sensor data

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Sensor Data. 한국기술교육대학교 민준기. Sensor Data Management. Wireless Sensor Network Limited Energy Power Limited Computing Power Sensor Data Management Navie Approach Each Sensor sends data to the base station Do data processing at the base station Problem - PowerPoint PPT Presentation

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Sensor Data

한국기술교육대학교 민준기

Wireless Sensor Network◦ Limited Energy Power◦ Limited Computing Power

Sensor Data Management◦ Navie Approach

Each Sensor sends data to the base station Do data processing at the base station

◦ Problem Each sensor waste its energy quickly in order to send its read-

ing continuously◦ Minimize Energy Consumption◦ In-Network Processing

Sensor Data Management

Data Aggregation

Data Gathering Query Processing

Major Research Topics

TAG (Tiny Aggregation)◦ In-Network Aggregation◦ Tree Routing Based

◦ Simple Approach◦ Cost for Median is very high

Aggregation(1/5)

2

4 3

5 3 2 2

Sum(2,12, 7)

Sum(4,5,3) Sum(3,2,2)

Q-Digest[2]◦ Capture the distribution of sensor data approximately◦ Digest property

count(v) <= floor(n/k) (except leaf node) count(v)+count(vp)+count(vs) >= floor(n/k) (except root node), where v is a node, vp is the parent of v, vs is the sibling of v.n is the number of data, k is compression parameterσ is the range of data

◦ Size of q-Digest <= 3k Each Sensor build q-Digest Parent node

◦ Merges q-Digests of Children◦ Compression

Aggregation(2/5 )

compression

Quantile Query◦ Find value whose rank in n values is qn, where q (0,1)

If q = 0.5, find median<[1,8],1> <[5,6], 2> <[7,8], 2> <[3,3],4> <[4,4], 6>Sorting in increasing right end point <[3,3],4> <[4,4], 6> <[5,6], 2> <[7,8], 2> <[1,8],1> <[4,4],6> exceed 0.5*15= 7.5Thus, 4 is an estimated median

Aggregation(3/5 )

Multiple Aggregation◦ Equivalence Class Reduction[3]

Q = {q1 = {1+2+3}, q2 ={1+2}, q3 = {3}} Equivalent class = set of sensors supports same

query set EC1 = {1,2} , EC2 = {3} Bit Vector EC1 = [1,1,0]T, EC2 = [1,0,1]T

EC1 EC2Q1 1 1 basisQ2 1 0 x v1 = {1+2} 1 0 x v1Q3 0 1 v2 = {3} 0 1 v2

Aggregation(4/5 )

Multiple Aggregation◦ Segmentation Based Method[4]

Dynamic routing, Not tree routing Segment == equivalent class A sensor sends data to a node including same segment as possible STG vs STS

Node 6 can send data to node 5 and 7, in case, node 6 sends data to node 7 STG : node 4 sends data for q2 (=4, 7, 8) and q1+q2 (=4,5)

node 1 receives 3 messages ( from node 2 - 1 message, node 4- 2 messages) STS: multiple routing

node 4 sends data for q2 (=4,5,6,7) to node 1 and q1(=4,5) to node 2 node 1 receives 2 messages

Aggregation(5/5)

In-network aggregation provides a great opportunity for reducing the communication overhead

Since a single aggregated value represents the overall sensing field, it may be insuffi-cient to analysis the correlation among sub-regions of the sensor field

Sensor Data Gathering◦ Exact Data Gathering waste Energy◦ Solution reduce the number of transmission

Gathering(1/8 )

Basic Approach◦ Temporal Suppression

A node does not transmit a value if it has not change since last reported

◦ Spatial Suppression A node suppresses it value if it is identical to those of

its neighboring Approximate Gathering

◦ Sensor readings have errors intrinsically◦ Sensor readings have strong correlations

Gathering(2/8 )

Approximate Data Gathering◦ Each Sensor has a tool to estimate future value◦ The base Station also keep tools

If a sensor does not send data estimation correct If a sensor sends data estimation incorrect

Update tools of the sensor and the basestation

◦ Model Based BBQ[5] KEN[6] PAQ[7]

◦ Filter Based Dual Kalman[9]

◦ Compression Based Wavelet, DFT, SBR[8]etc. A collection of readings of a sensor is transmitted periodically

Gathering(3/8)

Model Based Approach◦ Linear Regression

Xt+1 = aXt+b◦ BBQ, KEN

Multivariate Gaussian model Probability density function: P(X1, X2, X3, …, Xn)

Xi: random variable for sensor readings

Gathering (4/8 )

Approximate Gathering◦ PAQ

Linear Regression and Gaussian model require much time to construct correct model, and much data

AutoRegression(3) model A data Vt = mt+X(t) Vt - mt= X(t) X(t) = aX(t-1)+bX(t-2)+cX(t-3)+b(w)N(0,1) mt is a mean of V to time t, a,b,c is real constants,

b(w) is white noise Predictor P(t) = mt+ a(vt-1 – mt-1)+ b(vt-2 – mt-2) + c(vt-3

– mt-3)

Gathering(5/8)

PAQ◦ Lemma)Let e = v b(w), where v > 1. Then the actual

value at time t is contained in [P(t)-e , P(t)+e)] with probability at most 1/v2.

Proof) Chebychev inequality P(|vt- P(t)| > e) <= b(w)2/e2 = b(w)2/v2b(w)2 = 1/v2

◦ Generally v is 6 or 7◦ Using above Lemma, PAQ decide when it updates its

model.

Gathering(6/8)

-e -d d -e

Well fit Parital fit Outlier

Filter Based◦ Mode Based Approach requires much data to con-

struct models◦ Each node has the filter according to the last re-

ported sensor reading |Vnew – Vold| > e, the reading is sent to the base sta-

tion

Gathering(7/8)

Dual Kalman Filter◦ Base station has as many filters as the number of

sensors◦ Discrete Kalman Filter◦ Ex) moving object

State model : xt = vt-1*dt+xt-1

vt = vt-1 Measure model: z (real position)

z = [1 0]T x +vt

, where vt is measurement white Guassion noise

Gathering(8/8 )

project current state

Estimatenext state

Prediction stepComputeKalman gain

Updatesystem state

Correction step

Updateerror covariance

Initial state

Join Operation◦ An important operator◦ It allows to relate measurements taken at differ-

ent nodes.

Query Processing(1/6)

L R

General Join Plans[12,13]

Query Processing(2/6)

L R

Naive

L RSequential

L RCentroid

Optimal Join Location[14]◦ Weighted Fermat Problem

One wants to find the point with the property that the weighted sum of the distances from the point to the vertexes of a triangle is minimized.

Query Processing(3/6)

Synopsis Join[13]◦ Prunes non-candidate tuples and only joins candi-

date tuples◦ Preliminary Join

Eliminate non-candidate tuples

◦ Final Join

Query Processing(4/6)

TPSJ [10]◦ Preprocessing: Query Decomposition

Query Q

Decomposed Queries Q1 Q2

Page 21

Query Processing(5/6)

TPSJ◦ Fist phase

Query Q1 execute◦ Second phase

Query Q2 is executed with the injecting of R1 into the network

Page 22

Query Processing(6/6)

Sensor◦ Light weight◦ Wireless

Sensor Data Management◦ Reduce Energy consumption

In-network Processing Aggregation Gathering Query Processing

Conclusion

[1] S. Madden et.al., “TAG: Aggregation Service for Ad-Hoc Sensor Networks”, OSDI, 2002 [2] N. Shrivastava et.al., “Medians and Beyond: New Aggregation Techniques for Sensor Networks,”

ACM Sensys 2004 [3] N. Trigoni et.al., “Multi-Query Optimization for Sensor Networks” DCOSS 2005 [4]N. Trigoni, et.al., "Routing and Processing Multiple Aggregate Queries in Sensor Networks,“ ACM

SenSys, 2006. [5] A. Deshpande et.al., "Model-Driven Data Acquisition in Sensor Networks,“ VLDB, 2004. [6] D. Chu et.al., "Approximate Data Collection in Sensor Networks using Probabilistic Models,“

ICDE, 2006 [7] D. Tulone et. al., “PAQ: Time Series Forecasting For Approximate Query Answering In Sensor

Networks,” European Conf. Wireless Sensor Networks, 2006 [8] A. Deligiannakis et.al., “Compressing Historical Information in Sensor Networks,” ACM SIGMOD

2004 [9] A. Jain et.al., “Adaptive Stream Resource Management Using Kalman Filters,” ACM SIGMOD 2004 [10] X. Yang et.al., “In-Network Execution of Monitoring Queries in Sensor Networks,” ACM SIGMOD

2007. [11]M. Stern et.al., “Towards Efficient Processing of Gneral-Purpose Joins in Sensor Networks,” ICDE

2009. [12]A. Pandit et.al, “ Communication-Efficient Implementation of Range-Joins in Sensor Networks,”

International Conference on Database Systems for Advanced Applications (DASFAA), 2006 [13] H. Yu et.al, “In-Network Join Processing for Sensor Networks,” APWeb 2006. [14] A. Coman et.al, “On Join Location in Sensor Networks,” MDM 2007.

Reference

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