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