Network Design and In-network Data Analysis for Energy-Efficient Distributed Sensing The majority of my talk today is mostly based on my LONGLAB’s collaborative.

Network Design and In-network Data Analysis for Energy-Efficient Distributed Sensing
The majority of my talk today is mostly based on my LONGLAB’s collaborative work with Lehigh’s civil engineering colleagues at ATLSS, a U.S. National Engineering Research Center of Advanced Technology for Large Structural Systems. 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
Outline Our research in distributed sensing sponsored by NSF Wireless sensor networks for bridge monitoring Network design for interference mitigation Distributed in-network data analysis Conclusions In this talk, I will first introduce the background of the research and the challenges of applying wireless sensor networks in the context of bridge monitoring applications. Then I will present our solutions in the network design and in-network processing aspects. Specifically I will discuss the history of the critical communication radius problem in sensor network design and how our result of determinate upper and lower bounds of the critical radius would bridge the gap between theory and practice for sensor network design. These are some ongoing research topics in my group and my presentation will include both what we have solved and what we are trying to solve. I feel that it would be better to discuss both solved and unsolved when we could interact face-to-face here rather than just explain things that have already been published in our papers. Liang Cheng, Ph.D., LONGLAB, Lehigh CSE

Subsurface monitoring techniques
GPR TDR air underground Sensing Area Wireless Sensor Node Wireless Sensor Node Subsurface monitoring has been accomplished through traditional techniques including direct soil sampling, probing and soundings, and using geophysical mapping tools. Although these techniques have been successfully implemented to characterize the global state of geo-media in an interested site, there are challenges associated with these techniques including difficulties in providing real-time data to track geo-hazards and deployment challenges specifically the requirement of wired connections in some conventional techniques. To address these challenges, wireless sensor networks have been used recently for subsurface monitoring. However, wireless sensor nodes in existing solutions only provide point measurements and are incapable of providing global measurement for characterization of subsurface medium. The key contribution of this dissertation is to bridge the gap between real-time monitoring and global measurements by introducing a novel concept of Wireless Signal Networks (WSiNs) with a proof of concept for subsurface monitoring using actual wireless sensor nodes (i.e., MICAz) and a system design with a reconfigurable radio platform. Wireless signal networks use the real-time link quality signals among distributed wireless sensor nodes as the main indicator of an event in the physical domain. Our thesis is that "the variation of the link quality between wireless sensor nodes can be used as an effective global sensing mechanism which reflects characteristics of geo-media subjected to various geo-events". To prove this thesis, the dissertation proposes an accurate and simple radio propagation model for underground environments, and the model quantitatively explains that the changes of soil properties and conditions affect the link quality and strength of the radio waves within the region of the event. Then, the dissertation presents real-time global subsurface monitoring applications with wireless signal network concepts based on the proposed underground propagation model including experimental evaluations. Experiments demonstrated that calibrated wireless signal strength variations can be used as indicators to sense changes in the subsurface. To extend underground communication distance, prolong network lifetime, and provide adaptive topology construction, we propose a system design for wireless signal networks with the reconfigurable radio platform. Based on the theoretical and empirical analysis of the radio propagation model, the dissertation proposes practical solutions of extending the underground communication distance and an evaluation platform for the system design with reconfigurable radio (i.e., Universal Software Radio Peripheral). The dissertation describes a novel topology control mechanism by introducing a new control dimension, i.e., frequency control, with the reconfigurable radio that supports underground communication distance extension, network lifetime enhancement, and adaptive topology construction in underground environments of high signal attenuation affected by geo-events. Wireless Signal Networks Crimp in cable Global Sensing Soil Moisture Sensor 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. Liang Cheng, Ph.D., LONGLAB, Lehigh CSE

Experiments: point vs. global sensing
Wireless Vantage Pro2 Water Leakage #2 Water Leakage #1 Subsurface monitoring has been accomplished through traditional techniques including direct soil sampling, probing and soundings, and using geophysical mapping tools. Although these techniques have been successfully implemented to characterize the global state of geo-media in an interested site, there are challenges associated with these techniques including difficulties in providing real-time data to track geo-hazards and deployment challenges specifically the requirement of wired connections in some conventional techniques. To address these challenges, wireless sensor networks have been used recently for subsurface monitoring. However, wireless sensor nodes in existing solutions only provide point measurements and are incapable of providing global measurement for characterization of subsurface medium. The key contribution of this dissertation is to bridge the gap between real-time monitoring and global measurements by introducing a novel concept of Wireless Signal Networks (WSiNs) with a proof of concept for subsurface monitoring using actual wireless sensor nodes (i.e., MICAz) and a system design with a reconfigurable radio platform. Wireless signal networks use the real-time link quality signals among distributed wireless sensor nodes as the main indicator of an event in the physical domain. Our thesis is that "the variation of the link quality between wireless sensor nodes can be used as an effective global sensing mechanism which reflects characteristics of geo-media subjected to various geo-events". To prove this thesis, the dissertation proposes an accurate and simple radio propagation model for underground environments, and the model quantitatively explains that the changes of soil properties and conditions affect the link quality and strength of the radio waves within the region of the event. Then, the dissertation presents real-time global subsurface monitoring applications with wireless signal network concepts based on the proposed underground propagation model including experimental evaluations. Experiments demonstrated that calibrated wireless signal strength variations can be used as indicators to sense changes in the subsurface. To extend underground communication distance, prolong network lifetime, and provide adaptive topology construction, we propose a system design for wireless signal networks with the reconfigurable radio platform. Based on the theoretical and empirical analysis of the radio propagation model, the dissertation proposes practical solutions of extending the underground communication distance and an evaluation platform for the system design with reconfigurable radio (i.e., Universal Software Radio Peripheral). The dissertation describes a novel topology control mechanism by introducing a new control dimension, i.e., frequency control, with the reconfigurable radio that supports underground communication distance extension, network lifetime enhancement, and adaptive topology construction in underground environments of high signal attenuation affected by geo-events. Soil moisture sensor MICAz (WiSNS) Liang Cheng, Ph.D., LONGLAB, Lehigh CSE

Point sensing vs. global sensing
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. Subsurface monitoring has been accomplished through traditional techniques including direct soil sampling, probing and soundings, and using geophysical mapping tools. Although these techniques have been successfully implemented to characterize the global state of geo-media in an interested site, there are challenges associated with these techniques including difficulties in providing real-time data to track geo-hazards and deployment challenges specifically the requirement of wired connections in some conventional techniques. To address these challenges, wireless sensor networks have been used recently for subsurface monitoring. However, wireless sensor nodes in existing solutions only provide point measurements and are incapable of providing global measurement for characterization of subsurface medium. The key contribution of this dissertation is to bridge the gap between real-time monitoring and global measurements by introducing a novel concept of Wireless Signal Networks (WSiNs) with a proof of concept for subsurface monitoring using actual wireless sensor nodes (i.e., MICAz) and a system design with a reconfigurable radio platform. Wireless signal networks use the real-time link quality signals among distributed wireless sensor nodes as the main indicator of an event in the physical domain. Our thesis is that "the variation of the link quality between wireless sensor nodes can be used as an effective global sensing mechanism which reflects characteristics of geo-media subjected to various geo-events". To prove this thesis, the dissertation proposes an accurate and simple radio propagation model for underground environments, and the model quantitatively explains that the changes of soil properties and conditions affect the link quality and strength of the radio waves within the region of the event. Then, the dissertation presents real-time global subsurface monitoring applications with wireless signal network concepts based on the proposed underground propagation model including experimental evaluations. Experiments demonstrated that calibrated wireless signal strength variations can be used as indicators to sense changes in the subsurface. To extend underground communication distance, prolong network lifetime, and provide adaptive topology construction, we propose a system design for wireless signal networks with the reconfigurable radio platform. Based on the theoretical and empirical analysis of the radio propagation model, the dissertation proposes practical solutions of extending the underground communication distance and an evaluation platform for the system design with reconfigurable radio (i.e., Universal Software Radio Peripheral). The dissertation describes a novel topology control mechanism by introducing a new control dimension, i.e., frequency control, with the reconfigurable radio that supports underground communication distance extension, network lifetime enhancement, and adaptive topology construction in underground environments of high signal attenuation affected by geo-events. No Change Water Leakage Event #1 Water Leakage Event #2 Liang Cheng, Ph.D., LONGLAB, Lehigh CSE

Outline Our research in distributed sensing sponsored by NSF Wireless sensor networks for bridge monitoring Network design for interference mitigation Distributed in-network data analysis Conclusions In this talk, I will first introduce the background of the research and the challenges of applying wireless sensor networks in the context of bridge monitoring applications. Then I will present our solutions in the network design and in-network processing aspects. Specifically I will discuss the history of the critical communication radius problem in sensor network design and how our result of determinate upper and lower bounds of the critical radius would bridge the gap between theory and practice for sensor network design. These are some ongoing research topics in my group and my presentation will include both what we have solved and what we are trying to solve. I feel that it would be better to discuss both solved and unsolved when we could interact face-to-face here rather than just explain things that have already been published in our papers. Liang Cheng, Ph.D., LONGLAB, Lehigh CSE

Why bridge monitoring? Critical to the economy and public safety Bridge infrastructure is critical for the economy and public safety. As of Year 2008, over 25 percent of the bridges in the United States were rated as being structurally deficient or functionally obsolete by the Federal Highway Administration. Using information collected from the sensors mounted on bridges, timely and cost-effective maintenance can be performed and the life-cycle performance of bridges can be enhanced. (a) Collapsed I-35W Bridge in Minneapolis (2006), (b) cracks in a support pillar of I-95 Philadelphia (2008) The picture shows the Route 33 Lehigh River Bridge, which is used for the field experiments of bridge monitoring by researchers at Lehigh in order to better understand the distribution of stresses and strains within the structure. FHWA 2008: 25% Liang Cheng, Ph.D., LONGLAB, Lehigh CSE

Why wireless sensing? Routine visual inspection Wired monitoring the Stone Cutter Bridge in Hong Kong has more than 1200 sensors Data acquisition, one of the fundamental parts in SHM systems, is traditionally based on wired sensors connected to a centralized data repository. All sensor data is collected to this repository, where data processing takes place to extract structural features and information. This centralized data acquisition and processing approach in a wired sensor network is common practice in traditional SHM systems; however, high costs and the installation difficulties have prevented SHM from wider adoption in large-scale civil structures. For example, the SHM system for the Stone Cutter Bridge in Hong Kong has more than 1200 sensors with an extensive cabling system connecting the sensors to a central base station; installation of such systems is well-known to be both costly and time-consuming. Thus utilizing wireless sensor networks is an appealing solution because of simple deployment effort required, compared to the wired instrumentation complexity shown Liang Cheng, Ph.D., LONGLAB, Lehigh CSE

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 To date, the majority of smart sensor research has focused on emulation of traditional wired sensor networks employing centralized data acquisition and processing (see Figure 1.1a). Such approaches are not scalable due to the enormous amount of data that must be transferred using wireless communication. To illustrate this point, Pakzad et al. (2008) reported that 10 hours was required to transmit 80 seconds of data sampled at 1000 Hz for 56 wireless sensors back to the base station. In May 2006, a group of researchers from the University of California, Berkeley, installed a WSN on the main-span and a tower of the Golden Gate Bridge (GGB), which consisted of 256 accelerometers. After the initial installation phase, the network operated on the bridge from June to September 2006, periodically collecting acceleration and temperature data and transmitting them to a base-station located inside the south tower. During this period, at least three earthquakes occurred in Northern California, the Glen Ellen shaking of magnitude 4.4 on August 2, 2006 being the largest amongst them. The sensor network on the bridge did not collect data during any of these earthquakes [7] because it was not alert for their arrival: the network was either asleep or transmitting ambient vibration data collected prior to the arrival of the earthquake. 20 sensors: The total number data transmitted in the network is about 1e5, and in the test, it took approximately 30 minutes to finish the transmission. the total data transmitted using our method is about 4e4, and it took about 23 minute for the gateway to receive the calculated modal parameters. 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 , 2009. Liang Cheng, Ph.D., LONGLAB, Lehigh CSE

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

Outline Our research in distributed sensing sponsored by NSF Wireless sensor networks for bridge monitoring Network design for interference mitigation Distributed in-network data analysis Conclusions Liang Cheng, Ph.D., LONGLAB, Lehigh CSE

Mitigating exposed interference
Critical radio range determination Reduce wireless collision probability Prolong network lifetime More realistic models take into account additional factors in urban environment such as building blocks, foliage, surface reflection and absorption, and so forth. Liang Cheng, Ph.D., LONGLAB, Lehigh CSE

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 A graph is said to be connected if every pair of vertices in the graph are connected Liang Cheng, Ph.D., LONGLAB, Lehigh CSE

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 While the Bernoulli Graph does not restrict the length of each edge, most implemented wireless multi-hop networks consist of nodes with limited communication ranges. Liang Cheng, Ph.D., LONGLAB, Lehigh CSE

Gap between theory and practice
Rc=[logN+c(N)]/N Wireless sensor locations While the Bernoulli Graph does not restrict the length of each edge, most implemented wireless multi-hop networks consist of nodes with limited communication ranges. Liang Cheng, Ph.D., LONGLAB, Lehigh CSE

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 While the Bernoulli Graph does not restrict the length of each edge, most implemented wireless multi-hop networks consist of nodes with limited communication ranges. Liang Cheng, Ph.D., LONGLAB, Lehigh CSE

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) While the Bernoulli Graph does not restrict the length of each edge, most implemented wireless multi-hop networks consist of nodes with limited communication ranges. Liang Cheng, Ph.D., LONGLAB, Lehigh CSE

Mitigating hidden interference
Hidden terminal problem Collision at will Aloha (1971) Collision avoidance IEEE (1997) Collision detection ? More realistic models take into account additional factors in urban environment such as building blocks, foliage, surface reflection and absorption, and so forth. Liang Cheng, Ph.D., LONGLAB, Lehigh CSE

Messages vs. pulses Liang Cheng, Ph.D., LONGLAB, Lehigh CSE

Hidden terminal revisited
Hidden terminal no longer hidden! Collision detection Liang Cheng, Ph.D., LONGLAB, Lehigh CSE

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 , 2007. Liang Cheng, Ph.D., LONGLAB, Lehigh CSE

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

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

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

Distributed modal identification
To date, the majority of smart sensor research has focused on emulation of traditional wired sensor networks employing centralized data acquisition and processing (see Figure 1.1a). Such approaches are not scalable due to the enormous amount of data that must be transferred using wireless communication. To illustrate this point, Pakzad et al. (2008) reported that 10 hours was required to transmit 80 seconds of data sampled at 1000 Hz for 56 wireless sensors back to the base station. In May 2006, a group of researchers from the University of California, Berkeley, installed a WSN on the main-span and a tower of the Golden Gate Bridge (GGB), which consisted of 256 accelerometers. After the initial installation phase, the network operated on the bridge from June to September 2006, periodically collecting acceleration and temperature data and transmitting them to a base-station located inside the south tower. During this period, at least three earthquakes occurred in Northern California, the Glen Ellen shaking of magnitude 4.4 on August 2, 2006 being the largest amongst them. The sensor network on the bridge did not collect data during any of these earthquakes [7] because it was not alert for their arrival: the network was either asleep or transmitting ambient vibration data collected prior to the arrival of the earthquake. 20 sensors: The total number data transmitted in the network is about 1e5, and in the test, it took approximately 30 minutes to finish the transmission. the total data transmitted using our method is about 4e4, and it took about 23 minute for the gateway to receive the calculated modal parameters. Liang Cheng, Ph.D., LONGLAB, Lehigh CSE

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 , May 2013. Liang Cheng, Ph.D., LONGLAB, Lehigh CSE

Outline Our research in distributed sensing sponsored by NSF Wireless sensor networks for bridge monitoring Network design for interference mitigation Distributed in-network data analysis Conclusions Liang Cheng, Ph.D., LONGLAB, Lehigh CSE

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

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

Q & A Liang Cheng Computer Science & Engineering 19 Memorial Drive West, Bethlehem, PA 18015 Liang Cheng, Ph.D., LONGLAB, Lehigh CSE

Evaluation Scenarios Wireless sensor locations The WSN on Easton-Phillisburg Toll Bridge on Route 22 over the Delaware River Liang Cheng, Ph.D., LONGLAB, Lehigh CSE

Resource constraints of sensor nodes
Imote2 Transceiver: CC2420 Battery Rechargeable: 300 mWh/cm3 Zinc-air: mWh/cm3 CPU: 13–416 MHz Memory: 256kB SRAM, 32MB FLASH, 32MB SDRAM Demo A freshman lab project of my Eng5 students Liang Cheng, Ph.D., LONGLAB, Lehigh CSE

Network Design and In-network Data Analysis for Energy-Efficient Distributed Sensing The majority of my talk today is mostly based on my LONGLAB’s collaborative.

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Network Design and In-network Data Analysis for Energy-Efficient Distributed Sensing The majority of my talk today is mostly based on my LONGLAB’s collaborative.

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Presentation on theme: "Network Design and In-network Data Analysis for Energy-Efficient Distributed Sensing The majority of my talk today is mostly based on my LONGLAB’s collaborative."— Presentation transcript:

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