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for paper Bichromatic Reverse Nearest Neighbours in Mobile P2P Networks, PERCOM 2013 by Thao P. Nghiem
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Bichromatic Reverse Nearest Neighboursin Mobile P2P Networks
Jessie Nghiem, Kiki Maulana
Agustinus Borgy Waluyo, David Green, David Taniar
Earthquake and rescue teamsInspiring example
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Motivation
Advances in mobile technology
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Motivation
Limitations of Centralized Systems
(a) Centralized Systems (b) P2P Systems
Moving objects
Interest objects
Wide-range comm.
P2P comm.
1. Scalability
2. Bottleneck
3. Low fault-tolerance
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Ultimate Aim
“… to harness collaborative power of peers
for spatial query processing in Mobile Environment”
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Problem definition– Bichromatic Reverse Nearest Neighbour (BRNN)
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Moving objects
Objects of interest
i0
i1
Circle from the object of interest to its nearest moving object
io and i1 are the results of the RNN query from q
Query point
Bichromatic
Related work
– Tao, Y., Papadias, D., Lian, X.: Reverse knn search in arbitrary dimensionality. In: Proceedings of the Thirtieth international conference on Very large data bases , VLDB '04.
• Limitations:
– Centralized approach
– Only deal with monochromatic RNNs
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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.
H-(p0)
Propose:
P2P
Bichromatic
Definitions
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Query node
Peer node
Positive half planeNegative half plane
Boundary line
Definitions
• 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.
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Boundary polygon B
Object of interest o
How to build a tight polygon
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Boundary polygon
Farthest vertex
Reflection point of q thru v0
C(q, qq0)
The next processing peer is q4
outside C
TIGHT
q0
p4
P = {p0, p1, …, p4,,p5, p6, …} is a priority queue
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Construct the polygon for filtering objects of interest
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Exhaustive Search vs Centralized Search
Remarkably efficient in saving energy and time
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Only sends query to the peers that build B
Optimized Search versa Exhaustive Search
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Approximate accuracy rate with less mean latency
Simulation framework
- Based on OMNet++ and MiXiM- Using network interface card which follows IEEE 802.15.4 standard forbluetooth networks
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Simulation framework
Simulation model
Parameters Value
Playground 87.1km2
No. of MOs 7600
No. of IOs 550
Cache Size 50
Expected no. of queries/MO
2
Simulation time 30s
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Simulation Results – P2P Search versa Centralized Search
Optimized Search versa Exhaustive Search
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Simulation Results – No. of Peers Pruned and Stop Hits
3/31/2013 19Optimized Search Algorithm
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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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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System Overview
Query
NodePeers
Beacon message
Ack. message
Query message
Reply message
Communication between Query node and Peers
Three phases: 1. Initialization and Peer Discovery2. Constructing a Boundary Polygon and
Sending Queries 3. Pruning Interest Objects
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Definitions
• q, p
• P ={p1,…. pH}
is a priority queue of peers of q. |P| = H.
• Boundary line (b1)
•
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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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Simulation framework
• Based on OMNeT++
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WorldConnection Manager
Moving object
Object of interest