HOME NETWORK AND HUMAN INTERACTION SYSTEM

Rudolf Volner

Department of Air Transport, Faculty of Transportation Science

Czech Technical University in Prague, Horská 3, 128 03 Prague 2

Keywords: Network, human system, mobility system.

Abstract: The term security network intelligence is widely used in the field of communication security network. A

number of new and potentially concepts and products based on the concept of security network intelligence

have been introduced, including smart flows, intelligent routing, and intelligent web switching. Many

intelligent systems focus on a specific security service, function, or device, and do not provide true end-to-

end service network intelligence. True security network intelligence requires more than a set of

disconnected elements, it requires an interconnecting and functionally coupled architecture that enables the

various functional levels to interact and communicate with each other.

1 INTRODUCTION

The study of security network intelligence is an

extremely active area in the field of

communications. Thanks to the latest advances in

data communications – especially in the services

sector and in the communications software,

photonics, and programmable technologies areas –

service providers are spending millions of dollars a

year on an increasingly intelligent communication

infrastructure and applications. Research in the areas

of learning automata, intelligent agents technologies,

intelligent data-mining, knowledge discovery, data-

driven task sequencing, intelligent databases, wire-

speed real-time databases, virtual modelling, and

sophisticated communication network modelling has

provided insights into intelligent computing

processes. Significant progress has been made in

rule-based reasoning, planning, and problem

solving.

Future generation networking will be

characterized by the need to adapt to the demands of

agile networking, which include rapid response to

changing customer requirements, automated design

and engineering, lower-cost services, transparent

distributed networking, resource allocation on

demand, real-time planning and scheduling,

increased quality, reduced tolerance for error, and

in-process measurement and feedback. Future

networking systems will require automated

intelligent networking features that apply

intelligence to the domain of networking in such a

way as to make possible the realization of a full

range of agile and adaptable networks.

Cable operators will have to face the commercial

and operational strategy for:

• Building out or upgrading to bi-directional (two

way) networks,

• Offering voice telephony to residential and

business consumers,

• Offering multi-channel digital television,

• Video-on-demand,

• Home shopping,

• Home banking,

• Residential and business telephony,

• High-speed Internet,

• Home security.

The distributed interactive information system

can be structured in a hierarchical way for system

scalability and evolution – Figure 1, Figure 2. It can

start from an initial two level system with a central

information server and several local information

servers to a system with as many levels of the

hierarchy as needed. The number of levels needed

depends on the network size, network costs, and

network performance requirements.

323

Volner R. (2007).

HOME NETWORK AND HUMAN INTERACTION SYSTEM.

In Proceedings of the Ninth International Conference on Enterprise Information Systems - HCI, pages 323-327

DOI: 10.5220/0002400103230327

 SciTePress

2 INTELLIGENT SECURITY

AND COMMUNICATION

NETWORKS

Intelligent security and communication networks

must at least be able to understand the security and

communication environment, to make decisions, and

to use and manage network resources efficiently.

More sophisticated levels of security network

intelligence include the ability to recognize user,

application, service provider, and infrastructure

needs, as well as expected and unexpected events,

the ability to present knowledge in a world model,

and the ability to reason about and plan for the

future.

Network intelligence will evolve through growth

in computational power and through the

accumulation of knowledge about the types of input

data needed for making decisions concerning

expected response, and about the algorithmic

processing required in a complex and changing

communications environment. Increasingly

sophisticated network intelligence makes possible

look-ahead planning, management before

responding and reasoning about the probable results

of alternative actions. These intelligent network

capabilities can provide service providers with

competitive and operational advantages over

traditional networks.

3 THE HS/ATM ARCHITECTURE

For a network, the serving area is partitioned into a

number of basic service areas designated as cells.

Each cell is served by a base station – centre HS,

which exchanges radio signals with mobile terminals

– home control centers. Mobility is central to

networks. To provide mobility, tracking mobile

terminal locations becomes an important and

primary function of network and hence some

databases are introduced to support such a

capability. In HS/ATM networks, each HS network

covers a large geographical area and incorporates a

number of base stations. Meanwhile, the location

database of wireless cluster manager is broken into

two parts:

• one for the mobile terminals which are

permanently registered in the community, the

home community – home part,

• the other for the mobile terminals which are

visiting the community – visiting part.

4 MODELS OF MOBILITY

Mobility models describe a mobile unit’s movement

through a geographical area. A number of systematic

and ad hoc models have appeared in the literature,

but they do net reflect realistic actual movement

patterns in many respects. Nor, being idealizations

for specific purposes, are they intended to describe

adequately the range of subscriber behaviours that

will appear in a mobile multimedia network

covering a large geographical area.

The mobile VCE (The Virtual centre of Excellence

in Mobile and Personal Communications) model

consists of a series of poles - places where mobile

users gather, such as a city centre, a shopping mall

or a road (hence the need to include direction).

Movement between poles is defined by four

properties, which between them determine the

spatial and temporal behaviours of the users:

• gravity, reflecting the attraction to a pole,

• elasticity, reflecting the reaction of restoring

equilibrium after changes of attraction,

• entropy, modeling the disorder at poles and in

the flows between them,

• viscosity, representing the spatial spreading

variations of the flow populations.

The model is a network of poles through which

circulates a population of mobiles, whose velocity is

determined by the configuration of the above four

elements. A mobile can be seen either as an

individual or as a mass. Every mobile belongs to a

specified mobility class, of which there are four:

• business,

• leisure,

• shopping,

• residential.

As shown in Figure 4, the model can be decomposed

into three distinct sub-models:

• the physical sub-model defines the topology

and the quantitative spatial distribution of the

mobiles,

• the gravity sub-model controls the temporal

evolution of the attraction of all the poes,

• the fluid sub-model fixes the laws of circulation

of each mobile between the poles.

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5 CHARACTERIZATION OF

SERVICES, TRAFFIC

SOURCES AND SYSTEM

TELETRAFFIC

Traditional traffic modeling of data sources assumed

that the inter-arrival times of traffic packets were

basically exponential in distribution and independent

of one another, which means that the process is

memory-less. However, recent studies of the

behaviour of individual multimedia sources and

system-level activity show that traffic traces are

distributed in ways more complex than this.

Our analysis has aimed at improving the best-

fitting model for a given traffic scenario when the

underlying flow keeps changing over time and

space. To be confident that the results are useful a

model was sought that:

• was as simple as possible in a computational

sense without compromising accuracy,

• had a physical explanation in the network

context,

• can be related to real measurements for

verification purposes by the operators.

The investigation focused on extensions that could

retain tractability, in two steps as described below:

• statistical multiplexing,

• parameterization.

Traffic generation – if the traffic is memory-less,

generation of traffic to support the simulations can

be achieved simply by a negative exponentially

distributed process to specify packet inter-arrival

time. However, modeling self-similar traffic is much

more complex (Volner, 2003), (Volner, 1995),

(ATM, 1996).

6 CONCLUSION

In this paper, we have proposed a HS/ATM network

for supporting multimedia communication to mobile

terminals. Here the network is partitioned into core

and edge networks. The advantage of this portioning

has been discussed. The network bandwidth is

allocated in such a way that each VP is semi-

permanently allocated a certain amount of using

existing optimization techniques. Cell scheduling

and queuing implementations were discussed. We

conclude, that based on the proposed bandwidth

management framework, all ATM service classes

can be served with reasonable QoS guarantees, the

CAC procedures easily implemented, and potential

rate-based ABR congestion control easily

incorporated.

REFERENCES

Volner, R.: CATV – Interactive Security and

Communication System, proceedings the institute of

electrical and electronics engineers, 34th Annual

2000 International Carnahan Conference on Security

Technology, October 2000 Ottawa, Canada, pp. 124-

136, IEEE Catalog Number 00CH37083, ISBN 0-

7803-5965-8

Volner, R.: Home security system and CATV, 35th

Annual 2001 International Carnahan Conference on

Security Technology, October 2001 London, England,

pp. 293 – 306, IEEE Catalog Number 01CH37186,

ISBN 0-7803-6636-0

Volner, R.: CATV Architecture for Security, 36th Annual

2002 International Carnahan Conference on Security

Technology, October 2002, Atlantic City, New Jersey,

USA, pp. 209 – 215, IEEE Catalog Number

02CH37348, ISBN 0-7803-7436-3

Volner, R., Poušek, L.: Intelligence Security Home

Network, 37th Annual 2003 International Carnahan

Conference on Security Technology, October 2003

Taipei, Taiwan, pp. 30 – 37, IEEE Catalog Number

03CH37458, ISBN 0-7803-7882-2,

Volner, R.: Intelligence CATV – Traffic models, Design

and Analysis, International Conference on Computer,

Communication and Control Technologies CCCT’03

and The 9

International Conference on Information

Systems Analysis and Synthesis ISAS 03, Proceeding

volume IV, July 2003, Orlando, Florida, USA, pp. 340

-345, ISBN980-6560-05-1, CD - ISBN 980-6560-10-8

Volner, R. et al.: CATV In Multimedia Transmission

Systems, Electronic Horizont, Vol.55, Nov./ Dec.

1995

ATM Forum Technical Committee: Traffic management

specification version 4.0, AFTM 0056.000, Apr. 1996

ATM Forum technical committee: Flow controlled

connections proposal for ATM traffic management,

September 1994

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Figure 1: Basic home network – human interactive Subsystem.

Figure 2: The HS interactive system can be structured in a hierarchical way for system scalability and evolution.

Fibre Optic System

BMHS network

Centre

BMHS

Other centre BMHS

Security system -

Metro police

Audio

Text Data

WAN

MAN

Home control

centre

Security system

BMHS

Telemetrically

security system

Telemetric

systems

Security cameras

Video and audio

identification

Biometric

identification

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Figure 3: Mobility model into three sub-models.

Figure 4: Mobile awareness service.

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