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Passive Interference Measurement in Wireless Sensor Networks. Shucheng Liu 1,2 , Guoliang Xing 3 , Hongwei Zhang 4 , Jianping Wang 2 , Jun Huang 3 , Mo Sha 5 , Liusheng Huang 1 1 University of Science and Technology of China,

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passive interference measurement in wireless sensor networks

Passive Interference Measurement in Wireless Sensor Networks

Shucheng Liu1,2, Guoliang Xing3, Hongwei Zhang4,

JianpingWang2, Jun Huang3, Mo Sha5, Liusheng Huang1

1University of Science and Technology of China,

2City University of Hong Kong, 3Michigan State University,

4Wayne State University, 5Washington University in St. Louis

outline
Outline

Motivation

Understanding the PRR-SINR interference model

Passive Interference Measurement (PIM) protocol

Testbedevaluation

data intensive sensing applications
Data-intensive Sensing Applications

acoustic sensors detecting AAV

http://www.ece.wisc.edu/~sensit/

100 seismometers in UCLA campus [Estrin 02]

  • Real-time target detection & tracking, earthquake monitoring, structural monitoring etc.
    • Ex: accelerometers must sample a structure at 100 Hz
challenges
Challenges

Wireless sensors have limited bandwidth

Excessive packet collisions in high-rate apps

Energy waste and poor communication quality

Interference mitigation schemes

TDMA, link scheduling, channel assignments…

Rely on accurate interference models

interference models
Interference Models

PRR=100%

  • Protocol model
    • Perfect comm. range
    • Binary packet reception
  • PRR-SINR model
    • Packet reception ratio vs. signal to interference plus noise ratio

Ganesan 2002

empirical study on prr sinr model
Empirical Study on PRR-SINR Model

Measurement in different times

Measurement at different locations

Significant spatial and temporal variation

Real-time interference model measurement is necessary

a state of the art measurement method
A State-of-the-Art Measurement Method

SINR measurement

Synchronization

Received?

Sender

Noise Level Measurement

Receiver

Interferer

Time

Receive/measure event

Send event

Measuring multiple (PRR,SINR) pairs for many nodes

 Prohibitively high overhead!

outline1
Outline

Motivation

Understanding PRR-SINR model

Passive Interference Measurement (PIM) protocol

Performance evaluation

key observations
Key Observations

SINR=1dB

SINR=2dB

SINR=5dB

Data traffic generates many packet collisions

Spatial diversity leads to different SINRs

overview of pim
Overview of PIM
  • Measure M-node’s PRR-SINR model
  • R-node selection
  • Information collection
  • Interference detection
  • Model generation

R-node 1

R-node 2

M-node

base station

Interference link

Data link

information collection
Information Collection

Aggregator

  • RSS measurements of collision-free packets

Received Signal Strength

R-node 1

R-node 2

p1

p1

p2

p2

M-node

information collection1
Information Collection

Aggregator

  • TX/RX statistics of colliding packets

r-node 1

r-node 2

p3

p4

m-node

Receive with collision

information collection2
Information Collection

Aggregator

  • Colliding packets for TX/RX statistics

r-node 1

r-node 2

p5

p6

m-node

Lost due to collision

interference detection
Interference Detection
  • Detect interferer with collected timestamps
  • Remove fake collisions
    • Packets may overlap without interference!
    • Remove using measured RSS information

p4 collides with p3, but received by M

p6 collides with p5, lost at M

model generation
Model Generation
  • Derive SINR for collision of p3, p4
  • SINR(p3+p4) = RSS(p4) – RSS(p3) – Noise
  • = RSS(p2) – RSS(p1) – Noise
  • Compute PRR
  • PRR = 50%

p4 collides with p3, but received by M

p6 collides with p5, lost at M

r node selection
R-Node Selection
  • Minimize the number of r-nodes used to measure the (PRR,SINR) pairs of all M-nodes
  • Proved to be NP-hard
  • Designed a efficient greedy algorithm

R-Nodes Set {R1, R2, R3}

experimental setup
Experimental Setup
  • Implemented on TelosB with TinyOS-2.0.2
  • Both a 13-node portable testbed and a 40-node static testbed
  • Compared with the ACTIVE method
slide18

Accuracy of PIM

  • Create a model using 5 min statistics
  • Predict the throughput of from another sender
  • Baseline methods
    • Active method w/ 256 and 1024 control packets
    • Analytical model in Tinyos2.1
conclusions
Conclusions
  • Empirical study of PRR-SINR interference model
  • Passive interference measurement
    • Significantly lower overhead
    • High accuracy of PRR-SINR modeling
    • Real time interference modeling
  • Performance evaluation on real testbeds
remove fake interfering packets
Remove Fake Interfering Packets
  • Rule 1: If a interfering packet set of node v maintains the same SINR when removing packet w, then the forwarder/sender of w is a fake r-node of node v.
  • Rule 2: If node u is a fake r-node of node v, then any packet sent by u does not interfere with any packet received by v.
example
Example
  • Fake r-node of N4:
    • N7
    • N5
overview
Overview

chosen to help measure the PRR-SINR model of the m-node

records the time when an r-node forwards each packet

records the time when an m-node receives each packet

records the RSS values of the received packets.

The system architecture of PIM

whose PRR-SINR models are to be measured

collects information and generates the PRR-SINR models of m-nodes

overview1
Overview

decreases overhead by identifies interferers of m-nodes

generates PRR-SINR models of m-nodes

detects interferer using collected information

The system architecture of PIM

information collection3
Information Collection
  • Timestamping
    • Record the time of forwarding/sending and receiving packet
  • RSS measurement
    • Record the RSS value of received packet
  • All the recorded informations are then transmitted to the aggregator
why prr sinr model

received signal power of packet

probability of receiving packet

Why PRR-SINR Model?

s2

  • Packet-level physical interference model
  • Easy to estimated based on packet statistics
  • Directly describes the impact of dynamics
    • Environmental noise
    • Concurrent transmissions

s1

r

collisions

received signal power of interfering transmissions

average power of

ambient noise