Data Dissemination for Spot Applications in Ad-Hoc Networks

1. Alaeddine EL-FAWAL Thesis director : Jean-Yves Le Boudec Data Dissemination for Spot Applications in Ad-Hoc Networks Private Defense, 3 April 2009, EPFL

2. Overview on the PhD Work • Supported by 2 projects: • Haggle: European project in Situated and Autonomic Communications • NCCR MICS: National Center of Competence in Research – Mobile Information and Communication Systems • 2 parts in my PhD work: • Data dissemination for spot applications over WIFI technology (Haggle) • Cross-layer optimization for UWB systems (MICS) Dealing with different facets of uncoordinatedad-hoc wireless networks and deals with challenges at all networking layers

3. OUTLINE Introduction SLEF: Our Data Dissemination Middleware Performance Validation Prototyping and Testbed Conclusions Achievements

4. Open-Ended Environment • Characteristics: • Involves from few to thousands of nodes. • Challenging circumstances: • Highly dynamic • Unpredictable • Uncoordinated • Short contact time • Quickly changing from dense to sparse, non-congested to congested Caused by: Dramatic expansion of WIFI interfaces: laptops, PDAs, mobile phones, video games and even with peripherals and vehicles.

5. Spot Applications Description • Destination: all nodes within the spot (multi-hop). • The spot might be the entire network (campus). • Example: ad applications, traffic info, support routing, resource discovery, bootstrapping phases for application layer.

6. Spot Applications Requirements: • Efficient and reliable data dissemination service • Trade-off: spread-application rate • Spread: number of nodes within the application spot (number of nodes that receive a packet). • The spot size is variable according to the network conditions. • Delivery ratio can not be used… instead, we talk about spread.

7. Open-Ended Envir. : Variation and Diversity of Scenarios Relay Transmission range Source / Relay • Density: average • Few sources: little new injected traffic

8. Open-Ended Envir. : Variation and Diversity of Scenarios Relay Transmission range Source / Relay • Density: average • almost all are sources: a lot of new injected traffic

9. Open-Ended Envir. : Variation and Diversity of Scenarios Relay Source / Relay • Very high density (traffic jam) • almost all are sources: a huge amount of new injected traffic • One hop: + 200 neighbors

10. Open-Ended Envir. : Variation and Diversity of Scenarios Relay Source / Relay • Density: very sparse. • No communication without mobility (opportunistic communication) An autonomic mechanism for data dissemination that adapts to this diversity in scenarios is a must, otherwise network failure

11. OUTLINE Introduction SLEF: Our Data Dissemination Middleware Performance Validation Prototyping and Testbed Conclusions Achievements

12. SLEF: Self Limiting Epidemic Forwarding • We propose the SLEF middleware. • Delivers an efficient and reliable data dissemination service to the spot applications • Deals with the tradeoff: spread-application rate • The network conditions define the TTL limit (spread control). • Functional between the application and the transmission sockets (UDP or raw sockets). Data dissemination through limited epidemic forwarding : • A source transmits packets in broadcast mode. • Nodes forward each packet they receive Forwarding Factor times. • Packets are forwarded within a limited hop count.

13. SLEF: Self Limiting Epidemic Forwarding • SLEF Features: • Autonomic: Adapts itself to any change in the network. • Density increases  forwarding factor decreases • Traffic load increases  TTL limit decreases • Complete design of a middleware • Does not need / exchange any topology information. Uses only local information to the node (very short contact time). SLEF is designed to hold in all scenarios, in particular in very dense and very sparse ones

14. Essential Functions for Multi-Hop Broadcast SLEF implements 6 essential functions needed for a sustainable service 1. Congestion control: first mechanism proposed for broadcast in ad hoc networks • Efficient use of MAC broadcast: • 802.11 broadcast does not implement any exclusion mechanism (RTS/CTS) and it performs poorly (similar to Aloha). We replace it by a new scheme that we call pseudo-broadcast. • 802.11 broadcast does not implement Ack. Pkts might be transmitted in the vacuum. We implement a presence indicator that does not need any message exchange. Scheduler / fairness: A scheduler is needed to decide which packet to serve. It is based on Source ID to ensure some level of fairness. 4. Spread control (adaptive TTL) 5. Forwarding factor control 6. Buffer management

15. Spread Control: Adaptive TTL Trade-Off Spread vs. Application Rate Spread: number of nodes that receive a packet. How: • We use an Aging mechanism • Adaptive TTL: Aging adapts locally the TTL to the different network setting, based on the send/receive events. • The idea is as follows: With fixed TTL • Density => Spread Rate TTL limit= 2 With SLEF • Density =>TTL : In a traffic jam: TTL = 1 • Density => TTL : In a very sparse network: TTL = 10 N : Spread FF: Forwarding Factor R0: Nominal Rate of MAC Layer λ : Application Rate

16. Aging Age adaptive Age Real time Age hop count Send/receive the same pkt Age = Age +K0 receive any pkt Age = Age + K1 Constant increase by time: 8h -> 255 Age > 255 Drop packet • New created pkt: Age = 0 • Pkt received for the first time: Age = 255 – TTL • Age manipulated locally. • when transmitting: TTL = 255 -Age • Hop count: plays the role of a fixed TTL (=255/K0) if the network is not congested. • Adaptive Age: adapts the TTL to the network activity, which reflects the density and the traffic load • Real time Age: A pkt lives at most 8 hours (work cycle).

17. Spread-Rate Balance Adjusting the spread-rate balance according to the application needs Default values for K0 and K1 are computed in the thesis • SLEF maintains a spread-rate balance • 2 parameters to adjust: K0, K1 • Once Adjusted, they work well with all settings

18. Forwarding Factor Control One send/receive event Two send/receive events: The green nodes are inhibited:smaller forwarding factor Transmission would be redundant Why:To Minimize redundancy (save resources) How: • Inhibit nodes from transmitting over sent/received pkts. • We compute a virtual rate for each packet based on the send/receive events. • Adaptive:thevirtual rateAdapts locally the Forwarding Factor to the different network settings

19. Virtual Rate Virtual Rate: The max rate a packet is transmitted with For each send / receive event on a given pkt: 1- The virtual rate is computed as follows: R0 : nominal MAC rate [pkts/s] RcvCount : number of times the pkt is received SendCount : number of times the pkt is sent aandb : are coefficient less than 1: a=0.1, b=0.01 3- The pkt is allowed to be transmitted only after: current time + (similar to a back-off system) Unlike other mechanisms, the virtual rate based forwarding factor control allows transmitting the pkt multiple times if needed. 2- vRate decreases exponentially with send/receive events. The smaller the vRate is, the longer the back-off time is: The pkt might be dropped before being transmitted

20. Buffer Management Highest age Lived the highest number of send/receive events • Cleans the buffer in order to keep space for new incoming packets. • Based on Aging. • Drops packets with the highest age

21. OUTLINE Introduction SLEF: Our Data Dissemination Middleware Performance Validation Prototyping and Testbed Conclusions Achievements

22. Setting • Scenarios: • Vehicular networks. • Different network settings: node density, traffic load… Network Simulator: JIST-SWANS, A JAVA simulator for Ad Hoc networks Vehicular Mobility Simulator: STRAW, an extension of JIST-SWANS. It provides a mobility model based on the operation of the real vehicular traffic. Topo : 2-lanes road, speed limit 80Km/h MAC : 802.11/b Channel : Fading K0 = 25 K1 = 0.1 Range : 300 m in average

23. Adaptation of the Spread to the Rate Spread Rate Density: 12 vehicles/Km Adaptive TTL

24. Adaptation of the Forwarding Factor to the Density Very sparse (Death Valley) Very dense (Traffic jam) +200 neighbors

25. Importance of the Pseudo-Broadcast Very dense (Traffic jam): +200 neighbors Idea: implement a mutual exclusion mechanism for broadcast in order to avoid collision

26. OUTLINE Introduction SLEF: Our Data Dissemination Middleware Performance Validation Prototyping and Testbed Conclusions Achievements

27. SLEF Prototyping 2 architectures: • It is independent of the IP address • Practical in the absence of a centralized coordination where assigning IP @ is challenging • Complex with Windows • IP networking needs to be initialized • Straightforward with all platforms Spot application SLEF UDP sockets Spot application UDP SLEF IP Raw sockets MAC MAC

28. SLEF Prototyping 4 platforms: Resource-constrained devices: • Smartphone HTC S620: • Windows Mobile • 64MB RAM, 128 ROM • 201 MHz • ASUS WL-500 GP wireless router • OpenWrt • 32MB RAM, 8MB Flash • 266MHz • Windows XP: J2SE, C++ • Windows Mobile: J2ME, C++ • Linux: J2SE, C++ • OpenWrt (Linux-like firmware for embedded system): C++ SLEF is practical and performs well on very resource-limited devices

29. Testbed Stress test: Testbed features: Performance evaluation of SLEF through measurements • Wireless router: ASUS WL-500GP • Technical specifications: 8MB Flash, 32MB RAM, 266MHz, 2 USB 2.0 ports • Firmware: OpenWrt • Configurable wireless interface: using Atheros card with MadWIFI driver (setting RTS/CTS Th, Tx power, promiscuous mode, monitor mode, Tx queue length…). • Mobility: using plumb batteries, +4 hrs lifetime with full power transmission at full rate • Robustness. • More than 50 devices communicating with each other for long time.

30. Measurement Design Nodes are distributed over 12 buildings in EPFL

31. Measurement Design • Application: injects packets at fixed rate. • Application rate: can be reduced by the congestion control mechanism. • Comparison: SLEF vs. fixed-TTL • Fixed-TTL: • Implements all functions of SLEF, otherwise it is not functional. • Spread control is replaced by TTL (decremented by one for each hop) • TTL limit is fixed • Buffer management is based on the TTL and not on the age: packets with the smallest TTL are dropped first when the buffer is full • Fixed-TTL limits the spread through 2 parameters: TTL limit and the buffer size. • Buffer size of fixed-TTL: • Small buffer size: We ran SLEF and we use the average buffer occupation obtained (620 packets). • Large buffer size: 10 000 packets.

32. Measurement Results Higher redundancy Lower rate Smaller spread • With fixed TTL: • Setting the buffer size per scenario is needed: • Large buffer size  radundancy and low rate • Small buffer size  small spread even in non-congested network • TTL based buffer management does not perform well.

33. Measurement Conclusions Fixed-TTL SLEF • 2 parameters to adjust: K0, K1 • Once adjusted, they work well with all settings • Aging based buffer management performs well • Max Buffer size is 255/K1 (little formula) • 2 parameters to adjust: MaxTTL and buffer size • Needs to adjust whenever the network setting (density, traffic load, mobility,…) changes • TTL based buffer management performs poorly.

34. OUTLINE Introduction SLEF: Our Data Dissemination Middleware Performance Validation Prototyping and Testbed Conclusions Achievements

35. Conclusions We propose SLEF: data dissemination middleware for spot applications Prototyping SLEF for 4 platforms Building a testbed for wireless networks protocols SLEF performs better than fixed-TTL: • Fixed-TTL is not adaptive • TTL-based bufer management performs poorly • Autonomic: Adapts itself to any change in the network. • Complete middleware • Does not need/exchange any topology information. Uses only local information to the node. • Works even in extreme scenarios (very dense/sparse…) • Performs well on resource constrained devices

36. Perspectives Performance evaluation of SLEF: • Factorial analysis applied on measurement results. • Measurements will consider varying scenarios: mobility, intermittent connectivity, different traffic load and density. • Comparison with different variants of data dissemination protocols Collaboration with LCA3: Prof. Patrick Thiran and Adel Aziz • Extensive series of measurements for wireless network protocols

37. OUTLINE Introduction SLEF: Our Data Dissemination Middleware Performance Validation Prototyping and Testbed Conclusions Achievements

38. Achievements Cross-Layer Optimization for UWB IR Networks (MICS) • Robust Signal Acquisition in UWB Ad Hoc Networks: • One journal paper, one conference paper and one patent (adopted by MICS). • Sleeping Mode for UWB IR Ad-hoc Networks: • One journal paper. Data Dissemination for Spot Applications (Haggle) • Multi-Hop Broadcast Middleware (SLEF): • One conference paper. • Vulnerabilities in Epidemic Forwarding: • One conference paper. • SLEF Prototyping and Experimental Testbed.

39. Robust Signal Acquisition in UWB Ad Hoc Networks: Problem: Conventional detection method Near-far scenario S1 D1 • assume power control, otherwise it fails. • Power control impractical in the absence of a centralized coordination (CDMA). D2 S2 Solution: Power independent detection method. Performance Evaluation: 10 users, LOS Indoor Office Channel model by IEEE P802.15.4a Total Error: Et=PMD + PFA Proba of Misdetection: PMD PMD

40. Issues Addressed for the First Time • Spot Applications (introduced for the first time). • Trade-off: Spread – Application rate. • Spread control. • Congestion control in ad-hoc networks in broadcast mode. • Fairness with epidemic forwarding. • Identifying vulnerabilities that are specific to epidemic forwarding. • First prototype of network coding for ad-hoc networks. • Power independent signal acquisition for UWB in uncoordinated wireless ad-hoc networks (problem identification and solution). • Identifying key design elements for power saving with UWB IR systems.

41. Used Expertise • Queuing theory • Performance evaluation tools • Wireless networks • Security • All layers of TCP/IP stack, mainly: • MAC (Medium Access Control) • Physical Layer (UWB IR and 802.11) • Vehicular networks • Network coding • Different channel models • Signal processing. • Simulation • ns-2, Jist-Swans, Straw (vehicular traffic simulator), Matlab. • System programming • Windows XP, Windows Mobile, Linux, OpenWrt • C++, J2SE, J2ME, raw sockets

42. Demo: Ad-Hoc Ventes Flash • Spot application • Needs SLEF • Windows platform: XP and Mobile • C++ Client Sender (shop) • Injects ads: • Shop name • Product features: • Name • Category • Price • … • Receive ads: • Filtering at the application level: • Shop name • Category • Vote

43. Demo: Ad-Hoc Ventes Flash X X X X X X Scenario: Persistent broadcast in presence of intermittent connectivity Shop Client 1 Client 2 Application on Application on Application off Application on Application off

44. List of Publications at EPFL Journal Articles: Proceedings: Pending Patent: Demo El Fawal, Alaeddine ; Le Boudec, Jean-Yves: A Robust Signal Detection Method for Ultra Wide Band (UWB) Networks with Uncontrolled Interference. In: IEEE Transactions on Microwave Theory and Techniques (MTT,) 2006. El Fawal, Alaeddine ; Le Boudec, Jean-Yves et al. Tradeoff Analysis of PHY-aware MAC in Low-Rate, Low-Power UWB networks.In: IEEE Communications Magazine, vol. 43, num. 12, 2005, p. 147. Raya, Maxim ; Aad, Imad ; Hubaux, Jean-Pierre ; El Fawal, AlaeddineDOMINO: Detecting MAC layer greedy behavior in IEEE 802.11 hotspots. In: IEEE Transactions on Mobile Computing, December 2006 El Fawal, Alaeddine ; Le Boudec, Jean-Yves ; Salamatian, KaveMulti-hop Broadcast from Theory to Reality: Practical Design for Ad Hoc Networks. In: First International Conference on Autonomic Computing and Communication Systems, October 2007. El Fawal, Alaeddine ; Le Boudec, Jean-Yves ; Salamatian, KaveVulnerabilities in Epidemic ForwardingIn: The First IEEE WoWMoM Workshop on Autonomic and Opportunistic Communications (AOC2007), 2007 Merz, Ruben ; El Fawal, Alaeddine ; Le Boudec, Jean-Yves ; Radunovic, Bozidar et al. The Optimal MAC Layer for Low-Power UWB is Non-Coordinated.In: IEEE International Symposium on Circuits and Systems (ISCAS 2006), 2006 El Fawal, Alaeddine ; Le Boudec, Jean-YvesA Power Independent Detection Method for UltraWide Band (UWB) Impulse Radio Networks. In: IEEE International Conference on Ultra-Wideband (ICU 2005), 2005 El Fawal, Alaeddine ; Le Boudec, Jean-Yves Synchronizing Method for Impulse Radio Networks, Date: 2005 El Fawal, Alaeddine ; Salamatian, Kave et al. A framework for network coding in challenged wireless network.  Presented at: MobiSys 2006, Uppsala - Sweden, June 19-22, 2006.

45. The End MERCI DE VOTRE ATTENTION

46. Testbed Testbed features: Wireless router: ASUS WL-500GP Technical specifications: 8MB Flash, 32MB RAM, 266MHz, 2 USB 2.0 ports Firmware: OpenWrt Configurable wireless interface: using Atheros card with MadWIFI driver (setting RTS/CTS Th, Tx power, Tx queue length…). Mobility: using plumb batteries, +4 hrs lifetime with full power transmission at full rate Robustness.