Download - Sensor Data
![Page 1: Sensor Data](https://reader035.vdocuments.pub/reader035/viewer/2022062812/5681642f550346895dd5f914/html5/thumbnails/1.jpg)
Sensor Data
한국기술교육대학교 민준기
![Page 2: Sensor Data](https://reader035.vdocuments.pub/reader035/viewer/2022062812/5681642f550346895dd5f914/html5/thumbnails/2.jpg)
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
![Page 3: Sensor Data](https://reader035.vdocuments.pub/reader035/viewer/2022062812/5681642f550346895dd5f914/html5/thumbnails/3.jpg)
Data Aggregation
Data Gathering Query Processing
Major Research Topics
![Page 4: Sensor Data](https://reader035.vdocuments.pub/reader035/viewer/2022062812/5681642f550346895dd5f914/html5/thumbnails/4.jpg)
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)
![Page 5: Sensor Data](https://reader035.vdocuments.pub/reader035/viewer/2022062812/5681642f550346895dd5f914/html5/thumbnails/5.jpg)
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
![Page 6: Sensor Data](https://reader035.vdocuments.pub/reader035/viewer/2022062812/5681642f550346895dd5f914/html5/thumbnails/6.jpg)
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 )
![Page 7: Sensor Data](https://reader035.vdocuments.pub/reader035/viewer/2022062812/5681642f550346895dd5f914/html5/thumbnails/7.jpg)
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 )
![Page 8: Sensor Data](https://reader035.vdocuments.pub/reader035/viewer/2022062812/5681642f550346895dd5f914/html5/thumbnails/8.jpg)
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)
![Page 9: Sensor Data](https://reader035.vdocuments.pub/reader035/viewer/2022062812/5681642f550346895dd5f914/html5/thumbnails/9.jpg)
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 )
![Page 10: Sensor Data](https://reader035.vdocuments.pub/reader035/viewer/2022062812/5681642f550346895dd5f914/html5/thumbnails/10.jpg)
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 )
![Page 11: Sensor Data](https://reader035.vdocuments.pub/reader035/viewer/2022062812/5681642f550346895dd5f914/html5/thumbnails/11.jpg)
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)
![Page 12: Sensor Data](https://reader035.vdocuments.pub/reader035/viewer/2022062812/5681642f550346895dd5f914/html5/thumbnails/12.jpg)
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 )
![Page 13: Sensor Data](https://reader035.vdocuments.pub/reader035/viewer/2022062812/5681642f550346895dd5f914/html5/thumbnails/13.jpg)
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)
![Page 14: Sensor Data](https://reader035.vdocuments.pub/reader035/viewer/2022062812/5681642f550346895dd5f914/html5/thumbnails/14.jpg)
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
![Page 15: Sensor Data](https://reader035.vdocuments.pub/reader035/viewer/2022062812/5681642f550346895dd5f914/html5/thumbnails/15.jpg)
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)
![Page 16: Sensor Data](https://reader035.vdocuments.pub/reader035/viewer/2022062812/5681642f550346895dd5f914/html5/thumbnails/16.jpg)
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
![Page 17: Sensor Data](https://reader035.vdocuments.pub/reader035/viewer/2022062812/5681642f550346895dd5f914/html5/thumbnails/17.jpg)
Join Operation◦ An important operator◦ It allows to relate measurements taken at differ-
ent nodes.
Query Processing(1/6)
L R
![Page 18: Sensor Data](https://reader035.vdocuments.pub/reader035/viewer/2022062812/5681642f550346895dd5f914/html5/thumbnails/18.jpg)
General Join Plans[12,13]
Query Processing(2/6)
L R
Naive
L RSequential
L RCentroid
![Page 19: Sensor Data](https://reader035.vdocuments.pub/reader035/viewer/2022062812/5681642f550346895dd5f914/html5/thumbnails/19.jpg)
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)
![Page 20: Sensor Data](https://reader035.vdocuments.pub/reader035/viewer/2022062812/5681642f550346895dd5f914/html5/thumbnails/20.jpg)
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)
![Page 21: Sensor Data](https://reader035.vdocuments.pub/reader035/viewer/2022062812/5681642f550346895dd5f914/html5/thumbnails/21.jpg)
TPSJ [10]◦ Preprocessing: Query Decomposition
Query Q
Decomposed Queries Q1 Q2
Page 21
Query Processing(5/6)
![Page 22: Sensor Data](https://reader035.vdocuments.pub/reader035/viewer/2022062812/5681642f550346895dd5f914/html5/thumbnails/22.jpg)
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)
![Page 23: Sensor Data](https://reader035.vdocuments.pub/reader035/viewer/2022062812/5681642f550346895dd5f914/html5/thumbnails/23.jpg)
Sensor◦ Light weight◦ Wireless
Sensor Data Management◦ Reduce Energy consumption
In-network Processing Aggregation Gathering Query Processing
Conclusion
![Page 24: Sensor Data](https://reader035.vdocuments.pub/reader035/viewer/2022062812/5681642f550346895dd5f914/html5/thumbnails/24.jpg)
[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