Data Dissemination in Vehicular Networks

1. Data Dissemination in Vehicular Networks Vinod Kone MAE Presentation

2. Motivation: Scenario I 2

3. Motivation: Scenario II Imagine driving to a new city and you want to find the best parking lot 3

4. So what kind of a system do we need? Desirable system properties Data collection and distribution in a local environment Low information delivery latency Cheap deployment and communication Probable solutions Cellular ? Service fees Satellite ? High latency Vehicular Networks ? What is a vehicular network? Vehicles are equipped with sensing, computing and wireless devices Vehicles talk to road-side infrastructure (V2I) and other vehicles (V2V) Has all the desirable properties 4

5. Outline Vehicular Networks and Applications Research overview Data dissemination approaches and tradeoffs Open problems 5

6. Vehicular Networks What does road-side infrastructure (Infostation) mean? High bandwidth & Low cost device Coverage is less compared to a cellular base station Advantages of infrastructure support Low latency communication with vehicles Gateway to the Internet and extend connectivity Distributing time-critical data (e.g. accident notifications, traffic jam) near the affected area is efficient 6

7. Applications & Challenges Lots of potential applications Safety: Emergency brake light, Collision warning etc Comfort: Toll services, Parking space locator etc Commercial: Map updates, Video download, Services etc What makes vehicular networks challenging? Combination of V2V + V2I communications They face the �worst case� scenarios in real world 7

8. Who all are working on vehicular networks? 8 This is not something of only academic interestThis is not something of only academic interest

9. Research on Vehicular Networks: The BIG Picture 9

10. MAC & Physical Layer Dedicated Short Range Communications (DSRC) Protocol for vehicles to talk to each other and infrastructure Operates in 75 MHz licensed band at 5.9 GHz (USA), 5.8GHz (Europe and Japan) Characteristics Based on 802.11a PHY and 802.11 MAC Supports high mobility of vehicles (120 mph) High data rate (27 Mbps), short range (1 km), multi-channel (7) Studies have shown that vehicle-to-infrastructure communication is feasible [Ott�04] [Bychkovsky�06] 10

11. Outline Vehicular Networks and Applications Research overview Data dissemination approaches and tradeoffs Open problems 11

12. Data Dissemination Characteristics High mobility Dynamic topology Receivers are a priori unknown Large scale High density Low penetration ratio Challenges Maintaining routing tables is difficult Scalability Dealing with partitions 12

13. Classification of Dissemination Approaches V2I / I2V dissemination Push based Pull based V2V dissemination Flooding Relaying How to deal with network partitions ? Opportunistic forwarding 13

14. Push based dissemination Infostation pushes out the data to everyone Applications: Traffic alerts, Weather alerts Why is this useful? Good for popular data No cross traffic ? Low contention Drawback Everyone might not be interested in the same data 14

15. Pull based dissemination Request � Response model Applications: Email, Webpage requests Why is this useful? For unpopular / user-specific data Drawback Lots of cross traffic ? Contention, Interference, Collisions 15

16. Classification of Dissemination Approaches V2I / I2V dissemination Push based Pull based V2V dissemination Flooding Challenges Solutions & Drawbacks / Limitations Discovery of Parking Places problem Relaying How to deal with network partitions ? Opportunistic forwarding 16

17. Flooding Basic Idea Broadcast generated and received data to neighbors Usually everyone participates in dissemination Advantages �Good� for delay sensitive applications Suitable for sparse networks Key Challenges How to avoid broadcast storm problem? 17

18. Techniques to avoid the broadcast problem Simple forwarding Timer based [Linda�00] Hop limited [Nandan�06] Map based / Geographic forwarding Directed flooding [Sormani�06] Aggregation [Wischhof�04] [Nadeem�06] [Caliskan�06] 18

19. Drawbacks / Limitations of Flooding Flooding in general High message overhead ? Not scalable Map based / Geographic Geographically closest doesn�t necessarily reflect the best path! Depend on a location based service Aggregation techniques tradeoff with accuracy 19

20. Decentralized Discovery of Parking Places Push + Map based + Flooding solution [Caliskan�06] Parking lots periodically broadcast occupancy and price information to nearby vehicles City map is divided into a quad-tree like structure 20

21. Decentralized discovery algorithm Information of a single parking lot is distributed only in proximity Aggregate information of a region is distributed over wide area Why this particular solution? Lots of vehicles are interested in the data ? Push Fast transmission of the information ? Flooding To avoid broadcast storm ? Map based 21

22. Classification of Dissemination Approaches V2I / I2V dissemination Push based Pull based V2V dissemination Flooding Relaying 2 Challenges Solutions & Drawbacks / Limitations How to deal with network partitions ? Opportunistic forwarding 22

23. Relaying Basic Idea Instead of flooding the network, select a relay (next hop) Relay node forwards the data to next hop and so on Advantages Reduced contention ? Scalable for dense networks Key Challenges How to select the relay neighbors? How to ensure reliability? 23

24. How to select a relay neighbor? Simple forwarding Select the node farthest from source [Korkmaz�04] [Zhao�07] [Our work] Map based / Geographic forwarding Closest to the destination [Kikaiakos�05] Abstract topology into a weighted directed graph [Zhao�06] [Wu�04a] Drawback / Limitations Locally best next hop may not be globally best ! 24

25. How to ensure reliability? Use RTS/CTS & ACK [Korkamaz�01] [Zhao�07] Use indirect acknowledgments [Benslimane�04] [our work] Drawbacks / Limitations RTS/CTS incurs lot of overhead Interference affects indirect acknowledgments 25

26. Classification of Dissemination Approaches V2I / I2V dissemination Push based Pull based V2V dissemination Flooding Relaying How to deal with network partitions ? Opportunistic forwarding 26

27. Opportunistic Forwarding Problem with partitioned networks Next hop is not always present Opportunistic Forwarding Basic Idea: Store and Forward Challenge: What is the right re-broadcast interval? Solutions Broadcast repeatedly [Linda�00b][Uichin�06][Wischhof�04] Cache at infostations [Lochert�07a] 27

28. Opportunistic: Drawbacks / Limitations It is difficult to select the correct re-broadcast interval Too soon ? high overhead Too late ? doesn�t deal with partitions effectively Maintaining a neighbor list induces high overhead and contention 28

29. Dissemination Approaches: The BIG Picture 29

30. Take Away 30

31. Outline Vehicular Networks and Applications Research overview Data dissemination approaches and tradeoffs Open problems 31

32. Some interesting open problems Not much literature on V2I / I2V communication How to deal with cross-traffic in the pull scheme Scheduling transmissions? How to combine push and pull ? What is hybrid ? Mobility traces for evaluation of dissemination Real traces (e.g. NGSIM) are expensive to collect Not enough data points for simulation Need to extrapolate 32

33. Some interesting open problems (contd�) Imagine a service provider wants to install infostations What is the minimum infostation density required Impact of application parameters (size, lifetime) Analytical models Understand the bounds on performance Modeling network partitions ? Better opportunistic schemes 33

34. Some interesting open problems (contd�) Real experiments Equip vehicles with wireless devices and observe dissemination performance Can obtain real movement traces Designing and testing sample applications Real experiments might invalidate the design! Re-design the schemes based on the real observations Repeat! 34

35. Future Work Hybrid dissemination in vehicular networks Developing accurate analytical dissemination models Real experiments 35

36. Thank You for Listening 36

37. References [Nadeem�06] Comparative study of data dissemination models for vanets, Mobiquitous. [Wu�04a] MDDV: A Mobility-Centric Data Dissemination Algorithm for Vehicular Networks, VANET. [Korkamaz�04] Urban multi-hop broadcast protocol for inter-vehicle communication systems, VANET. [Sun�00] GPS-Based Message Broadcasting for Inter-Vehicle Communication, ICCPP. [Zong�01] Ad Hoc Relay Wireless Networks over Moving Vehicles on Highways, Mobihoc. [Wu�04b] Analytical Models for Information Propagation in Vehicle-to-Vehicle Networks, VTC. [Linda�00a] Disseminating Messages among Highly Mobile Hosts based on Inter-Vehicle Communication, IV. [Sormani�06] Towards Lightweight Information Dissemination in Inter-Vehicular Networks, VANET [Zhao�06] VADD-Vehicle-Assisted Data Delivery in Vehicular Ad Hoc Networks, INFOCOM. [Caliskan�06] Decentralized Discovery of Free Parking Spaces, VANET [Basu�04] Wireless Ad Hoc Discovery of Parking Meters, WAMES. [Zhao�07] Data Pouring and Buffering on The Road: A New Data Dissemination Paradigm for Vehicular Ad Hoc Networks, Transactions on VT 37

38. References [Lochert�07a] The Feasibility of Information Dissemination in Vehicular Ad-Hoc Networks, WON [Wischhof�04] Information Dissemination in Self-Organizing Inter-vehicle Networks, Trans on ITS [Uichin�06] FleaNet: A Virtual Market Place on Vehicular Networks, MobiQuitos [Kikaiakos�05] VITP: An information transfer protocol for vehicular computing, VANET [Bai�06] Towards Characterizing and Classifying Communication-based Automotive Applications from a Wireless Networking Perspective, Research Report, GM [Nandan�06] Modeling Epidemic Query Dissemination in AdTorrent Network, CCNC [Linda�00b] Role-Based Multicast in Highly Mobile but Sparsely Connected Ad Hoc Networks, MobiHoc [Lochert�07b] Probabilistic Aggregation for Data Dissemination in VANETs, VANET [Luo�04] A Survey of Inter-Vehicle Communication, Technical Report [Varghese�06] Survey of Routing Protocols for Inter-Vehicle Communications, Mobiquitos [Bychkovsky�06] A Measurement Study of Vehicular Internet Access Using In Situ Wi-Fi Networks, MobiCom [Choo�06] Performance Study of Robust Data Transfer Protocol for VANETs, LNCS. 38

39. References [Bensilmane�04] Optimized dissemination of alarm messages in vehicular ad-hoc networks, HSNMC [Bala�07] Web Search From a Bus, CHANTS. [Burgess�06] MaxProp: Routing for Vehicle-Based Disruption-Tolerant Networks, INFOCOM. [Ott�04] Drive-thru Internet: IEEE 802.11b for �Automobile� Users, INFOCOM. [Hartenstein�01] Position-Aware Ad Hoc Wireless Networks for Inter-Vehicle Communications, MobiHoc [Namboodiri�04] A study on the feasibility of mobile gateways for vehicular ad-hoc networks [Shahram�04] PAVAN: A Policy Framework for Content Availability in Vehicular Ad-hoc Networks, VANET [Raya�07] Securing Vehicular Networks, INFOCOM [Harsch�06] Secure Position Based Routing for VANETs, VTC 39

40. Backup Traffic View [Nadeem�06] Formal models for data dissemination Bi-directional mobility considered Aggregation based flooding Not scalable for dense traffic Flooding is in general good for delay sensitive apps Targeted App: Traffic monitoring MDDV[Wu�04a] Opportunistic + trajectory + geographic forwarding Assumes vehicles have road map and know src, dest region Traffic flow information is fed to vehicles to abstract the road map and make forwarding decisions Group of vehicles near the message head can forward the data Forwarding phase to reach the destination region and then propagation phase to reach all the receivers in the region 40

41. Adhoc Relay [Zong�01] Opportunistic (pessimistic) forwarding based on store and forward approach Good for networks with low density Delay-sensitive applications cannot work with this Motion of vehicles significantly affect delivery latency Analytical Models [Wu�04b] Model an idealistic propagation scheme Consider partitioning of vehicles for information propagation Forward (intra-partition) and catchup(inter-partition) processes Models are for sparse (ignores in-partition propagation) and dense (traffic between cycles) networks Doesn�t model real networks VITP [Kikaiakos�05] Geographical routing to forward the query to the query region Nodes maintain a neighbor list Once query region is reached, nodes do flooding Reply is sent back to the source via flooding 41

42. Urban-Multihop [Korkamaz�01] Segment the road in the dissemination direction iteratively Select the node in the furthest segment as relay RTS/CTS like mechanism at MAC layer Distance from source decides black burst time Repeaters are used at intersections to propagate to different directions Dissemination Messages [Linda�00a] Flooding based solution Nodes wait a time proportional to the distance from the source before broadcast Role based Multicast [Linda�00b] [Linda�00a] + retransmissions based on change in the neighbor set Lightweight Dissemination [Sormani�06] Dissemination is based on propagation function Propagation function encodes destination region and trajectory Propose several flooding schemes (basic, probabilistic, function driven) Requires a map to create and evaluate the propagation function 42

43. VADD [Zhao�06] Pull based routing model to query a static location Map based information (trajectory, traffic) is used to select the next hop with least delay to the destination Models roads and intersections as graphs with estimated delays as weights Store and forward approach to tackle sparse networks Targeted App: To query a static information center DP, DP-IB [Zhao�07] Propose a data pouring and buffering dissemination scheme Nodes maintain neighbor list and select farthest node as relay RTS/CTS, Indirect Acks are used for reliability Ibers (Infostations) are deployed at intersections to rebroadcast data on the cross roads Analytical models developed for dissemination capacity and broadcast interval Feasibility [Lochert�07] Shows the feasbility of information dissemination w.r.t. penetration ratio in city Analytical model to show that connectivity decreases with length Propose installing SSUs (InfoStations), networked and stand-alone to improve dissemination by re-broadcasting the information Vehicles periodically broadcast information to neighbors (Locomotion + Wireless propagation) 43

44. SODAD/SOTIS [Wischhof�04] Data dissemination is achieved by abstracting the map into segments and aggregating information Analytical models (coverage processes) to show low penetration ratio leads to low multi-hop range Recurrent broadcasts to tackle with network partitions Adaptive broadcast interval based on provocation/mollification events to suit traffic conditions Targeted App: Vehicles sensing data for traffic info system FleaNet [Uichin�06] Proposed an architecture for buy/sell queries dissemination Dissemination is basically by contacts�vehicles that receive queries store it in their db and see if there is a local match Source broadcasts queries periodically to its neighbors (opportunistic) LER routing is used to send notifications from buyers?? sellers 44

45. Some stats Number of telemetric subscribers will reach >15 million by 2009 Smart traffic lights can reduce waiting time by 28% during rush hours 45

46. Mobility Models & Simulators How to evaluate vehicular network protocols? Synthetic mobility models: highly unrealistic Trace-driven Traces from microscopic traffic simulators CORSIM , VISSIM , TRANSIM close to reality but not real Real Traces (Source: NGSIM) very expensive to collect data not enough data points How can we solve this problem? We have to extrapolate the real data by some �modeling� Equip vehicles with sensors

47. Security and Privacy: Why is this important?

48. Secure solutions for VANETs