1. 2017/4/25
INDOOR LOCALIZATION SYSTEM USING RSSI MEASUREMENT OF WIRELESS SENSOR NETWORK BASED ON ZIGBEE STANDARD
Authors：Masashi Sugano, Tomonori Kawazoe, Yoshikazu Ohta, and Masayuki Murata
Publisher：Wireless Sensor Networks 2006
Present：Yu-Tso Chen
Date：March, 25, 2009
Department of Computer Science and Information Engineering
National Cheng Kung University, Taiwan R.O.C.
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2. Outline 1. Introduction 2. Localization System Model
2017/4/25
Outline
1. Introduction
2. Localization System Model
3. Effective Data Collection
4. Experiment Setup and Results
5. Conclusions and Future Works
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3. 2017/4/25
Introduction
Sensing data are meaningless if the sensor location is unknown.
RSSI has a larger variation because it is subject to the deleterious effects of fading or shadowing.
RSSI-based approach therefore needs more data than other methods to achieve higher accuracy.
Collecting a amount of data causes an increase in traffic & in the energy consumption.
We devised a data-collection technique in which sensors recognize the number of surrounding sensors.
Sensor擷取到的資料所在的位置也相當重要 比如說 空氣與水的品質若是不知道哪裡的品質也沒有幫助
RSSI有相當大的變異性，可能被遮蔽干擾，訊號時強時弱
所以RSSI-based的approach需要更多的data來達到高準確性
但是收集更多的資訊需會造成很大的traffic以及能量的消耗
所以提出了一個演算法來調節data collection
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4. Localization System Model
2017/4/25
Localization System Model
Target node 發送demand 給其他reference node 其他reference node 回傳給sink node
這裡直接回傳給sink node 沒有回傳給blind node 所以可能要記錄是哪一個blind node發出的request
Reference ndoe 知道自己位置且不會移動
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5. 2017/4/25
Sensor Node Placement
There is two ways in which a sensor node can learn its position.
Sensor node’s position in the sink node’s database
Can’t handle large number of randomly placed sensor nodes.
Places a few beacon nodes that know their own positions
Sensor nodes 有兩種方法可以知道自己的位置，一是把所有的sensor node資料都放在sink node的database內如果sensor node需要知道自己位置就由sink node告知
缺點是如果大量亂數擺放的sensor 很浪費空間 否則就是要照格子法擺放
另外一種是讓少數的sensor node知道自己的位置 其他sensor node 可以藉由他們知道自己的位置
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6. 2017/4/25
Data Collection
1. Measure the power of the packet and transform the RSSI into Distance
每個fixed sensor node收到target node的packets之後估算power並把RSSI轉成distance
包含sequence number當target發出就會增加
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7. Position Estimation Calculation at the Sink Node
2017/4/25
Position Estimation Calculation at the Sink Node
2. Sensors send the following data to sink node
Sensor ID, Target ID, Packet Number, and Sensor-to-Target Distance
3. Use a maximum-likelihood (ML) estimation to estimate the position of a target
Sensor node發送下列訊息給sink node
利用ML去估算target的位置
而ML可以轉換成MMSE
4. ML estimation of a target’s position can be obtained using the Minimum Mean Square Error (MMSE) [18]
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8. Position Estimation Calculation at the Sink Node
2017/4/25
Position Estimation Calculation at the Sink Node A B C P
1. 這邊是三角定位的公式推導 dpA為A到P距離
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9. Position Estimation Calculation at the Sink Node
2017/4/25
Position Estimation Calculation at the Sink Node
整理上面的算是可以寫成矩陣
要求的為P位置 則使用矩陣運算可以求出
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10. Effective Data Collection
2017/4/25
Effective Data Collection
A user can decide the number of data to collect based on prior knowledge.
Targets can inform sensors of the number of data by sending packets
Sensor nodes send data depends on the deployment density of sensor nodes itself and the distance between the sensor node and the target.
使用者可以自行決定 要收集多少個sensor node的data
Sensor node依照兩個值來決定是否要傳送data 一個是 周圍sensor 的密度 二是 是否與target node非常的靠近
Sensor node 每隔一段時間會通知周圍的node
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11. Effective Data Collection (cont.)
2017/4/25
Effective Data Collection (cont.)
R is the communication range and Mi is the number of sensor nodes.
Define the number of data required by the system as Z
Sensor node i sends data if the measured distance is less than Di
Di depends on the density around sensor node i
密度公式可以寫成下面這個公式很trival sensor 個數除以範圍面積
定義Z為需要多少資料量
決定node i要不要發送資料給sink node的條件是sensor與target距離要小於Di
Di與D成比例
Di主要與sensor node i的密度相關 如果密度高距離就可以短一些 這樣的話 sink node收到的sensor node個數不會變少
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12. Implementation of Localization System
2017/4/25
Implementation of Localization System
We set the threshold value of RSSI in each sensor node
Sensor node decides to transmit a packet to a sink node only when the received signal from a target exceeds this value
We can change the number of data to collect by changing this threshold value
每個sensor node我們都會設定一個RSSI threshold值
當收到的RSSI值大於threshold值 我們才會傳送封包給sink node
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13. Position Estimation Procedure
2017/4/25
Position Estimation Procedure
1. Sensor nodes’ positions are stored in a database on a PC.
The RSSI threshold is set in sensor nodes.
2. A measurement demand message is broadcast to sensor nodes from a target.
3. Sensor node measures RSSI, if exceeds the preset threshold value, a sensor node transmits the target ID and sequence number to the sink node
Sensor node的位置都存在PC上 以及每個sensor node都設定好RSSI 的threshold
Target node broadcast measurement demand message 到所有的sensor nodes
Sensor node估計RSSI值 如果超過threshold值，則sensor node傳訊息(target ID& Seq. # )給sink node
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14. Position Estimation Procedure (cont.)
2017/4/25
Position Estimation Procedure (cont.)
4. Sink node collects the ID and sequence number of the target, and the ID and RSSI of each sensor node.
If three or more RSSI values with the same target ID and sequence number are collected, the target’s position can be estimated.
4. Sink收集了Target的ID & Sequence number 以及sensor node的ID&RSSI
5. 如果有超過三個同樣target ID&Sequence number的RSSI 就可以算出位置
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15. Experimental Results 2017/4/25
1. Relationship between communication distance and RSSI value
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16. Positions of sensor nodes and targets in the conference room
2017/4/25
Positions of sensor nodes and targets in the conference room
7.08m x m
number of data that were predicted to be collectable for various RSSI thresholds
表示多少的threshold值可以截取到多少個sensor node的資料
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17. Predicted & Actually Obtained Data Collection Numbers
2017/4/25
Predicted & Actually Obtained Data Collection Numbers
Paper說差異會在 retransmissions in IEEE , which is five 五次 但看不出為何
由實驗結果可以看出來不需要收集7個以上的訊號 可以省略一些訊號
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18. Conclusions & Future Works
2017/4/25
Conclusions & Future Works
Density of sensor nodes was set to 0.27 nodes/m2 the position estimation error could be reduced to m.
The collected numbers of data could be controlled by changing the RSSI threshold.
當密度約為0.27時 則誤差可以縮小到1.5 – 2m但是這密度平均 不太公平 sensor node 沒有均勻分配 target node也是剛好都在中間
做到控制收集資料數可以藉由RSSI的threshold來達成
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