PeopleNet: Engineering A Wireless Virtual Social Network

1. PeopleNet: Engineering A Wireless Virtual Social Network Authors: Mehul Motani, Vikram Srinivasan and Pavan S. Nuggehalli Presented by: Sharon Smith

2. Observation: People often use social contacts for: time, location and community-specific information rather than using powerful search engines or libraries! Social contacts are generally good sources of this information. • Inference: There is a need for a simple and efficient mechanism to find location, time, and community-specific information between people. Motivation:

3. PeopleNet Solution: Solution: A seamless wireless virtual social network utilizing an architecture called PeopleNet to meet the needs of these information seekers. • PeopleNet is a query matching system exploits the: • (1) Natural behaviors of social networking and social mobility • (2) Pervasiveness of mobile phones and their p2p capabilities

4. PeopleNet System Perspective: • PeopleNet has 4 KEY advantages: • Convenient • $$ Cost-savings • Scalable • Lightweight

5. PeopleNet System Description: • Main components: • Long distance fixed infrastructure • Short distance p2p interfaces Cellularor WiMax Bluetooth or WiFi • Divide area into non-overlapping regions calledbazaars • Each bazaar handles specific types of queries

6. Bazaar 1 Bazaar 4 Bazaar 2 Bazaar 5 Bazaar 3 Bazaar 6 PeopleNet System Description: Fixed cellular infrastructure PeopleNet clustered cells form 6 bazaars

7. A bazaar would consists of several base stations controlled by a MSC = Mobile Switching Center A PC = PeopleNet Coordinator would be located at the MSC to provide features for PeopleNet PeopleNet Fixed Infrastructure: Incoming Query: PC chooses k users from MSC’s VLR= Visitor Loation register and transmits query Reference: www.althos.com Outgoing Query: PC with LUT maps query type to respective bazaar’s PC

8. Query Hierarchical Format: Query description is hierarchical with i higher than j, i > j A match occurs when all the specified levels of the request are identical to the response

9. Random Spread: Propagating Queries in P2P Mode: nA’s Buffer nB’s Buffer 4 1(A) 5 5(Q) 5(Q) 1 3(Q) nA nB 3(Q) 3(Q) 3(Q) 3 … 2 3(Q) … 6 5(Q) Random Swap: nA’s Buffer nB’s Buffer 1(A) 4 6 5(Q) 3 5(Q) 3(Q) nA nB 3(Q) 5(Q) 3(Q) 5(Q) 3(Q) 1 … 2 3(Q) 5 …

10. PeopleNet’s Metrics for Analysis: • Probability of match • 2) Time to match • 3) Time in system • 4) Number in system • 5) Number of distinct matches QoS for user Rate to re-inject queries Useful in buyer/seller application

11. PeopleNet’s Review Notation: • Guide to PeopleNet’s notation and metrics: N= Number of nodes in a bazaar l= Arrival rate of new queries in the system 2M= Total number of query types k= Total number of nodes in a bazaar that receive query from cellular network B= Size of buffer at each node L= Number of queries exchanged when nodes meet W*W= Square grid distance of location, W*W/m= bazaar size

12. PeopleNet’s Review Assumptions: 1) Every node occupies one grid position 2) Nodes can move either: random walk or i.i.d. walk 3) For every type of query this is a unique matching query type 4) Time is discrete 5) Probability of queries arriving = l 6) Query types are U (1, 2M) 7) Arrival rates are randomly distributed to k with N nodes 8) Transmission radius is sqrt(2) units

13. Metrics utilized for RePast simulations: W=32, N=30, B=3, M=30, lambda=.5 Swap vs. Spread Comparison: Time in System Avg. Copies

14. Metrics utilized for RePast simulations: W=32, N=30, B=3, M=30, lambda=.5 Swap vs. Spread Comparison: Matching Probability No. of Distinct Matches

15. Smaller bazaar performs better! • Theorem 1: The expected number of queries of a certain type will be • constant given by: • The mean is independent of k and lambda, however the variance is not. Qualitative Impact of Bazaars: Unfortunately, due to mobility patterns in real life, bazaars can’t be made very small. 

16. Swap vs. Spread Analysis: Where,

17. Probability of Match vs. Buffer Size Swap Probability of Match: As Buffer Size increases, matching prob. increases. Its maximal values occurs when L = B/2. Recall, L= number queries swapped when two nodes meet!

18. Maybe nodes could “employ some intelligence in swapping queries by prioritizing.” Random Swap: Meta-information Exchange: How do we decide which and how many queries to exchange? X(Q) nA nB Y(Q)

19. Meta-information Exchange: Using a summary snapshot of each node’s data, known as meta-information the average number of distinct queries can be improved. Given buffer information: Bx= [3Q, 3Q, 3Q, 4A] By= [3A, 3A, 4Q, 4Q] The meta-information would look as follows: X= [(3Q, 3), (4A, 1)] Y= [(3A, 2), (4Q, 2)]

20. Meta-information Exchange: Greedy meta-rank: Only takes into account highest count and chooses to match this way. Problem: Possibility of exchanging queries that it already matched. Smart meta-rank: Uses a thought experiment, by choosing largest query type first and then reorders the meta-information of peer and finds new largest query type count.

21. Greedy: Greedy vs. Smart Meta-rank Algorithms: 10 matches! Smart: 11 matches!

22. 12% better 40% reduced 8x Naive vs. Smart Results for Swap: With the following parameters: N=30, k=5, W=32, M=500, B=50, lambda=.5

23. System Security: • Data Integrity • Viruses and worms targeted to mobile phones • Buffer Management (deleting queries in buffer): • Time to Date (TTD) • Matches to Date (MTD) • System Capacity: • Depends on lambda and M parameters • Clear understanding necessary for maximal benefits • Swap vs. Spread: • Infinite Buffers vs. Finite Buffers • Query Database: • Need to decide query types and inform users Drawbacks and Extensions:

24. Mobility Patterns • Random walk mobility model is not ideal for mobile users • Need to find more accurate mobility model • Bluetooth or P2P pervasiveness • Is it fair to assume all mobile phones have p2p capability? • Persuading users to support the PeopleNet architecture • What benefits does a relaying mobile phone user have? • Why should they bother? Drawbacks and Extensions Cont’d:

25. Questions/Comments?