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Wireless Sensing Applications for Critical Industrial Environments Wireless Sensing Applications for Critical Industrial Environments Fabien Joseph Chraim Electrical Engineering and Computer Sciences University of California at Berkeley Technical Report No. UCB/EECS-2014-215 http://www.eecs.berkeley.edu/Pubs/TechRpts/2014/EECS-2014-215.html December 13, 2014 Copyright © 2014, by the author(s). All rights reserved. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission. Wireless Sensing Applications for Critical Industrial Environments by Fabien Joseph Chraim A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Electrical Engineering and Computer Sciences in the GRADUATE DIVISION of the UNIVERSITY OF CALIFORNIA, BERKELEY Committee in charge: Professor Kristofer S.J. Pister, Chair Professor Alexandre Bayen Professor Steven Glaser Fall 2014 Abstract Wireless Sensing Applications for Critical Industrial Environments by Fabien Joseph Chraim Doctor of Philosophy in Electrical Engineering and Computer Sciences University of California, Berkeley Professor Kristofer S.J. Pister, Chair The widespread deployment of the World Wide Web over the past few decades has connected people globally. The next logical step in this evolution was to connect people to their environment. As public interest in the aging infrastructure grew, so did the desire to make this infrastructure safer and more environmentally friendly. This required the devel- opment of low-power wireless sensor networks to monitor and control this infrastructure, the so-called Internet of Things. With the introduction of low-power communication stan- dards rooted in the Time Slotted Channel Hopping (TSCH) mechanism, the ability to have sensors communicate robustly (99.999%) over long periods of time (10 years) using battery power, appears to have been largely achieved. This thesis is concerned with exploring three significant applications of these new communi- cation networks. It also introduces two modeling tools useful in designing new applications for this space. A process automation solution centered around rotary valve position mon- itoring is presented first. The “peel-and-stick” MEMS device shows an accuracy of ±5◦ for quarter-turn valves, and an accuracy of ±10% of a turn was obtained with the multi- turn valves. This cost-effective solution is designed to be densely deployed on most of the plant’s rotary valves. Next, the design and implementation of fence line perimeter security application is developed and verified. Combining a MEMS accelerometer with a hypoth- 1 esis testing algorithm, all 91 known intrusions were detected with no false alarms over a period of approximately two months. Finally, a wireless gas leak detection and localization is evaluated from a sensory swarm perspective. This application highlights the detection challenges when working with noisy sensors. A gas plume detection rate higher than 90% is demonstrated, with 7 false alarms over a period of three days and an average detection delay of 108s. In terms of localization, the system estimated the leak locations to within three meters of the actual leak source. The design of the new system tools is controlled by the energy trade-off between trans- mitting and locally processing captured data. This has direct implications on the Quality of Service (robustness, delay, lifetime, etc.). An adaptable energy consumption model for TSCH networks is presented and then generalized to an application energy consumption model. These design-time tools allow developers to understand the feasibility of the new application and to refine its hardware and software design in order to meet predefined specifications. The application energy model is presented by taking a motor vibration ap- plication as an example, while combining the sensing and communication hardware with energy scavenging. The work done in this thesis is experimental in nature and care is taken to bring the proposed ideas as close to real implementations as possible: the hardware is built using commercial off-the-shelf components, the networks are configured for the application at hand, and algorithms are devised and validated in each setting. 2 To Elias Nabhan, who never made it to engineering school... i Contents Contents ii List of Figures v List of Tables x Acknowledgements xi Introduction 1 1 Valve Position Monitoring: a MEMS Approach 5 1.1 ExperimentalSetup ............................... 6 1.2 Detection with Noisy Sensors . 8 1.2.1 Gyroscope Temperature Self-Calibration . 9 1.2.2 Issues with Rigid-Body Dynamics . 11 1.2.3 Gyroscope Motion Drift . 12 1.2.4 Filtering Accelerometer Noise . 13 1.3 Algorithm . 14 1.4 Results....................................... 18 1.4.1 Quarter-Turn Valve . 18 1.4.2 Multi-TurnValve............................. 19 1.4.3 SystemArchitecture ........................... 20 1.5 Concludingremarks ............................... 23 2 Smart Fence: Decentralized Sequential Hypothesis Testing for Perimeter Security 25 2.1 SequentialHypothesisTesting. 27 ii 2.1.1 Centralized Sequential Hypothesis Testing . 28 2.1.2 Decentralized Sequential Hypothesis Testing . 29 2.2 ExperimentalSetup ............................... 31 2.2.1 Hardware . 32 2.2.2 SystemArchitecture ........................... 33 2.3 Detection Algorithm . 34 2.3.1 PreliminaryTesting ........................... 34 2.3.2 Applying D-SPRT to Fence Monitoring . 35 2.4 DeploymentsandTestResults. 37 2.5 ConcludingRemarks............................... 41 3 Wireless Gas Leak Detection and Localization 42 3.1 Literature Review and State of the Art . 43 3.1.1 Gas Leak Detection Systems . 43 3.1.2 Detection and Localization Using Multiple Sensors . 45 3.2 AWirelessDistributedSensingApproach . 46 3.2.1 Hardware . 46 3.2.2 SystemArchitecture ........................... 48 3.2.3 Detection Algorithm . 51 3.2.4 Localization Algorithm . 58 3.2.5 Possible Improvements to our Method . 58 3.3 Experimental Validation . 60 3.3.1 DetectionResults............................. 61 3.3.2 Localization Results . 68 3.4 Final Thoughts . 68 4 A Realistic Energy Consumption Model for TSCH Networks 72 4.1 EnergyModel................................... 74 4.1.1 TSCH Slot Template . 75 4.1.2 Slot Energy Consumption Modeling . 76 4.1.3 Experimental Verification . 77 4.1.4 Slot-Frame Energy Consumption Modeling . 83 4.1.5 Relay and Leaf Node evaluation . 85 4.2 TSCH optimizations . 87 iii 4.2.1 Synchronization Policy . 87 4.2.2 The Cost of Overprovisioning . 89 5 When Scavengers Meet Industrial Wireless 92 5.1 RelatedWork................................... 93 5.2 A Model for Early Power Estimation . 95 5.2.1 A Model for TSCH Networks Energy . 96 5.2.2 Modeling Data Acquisition Energy . 98 5.2.3 Modeling Local Data Processing Energy . 99 5.3 Application Oriented Model Validation . 100 5.3.1 Network Energy Profiling . 101 5.3.2 Data Acquisition Profiling . 102 5.3.3 Data Processing Profiling . 104 5.3.4 Joint Model Validation . 106 5.4 The Model in Action . 109 5.4.1 Application Energy Balance . 109 5.4.2 Scavenging . 111 5.4.3 Dynamic Scavenging . 111 5.5 Final Thoughts on the Model . 113 Conclusion 116 Bibliography 118 iv List of Figures 1.1 The experimental setup showing an instrumented ball valve (left) and an instrumentedgatevalve(right) . 7 1.2 Plot of the valve angle over time, as inferred from the magnetometer. The valve was steady for the first 120 seconds (no motion), then it was turned from one end to the other repeatedly. Clearly the magnetometer recorded significant motion when there was none, then showed a repetitive swing of around 40◦ instead of 90◦ ............................ 9 1.3 The plot of the gyroscope axis reading v/s temperature shows the linear dependency between the two. This data was recorded for one minute of inactivity. ..................................... 10 1.4 Integrating the gyroscope data yields a significant drift. This drift occurs only during motion, as the valve is moved from one end to the other (0◦ to 90◦) ........................................ 13 1.5 Unfiltered Accelerometer readings (in units of g’s) on all three axes during the motion of the ball valve. Motion starts 70 seconds in, and the valve is moved between 0◦ and 90◦ repeatedly ..................... 15 1.6 Same time series as in figure 1.5, but at the output of the LOWESS smoothing filter........................................ 16 1.7 Results of five experiments run with the ball valve. In each time series, the first minute (approximately) of inactivity is reserved for temperature calibration. Then the valve is moved from one end to the other, with varying motion types. A ‘∗’ represents the places at which the accelerometer was usedtoremovethegyroscopedrift.. 21 1.8 Results of five experiments run with the gate valve. In each time series, the valve is closed and opened twice (8 full turns each way). The ‘∗’ sign shows alignment with the origin and a zeroing of the angle, while a ‘T’ marks a tightclose. .................................... 22 2.1 A system of K sensors and a fusion center . 28 2.2 The hardware platform: GINA/WHIM . 32 v 2.3 The IP-65 enclosure (rain and dust resistant) that houses the hardware plat- form [courtesy of Hammond Inc] shown next to one sensor as deployed in the middleofafencesection............................. 32 2.4 System architecture for the Smart Fence. The detection algorithm can run only on the motes, or only on the server, or using a hybrid approach where some computations happen in the motes and their output is relayed to the servertomakethefinaldecision. 34 2.5 Singular value decomposition of the sensor data including all axes. This plot shows that only three axes contain the majority of the information. Namely, the z-axis of the accelerometer into the fence and the x-axis and y-axis of the gyroscope in the plane of the fence.
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