Ambient and Cognitive Networks

1. Ambient and Cognitive Networks Youn-Hee Han yhhan@kut.ac.kr May 2009 Korea University of Technology and EducationLaboratory of Intelligent Network http://link.kut.ac.kr

2. Ambient Networks 2/29

3. EU’s FP6 & Ambient and Cognitive Networks • EU’s FP6 (6th Framework Program) • 유럽연합의 6차 연구개발 프로그램 (2002~2006) • Goal & Vision • 유럽단일연구공간(ERA: European Research Area)의 실현 • 7개 중점 연구 분야와 IST, WWI, Ambient Networks의 관계 XXX [IST내의통합 관리 프로젝트] yyy … WWI (Wireless World Initiative, 2004~)

4. Overview of Ambient Networks • Ambient Networks (AN) • A software-driven dynamic network integration solution over any access technology and any type of network • Phase I (2004-2005) • Fundamental concepts & the overall architecture of ANs • Phase II (2006-2007) • The engineering of the overall solution • Develop real prototypes • Overall Review • Fatna Belqasmi, Roch H. Glitho, and Rachida Dssouli, “Ambient Network Composition,” IEEE Network, Vol. 22, No. 4, pp. 6-12, July/August 2008. [Ambient] : existing or present on all sides : of the surrounding area or environment

5. Overview of Ambient Networks • Four innovations (design paradigm) of AN Network Composition Enhanced Mobility Network Heterogeneity Support + Context Awareness To provide common control functions to a wide range of different applications and air interface technologies

6. Technology of Ambient Networks • Ambient Control Space (ACS) • 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

7. Technology of Ambient Networks • Network Composition (1/2) • Network Integration • Involved networks merge intoone common network • E.g. a new PAN creation integrating two different PANs • Control Delegation • One AN delegates certain control functions to the other AN • E.g. 3GPP-WLAN interworking:WLAN delegates authentication, authorization and charging to 3GPP network • Network Interworking • Cooperation but no control delegation • E.g. dynamic roaming agreements Increasing control plane interworking

8. Ambient Control Space Ambient Control Space FE5 FE5 FE1 FE1 AmbientConnectivity AmbientConnectivity FE2 FE2 FE6 FE6 Composition FE Composition FE FE 4 FE 4 FE 3 FE 3 Technology of Ambient Networks • Network Composition (2/2) GANS GANS: Generic Ambient Networks Signaling Overlay Control Space

9. Technology of Ambient Networks • Generic Link layer (GLL) for a Multi-Radio Access • 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

10. Scenarios of Ambient Networks • Scenario 1

11. Scenarios of Ambient Networks • Scenario 2

12. Research Results of Ambient Networks • Active Research and Much Results • Selected Review #1 • Instant Media Services for Users on the Move 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.

13. Research Results of Ambient Networks • Selected Review #2 • New Handover Strategy & Business Map 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 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.

14. Research Results of Ambient Networks • Selected Review #3 • Ambient Network Advertising Broker Access Broker (Auction-based) : Dynamic allocation per Call L. Ho, J. Markendahl, M. Berg, “Business Aspects of Advertising and Discovery Concepts in Ambient Networks,” IEEE 17th International Symposium on PIMRC, 2006.

15. Cognitive Networks 15/29

16. Motivation • 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 A school of fish A termite mound

17. Motivation • 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 • 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.

18. Definition • 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 [by Ryan Thomas @ Virginia Tech., 2005] Global Internet Map(www.siencedaily.com)

19. Cognitive Network vs. Cross-layering • 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) [by Ryan Thomas @ Virginia Tech.]

20. Cognition Process • Basic Decision Model • OODA Loop [John Boyd] • Decision based on observation ofnetwork 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 분석 및 계획 [by Ryan Thomas @ Virginia Tech.]

21. Cognitive Network Framework • Inputfrom 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 decisionsthat 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 [by Ryan Thomas @ Virginia Tech.]

22. Case Study: Mobile Robots & Sensor Network • Cooperative Mobile Robots • Usage Scenarios [University of Tübingen] [USC @ LA] Environmental Robotics [Robot Army] [Exploring the unknown] [Disaster Area]

23. Sensor Robot Mobility • How to MOVE? • Cognition (Perception) of Obstacles and Other Sensors • Supersonic Wave, Artificial Vision, … • Force based on Potential Fields • ForceAccelerationVelocityPosition

24. Coverage Level • 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: 28.37% Coverage Level: 76.14% Coverage Level: 98.56%

25. Connectivity Level • How to make the network connection robust? • A self-deployment algorithm to achieve the max connectivity level • Cognition of connectivity level in distributed manner Avg. # of Neighbor: 2.6 Avg. # of Neighbor: 3.32 trade-off Coverage Level  Connectivity Level

26. Overlay Level • How to make the overlay level high? • An optimized grouping algorithm to achieve the max energy efficiency Active - Group #1(of 35 Active Sensors) Sleep - Group #2(of 35 Sleep Sensors) Active - Group #2(of 35 Active Sensors) Sleep - Group #1(of 35 Sleep Sensors) 70 Active Sensors

27. Cognition Scheme in Mobile Sensor Networks Area Border Location Obstacle Location, Other Sensor Location, Sensing Range, Communication Range, Current Levels of Coverage, Connectivity, and Overlay Sensing Areas, Obstacles, Other Sensors, Environment Status… Optimization Algorithms to maximize “Coverage Level”, “Connectivity Level”, and “Overlay Level” - Heuristic Algorithms (Greedy Algorithm…) - Intelligent Algorithms (Genetic Algorithm…) Autonomic Self-deployment of Sensors New Position of Sensor Robots

28. References of Mobile Sensor Networks • 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

29. [Demo] • Demo animation for mobile sensor network deployment