1. Location Resident Services Emmanouil Koukoumidis Princeton University Group Talk on 04/15/09 1

2. Location Determines Usefulness of Information Based on one’s location specific information may be of particular interest: – Nearby parking spots availability. – Traffic ahead and nearby roads. – Potholes and accidents ahead. – Cultural events taking place in the vicinity. – Localized advertisements. Several types of information are most useful to people within a certain geographical region and make sense to be provided only to them. 2

3. Location-specific Services Location-specific service: Provision of such location specific information. Problem: – How to make these location specific services available? Solution: – Location Resident Services. – Ad-hoc distributed coordination scheme. 3

4. Outline Motivation Location Specific Services Design Space (Related Work). Our solution: Location Resident Services/ Tri- fold LRS problem LRS early results Future Work 4

5. R Location Specific Services Design Space Problem: Give data D to all interested nodes that enter region R within the service lifetime T. Infrastucture-based approach. – Need to deploy enough fixed nodes so as to cover all region of interest. – $$$ Grassroots approach. – Nodes collaboratively disseminate the information to each other. mobile node w/ data mobile node w/o data R R 5 mobile node without interest in data

6. Other Approaches Abiding geocast [Maihoefer, VANET, 2005]. – Server approach. Server stores data. Periodically initiates geocasts – Pros: Simple – Cons: Relay nodes overhead. Periodic flooding. – Too wasteful in dense networks. Broadcasts when not necessary. – Nodes might be unreachable in sparse networks. Centralized and infrastructure based. 6 server relay nodes unicast broadcast R

7. Other Approaches Abiding geocast [Maihoefer, VANET, 2005]. – Election approach. Node within region is elected to store data and periodically initiates broadcasts. All others just forward it. – Cons: Election overhead. Periodic flooding. Need multiple elected nodes for fault tolerance -> Increased flooding. In sparse network its hard to ensure coordination for election and fault tolerance. 7 Elected node R

8. Other Approaches Abiding geocast [Maihoefer, VANET, 2005]. – Neighbor approach. All nodes within region store data. Keep list of encountered nodes that they forwarded the data to. On encountering a new one: – Forward the data. – Add encountered node to list. – Pros: Fully distributed and fault tolerance scheme. – Cons: Maintained lists have local view -> A node will get the data from all nodes that it encountered (wasteful). 8 R

9. Outline Motivation Location Specific Services Design Space (Related Work). Our solution: Location Resident Services/ Tri- fold LRS problem LRS early results Future Work 9

10. Location Resident Services Design Fully decentralized scheme that ensures that the location specific service will remain(reside) within the region of interest passed on to all interested nodes. Other approaches ([Maihoefer, VANET, 2005], Impala [Liu, PPoPP, 2003], Trickle [Levis, NSDI, 2004]) disseminate data to ALL nodes in region. 10 R Approach: Try to achieve a targeted degree of service replication across the region R. Only SOME nodes carry the data NOT ALL. Target: Maintain N carriers in R. Service carriers detect nodes that need the data and update them in an epidemic style (like in Impala [Liu, PPoPP, 2003], Trickle [Levis, NSDI, 2004]). Service carrier mobile node w/o data mobile node with no interest in data

11. LRS Tri-fold problem Problem: Maintain N carriers in region R. Nodes move in and out. Nodes may also fail. In order to maintain fixed number of carriers in R in a distributed way carriers need to able to: – Estimate total number of carriers in region. – Spawn new carriers when necessary ie too few carriers currently exist. 11 1) Service carrier number estimation. 2) Spawn policies: When to spawn? 3) Carrier selection criteria: How to spawn?

12. Carrier Number Estimation Target number of nodes N →Target node density D. Nodes as they move try to estimate D. How to measure carrier densities? – In epidemic protocols carriers periodically (every T sec) broadcast a small version advertisement. – Every T carriers calculate: – So estimation of D can be done with zero communication overhead. 12

13. Density Estimators Cumulative Moving Average (CMA) Exponential Moving Average (EMA) The newer the measurement the more important it is. 13

14. Collaborative Density Estimation 1) Nodes as they move estimate node densities: 14 R A B

15. Collaborative Density Estimation 1) Nodes as they move estimate node densities: 15 R AB 2) When nodes meet they exchange: Their density estimation: d k, A Their Estimation Quality Factor ie their confidence about the quality of their estimation. EQF = 1 + f+ f 2 + … + f k

16. Collaborative Density Estimation 1) Nodes as they move estimate node densities: 16 R A B 2) When nodes meet they exchange: Their density estimation: d k, A Their Estimation Quality Factor ie their confidence about the quality of their estimation. EQF = 1 + f+ f 2 + … + f k 3) Merging of estimations:

17. More on Quality of Estimations The quality of an estimation should also be a factor of node’s mobility. – Node speed – Area travelled When merging the more disjoint the areas the nodes have covered the better. 17 R A B R A B

18. Experimental Setup: ORBIT testbed Grid of 20x20 linux PCs with 802.11a/b/g Atheros cards. Wireless interfaces configured in 802.11a ad-hoc demo mode. Mobility is emulated: Socket receive() has been wrapped. Filters out packets from nodes that are “out of range”. Range = 100m. Mobility scheme: Random teleport every 10sec. Currently working on more realistic schemes. 18

19. Some graphs now… 19 100 nodes in 850x850 m 2 → 4 nodes in range on average. Density estimations based on CMA. Collaborative estimation helps carriers get better estimates and faster…

20. Denisty Estimators Evaluation 20 100 nodes in 1700x1700 m 2 → 1 nodes in range on average. Density estimations based on CMA. Collaborative estimation helps carriers get better estimates and faster…

21. Spawn Policies When should a carrier decide to spawn a new one? Policy 1: – if (estimated_density < targeted_density) spawn() Policy 2: – if (estimated_density EQF THRESHOLD ) spawn() What EQF is good enough to make a reasonable decision to spawn? 21 Policy 3: Why rush to spawn? A carrier can estimate based on its trajectory the amount of time it will stay within region… So it knows if it can afford to wait before spawning…

22. Spawn Policies 22 spawn policy 1: spawn policy 2: spawn policy 3:

23. Carrier Selection How should potential carriers be selected among available nodes? – Randomly – Based on estimated residence within region R. B, C etc transmit to A their location, and velocity vector. A using that can estimate their time of residence within the area. Will evaluate savings achieved in number of spawns necessitated. R A B C 23

24. Future Work Short future: – Improve mobility schemes: Random Waypoint model Real traces from crawdad – Lots left to do for: Density estimators Spawn policies Carrier selection criteria Far future: – Improve node density estimation when in non-optimal communication environments. – Weak service consistency. 24

25. Conclusions Collaborative density estimation can significantly help nodes get better estimations and faster… LRS seems a viable scheme for the provision of location-specific services especially when only a subset of the nodes needs the data. More work to evaluate the tri-fold problem is necessary… 25

26. 26 Questions ? No Thank you! (good choice) Think about it again…. Yes

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