Ambient and Cognitive Networks Youn-Hee Han [email protected] Korea University of Technology and Education Internet Computing Laboratory

Ambient and Cognitive Networks

Youn-Hee [email protected]

Korea University of Technology and EducationInternet Computing Laboratory

http://icl.kut.ac.kr

2008 KREONET Workshop

Ambient Networks

2/30


EU’s FP6 (6th Framework Program) 유럽연합의 6 차 연구개발 프로그램 (2002~2006) Goal & Vision

유럽단일연구공간 (ERA: European Research Area) 의 실현 유럽의 과학기술 지형과 역량을 단일한 연구 공간 및 역량으로 통합 유럽을 21 세기 최고의 지식사회로 구축하고자 함 실질적인 유럽통합을 실현할 정책적 툴로 활용

주요 내용 및 예산

EU’s FP6

3/30

구분 내용 예산 (Euro)

연구 활동의 집중과 통합 사업

7 개 중점 연구 분야 핵심 연구 사업

132.85 억( 전체 예산의 3/4)

합동연구센터 사업7 개 중점 연구 분야에서 이루어지는 연구 개발 활동을 보완

2.9 억

ERA 구조화 인적자원 및 교류 , 연구인프라 26.55 억

ERA 기반강화 연구사업 조정 , 정책 개발 3.3 억

원자력 에너지 핵융합 , 방사성 폐기물 9.4 억

계 (2002 ~ 2006) 175 억 Euro (EU 전체 예산의 4%)


7 개 중점 연구 분야와 IST, WWI, Ambient Networks 의 관계

EU’s FP6 & Ambient and Cognitive Networks

4/30

연구 분야 예산 (Euro)

1. 생명과학 , 게노믹스 , 생명공학 22 억

2. 정보사회기술(Information Society Technologies, IST)

36 억

3. 나노기술 및 나노과학 , 새로운 생산공정 및 디바이스 13 억

4. 항공우주 10.75 억

5. 식품의 질 및 안전성 6.85 억

6. 지속가능한 발전 , 전지구적 변화 및 생태계 21.2 억

7. 지식기반사회에서의 시민과 통치 2.25 억

기타 13.2 억

계 132.85 억

…

WWI (Wireless World Initiative, 2004~)

yyyXXX

[IST 내의 통합 관리 프로젝트 ]

Ambient Network

Cognitive Network


Ambient Networks (AN) The name of a project within EU’s FP6 A software-driven dynamic network integration solution

Design Paradigm of AN To support network composition, mobility, multiple radio

interfaces, context awareness To offer common control functions to a wide range of

different applications and air interface technologies

Overview of Ambient Networks

5/30

[Ambient]: existing or present on all sides: of the surrounding area or environment


Four innovations of AN


6/30

Network Composition

Enhanced Mobility

Network Heterogeneity Support

Context Awareness

+


Work Areas & Work Packages


7/30


Ambient Control Space & Network Composition

Technology of Ambient Networks

8/30

Ambient Network Interface (ANI): Standardized single interface to connect the network instead of just connection of nodes: Offer a simple plug & play connection

Ambient Service Interfaces (ASI): Even in a composed Ambient Network, only a single homogeneous control space is visible to external entities : An application or service will always find the same environment


Ambient Control Space & Network Composition


9/30

GANS: Generic Ambient Networks Signalling Overlay Control Space


Generic Link layer (GLL) for a Multi-Radio Access


10/30

Generic Link Interface (GLI): It provides compatible radio link layers for different radio access technologies: A reconfiguration of the GLL (generic link layer) due to a change of radio access technology will be seamless


Scenario 1

Scenario 2

Scenarios of Ambient Networks

11/30

3G Base Station

Sensor NodeSink Node

WiMax/WiBro RAS (Base Station)


Scenario 3

Scenarios of Ambient Networks

12/30


Active Research and Much Results

Instant Media Services for Users on the Move

Research Results of Ambient Networks

13/30

M. Vorwerk, S. Schuetz, R. Aguero, J. Choque, S. Schmid, M. Kleis, M. Kampmann, M. Erkoc, “Ambient networks in practice - instant media services for users on the move,” 2nd International Conference on TRIDENTCOM, 2006.


Active Research and Much Results New Handover Strategy & Business Map


14/30

P. Poyhonen, J. Tuononen, T. Haitao, O. Strandberg, “Study of Handover Strategies for Multi-Service and Multi-Operator Ambient Networks,” 2nd International Conference on CHINACOM, 2007.

DS: Discovered Sets (of Access Networks)CST: Candidate Sets based on Terminal’s policy CSN: Candidate Sets based on Network’s policy AS: Finally selected Active Sets

Business Map


Active Research and Much Results Ambient Network Advertising Broker


15/30

L. Ho, J. Markendahl, M. Berg, “Business Aspects of Advertising and Discovery Concepts in Ambient Networks,” IEEE 17th International Symposium on PIMRC, 2006.

Access Broker (Auction-based): Dynamic allocation per Call


Cognitive Networks

16/30


Three motivating problems for Cognitive Networks Complex

Large numbers of highly interconnected, interacting elements and instances of self-organization and emergent behavior

Network need to be able to deal with and adapt to complex environment with minimal or zero user interaction

Motivation

17/30

A school of fish

A termite mound


Three motivating problems for Cognitive Networks Wireless and Its heterogeneity

Large numbers of standards IEEE 802.11, Bluetooth, WiMAX, CDMA2000, UMTS…

Ad-hoc networks are highly dynamic should be capable of self-organization In research papers, simulation is usually used because of the

difficulty in using forms of analysis SDR (Software-defined Radio) creates limitless number of

operating states Difficulty in QoS of Layered Architecture

People wants a sort of end-to-end guarantees It is a very difficult research area because most all

networking stacks do not operate on an end-to-end paradigm.

Current approaches are typically reactive.

Motivation

18/30


Cognitive Network (CN) A network composed of elements that, through learning

and reasoning, dynamically adapt to varying network conditions in order to optimize end-to-end performance

Features Decisions are made to meet the requirements of the network

as a whole (not individual network components) A Cognitive Process

perceive conditions, plan, decide, and act on those conditions

Definition

19/30

Global Internet Map(www.siencedaily.com)

[by Ryan Thomas @ Virginia Tech.]


Similarities Operates in parallel to stack Increases information available to participating layers Optimizes on goals that require multiple layers to achieve

Differences Cognition (as opposed to reactive, localized schemes) Multiple and End-to-end goals (as opposed to single goal at

layer level)

Cognitive Network vs. Cross-layering

20/30



Basic Decision Model OODA Loop [John Boyd] Decision based on observation of

network environments

Implementation It depends on

Goals, Controllable Network Elements System Structure, States

Critical Design Issues Behavior: Selfish vs. Cooperation Computational: Level of ignorance Physical: Amount of control

Cognition Scheme

21/30



Requirements Layer End-to-End Goals Cognitive Specification Language

Converts end-to-end goals into cognitive elements goals

Cognitive Elements Adapt and learns to make decisions

that meet end-to-end goals

Software Adaptable Network (SAN) API Configurable Elements

Points of network control for cognitive process Network Status Sensors

Reads status of the network

Cognitive Network Framework

22/30



Cooperative Mobile Robots

Usage Scenarios

Case Study: Mobile Robots & Sensor Network

23/30

[University of Tübingen][USC @ LA]

[Disaster Area]

[Exploring the unknown]

[Robot Army]

[Exploring the unknown]


How to MOVE? Cognition (Perception) of Obstacles and Other Sensors

Supersonic Wave, Artificial Vision, … Force based on Potential Fields

ForceAccelerationVelocityPosition

Sensor Robot Mobility

24/30


How to expand the covering area? A self-deployment algorithm to achieve the max coverage

level Cognition of coverage level in distributed manner

Coverage Level

25/30

Coverage Level: 28.37% Coverage Level: 76.14% Coverage Level: 98.56%


How to make the network connection robust? A self-deployment algorithm to achieve the max

connectivity level Cognition of connectivity level in distributed manner

Connectivity Level

26/30

Avg. # of Neighbor: 2.6 Avg. # of Neighbor: 3.32

Coverage Level Connectivity Level


How to make the overlay level high? An optimized grouping algorithm to achieve the max

energy efficiency

Overlay Level

27/30

70 Active Sensors

Active First Group (of 35 Active Sensors)

Sleep Second Group(of 35 Sleep Sensors)

Active Second Group (of 35 Active Sensors)

Sleep First Group(of 35 Sleep Sensors)


Cognition Scheme in Mobile Sensor Networks

28/30

Sensing Areas,Obstacles, Other Sensors,

Environment Status…

Area Border LocationObstacle Location, Other Sensor Location, Sensing

Range, Communication Range, Current Levels of Coverage, Connectivity, and

Overlay

Optimization Algorithms to

maximize “Coverage Level”,

“Connectivity Level”, and“Overlay Level”

Autonomic Self-deployment of

Sensors

New Position of Sensor Robots


A. Howard, M. J. Mataric, and G. S. Sukhatme, “Mobile Sensor Network Deployment using Potential Fields: A distributed, scalable solution to the area coverage problem,” The 6th International Symposium on Distributed Autonomous Robotics Systems (DARS02), June 2002.

Y. Zou and K. Chakrabarty, “Sensor Deployment and Target Localization based on Virtual Forces,” IEEE INFOCOM 2003, Vol. 2, pp. 1293-1303, March 2003.

S. Poduri and G. S. Sukhatme, “Constrained Coverage for Mobile Sensor Networks,” IEEE International Conference on Robotics and Automation, pp. 165–172, May 2004.

G. Wang, G. Cao and T. L. Porta, “Movement-assisted Sensor Deployment,” In Proc. of IEEE INFOCOM 2004, Vol. 4, pp. 2469-2479, March 2004.

B. Liu, P. Brass, O. Dousse, P. Nain and D, Towsley, “Mobility Improves Coverage of Sensor Networks,” ACM MobiHoc 2005, pp. 300-308, May 2005.

J. Wu and S. Yang, “SMART: A Scan-Based Movement-Assisted Sensor Deployment Method In Wireless Sensor Networks,” In Proc. of INFOCOM 2005, pp.2313-2324, March 2005.

G. Wang, G. Cao, T. L. Porta and W. Zhang, “Sensor Relocation In Mobile Sensor Networks,” In Proc. of INFOCOM 2005, pp. 2302-2312, March 2005.

H. Yu, J. Iyer, H. Kim, E. J. Kim, K. H. Yum and P. S. Mah, “Assuring K-Coverage in the Presence of Mobility in Wireless Sensor Networks,” in Proceedings of IEEE GLOBECOM 2006 (selected for best papers), 2006.

D. Wang, J. Liu and Qian Zhang, “Mobility-Assisted Sensor Networking for Field Coverage,” In Proc. of IEEE GLOBECOM '07. pp. 1190-1194, Nov. 2007.

Wang, H. Wu, and N.-F. Tzeng, “Cross-layer Protocol Design and Optimization for Delay/Fault-tolerant Mobile Sensor Networks, IEEE Journal on Selected Areas in Communications, Vol. 26, No. 5, pp. 809-819, June 2008

References of Mobile Sensor Networks

29/30


Conclusions

30/30

Ambient Networks Network Composition Enhanced Mobility Network Heterogeneity Support

Cognitive Networks Dynamically adapt to varying network conditions Meet the given network requirements and goals

Case Study New Handover Strategy, Business Map, Ambient Network

Broker Cognitive Sensor Network over Mobile Robots

Documents

Ambient and Cognitive Networks Youn-Hee Han [email protected] Korea University of Technology and Education Internet Computing Laboratory