Network Design and In-network Data Analysis for Energy-Efficient Distributed Sensing

Network Design and In-network Data Analysis for Energy-Efficient Distributed Sensing

Liang Cheng, Ph.D., Associate Professor

Laboratory Of Networking Group (LONGLAB)

Department of Computer Science and Engineering

In Collaborations with ATLSS Colleagues

Liang Cheng, Ph.D., LONGLAB, Lehigh CSE 2

Outline Our research in distributed sensing sponsored by

NSF http://www.cse.lehigh.edu/~cheng/

LONGLAB_Liang_Cheng.pdf Wireless sensor networks for bridge monitoring

Network design for interference mitigation Distributed in-network data analysis

Conclusions


Subsurface monitoring techniques

air

underground

Crimp in cable

GPR TDR

Soil Moisture Sensor

Wireless Sensor Node

Wireless Sensor Node

Wireless Signal Networks

Sensing Area

Global Sensing

S. Yoon, L. Cheng, E. Ghazanfari, S. Pamukcu, and M. T. Suleiman, A radio propagation model for wireless underground sensor networks, IEEE Globecom, Houston, TX, December 2011.


Experiments: point vs. global sensing

Wireless VantagePro2

Soil moisture sensor

MICAz(WiSNS)

WaterLeakage #2

WaterLeakage #1


Point sensing vs. global sensing

0 5 10 15 20 25 30 35 40 450

10

20

Wat

er C

on

ten

t (%

)

Time (Min.)

0 5 10 15 20 25 30 35 40 45-100

-50

RS

SI (

dB

m)

WC in Point SensingRSSI for WiSNs

Water Leakage Event #1 Water Leakage Event #2

No Change

S. Yoon, E. Ghazanfari, L. Cheng, S. Pamukcu, M. T. Suleiman, Subsurface event detection and classification using wireless signal networks, Sensors, Vol. 12, No. 11, 2012.


Outline Our research in distributed sensing sponsored

by NSF Wireless sensor networks for bridge monitoring


Conclusions


Why bridge monitoring?

Critical to the economy and public safety

FHWA 2008: 25%


Why wireless sensing?

Routine visual inspection Wired monitoring

the Stone Cutter Bridge in Hong Kong has more than 1200 sensors


Wireless sensor network challenges

Network agility June – September 2006

Glen Ellen shaking magnitude 4.4 on 08/02/2006

3:0 Multi-hop (2008)

10 hours for getting 80 seconds of data (1KHz) from 56 sensors

Single-hop (2011) 5 minutes for 240KB

data from 20 sensors

Liang Cheng and Shamim Pakzad, Agility of Wireless Sensor Networks for Earthquake Monitoring of Bridges, the Sixth International Conference on Networked Sensing Systems (INSS'09), Carnegie Mellon University, Pittsburgh, USA, June 17 - 19, 2009.


Energy-efficient wireless sensor networks with resource constraints Network design

Critical radio range determination Hidden terminal problem solution

In-network data analysis Distributed system identification

…





Conclusions


Mitigating exposed interference

Critical radio range determination Reduce wireless collision probability Prolong network lifetime


Bernoulli graphs

Infinite radius, unreliable links Bela Bollobas, Random Graphs, Cambridge

University Press, 1985 A graph consists of N nodes where edges are

chosen independently and with probability p Find the critical p ensuring a connected graph

Pc=[logN+c(N)]/N


2D wireless networks

Finite radius, reliable links Gupta and Kumar, Critical power for asymptotic

connectivity in wireless networks, Stochastic Analysis, Control, Optimization & Applications, 1998.

A unit area containing N nodes, each having the same communication radius r Find the critical r ensuring a connected graph

Rc=[logN+c(N)]/N


Gap between theory and practice

Rc=[logN+c(N)]/N

Wireless sensor locations


1D wireless networks

Finite radius, reliable links Li and Cheng, Determinate Bounds of Design

Parameters for Critical Connectivity in Wireless Multi-hop Line Networks, IEEE WCNC 2011.

A unit length containing N nodes, each having the same communication radius r

Find the critical r ensuring a connected graph lnN/N =< Rc <= 2lnN/N


A bridge sensor network

Finite radius, unreliable links A unit length containing N nodes, each having

the same communication radius r with link connectivity probability p

Find the critical r ensuring a connected graph lnN/N =< Rc <= 2lnN/(pN)


Mitigating hidden interference

Hidden terminal problem Collision at will

Aloha (1971)

Collision avoidance IEEE 802.11 (1997)

Collision detection ?


Messages vs. pulses


Hidden terminal revisited

Hidden terminal no longer hidden! Collision detection


Throughput increased

J. Peng, L. Cheng, and B. Sikdar, A Wireless MAC Protocol with Collision Detection, IEEE Transactions on Mobile Computing, Vol. 6, No. 12, pp. 1357-1369, 2007.


Outline Our research in distributed sensing sponsored by

NSF Wireless sensor networks for bridge monitoring

Network design for interference mitigation Critical radio range determination Hidden terminal problem solution

Distributed in-network data analysis Distributed system identification

Conclusions


Modal parameters of dynamic systems Eigenvalue decomposition of the state matrix

(Ad) results in the matrices of eigenvalues (λi’s) and eigenvectors (ψi’s) The natural frequencies ωi and damping ratios ζi

)()()1( nuBnxAnx dd

)()()( nDunCxny

ti

ci

)ln(

21, iiiicc jii


Traditional modal identification Expectation-Maximization (EM)

estimates unknown parameter (Ѳ), given the measurement data (Y) in the presence of some hidden variables (Ŷ ) (Dempster, 1977)

)]/(log[)( YpL )](max[. LArg


Distributed modal identification


Evaluation results O(1/n) consumed energy comparing to the

centralized method in n-hop WSNs S. Dorvash, S. Pakzad, and L. Cheng, An iterative modal

identification algorithm for structural health monitoring using wireless sensor networks, Earthquake Spectra, Vol. 29, No. 2, pp. 339-365, May 2013.





Conclusions


Conclusions Energy-efficient wireless sensor networks with

resource constraints Network design

Critical radio range determination (1985, 1998, 2011) Hidden terminal problem solution (1971, 1997, 2007)

In-network data analysis Distributed system identification (Expectation-maximization

1977, frequency responses 2004, distributed modal identification 2011)


Acknowledgement

National Science Foundation (NSF)

Commonwealth of Pennsylvania Department of Community and Economic Development via PITA

Christian R. & Mary F. Lindback Foundation

Siavash Dorvash, Xu Li, Dr. Shamim Pakzad, Dr. Jun Peng


Q & A

[email protected]

610-758-5941

Liang Cheng

Computer Science & Engineering

19 Memorial Drive West, Bethlehem, PA 18015


Evaluation Scenarios

Wireless sensor locations


Resource constraints of sensor nodes Imote2

Transceiver: CC2420 Battery

Rechargeable: 300 mWh/cm3 Zinc-air: 1050-1560 mWh/cm3

CPU: 13–416 MHz Memory: 256kB SRAM, 32MB FLASH, 32MB SDRAM

Demo A freshman lab project of my Eng5 students

Documents

Network Design and In-network Data Analysis for Energy-Efficient Distributed Sensing