for paper Bichromatic Reverse Nearest Neighbours in Mobile P2P Networks, PERCOM 2013 by Thao P. Nghiem

1. Bichromatic Reverse Nearest Neighbours
in Mobile P2P Networks
Jessie Nghiem, Kiki Maulana
Agustinus Borgy Waluyo, David Green, David Taniar

2. Inspiring example Earthquake and rescue teams
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3. Motivation
Advances in mobile technology
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4. Motivation
1. Scalability
Limitations of Centralized Systems 2. Bottleneck
Moving objects Wide-range comm.
3. Low fault-tolerance
Interest objects P2P comm.
3/31/2013 (a) Centralized Systems (b) P2P Systems 4

5. Ultimate Aim
“… to harness collaborative power of peers
for spatial query processing in Mobile Environment”
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6. Problem definition
– Bichromatic Reverse Nearest Neighbour (BRNN)
Query point
Moving
i0 objects
Circle from the object
of interest to its Bichromatic
nearest moving object i1
Objects of
interest
io and i1 are the results of the RNN query from q
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7. Related work Propose:
– Tao, Y., Papadias, D., Lian, X.: Reverse
knn search in arbitrary dimensionality.
• Limitations: P2P
In: Proceedings of the Thirtieth – Centralized approach
international conference on Very large
data bases , VLDB '04. – Only deal with
monochromatic RNNs
H-(p0) Bichromatic
Half-space pruning. Any point that lies in the
shaded half- space H-(p0)
is always closer to p0 than to q and cannot be the
RNN for this reason.
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8. Definitions
Negative half plane
Positive half plane
Query node
Peer node
Boundary line
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9. Definitions
Object of interest o
• Boundary region
• If B is closed, B: boundary polygon.
• The boundary polygon B is called a
tight polygon iff any object of interest
oi inside B regards q as the closest
moving object.
Boundary polygon B
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10. How to build a tight polygon The next processing peer is q4
outside C
p4 C(q, qq0)
TIGHT
Boundary polygon
Farthest vertex
q0
P = {p0, p1, …, p4,,p5, p6, …} is a priority queue Reflection point of q thru v0
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11. Construct the
polygon for filtering
objects of interest
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12. Exhaustive Search vs Centralized Search
Remarkably efficient in saving energy and time
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13. Only sends query to
the peers that build B
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14. Optimized Search versa Exhaustive Search
Approximate accuracy rate with less mean latency
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15. Simulation framework
- Based on OMNet++ and MiXiM
- Using network interface card
which follows
IEEE 802.15.4 standard for
bluetooth networks
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16. Simulation framework
Simulation model
Parameters Value
Playground 87.1km2
No. of MOs 7600
No. of IOs 550
Cache Size 50
Expected no. of 2
queries/MO
Simulation time 30s
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17. Simulation Results – P2P Search versa Centralized Search
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18. Optimized Search versa Exhaustive Search
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19. Simulation Results – No. of Peers Pruned and Stop Hits
3/31/2013 Optimized Search Algorithm 19

20. Conclusion
• P2P Search significantly save communication cost and 43%
processing time compared to Centralized Search
• Optimized Search reduces the number of queried peers and then
response time while it maintains accuracy rate approximate to that
of Exhaustive Search.
• A practically feasible option for a large-scale and busy network
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22. 3/31/2013 22

23. Problem Statement
• Let P and O be two sets of points in the same data space.
• Given a point p є P, a BRNN query finds all the points o є O whose
nearest neighbours in P are p, namely, there does not exist any other
point p0 є P such that d(o, p0) < d(o, p).
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24. System Overview
Beacon message
Ack. message
Query
Peers
Node Query message
Reply message
Communication between Query node and Peers
Three phases:
1. Initialization and Peer Discovery
2. Constructing a Boundary Polygon and
Sending Queries
3. Pruning Interest Objects
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25. Definitions
• q, p
• P ={p1,…. pH}
is a priority queue of peers of q. |P| = H.
• Boundary line (b1)
•
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26. Lemma – How to build a tight polygon
If ∃pi є priority queue P, such that dist(q; pi) ≥ dist(q; vj), then B is a
tight polygon.
Put another way, we do not need to consider remaining peers left in the
queue P and stop creating the polygon.
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27. Simulation framework
Connection Manager
World
• Based on OMNeT++
Moving object
Object of interest
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