the Optimised Link State Routing protocol are discussed, with special emphasis on its functional and structural characteristics related to the field of applicability and the assumed messaging structure. Apart from the proactivity-driven relevance to mobile ad hoc network scenarios, special attention is paid to the multi-point relay station selection heuristics with the incorporation of certain small alignments. Additionally, the information storage repositories are analysed in order to provide the required context for further developments, and specifically to introduce new elements in the form of both the VAA selector set and its related VAA selector tuples, intended to become the enablers of the target concept of enhancing cooperative transmission with routing information. What follows directly are the developments originating from the routing information enhanced algorithm for cooperative transmission, conceived by the author as a method for applying the additional information collected by the Optimised Link State Routing protocol inherent in the network layer, and its modified version in particular, for the sake of both enabling and orchestrating cooperative transmission at the link layer. To this end, the justification for the introduction of the routing information enhanced algorithm for cooperative transmission is provided with particular emphasis on the relevant algorithmic description, where, additionally, certain elements and nomenclature of the Optimised Link State Routing protocol are assumed, predominantly because of a fairly direct usage of the outcome of the multi-point relay station selection heuristics.
Given such a context, the elevated concept of the extended routing information enhanced algorithm for cooperative transmission is outlined along with the evolved messaging structure in order to lay the groundwork for the target Autonomic Cooperative Networking Protocol. In this respect, both the very vital topics of address auto-configuration and duplicate address detection are considered, before the focus shifts more towards the umbrella formed by the function level overlay logic. In this way the workings of the Autonomic Cooperative Networking Protocol are outlined, covering the role of the extended routing information enhanced algorithm for cooperative transmission in its conception, as well as justifying the place of the evolved messaging structure in the process of Autonomic Cooperative Node preselection, along with the layout and the reasoning for the related design of the routing table. The extended algorithmic description defining the logic of the cooperation management decision element is then examined in reference to what was previously outlined for the original routing information enhanced algorithm for cooperative transmission. Based on this it becomes possible not only to evaluate the advantages thereof by means of simulation analysis, but also address the overhead aspects of the evolved messaging structure. Finally, the entire analysis is elevated even further to conclude with aspects of the architectural integration, covering the roots of the Autonomic Cooperative Networking Protocol, the conceptual transitions, and the related dependencies among its architectural entities.
In the final chapter the standardisation-orientated design is introduced, assuming a research and investment driven perspective, in order to explain the origins of the Autonomic Cooperative Networking Architectural Model by touching on issues related to the standardisation of the Open Systems Interconnection Reference Model, as well as emphasising the role of prestandardisation related to the Generic Autonomic Network Architecture. What naturally follows is a description of the staged instantiation of the Generic Autonomic Network Architecture Reference Model, depicting the progression of various levels of abstraction in an incremental manner. This introductory part is concluded with certain cross-specification-related considerations intended to incorporate select concepts from software-defined networking, machine-to-machine communications, and intelligent transport systems into the bigger context of the Autonomic Cooperative Networking Architectural Model. Then another, highly practical, deployment scenario in the form of an emergency communications network is considered, which becomes especially interesting because of its being driven by a combination of specifically tailored requirements, where safety appears to take priority over the latest technological advancements. In particular, it is emphasised that the system operation becomes bound to exercise the hierarchy between chief first responders and their respective first responders, as implied by human established relations. In this respect, the relevant network topologies are discussed along with the related configurations of chief first responders.
The way is thus prepared for further incorporation of autonomic routines, since, after the cooperative mode of operation has been introduced and the proactive and reactive resiliency process has been outlined, the integration of the emergency communications network into the ultimate Autonomic Cooperative Networking Architectural Model may be discussed. Following the complementary justification for the cooperative enhancement in question, supported with performance evaluation analysis, the related network level overlay logic is introduced to the overall picture to encompass any still outstanding or not comprehensively addressed workings of the Autonomic Cooperative Networking Architectural Model. In this way the mutual relation between the Autonomic Cooperative Networking Protocol and the Autonomic Cooperative Behaviour is presented from the perspective of the priority between the two, on the grounds of their being inherent in the respective dimensions of the Open Systems Interconnection Reference Model and the Generic Autonomic Network Architecture. Based on this, the notion of the cooperation orchestration decision element is introduced in a way emphasising more tangibly when the Autonomic Cooperative Behaviour may be prioritised over the Autonomic Cooperative Networking Protocol. In particular, the relay-enhanced cell scenario is revisited under certain additional assumptions allowing for a more accurate evaluation of the second hop. Finally, the architectural integration aspects are raised to address the mutual operation of all the discussed decision elements to introduce additional synergy to the fairly exhaustive depiction of the Autonomic Cooperative Networking Architectural Model.
Chapter 2Autonomically Driven Cooperative Design
2.1 Introduction
This opening chapter begins with an analysis of the rationale behind the introduction and the role of the visionary concept of autonomic computing for the needs of accounting for its rapid translation into and convergence with the domain of state-of-the-art networked systems. Although it is of an introductory or context-setting nature, this part of the book provides an extensive commentary on and insight into the entire design to allow a comprehensive view of the current status of and future advancements in the realm of autonomics. For this reason, not only are the major aspects of the original approach detailed, but the relevant conceptual and architectural changes are indicated to allow for the introduction of the concept of Autonomic Intelligence Evolved Cooperative Networking. In particular, once the general vision has been introduced, the main emphasis shifts towards an explanation of the workings of the classic approach, advocating the adoption of the mechanisms governing the human autonomic nervous system into the architecture responsible for the management of complex networked systems. As such, it involves discussion of the aspects of self-configuration, self-optimisation, self-healing, and self-protection, together constituting the notion of self-management, while the pertinent architectural assumptions and variations follow, developing into a discussion regarding the complementary nature of autonomics and agent systems. In particular, the ultimate issue of convergence is emphasised together with the role of self-awareness with regard to the design principles driving the respective architectural considerations.
In fact, based on such an introductory analysis, originally intended to provide a comprehensive and consistent explanation of the current role and place of autonomics, thereby primarily covering autonomic computing, yet also being highly pertinent to autonomic networking per se, the focus then shifts to address the investigation and presentation of the latter, with special emphasis on state-of-the-art architectural advancements of relevance, to prepare for the introduction of the idea of the ultimate Autonomic Cooperative Networking Architectural Model. To this end, the Generic Autonomic Network Architecture, playing a referential role, is introduced and scrutinised as the baseline solution for the further incremental conceptual development to be outlined in the remainder of this chapter. In this respect, first, the origins of the Generic Autonomic Network Architecture are detailed, with special attention being paid to account not only for its composition based on functional planes, but also for the decision element driven orchestration processes. What follows is a presentation of the key notion of hierarchical autonomic control loops, being tightly correlated with their corresponding levels of abstraction, as well as an explanation of their role from the bottom up, i.e. from the protocol level, through the function level and node level, up to the network level. The entire discussion is carried out keeping in mind the notion of legacy autonomic networking, yet assuming a rather more modern perspective outlined in the Group Specifications which the author of this book coauthored and coedited under the auspices of the European Telecommunications Standards Institute.
Moving towards the end of this chapter, the concept of the Autonomic Cooperative Networking Architectural Model is drafted in more detail, with a special emphasis on the enabling Vertical Technological Pillars and the relevant Horizontal Architectural Extensions. To this end certain assumptions are made, according to which the orientation of the layers of interest belonging to the Open Systems Interconnection Reference Model, including the physical layer, link layer, and network layer, is changed so that they become perpendicular, if not orthogonal, to the levels inherent in the Generic Autonomic Network Architecture, most of all for design transparency reasons. Such a positioning allows to identify the key architectural challenges to be addressed throughout the entire book. What is more, an incremental conceptual outline follows, where the reader is acquainted with the major notions, including the Autonomic Cooperative Node, Autonomic Cooperative Behaviour, and Autonomic Cooperative Networking Protocol, as well as all the decision elements of relevance are introduced. In particular, first comes the cooperative transmission decision element of the protocol level intended to orchestrate distributed space-time block coding. Next is the cooperative re-routing decision element of the function level, being the main enabler of increased reliability and resiliency of transmission in general. The cooperation management decision element of the node level follows, to be responsible for the integration of cooperative relaying with the related routing mechanisms. Last, but not least, comes the cooperation orchestration decision element of the network level, responsible for comprehensive system oversight.
2.2 Biologically Inspired Autonomics
2.2.1 Rationale and Vision
As much as it may be perceived a sign of the times, the current profound interest in the visionary concept of autonomic computing (AC) appears to be primarily attributable to the related advancement in networked system design (Wódczak, 2014). Even though there is no denying that the natural drive for automation had been followed well before the conception of autonomic computing per se, it also transpires that the very foundations laid with the emergence of the same opened a completely new era in the development of its networking-related counterpart (Wódczak, 2012). Most obviously, this phenomenon is rooted in the rapid progression of unprecedentedly complex distributed systems featuring a multitude of interconnected appliances, each overseen by sophisticated software and, thus, altogether posing a critical demand in terms of being properly configured and maintained with, virtually, no delay. In order to provide an exhaustive explanation in this respect, it is necessary to investigate the non-technical, biological origins of autonomics, and to analyse its business-orientated and technologically varying enablers. In this way one may not only be able to discern the technological shift, but also obtain a much better insight into the relevant convergence-driven and hardware-based similarities between computing and networking in general. Yet, no sooner may the entire picture become more complete than the prevailing role of software has been disguised in the sense that it is not only responsible for the overseeing of its underlaying hardware, but especially accountable for the orchestration of its own routines (Wódczak, 2014).
Looking from the intrinsic perspective, as indicated by Kephart and Chess (2003), the origins of the idea behind legacy autonomic computing are undoubtedly profoundly rooted in inspiration drawn from the functioning of the human autonomic nervous system (HANS),1 naturally intended to orchestrate the behaviour of the internal organs through the monitoring of numerous related parameters, such as heart rate or body temperature, for the sake of releasing the brain itself from controlling them in a fully aware manner. This appears to be the key point as to the explanation of the actual rationale behind autonomics, especially given the fact that, even though there is an abundance of such parameters that are considered to be crucial for the uninterrupted functioning of a human organism, the said process of orchestration is carried out in a somewhat detached, if not distributed, fashion, as explained by Paulson (2002). For this reason, the structure of such an autonomic system (AS) is said to resemble a recursively arranged hierarchy, where each of the numerous entities that display the capability of self-management at a given level of abstraction (LoA) should encompass the similarly numerous and conceptually alike, yet functionally varying, components one level below, and so forth, as advocated by Kephart and Chess (2003). Attempting to apply a more descriptive terminology, one could equally well approximate the above-mentioned conceptual construction by instantiating it with a rather generic pattern, where the structure itself would originate from the lowest molecular level, just to move forward through human markets and societies, in order to approach the top level of world's global socio-economy (Kephart and Chess, 2003).
Yet, assuming a more extrinsic viewpoint, it seems that the business model has undergone so substantial a change, over little more than a decade, that it became the major enabling factor in the said respect. In fact, originally, the objective of autonomics2
