1. Non-Cooperative Behavior in Wireless Networks Márk Félegyházi (EPFL) May 2007

2. Márk Félegyházi (EPFL) 2 Prospective wireless networks Relaxing spectrum licensing: ► small network operators in unlicensed bands – inexpensive access points – flexible deployment ► community and ad hoc networks – no authority – peer-to-peer network operation ► cognitive radio – restricted operation in any frequency band – no interference with licensed (primary) users – adaptive behavior

3. May 2007Márk Félegyházi (EPFL) 3 Motivation ► more complexity at the network edges ► decentralization ► ease of programming for wireless devices ► rational users  ► more adaptive wireless devices ► potential selfish behavior of devices TRENDS OUTCOME What is the effect of selfish behavior in wireless networks?

4. May 2007Márk Félegyházi (EPFL) 4 Related work (1/2) ► Peer-to-peer networks – free-riding [Golle et al. 2001, Feldman et al. 2007] – trust modeling [Aberer et al. 2006] ► Wired networks – congestion pricing [Korilis et al. 1995, Korilis and Orda 1999, Johari and Tsitsiklis 2004] – bandwidth allocation [Yaïche et al. 2000] – coexistence of service providers [Shakkottai and Srikant 2005/2006, He and Walrand 2006] ► Wireless networks – power control [Goodman and Mandayam 2001, Alpcan et al. 2002, Xiao et al. 2003] – resource/bandwidth allocation [Marbach and Berry 2002, Qui and Marbach 2003] – medium access [MacKenzie and Wicker 2003, Yuen and Marbach 2005, Čagalj et al. 2005] – Wi-Fi pricing [Musacchio and Walrand 2004/2006]

5. May 2007Márk Félegyházi (EPFL) 5 http://secowinet.epfl.ch Related work (2/2) 1. Existing networks 2. Upcoming networks 3. Trust 4. Naming and addressing 5. Security associations 6. Secure neighbor discovery 7. Secure routing 8. Privacy protection 10. Selfishness in PKT FWing 11. Operators in shared spectrum 12. Behavior enforcement Appendix A: Security and crypto Appendix B: Game theory SecurityCooperation 9. Selfishness at the MAC layer

6. May 2007Márk Félegyházi (EPFL) 6 Summary of my research ► Ch 1: A tutorial on game theory ► Ch. 2: Multi-radio channel allocation in wireless networks ► Ch. 3: Packet forwarding in static ad-hoc networks ► Ch. 4: Packet forwarding in dynamic ad-hoc networks ► Ch. 5: Packet forwarding in multi-domain sensor networks ► Ch. 6: Cellular operators in a shared spectrum ► Ch. 7: Border games in cellular networks Part II: Non-cooperative users Part III: Non-cooperative network operators Part I: Introduction to game theory

7. Introduction to Game Theory

8. May 2007Márk Félegyházi (EPFL) 8 The channel allocation (CA) game ► two channels: c 1 and c 2 – total available throughput: and ► two devices: p 1 and p 2 ► throughput is fairly shared ► users of the devices are rational  ► Channel Allocation (CA) game: G CA = ( N, S, U ) – N – players: p 1 and p 2 – S – strategies: choosing the channels and – U – payoff functions: received throughputs and c1c1 c2c2 f1f1 f2f2 f3f3 strategy of player i payoff of player i strategy profile

9. May 2007Márk Félegyházi (EPFL) 9 Strategic form ► the CA game in strategic form p2p2 c1c1 c2c2 p1p1 c1c1 1.5,1.53,2 c2c2 2,31,1

10. May 2007Márk Félegyházi (EPFL) 10 Stability: Nash equilibrium p2p2 c1c1 c2c2 p1p1 c1c1 1.5,1.53,2 c2c2 2,31,1 Nash equilibrium: No player has an incentive to unilaterally deviate. Best response: Best strategy of player i given the strategies of others.

11. May 2007Márk Félegyházi (EPFL) 11 Efficiency: Pareto-optimality p2p2 c1c1 c2c2 p1p1 c1c1 1.5,1.53,2 c2c2 2,31,1 Price of anarchy: The ratio between the total payoff of players playing a socially-optimal (max. Pareto-optimal) strategy and a worst Nash equilibrium. Pareto-optimality: The strategy profile s po is Pareto-optimal if: with strict inequality for at least one player i

12. Multi-Radio Channel Allocation in Wireless Networks Non-Cooperative Users

13. May 2007Márk Félegyházi (EPFL) 13 Related work ► Channel allocation – in cellular networks: fixed and dynamic: [Katzela and Naghshineh 1996, Rappaport 2002] – in WLANs [Mishra et al. 2005] – in cognitive radio networks [Zheng and Cao 2005] ► Multi-radio networks – mesh networks [Adya et al. 2004, Alicherry et al. 2005] – cognitive radio [So et al. 2005] ► Competitive medium access – Aloha [MacKenzie and Wicker 2003, Yuen and Marbach 2005] – CSMA/CA [Konorski 2002, Čagalj et al. 2005] – WLAN channel coloring [Halldórsson et al. 2004] – channel allocation in cognitive radio networks [Cao and Zheng 2005, Nie and Comaniciu 2005]

14. May 2007Márk Félegyházi (EPFL) 14 Problem ► multi-radio devices ► set of available channels How to assign radios to available channels?

15. May 2007Márk Félegyházi (EPFL) 15 System model (1/3) ► C – set of orthogonal channels (| C | = C) ► N – set of communicating pairs of devices (| N | = N) ► sender controls the communication (sender and receiver are synchronized) ► single collision domain if they use the same channel ► devices have multiple radios ► k radios at each device, k ≤ C

16. May 2007Márk Félegyházi (EPFL) 16 System model (2/3) ► N communicating pairs of devices ► C orthogonal channels ► k radios at each device number of radios by sender i on channel x → example: Use multiple radios on one channel ? Intuition:

17. May 2007Márk Félegyházi (EPFL) 17 System model (3/3) ► channels with the same properties ► τ t (k x ) – total throughput on any channel x ► τ(k x ) – throughput per radio

18. May 2007Márk Félegyházi (EPFL) 18 ► selfish users (communicating pairs) ► non-cooperative game G MRCA – players → senders – strategy → channel allocation – payoff → total throughput ► strategy: ► strategy matrix: ► payoff: Multi-radio channel allocation (MRCA) game

19. May 2007Márk Félegyházi (EPFL) 19 Lemma: If S * is a NE in G MRCA, then. Use of all radios Each player should use all of his radios. p4p4 p4p4 Intuition: Player i is always better off deploying unused radios. all channel allocations Lemma

20. May 2007Márk Félegyházi (EPFL) 20 Proposition: If S * is a NE in G MRCA, then d y,x ≤ 1, for any channel x and y. Load-balancing channel allocation ► Consider two arbitrary channels x and y in C, where k x ≥ k y ► distance: d x,y = k x – k y all channel allocations Lemma Proposition

21. May 2007Márk Félegyházi (EPFL) 21 Nash equilibria (1/2) Theorem 1: A channel allocation S * is a Nash equilibrium in G MRCA if for all i: ► d x,y ≤ 1 and ► k i,x ≤ 1. p2p2 Nash Equilibrium: p4p4 Use one radio per channel. all channel allocations Lemma Proposition NE type 1 ► Consider two arbitrary channels x and y in C, where k x ≥ k y ► distance: d x,y = k x – k y

22. May 2007Márk Félegyházi (EPFL) 22 Nash equilibria (2/2) Nash Equilibrium: Theorem 2: A channel allocation S * is a Nash equilibrium in G MRCA if: ► d x,y ≤ 1, ► for any player i who has k i,x ≥ 2, x in C, ► for any player i who has k i,x ≥ 2 and x in C +, k i,y ≥ k i,x – 1, for all y in C – Use multiple radios on certain channels. all channel allocations Lemma Proposition NE type 1 NE type 2 ► Consider two arbitrary channels x and y in C, where k x ≥ k y ► distance: d x,y = k x – k y ► loaded and less loaded channels: C + and C – C+C+ C–C–

23. May 2007Márk Félegyházi (EPFL) 23 Efficiency (1/2) Corollary: If τ t (k x ) is constant (i.e., ideal TDMA), then any Nash equilibrium channel allocation is Pareto-optimal in G MRCA. Theorem: In G MRCA, the price of anarchy is: where

24. May 2007Márk Félegyházi (EPFL) 24 Efficiency (2/2) ► In theory, if the total throughput function τ t (k x ) is constant  POA = 1 ► In practice, there are collisions, but τ t (k x ) decreases slowly with k x (due to the RTS/CTS method) G. Bianchi, “Performance Analysis of the IEEE 802.11 Distributed Coordination Function,” in IEEE Journal on Selected Areas of Communication (JSAC), 18:3, Mar. 2000

25. May 2007Márk Félegyházi (EPFL) 25 Convergence to NE (1/3) p1p1 p1p1 N = 5, C = 6, k = 3 p2p2 p2p2 p4p4 p1p1 p3p3 p2p2 p5p5 p4p4 p5p5 p3p3 p3p3 p4p4 p5p5 c1c1 c2c2 c3c3 c4c4 c5c5 c6c6 time p 5 : c 2 →c 5 c 6 →c 4 p 3 : c 2 →c 5 c 6 →c 4 c 1 →c 3 p 2 : c 2 →c 5 p 1 : c 2 →c 5 c 6 →c 4 p 1 : c 4 →c 6 c 5 →c 2 p 4 : idle channels p5p5 p3p3 p2p2 p1p1 p1p1 p4p4 Algorithm with imperfect info: ► move links from “crowded” channels to other randomly chosen channels ► desynchronize the changes ► convergence is not ensured

26. May 2007Márk Félegyházi (EPFL) 26 Convergence to NE (2/3) Algorithm with imperfect info: ► move links from “crowded” channels to other randomly chosen channels ► desynchronize the changes ► convergence is not ensured Balance: unbalanced (UB): best balance (NE): Efficiency:

27. May 2007Márk Félegyházi (EPFL) 27 Convergence to NE (3/3) N (# of pairs)10 C (# of channels)8 k (radios per device)3 τ(1) (max. throughput)54 Mbps

28. Summary and Future Work

29. May 2007Márk Félegyházi (EPFL) 29 Summary – Multi-radio channel allocation ► wireless networks with multi-radio devices ► users of the devices are selfish players ► G MRCA – multi-radio channel allocation game ► results for a Nash equilibrium: – players should use all their radios – load-balancing channel allocation – two types of Nash equilibria – NE are efficient both in theory and practice ► fairness issues ► coalition-proof equilibria ► algorithms to achieve efficient NE: – centralized algorithm with perfect information – distributed algorithm with imperfect information

30. May 2007Márk Félegyházi (EPFL) 30 Summary of my research ► Ch 1: A tutorial on game theory ► Ch. 2: Multi-radio channel allocation in wireless networks ► Ch. 3: Packet forwarding in static ad-hoc networks ► Ch. 4: Packet forwarding in dynamic ad-hoc networks ► Ch. 5: Packet forwarding in multi-domain sensor networks ► Ch. 6: Cellular operators in a shared spectrum ► Ch. 7: Border games in cellular networks Part II: Non-cooperative users Part III: Non-cooperative network operators Part I: Introduction to game theory

31. May 2007Márk Félegyházi (EPFL) 31 Future research directions (1/3) ► Cognitive networks – Chapter 2: multi-radio channel allocation – adaptation is a fundamental property of cognitive devices – selfishness is threatening network performance primary (licensed) users secondary (cognitive) users – incentives are needed to prevent selfishness frequency allocation interference control submitted: M. Félegyházi, M. Čagalj and J.-P. Hubaux, “Efficient MAC in Cognitive Radio Systems: A Game-Theoretic Approach,” submitted to IEEE JSAC, Special Issue on Cognitive Radios, 2008

32. May 2007Márk Félegyházi (EPFL) 32 Future research directions (2/3) ► Coexistence of wireless networks – Chapter 6 and 7: wireless operators in shared spectrum – advancement of wireless technologies – alternative service providers small operators social community networks – competition becomes more significant – coexistence results in nonzero-sum games mechanism to enforce cooperation competition improves services in preparation: M. H. Manshaei, M. Félegyházi, J. Freudiger, J.-P. Hubaux, and P. Marbach, “Competition of Wireless Network Operators and Social Networks”

33. May 2007Márk Félegyházi (EPFL) 33 Future research directions (3/3) ► Economics of security and privacy – cryptographic building blocks are quite reliable (some people might disagree) – implementation fails due to economic reasons (3C) confusion in defining security goals cost of implementation complexity of usage – privacy is often not among the security goals – incentives to implement correct security measures share liabilities better synchronization collaboration to prevent attacks submitted: J. Freudiger, M. Raya, M. Félegyházi, and J.-P. Hubaux, “On Location Privacy in Vehicular Mix-Networks”

34. Extensions

35. May 2007Márk Félegyházi (EPFL) 35 My research Non-cooperative users ► Multi-radio channel allocation in wireless networks ► Packet forwarding in static ad-hoc networks ► Packet forwarding in dynamic ad-hoc networks Non-cooperative network operators ► Packet forwarding in multi-domain sensor networks ► Cellular operators in a shared spectrum ► Border games in cellular networks

36. May 2007Márk Félegyházi (EPFL) 36 Thesis contributions (Ch. 1: A tutorial on game theory) ► facilitate the application of game theory in wireless networks M. Félegyházi and J.-P. Hubaux, “Game Theory in Wireless Networks: A Tutorial,” submitted to ACM Communication Surveys, 2006

37. May 2007Márk Félegyházi (EPFL) 37 Thesis contributions (Ch. 2: Multi-radio channel allocation in wireless networks) ► NE are efficient and sometimes fair, and they can be reached even if imperfect information is available ► load-balancing Nash equilibria – each player has one radio per channel – some players have multiple radios on certain channels ► NE are Pareto-efficient both in theory and practice ► fairness issues ► coalition-proof equilibria ► convergence algorithms to efficient NE M. Félegyházi, M. Čagalj, S. S. Bidokhti, and J.-P. Hubaux, “Non-cooperative Multi-radio Channel Allocation in Wireless Networks,” in Proceedings of Infocom 2007, Anchorage, USA, May 6-12, 2007

38. May 2007Márk Félegyházi (EPFL) 38 Thesis contributions (Ch. 3: Packet forwarding in static ad-hoc networks) ► incentives are needed to promote cooperation in ad hoc networks ► model and meta-model using game theory ► dependencies / dependency graph ► study of NE – in theory, NE based on cooperation exist – in practice, the necessary conditions for cooperation do not hold ► part of the network can still cooperate M. Félegyházi, L. Buttyán and J.-P. Hubaux, “Nash Equilibria of Packet Forwarding Strategies in Wireless Ad Hoc Networks,” in Transactions on Mobile Computing (TMC), vol. 5, nr. 5, May 2006

39. May 2007Márk Félegyházi (EPFL) 39 Thesis contributions (Ch. 4: Packet forwarding in dynamic ad-hoc networks) ► mobility helps cooperation in ad hoc networks ► spontaneous cooperation exists on a ring (theoretical) ► cooperation resistant to drift (alternative cooperative strategies) to some extent ► in reality, generosity is needed ► as mobility increases, less generosity is needed M. Félegyházi, L. Buttyán and J.-P. Hubaux, “Equilibrium Analysis of Packet Forwarding Strategies in Wireless Ad Hoc Networks - the Dynamic Case,” Technical report - LCA-REPORT-2003-010, 2003

40. May 2007Márk Félegyházi (EPFL) 40 Thesis contributions (Ch. 5: Packet forwarding in multi-domain sensor networks) ► sharing sinks is beneficial and sharing sensors is also in certain scenarios ► energy saving gives a natural incentive for cooperation ► sharing sinks ► with common sinks, sharing sensors is beneficial – in sparse networks – in hostile environments M. Félegyházi, L. Buttyán and J.-P. Hubaux, “Cooperative Packet Forwarding in Multi-Domain Sensor Networks,” in PerSens 2005, Kauai, USA, March 8, 2005

41. May 2007Márk Félegyházi (EPFL) 41 Thesis contributions (Ch. 6: Cellular operators in a shared spectrum) ► both cooperation (low powers) and defection (high powers) exist, but cooperation can be enforced by punishments ► wireless operators compete in a shared spectrum ► single stage game – various Nash equilibria in the grid scenario, depending on cooperation parameters ► repeated game – R MIN (cooperation) is enforceable with punishments ► general scenario = arbitrary ranges – the problem is NP-complete M. Félegyházi and J.-P. Hubaux, “Wireless Operators in a Shared Spectrum,” in Proceedings of Infocom 2006, Barcelona, Spain, April 23-29, 2006

42. May 2007Márk Félegyházi (EPFL) 42 Thesis contributions (Ch. 7: Border games in cellular networks) ► operators have an incentive to adjust their pilot power on the borders ► competitive power control on a national border ► power control game – operators have an incentive to be strategic – NE are efficient, but they use high power ► simple convergence algorithm ► extended game corresponds to the Prisoner’s Dilemma M. Félegyházi, M. Čagalj, D. Dufour, and J.-P. Hubaux, “Border Games in Cellular Networks,” in Proceedings of Infocom 2007, Anchorage, USA, May 6-12, 2007

43. May 2007Márk Félegyházi (EPFL) 43 Selected publications (à la Prof. Gallager) ► M. Félegyházi, M. Čagalj, S. S. Bidokhti, and J.-P. Hubaux, “Non- Cooperative Multi-Radio Channel Allocation in Wireless Networks,” in Infocom 2007 ► M. Félegyházi, M. Čagalj, D. Dufour, and J.-P. Hubaux, “Border Games in Cellular Networks,” in Infocom 2007 ► M. Félegyházi, L. Buttyán and J.-P. Hubaux, “Nash Equilibria of Packet Forwarding Strategies in Wireless Ad Hoc Networks,” in IEEE Transactions on Mobile Computing (TMC), vol. 5, nr. 5, 2006

44. May 2007Márk Félegyházi (EPFL) 44 Fairness Nash equilibria (fair) Nash equilibria (unfair) Theorem: A NE channel allocation S * is max-min fair iff Intuition: This implies equality: u i = u j,  i,j  N

45. May 2007Márk Félegyházi (EPFL) 45 Centralized algorithm Assign links to the channels sequentially. p1p1 p1p1 p1p1 p1p1 p2p2 p2p2 p2p2 p2p2 p3p3 p3p3 p3p3 p3p3 p4p4 p4p4 p4p4 p4p4

46. May 2007Márk Félegyházi (EPFL) 46 Thesis contributions ► Ch 1: A tutorial on game theory – facilitate the application of game theory in wireless networks ► Ch. 2: Multi-radio channel allocation in wireless networks – NE are efficient and sometimes fair, and the fair NE can be reached even if imperfect information is available ► Ch. 3: Packet forwarding in static ad-hoc networks – incentives are needed to promote cooperation in ad hoc networks ► Ch. 4: Packet forwarding in dynamic ad-hoc networks – mobility helps cooperation in ad hoc networks ► Ch. 5: Packet forwarding in multi-domain sensor networks – sharing sinks is beneficial and sharing sensors is also in certain scenarios ► Ch. 6: Cellular operators in a shared spectrum – both cooperation (low powers) and defection (high powers) exist, but cooperation can be enforced by punishments ► Ch. 7: Border games in cellular networks – operators have an incentive to adjust their pilot power on the borders