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DESIGN AND IMPLEMENTATION OF ZIGBEE BASED WIRELESS SENSOR NETWORK FOR STRUCTURAL HEALTH MONITORING A Preliminary Thesis Report Submitted in Partial Fulfillment of the Requirement for the Award of Degree of MASTER OF ENGINEERING IN ELECTRONICS AND COMMUNICATION ENGINEERING TO PANJAB UNIVERSITY, CHANDIGARH

BY

KIRANDEEP KAUR (REG. NO. 13-TTC -229) (ROLL NO. 132608)

Under the Supervision of Prof. O.S. Khanna (Associate Professor) Amol P Bhondekar (Principal Scientist, Assistant Professor, CSIO AsCIR Chandigarh) DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING NATIONAL INSTITUTE OF TECHNICAL TEACHERS’ TRAINING AND RESEARCH, CHANDIGARH – 160019 AUGUST, 2015

DECLARATION I hereby declare that the thesis entitled “Design and Implementation of ZigBee based Wireless Sensor Network for Structural Health Monitoring” submitted by me in partial fulfillment of the requirements for the award of the degree of Master of Engineering (Electronics and Communication Engineering) of Panjab University, is record of my own work carried under the joint supervision and guidance of Prof. O.S Khanna, Associate Professor at NITTTR, Chandigarh and Mr. Amol P. Bhondekar, Principal Scientist, Assistant Professor, CSIO AsCIR Chandigarh.

I own the full responsibility for the information and results provided in this thesis work and have taken care in all respect to honor the intellectual property rights. I have acknowledged the contribution of others for using them in academic purpose. I further declare that in case of violation of intellectual property right or copyright, I, as the candidate will be fully responsible for the same. My honorable supervisors, Head of department and Institute should not be held for full or partial violation of any intellectual property right or copy right, if found at any stage of my degree. To the best of my knowledge, this thesis is my own work and effort and that it has not been submitted anywhere for award of any degree.

(Kirandeep Kaur) Date:

Reg. No. TTC-229

Place: NITTTR, Chandigarh

M.E. (Regular), ECE-2013

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CERTIFICATE Certified that the thesis entitled “Design and Implementation of ZigBee based Wireless Sensor Network for Structural Health Monitoring” submitted by Kirandeep Kaur, Roll no. 132608, Reg. No. 13-TTC-229 in the partial fulfillment of the requirement for the award of the degree of Master of Engineering (Electronic & Communication Engineering) of Panjab University, is a record of student’s own work carried under our supervision and guidance. To the best of our knowledge, this thesis has not been submitted to Panjab University or to any other university or institute for award of any degree. It is further understood that by this certificate the undersigned do not endorse or approve any statement made, opinion expressed or conclusion drawn therein but approve the thesis only for the purpose for which it is submitted.

Internal Supervisor Prof. O.S Khanna Associate Professor NITTTR, Chandigarh

External Supervisor Amol P. Bhondekar Principal Scientist, Assistant Professor CSIO, AsCSIR, Chandigarh

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Head of Department Dr.Maitreyee Dutta Prof. & Head, ECE Deptt NITTTR, Chandigarh

ACKNOWLEDGEMENTS

Successful accomplishment of this thesis is possible only with the co-operation of the people at various levels. A sincere effort has been made to offer a word of thanks to all of them for their valuable support. Foremost, I would like to express my sincere gratitude to my external supervisor Mr. Amol P. Bhondekar, Principal Scientist, Assistant Professor CSIO AcSIR, Chandigarh, for the opportunity to work on this thesis. His guidance, motivation, enthusiasm, and immense knowledge, helped me in all the time of research and writing of this thesis. He pushed me to perform to the best of my abilities and gave me opportunities and exposure. I also express my sincere gratitude to internal supervisor Prof. O.S Khanna, Associate Professor, Department of Electronics and Communication Engineering, NITTTR, Chandigarh for his valuable guidance, constant inspirations, helpful suggestions and unending support for carrying out this thesis work. I also wish to offer a word of thanks and deep sense of gratitude to my HOD Dr. Maitreyee Dutta, (Prof. & Head, ECE Deptt), Dr. S. B. L. Sachan,(Ex- Prof. & Head, ECE Deptt), NITTTR Chandigarh for their constant inspirations and helpful suggestions throughout the course of this thesis work. I would like to thank all the members of Agrionics Lab at CSIO. Ritesh Sir (Scientist), Bob Sir and Shambo Sir (Phd. Scholars) deserves a special mention for their support and valuable suggestions throughout this work. I appreciate Harmanpreet Kaur, my friend and project partner, for her patience and tolerance towards me. I’d like to thank all my friends at NITTTR and CSIO for their much valued friendship, for running the marathon with me.

Last but not the least, I would like to thank my family members and the Almighty, for the continuous support and encouragement from beginning to the end.

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TABLE OF CONTENTS Declaration

i

Certificate

ii

Acknowledgement

iii

Table of Contents

iv

Abstract

vi

List of Figures

vii

List of Tables

ix

List of Abbreviations

x

Chapter 1: INTRODUCTION

1-13

1.1 Overview of Wireless Sensor Networks 1.2 Types of WSNs 1.3 Factors influencing sensor network design 1.4 Applications of WSNs 1.5 Sensor node architecture 1.6 Node placement techniques 1.7 Routing strategies for WSNs 1.8 Introduction to Structural Health Monitoring 1.9 Wireless Sensor Networks for SHM 1.9.1 Advantages of WSNs for SHM 1.9.2 Architecture of sensor node for SHM 1.10 Corrosion Detection 1.10.1 Electrochemical impedance spectroscopy Chapter 2: LITERATURE SURVEY

14-22

2.1 Introduction 2.2 Literature Review 2.3 Inferences Drawn out of Literature Review 2.4 Problem Definition

iv

Chapter 3: PROPOSED WORK AND METHODOLOGY

23-31

3.1 Objectives 3.2 Design Methodology 3.3 Device selection 3.4 Sensor node design and configuration 3.4.1 Sensor node schematic design 3.4.2 Interfacing AD5933 with C8051F320 3.4.3 Interfacing Xbee S2 with C8051F320 Chapter 4: TESTING AND RESULTS

32-42

4.1 Introduction 4.2 Sensor node impedance measurement results 4.3 Network setup 4.4 Data acquisition in the network 4.5 Testing Chapter 5: CONCLUSION AND FUTURE SCOPE

43-44

5.1 Conclusion 5.2 Future Scope

Appendix A

45-46

Appendix B

47-51

Appendix C

52-54

List of Publications

xii

References

xiii

v

ABSTRACT Corrosion of steel reinforcements is one of the main cause of damage and early failure of reinforced concrete structures, leading to enormous costs for inspection, maintenance, restoration and replacement of the infrastructure worldwide. Conventional methods for detecting corrosion are based on visual inspection which is highly dependent on the expertise of the operator and cannot detect hidden corrosion.

Wireless monitoring has emerged in recent years as a promising technology that could greatly impact the field of structural monitoring for corrosion detection. The small size sensor devices used in WSNs are less expensive, robust and offer high fidelity data.

Structural health

monitoring systems based on WSN offer many advantages over conventional wired systems and provide coverage to the entire giant structure.

The proposed work is focused on the design and implementation of ZigBee based wireless sensor network for corrosion detection. The network comprises small size, low powered sensor nodes designed for the Non Destructive Evaluation of the structure. The nodes use Electrochemical Impedance Spectroscopy (EIS) process to detect the advent of corrosion. A miniaturized impedance measuring chip Ad5933 is used to implement EIS process. The exchange of information in the network is carried by ZigBee based RF modules which operates on low power. A query-reply mechanism is applied to extract the sensed information from the sensor nodes, in the network. The implemented network is tested under the laboratory conditions for data acquisition from the sensor nodes. The data is collected successfully from the nodes in the network and the proposed algorithm can be extended for data collection in expanded network.

Keywords: AD5933, Corrosion, EIS, Wireless Sensor Networks, Structural Health Monitoring, ZigBee.

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LIST OF FIGURES

FIGURE NO.

DESCRIPTION

PAGE NO.

Fig 1.1

General model of a WSN

1

Fig 1.2

Sensor Node Architecture

6

Fig 1.3

Routing Protocols for WSNs

8

Fig 1.4

Sensor Node design for SHM

11

Fig 1.5

Process of Corrosion

12

Fig 1.6

Electrical equivalent of Corrosion process

13

Fig 3.1

Block Diagram of the proposed sensor node

25

Fig 3.2

Schematic Diagram of the PCB of sensor node

26

Fig 3.3

Sensor node PCB

28

Fig 3.4

Flowchart for AD5933 Configuration

29

Fig 3.5

Designed Sensor Node

30

Fig 3.6

Flowchart of UART0 programming steps

31

Fig 4.1

Resistor Measurements taken at 30 KHz with Gain

33

Factor 512.453 × 10-12 Fig 4.2

Single Resistance at different

33

RC series network measurements at different

34

Measurement of frequencies

Fig 4.3

frequencies Fig 4.4

RC parallel network measurements at different

35

frequencies Fig 4.5

Network Setup in radial topology

36

Fig 4.6

Query transmission steps for Coordinator

38

Fig 4.7

Response from end device

39

Fig 4.8

Screenshot of python console

41

Fig A.1

Internal blocks of C8051F320

45

Fig B.1

Functional Block of AD5933

47

Fig B.2

I2C Timing Diagram for AD5933

49

vii

FIGURE NO. Fig B.3

DESCRIPTION

PAGE NO.

The impedance error as function of frequency for

50

ranges 1, 2 Fig B.4


50

ranges 3,4 Fig B.5


50

ranges 5,6 Fig B.6

Single point Gain Factor variation with frequency

51

Fig B.7

Impedance Variation with Temperature Using a 2-

51

Point Gain Factor Calculation Fig C.1

ZigBee Network Topology

53

Fig C.2

API frame structure

54

Fig C.3

Xbee frame types

54

viii

LIST OF TABLES TABLE NO. Table 3.1

DESCRIPTION

PAGE NO.

Output Excitation Voltage Levels for AD5933 at

26

3.3 V

Table 4.1

Network parameters of Xbee Modules

37

Table 4.2

Frame structure of a query originating at

40

Coordinator

Table B.1

Impedance Measurement ranges for AD5933

ix

48

LIST OF ABBREVIATIONS

ABBREVIATIONS

DETAILS

WSN

Wireless Sensor Network

SHIMMER

Secure Health with Intelligence, Modularity, Mobility & Experimental Reusability

FPGA

Field Programmable Gate Array

CPU

Central Processing Unit

MCU

Microcontroller Unit

SHM

Structural Health Monitoring

NDE

Non-destructive evaluation

EIS

Electrochemical impedance spectroscopy

OCP

Open Circuit Potential

SP

Surface Potential

RMSD

Root Mean Square Deviation.

SRAM

Static Random Access Memory.

IEEE

Institute of Electrical and Electronics Engineers

DAQ

Digital Acquisition

MFC

Macro Fiber Composite

PZT

Lead-Zirconate-Titanate

DSA

Data Signal Analyzer

WISAN

Wireless Intelligent Sensor and Actuator Network

GPMP

General Purpose Multi-hop

SSMP

Single-Sink Multi-hop

AODV

Ad-hoc On-demand Distance Vector

E3PSC

Enhanced Energy-Efficient Protocol with Static Clustering

MAC

Medium Access Control

LEACH

Low Energy Adaptive Clustering Protocol

EGAF

Energy-Saving Geographic Adaptive Fidelity

GAF

Geographic Adaptive Fidelity

LZW

Lempel-Ziv-Welch x

ABBREVIATIONS

DETAILS

MSFCP

Maximum Subtree First Collection Protocol

MSF

Maximum Subtree First

AR-ARX

Auto Regressive and Auto Regressive with eXogenous input

PCA

Principle Component Analysis

I2C

Inter-Integrated Circuit

SDA

Serial Data

SCL

Serial Clock

MCLK

Master Clock

G.F

Gain Factor

API

Application Programming Interface

AT

Attention

PAN

Personal Area Network

xi

Introduction

CHAPTER 1 INTRODUCTION

1.1 OVERVIEW OF WIRELESS SENSOR NETWORKS A wireless sensor network is a group of specialized sensors with a communication for monitoring and recording conditions at diverse locations. WSN is a network consisting of a large number of tiny nodes, deployed to collect the local information and make a global decision about the physical environment. The collaboration and synergy of sensing, processing, communication and actuation forms a wireless sensor network [1]. A WSN typically has little or no infrastructure. It comprises number of sensor nodes.

Figure 1.1 General model of a WSN [2].

1.2 TYPES OF WSNs There are broadly five categories of WSN depending on the environment they are deployed in: Terrestrial WSN, underwater WSN, underground WSN, multi-media WSN and mobile WSN.

Terrestrial WSNs typically consist of large number of inexpensive wireless sensor nodes deployed in a given area, either in an ad hoc or in a pre-planned manner. The sensor node can be driven by a rechargeable power source and can also have secondary power in the form of solar cells. For a terrestrial WSN, energy can be conserved with multi-hop optimal routing, short

ECE Department, NITTTR Chandigarh

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Introduction transmission range, in-network data aggregation, eliminating data redundancy, minimizing delays, and using low duty-cycle operations [3].

Underwater WSNs consist of number of sensor nodes and self governing, data gathering vehicles deployed under the water. This type of WSN is expensive and consists of lesser number of sensor nodes. The communication means for underwater WSN is the transmission of acoustic waves which have low bandwidth and suffers from long propagation delay. The network has a limited battery source which cannot be replaced or recharged.

Underground WSNs consist of a number of sensor nodes buried underground or in a cave or mine used to monitor underground conditions. Additional sink nodes are located above ground to relay information from the sensor nodes to the base station [3]. Underground sensor nodes are expensive and should be deployed in pre-planned manner. Like underwater WSN, underground type also has a limited power source which is difficult to replace or recharge.

Multi-media WSNs as the name suggests is used to collect data in form of multimedia such as video, audio and images. The sensor nodes of this WSN type are equipped with cameras and microphones and their deployment is usually pre-planned. The major challenges in multi-media WSNs are high bandwidth requirement, high energy consumption, data processing and compression techniques.

Mobile WSNs is built by a group of mobile sensor nodes communicating with the physical environment, they are deployed in. The mobile sensor nodes work in same fashion as the static nodes with the only difference of mobility i.e. they can sense, process and communicate like static nodes. Challenges in mobile WSN include deployment, localization, self-organization, navigation and control, coverage, energy, maintenance, and data process [3].

1.3 FACTORS INFLUENCING SENSOR NETWORK DESIGN A sensor network design is influenced by many factors, which include fault tolerance; scalability; production costs, operating environment; sensor network topology; hardware constraints; transmission media; and power consumption.


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Introduction Fault Tolerance: Sensor nodes may die due to power depletion or may get blocked or harmed due to environmental or physical interference. This scenario should not compromise the overall performance of the network. This is called fault tolerance. Fault tolerance is the ability to sustain sensor network functionalities without any interruption due to sensor node failures [4].

Scalability: Initially when sensor nodes are deployed, they can range from hundred to thousand in number. Depending upon the application the network may to be expanded, increasing the number of nodes from thousand to million. Hence network should be able to work with the increased number of nodes. All these issues are addressed under the term scalability.

Production costs: Since a network is built up from a large number of sensor nodes, therefore cost of single node matters. If an expensive node is used, it will increase the overall cost of the network. Hence to restrict the production cost of the network a low-cost node should be used.

Hardware Constraints: A sensor node has four basic units: power supply, communication, processing unit and sensors. They may also have application dependent additional components such as a location finding system, a power generator and a mobilizer. All these subunits fit into a small size module. Moreover the overall energy consumption of the node should be low and it should be capable of operating in autonomous and unattended way.

Transmission Media: In a multihop sensor network, communicating nodes are linked by a wireless medium. These links can be formed by radio, infrared or optical media. To enable global operation of these networks, the chosen transmission medium must be available. One option for radio links is the use of industrial, scientific and medical (ISM) bands, which offer license-free communication in most countries [4].

Power Consumption: Sensor node is a microelectronic device equipped with limited source of energy. The lifetime of sensor node depends upon battery lifetime because in some applications recharging of battery may be impossible. In a multihop ad hoc sensor network, each node plays the dual role of data originator and data router. The malfunctioning of few nodes can cause significant topological changes and might require re-routing of packets and re-organization of the network. Hence, power conservation and power management take on additional importance. The ECE Department, NITTTR Chandigarh

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Introduction main task of a sensor node in a sensor field is to detect events, perform quick local data processing, and then transmit the data. Power consumption can hence be divided into three domains: sensing, communication, and data processing

1.4 APPLICATIONS OF WSNs WSN applications can be classified into two categories: monitoring and tracking. Monitoring applications include indoor/outdoor environmental monitoring, health and wellness monitoring, power monitoring, inventory location monitoring, factory and process automation, and seismic and structural monitoring. Tracking applications include tracking objects, animals, humans, and vehicles [3].

Military Applications: Wireless sensor networks can be an integral part of military command. WSNs can be used for Battlefield surveillance, Reconnaissance of opposing forces and terrain, Targeting, Battle damage assessment. As wireless sensor networks consist of the dense deployment of low-cost sensor nodes, harm to some nodes by hostile actions does not affect a military operation as much as the destruction of a traditional sensor, which makes the WSNs quite useful for battlefields. PinPtr is an example of ad-hoc wireless sensor network that detects and accurately locates shooters even in urban environments. The system consists of a large number of cheap sensors communicating through an ad-hoc wireless network, thus it is capable of tolerating multiple sensor failures, provides good coverage and high accuracy, and is capable of overcoming multipath effects. The performance system is superior to that of centralized counter-sniper systems even in dense urban terrain [5]. PinPtr is used for locating and tracking. Another major category of possible applications is what is called environmental monitoring. The capability of sensing temperature, light, status of frames (windows, doors), air streams and indoor air pollution can be utilized for optimal control of the indoor environment. Motes can help in using heaters, fans and other relevant equipment at a reasonable and economic way, leading to a healthier environment and greater level of comfort for residents [1]. The networks can also be deployed for the outdoor environment monitoring. An example for environmental monitoring is the WSN consisting of 32 nodes at Great Duck Island [6]. The WSN was used for habitat monitoring. The sensor node used was based on Mica board and had photo-resistor, temperature, humidity and pressure sensors as part of its Mica weather board. WSNs are also used for volcanic eruption and earthquake sensing. ECE Department, NITTTR Chandigarh

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Introduction Some of the health applications for sensor networks are providing interfaces for the disabled; integrated patient monitoring; diagnostics; drug administration in hospitals; monitoring the movements and internal processes of insects or other small animals; tele-monitoring of human physiological data; and tracking and monitoring doctors and patients inside a hospital. An example of WSN based on health monitoring is Heart@Home. It is a wireless blood pressure monitor and tracking system. Heart@Home uses a SHIMMER mote located inside a wrist cuff which is connected to a pressure sensor. A user’s blood pressure and heart rate is computed using the oscillometric method. The SHIMMER mote records the reading and sends it to the T-mote connected to the user’s computer. A software application processes the data and provides a graph of the user’s blood pressure and heart rate over time [3].

1.5 SENSOR NODE ARCHITECTURE A sensor node has four basic units: power supply, communication, processing unit and sensors.

Power Supply: The power supply block consists of a battery and a dc-dc converter and has the purpose to power the node, since the sensor node needs energy to monitor the environment. A power management layer is necessary control the energy level of the node. The power management layer should monitor battery's voltage slope to dynamically alter the system performance. Another advantage is that an addition of another or secondary power source can be made in order to make best use of energy resources. The network protocols used for routing, clustering, addressing and synchronization should be energy-efficient.

Communication unit: Communication unit involves the medium used for transmission of data. Here the wireless mode is used for information exchange. There are three possibilities for wireless communication channel: optical, infrared and RF. Several aspects affect the power consumption of a radio, including the type of modulation, scheme used, data rate, transmit power. In general, radios can operate in four distinct modes of operation: transmit, receive, idle, and sleep. Most radios operating in idle mode results in high power consumption, almost equal to receive mode, thus, it is important to shutdown the radio [7]. In WSNs normally RF means of transmission is preferred because the size of data packets is small, low data rates are supported and there is also possibility of frequency reuse since short communication distances are served.


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Introduction These characteristics also make it possible to use low duty cycle radio electronics for sensor networks.

Processing unit: The processing unit is composed of memory to store data and applications programs, a microcontroller and an Analog-to-Digital Converter to receive signal from the sensing block [7]. Processing unit determines the energy and computational parameters of the node. Different algorithms are implemented at processing level to optimize these parameters. Availability of different microcontrollers, microprocessors and FPGAs leads to flexible implementations of CPU. The choice of MCU depends on application scenario. The ideal choice of microcontroller is the one that matches its performance level with application’s need.

Sensors: Sensors are the devices which converts the change in the physical environment to the electrical responses, which are processed by the previous unit. The sensors can be accelerometer, magnetometer, and light sensors. The type of sensor used depends upon the application. Algorithms for filtering and data fusion are necessary on the sensor unit since only relevant data should be passed on to CPU for processing.

Figure 1.2 Sensor Node Architecture.

1.6 NODE PLACEMENT TECHNIQUES A sensor dies when its energy source gets depleted and thus a WSN may be structurally damaged if many sensors exhaust their onboard energy supply. So the deployment of sensor node should be done in an optimal manner such that full coverage of the monitored area should be ensured.

Static positioning of nodes: Node placement schemes prior to network startup usually base their ECE Department, NITTTR Chandigarh

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Introduction choice of the particular nodes’ positions on metrics that are independent of the network state or assume a fixed network operation pattern that stays unchanged throughout the lifetime of the network [8]. The network periodically collects data over the preset routes.

Role based placement technique: The positions of nodes not only affect coverage but also significantly impact the properties of the network topology. These architectures often define roles for the employed nodes and pursue a node specific positioning strategy that is dependent on the role that the node plays. Generally, a node can be a regular sensor, relay, cluster-head or base-station [8].

Dynamic repositioning of nodes: If the sensor nodes placed near the base dies, then the network is compromised of its performance. Moreover the traffic pattern can vary from setup of network to the time it starts operating. In order to address such issues nodes should be repositioned dynamically while the network is operational. This improves the network performance.

1.7 ROUTING STRATEGIES FOR WSNs Routing is the process of selecting paths in a network along which to send network traffic. A routing protocol is a protocol that specifies how nodes communicate with each other and disseminating information that enables them to select routes between any two nodes on a network.

Based on the network structure the routing protocols can be either flat or hierarchical. In flat protocol all nodes play the same role and the network overhead is minimum. The routing protocols on this scheme impose a structure on the network to achieve energy efficiency, stability, and scalability. In this class of protocols, network nodes are organized in clusters in which a node with higher residual energy [9], called cluster head.

Based on the communication model routing can be query based where a destination node queries for data from node through the network. The other type in this category is Coherent and Non Coherent strategy. In coherent routing, the data is forwarded to aggregators after a minimum processing. In non-coherent data processing routing, nodes locally process the raw data before it


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Introduction is sent to other nodes for further processing [9]. The last protocol belonging to this category is negotiation based which uses meta-negotiation to reduce the transmission overhead.

Topology based protocols consists of location based and mobile agent based algorithms. In location based routing the position information is used for relaying data to different parts of the network. In mobile agent based protocol a mobile agent gathers data from different nodes.

The protocols covered under Reliable routing are more resilient to route failures either by achieving load balancing routes or by satisfying certain QoS metrics, as delay, energy, and bandwidth [9]. Multipath based reliable routing is dedicated towards load balancing and route failures. QoS based routing has to balance between energy consumption and data quality.

Figure 1.3 Routing Protocols for WSNs [9].

1.8 INTRODUCTION TO STRUCTURAL HEALTH MONITORING Civil infrastructures, which include bridges and buildings, begin to deteriorate once they are built and used. Maintaining safe and reliable civil infrastructures for daily use is important to the well being of all of us. Knowing the integrity of the structure in terms of its age and usage, and its level of safety to withstand infrequent but high forces such as overweight trucks, earthquakes, tornadoes, and hurricanes is important and necessary. The process of determining and tracking structural integrity and assessing the nature of damage in a structure is often referred to as health monitoring. Ideally, health monitoring of civil infrastructure consists of determining, by measured parameters, the location and severity of damage in buildings or bridges as they happen


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Introduction [10]. Structural monitoring systems are widely used to examine the behavior of structures during forced vibration testing or natural excitation like earthquakes, winds and so on.

Damage detection methods for structures can be categorized as: global-based and local-based damage detection methods. Global-based damage detection refers to numerical methods that consider the global vibration characteristics of a structure to identify damage. The global techniques detect the significant damages which can have large impact on the integrity of the entire structure. They help in knowing that damage has occurred. Most global health monitoring methods are centered on either finding shifts in resonant frequencies or changes in structural mode shapes.

Local-based damage detection methods attempt to identify damage based on screening structures at their component or subcomponent length-scales. The local techniques detect the small defects in a structure. Non-destructive evaluation (NDE) methods are used to find the damage. Methods such as ultrasonic guided waves used to measure stress, or eddy current are helpful in locating cracks and can determine the exact location and extent of the damage. Therefore, both global and local health monitoring are necessary.

The conventional NDE techniques used for detection of local -based damage require a trained professional to operate in the field, thereby raising their costs. Furthermore, the operator must have knowledge of potential damage regions to prioritize inspection of the complete structure. On the other hand for global-based damage detection, wired structural monitoring systems are used, which are expensive to install. The nodal densities of most systems is quite low, only 10– 20 sensors for a single structure. Such small numbers of sensors are poorly scaled to the localized behavior of damage, often rendering global-based damage detection difficult to implement

1.9 WIRELESS SENSOR NETWORKS FOR SHM Wireless monitoring has emerged in recent years as a promising technology that could greatly impact the field of structural monitoring. Sensing devices are becoming smaller, less expensive, more robust, and highly precise, allowing collection of high-fidelity data with dense instrumentation employing multi-metric sensors. Wireless sensor networks (WSNs) leverage ECE Department, NITTTR Chandigarh

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Introduction these advances to offer the potential for dramatic improvements in the capability to capture structural dynamic behavior and evaluate the condition of structures. Structural health monitoring (SHM) systems based on WSN offer many advantages over conventional wired systems, particularly for large civil infrastructure.

1.9.1 ADVANTAGES OF WSNs FOR SHM Wireless sensors provide rich information which SHM algorithms can utilize to detect, locate, and assess structural damage caused by severe loading events and by progressive environmental deterioration as well as economical realization of SHM system. Information from densely instrumented structures is expected to result in the deeper insight into the physical state of the structural system. The various advantages offered by WSNs for SHM are given as: 

Low cost attributes of WSNs motivated the researchers to implement the technology for SHM. The declining cost of computing and communication technologies has helped in the development of low-price networks.



The wireless mode of communication led to the eradication of long co-axial cables which reduced the installation costs.



With potentially hundreds of wireless sensors installed in a single structure, the wireless monitoring system is also better equipped to screen for structural damage by monitoring the behavior of critical structural components, thereby implementing local-based damage detection [11].



Wireless sensors are autonomous data acquisition nodes to which traditional structural sensors (e.g. strain gages, accelerometers, linear voltage displacement transducers and so on) can be attached. Wireless sensors a platform in which mobile computing and wireless communication elements co-exist with the sensing transducer.

1.9.2 ARCHITECTURE OF SENSOR NODE FOR SHM Within a wireless network for structural monitoring system, each wireless sensing unit will be responsible for three tasks: (i) collection of structural response data, (ii) local interrogation of collected measurement data, and (iii) wireless communication of response data or analysis results to a wireless network which comprises other wireless sensing units [12].


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Introduction

Figure 1.4 Sensor Node design for SHM [12].

Data acquisition subsystem: In order to capture global response of a civil structure, the sensor part of the node should be designed to record data response at sample rates below 100 Hz. The high-order response modes of structural components are needed for local-damage monitoring. Therefore sensing interface should be capable of recording at relatively high sample rates (greater than 500 Hz). For generalization of the sensing unit functionality, it should not be designed for a sensor. To convert analog data to a digital format, ADC with a resolution of 16 bits or higher is needed.

Computational Core: The computational core is responsible for collection of data from the sensor, execution of embedded computing procedures and managing the flow of data through the wireless communication channel. It consists of microcontroller for processing and memory for storing the measured data. To store measured data, rewritable RAM will be needed. Static ROM is needed for the storage of software written for operation of the unit.

Wireless Communication Channel: Two requirements of the selected wireless technology should be: reliability and range. The wireless communication channel must be highly reliable with minimum data loss due to channel interference, multi-path reflections and path losses. Spread spectrum wireless radios are preferred as they distribute data across entire spectrum, to maintain data integrity in case of interference. The large size of civil structures requires wireless communication ranges of at least 100 m.


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Introduction

1.10 CORROSION DETECTION Corrosion of reinforcement has been established as the predominant factor causing widespread premature deterioration of concrete construction worldwide. The most important causes of corrosion setup of reinforcing steel are the entrance of chloride ions and carbon dioxide to the steel surface. After initiation of the corrosion process, the corrosion products (iron oxides and hydroxides) get deposited in the restricted space around the steel. Their formation within this restricted space sets up expansive stresses, which causes cracks. This in turn results in progressive deterioration of the concrete. So maintenance of such structures requires a lot of spending. Quality control, maintenance and planning for the restoration of these structures need non-destructive inspections and monitoring techniques that detect the corrosion at an early stage. For measurement of the corrosion rate of reinforcing steel in concrete, many electrochemical and non-destructive techniques are available for monitoring corrosion. Some of them are as follows: 

Electrochemical impedance spectroscopy (EIS)



Open circuit potential (OCP) measurements



Surface potential (SP) measurements



Concrete resistivity measurement



Linear polarization resistance (LPR) measurement



Galvanostatic pulse transient method



Noise Analysis



Embeddable corrosion monitoring sensor and



Cover thickness measurements



Visual inspection

Figure 1.5 Process of Corrosion [43].


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Introduction 1.10.1 ELECTROCHEMICAL IMPEDANCE SPECTROSCOPY (EIS) In recent years, A.C. Impedance spectroscopy is being experimented as a useful non-destructive technique for quantifying corrosion of steel rebars. Impedance Z is the ratio of A.C. voltage to A.C. current. An alternating voltage of about 10 to 20 mV is applied to the rebar and the resultant current and phase angle are measured for various frequencies. The response to an A.C. input is complex impedance that has both real and imaginary components. From studying the variation of the impedance with frequency, an equivalent electrical circuit can be determined which would give the same response as the corrosion system being studied [13].

Figure 1.6 Electrical equivalent of Corrosion process [43].

In the impedance-based method, multiple numbers of a frequency range containing 20-30 numbers of peaks are usually chosen, since a number of peaks imply that there is a greater dynamic interaction over that frequency range. A higher frequency range (higher than 150 kHz) is found to be favorable in localizing the sensing, while a lower frequency range (lower than 70 kHz) covers more sensing areas. For damage quantification of the electro-mechanical impedance-based damage detection technique, root mean square deviation (RMSD) of the real part of the impedance signatures is utilized as a damage indicator. An advantage of the EIS technique is the use of very small excitation amplitudes, which causes minimum disturbance to the material.


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Literature Survey

CHAPTER 2 LITERATURE SURVEY

2.1 INTRODUCTION Wireless Sensor networks have emerged in recent years as a promising technology that could greatly impact the field of structural health monitoring. Wireless sensors provide information which can be used by SHM algorithms to detect, locate, and assess structural damage. Due to versatile nature of wireless sensor networks, it is feasible to deploy the sensor nodes in an application specific way. After deployment the sensor nodes organize themselves in a network which is self maintaining in nature to carry out the specific task. The main objective of the sensor node is to sense, collect and process data, and transmit the information to the desired location. To fulfill this objective, there is requirement of efficient data transfer between the sensor nodes.

2.2 LITERATURE REVIEW Park et al. in [14] have observed the hardware and software issues of impedance-based structural health monitoring based on piezoelectric materials. The basic concept of the method is to use high-frequency structural excitations to monitor the local area of a structure for changes in structural impedance that would indicate imminent damage. The impedance method used provides several advantages over traditional NDE approaches. Due to the high-frequency range employed, the method is very sensitive to incipient damage in a structure and unaffected by changes in boundary conditions, loading, or operational vibrations. The sensing regions of the impedance sensors are much larger than those of local ultrasonic or eddy current sensors.

Rice et al. in [15] have developed a flexible wireless smart sensor framework for full-scale, autonomous SHM that integrates the necessary software and hardware while addressing key implementation requirements. The Imote2 smart sensor platform is employed, providing the computation and communication resources SHM of civil infrastructure. A multi-metric Imote2 sensor board with onboard signal processing having ADC, temperature, humidity and light sensor is designed for SHM applications. The authors have flexible network management software combined with a sleep/wake cycle for enhanced power efficiency with threshold ECE Department, NITTTR Chandigarh

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Literature Survey detection for triggering network wide operations such as synchronized sensing or decentralized modal analysis.

Swartz et al. in [16] have designed a new wireless sensing unit that which has low power consumption, long transmission ranges, and the ability to calculate complicated structural engineering algorithms on-board. The new unit is capable of sensing up to four simultaneous channels from ADC, and ATmega128 and 128 KB SRAM forms the computational part of the node. The unit also utilizes the network communication scheme outlined in the IEEE 802.15.4 standard allowing it to form star and peer-to-peer network topologies. This makes the new unit suitable for distributed computing tasks in dense, ad-hoc networks that are completely scalable.

Baptista et al. in [17] have presented a new impedance measurement methodology for an SHM system based on the E/M impedance technique. The system offers precision, speed, low cost, and versatility. The authors used a DAQ device with a sampling rate limited to only 250 kS/s. The signal response acquisition is performed by a DAQ device, which is controlled by the software LabVIEW. The DAQ device used is multifunctional with analog channels based on 16-bit ADC and 16-bit DAC.

Park et al. in [18] have proposed a self-contained active sensor system for the practical use of an electro-mechanical impedance-based structural health monitoring (SHM) technique for civil infrastructures. The system consists of a miniaturized impedance measuring chip (AD5933) and a self-sensing macro-fiber composite (MFC) patch. The effectiveness of the proposed active sensor device has been verified through a series of experimental studies, inspecting loosening bolts in a bolt-jointed structure and detecting corrosion in an aluminum beam. The output of AD5933 impedance chip was compared with the conventional impedance analyzer, HP4914A.

Park et al. in [19] have presented an experimental study for wireless monitoring of corrosion damage. A simple beam structure made from an aluminum alloy was selected for corrosionmonitoring testing and a small lead-zirconate-titanate (PZT) patch-type sensor was surfacemounted to the structure. To obtain an electromechanical impedance data at the PZT sensor, a wireless impedance sensor node that consists of a miniaturized impedance-measuring chip, a ECE Department, NITTTR Chandigarh

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Literature Survey microprocessor, and radio frequency (RF) telemetry was employed. Three different corrosion cases with a different corroded area were artificially inflicted on the beam structure using hydrochloric (HCI) acid, and the electromechanical impedance data were collected in sequence from the wireless impedance sensor node. To quantify the corroded area, the variations of the resonant frequency that represents structural dynamic information were continuously tracked during all the damage cases. Conclusively, it has been found that the amount of the resonant frequency shift got increased when the corroded area got increased. Hence authors concluded that the proposed approach using the amount of resonant frequency shift at the measured electromechanical impedance can be effectively utilized for quantitative analysis of the corroded area in metallic structures.

Neto et al. in [20] have presented the architecture of a Switching and Signal Conditioning System that is proposed to be used in SHM applications. The use of phasors made the proposed hardware simpler, not requiring FFT capable DSA or high sampling rates. The authors presented low-cost design, using a DSP microcontroller as DSA. The authors tested the proposed prototype directly in an aircraft structure to validate its working principle. The results demonstrated the effectiveness of the system to detect a typical damage found in an aircraft panel.

Kottapalli et al. in [21] have implemented two tier network to improve the power efficiency of the network. The lower tier consists of clusters of sensor units operating on battery power and the upper tier is formed by local site masters operating on regular wall power supply with a battery backup. The sensor units transmit their data to their respective local site masters and the local site masters route these data to the central monitoring station. The communication protocol designed for the lower tier is TDMA based and helps the sensor units to conserve power by requiring them to operate their radios only when transmitting or receiving the data.

Pakzad et al. in [22] have developed a 64 nodes wireless sensor network on a long span bridge. An accelerometer-based sensor node is designed and calibrated to meet the requirements for monitoring structural vibrations. The nodes have four channels of accelerometers and a microcontroller for processing. The mode of communication is multi-hop. The sensor nodes sample and send their data to base station along with additional task of relaying information from ECE Department, NITTTR Chandigarh

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Literature Survey other nodes to base station. Software components have been implemented within the TinyOS operating system to provide a flexible software platform and scalable performance for structural health monitoring applications.

Niu et al. in [23] have designed a WSN based prototype of SHM system. Acceleration data, synchronously sampled in each sensor node based on IRIS, are transported to a data processing computer through a base station. In order to achieve a high network throughput, a time division multiple access (TDMA) approach is proposed to reduce the packet collision and energy consumption. To reduce the energy consumption further the power supply of the node is turned off after sampling of data and is switched again before the next sampling. The authors tested the system by deploying 17 nodes for collection of acceleration data.

Sazonov et al. in [24] have developed Wireless Intelligent Sensor and Actuator Network (WISAN) the tasks of structural health monitoring and SHM method, suitable for autonomous structural health monitoring. WISAN consists of microcontroller equipped with data acquisition part consisting of six ADC, two DAC and flexible general purpose data channels. The wireless communication module of the system is based in 802.15.4 protocol operating in ISM band of 2.5 GHz. Two-level cluster-tree architecture is used for information distribution, which improves the fault tolerance of the system.

Bocca et al. in [25] have implemented a wireless sensor network for structural health monitoring with a wireless sensor node, equipped with digital accelerometer, a temperature and humidity sensor. The device is capable of sampling data at frequencies with upper limit of 1 kHz. The deployed wireless sensor network allows the end-user operating at the sink node to configure various parameters of the application. During data transmission, the sink node establishes reserved communication links with the sensor nodes. The uses retransmission procedure to compensate for lost data packets during transmission. A MATLAB is used to visualize and process the data collected from the sensor nodes.

Paek et al. in [26] have developed a networked software system called TENET. The system is a two-tier networked system consisting of two classes of nodes: a higher-tier nodes with 32-bit ECE Department, NITTTR Chandigarh

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Literature Survey processors and IEEE 802.11b radios, and a lower-tier comprising sensor nodes operated with a battery,

less-capable processors and low-power IEEE 802.15.4 radios. The software runs

application code on the higher-tier nodes, and provides a interface for sensors and actuators. This separation of functionality simplifies application development by reducing application development time.

Nagayama et al. in [27] have proposed two complementary reliable multi-hop communication algorithms for monitoring of civil infrastructure. The first algorithm General Purpose Multi-hop (GPMH) supports adaptable any-to-any communication protocol. In the second approach, termed as Single-Sink Multi-hop (SSMH), an efficient many-to-one protocol utilizing all available RF channels is designed to minimize the time required to collect the large amounts of data generated by dense arrays of sensor nodes. Both protocols adopt the Ad-hoc On-demand Distance Vector (AODV) routing protocol, which provides any-to-any routing and multi-cast capability.

Yao et al. in [28] have analyzed an energy efficient optimization model by dividing the circular sensing field into rings with optimal size ratio , which makes the energy consumption of a CH (Cluster head) in every ring more balanced and prolongs the lifetime of network. Then the authors have proposed the performance evaluation model with queuing theory. Since the transmitting and queuing capacity of a CH exist constraint in the real world, the energy-efficient optimization model was combined with the performance analysis evaluation model to get the energy-efficient QoS supporting model.

Liu et al. in [29] have proposed a cluster-based modal analysis approach for WSN-based SHM systems. Two centralized and one distributed algorithms are proposed to solve the clustering problem. Through simulation and experiment, the effectiveness and efficiency of this clusterbased modal analysis along with the clustering algorithms have been demonstrated. Compared with traditional approach of streaming raw data back, WSN-based SHM systems using the proposed approach is much more energy efficient, particularly for large structures. The authors have observed that the proposed approach reduced energy by 40% without sacrificing the accuracy of identified modal parameters, when implemented on small scale WSN based SHM.


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Literature Survey Chaurasiya et al. in [30] have proposed an energy-efficient routing scheme called Enhanced Energy-Efficient Protocol with Static Clustering (E3PSC) which is basically a modification of an existing routing scheme, Energy-Efficient Protocol with Static Clustering (EEPSC). Similar to EEPSC, the proposed work partitions the network into distance-based static clusters. However, unlike EEPSC, cluster-head selection is performed by taking into account both the spatial distribution of sensors nodes in network and their residual energy with an objective to reduce the intra-cluster communication overhead among the nodes making the scheme more energyefficient. Both qualitative and quantitative analysis is performed to prove the energy efficiency of the proposed scheme. Authors have carried out a set of experiments to evaluate the performance of the scheme and to compare the results with EEPSC, which showed that E3PSC outperforms EEPSC in terms of network lifetime and energy consumption.

Priyankara et al. in [31] have discussed robust hybrid routing method which adaptively combines clustering and multi-hop communication methods, in order to maximize the network lifetime in the heterogeneous sensor network. The authors have described the network model and mathematical analysis method to define the optimal area of the multi-hop zone. The work defines inter-cluster, intra-cluster routing methods and routing in the boundary of multi-hop zone and clustering zone and finally, the overall network formation. The performance of the proposed method is compared with clustering network and multi-hop network.

Sazak et al. in [32] have presented a TDMA-based MAC protocol which offers data slot assignment by considering source nodes transmitting same data in event driven WSN applications. In this protocol, contention period is increased to transmit difference data in 4-bit time slots instead of 1-bit slot. On the other hand, energy saving is achieved by decreasing the number of assigned data slots. The proposed MAC protocol assigns a time slot to only one of the source nodes all with the same data. Thus it reduces data transmission redundancy and achieves energy savings.

Saravanakumar et al. in [33] have proposed a new routing protocol and data aggregation method in which the sensor nodes form the cluster and the cluster-head is elected based on the residual energy of the individual node calculation without re-clustering and the node scheduling scheme ECE Department, NITTTR Chandigarh

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Literature Survey is adopted in each cluster of the WSNs. The authors observed that in the node scheduling scheme (ACTIVE and SLEEP mode) the energy efficiency is increased near to 50% than LEACH protocol and lifetime of the networks also increased.

Yin et al. in [34] have implemented an energy-efficient clustering routing protocol for WSN on the Zheng Dian Viaduct Bridge for strain data and structural acceleration monitoring, MHop-CL. It uses the cluster head rotation metric to select cluster head and group nodes into cluster based on the nodes deployment information. Three timers are used in MHop-CL protocol. The timer1 is used for sending information among the intra-cluster nodes. The timer 2 is used for initiating new round for cluster head selection and the timer 3 is used for triggering the sample data event. Power consumption was reduced compared to multihop routing in TinyOS. Zhang et al. in [35] designed Energy-Saving Geographic Adaptive Fidelity (EGAF) routing protocol developed for bridge monitoring. Every node in network broadcasts to a fixed radius. Each node will receive the broadcast messages of others and get a view of the node density in its neighborhood. Each node sends information regarding node density, its ID and residual energy to the Base station. This information is broadcasted to all nodes which help in cluster head selection. After CH selection the nodes join the CH with the strongest signal. The observations reflect that the protocol balances the network energy consumption, and extends the network lifetime by 30% or more compared with LEACH and GAF.

Prabu et al. in [36] have presented Grade diffusion algorithm with LZW Compression for WSNs used for as structural health monitoring for bridges and tunnels, border surveillance, road condition monitoring. The grade diffusion algorithm is used to select the efficient routing path. GD algorithm creates routing for each sensor node and identifies its neighbor to reduce transmission load. Lempel-Ziv-Welch (LZW) compression is a dictionary based algorithm that replaces strings of characters with single codes in the dictionary. The algorithm sequentially reads in characters and finds the longest string that can be recognized by the dictionary. The authors observed that the proposed algorithm increases the number of active nodes, reduces the data loss and energy consumption.


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Literature Survey Kuroiwa et al. in [37] have introduced Maximum Subtree First Collection Protocol for SHM which requires high throughput, bulk data collection. MSFCP uses multichannel block transfer and adopts Maximum Subtree First (MSF) scheduling to reduce interference and enhance overall throughput. MSFCP adopts MSF scheduling, which is unsynchronized distributed scheduling based on node's own transmission buffer information. The key idea is to schedule transmission in parallel along multiple branches of the tree, and to keep the sink as busy receiving as possible. The authors examined from theoretical analysis that the protocol can achieve an overall throughput of 135 kbps on the IRIS Mote platform.

Nie et al. in [38] have proposed a cluster based damage detection for SHM. The damage detection system is based on Auto Regressive and Auto Regressive with eXogenous input [ARARX] model. Data compression is employed at each node to reduce the transmitted data. Data from multiple nodes is gathered in cluster head where principle component analysis (PCA) is implemented to process data before AR-ARX. A clustering strategy is designed to forward data form nodes to base station. Each CH calculates its trigger points and broadcasts them to its member nodes as reference data. All the nodes in the cluster perform the averaging procedure by using their own data and the reference data. An optimal clustering strategy is used to minimize the system’s energy consumption which uses genetic algorithm as its basis. Genetic algorithm is first carried out in base station, and the wireless sensor nodes are disjointed through the result.

2.3 INFERENCES DRAWN OUT OF LITERRATURE SURVEY 1. It can be observed from the literature survey that wireless sensor technology provides promising platform for the structural health monitoring of large civil infrastructures, over the conventional wired technology. 2. The advent of small size sensors has made corrosion monitoring in structures at inaccessible places. 3. The impedance based structural health monitoring can be very well implemented using small size sensor node, which can be deployed over various civil structures, for both local and global damage detection. 4. Different Wireless technologies are used to exchange information among nodes in WSNs implemented for SHM. Few examples are 802.15.4, Zigbee, 802.11. ECE Department, NITTTR Chandigarh

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Literature Survey 5. AD5933 has emerged has a promising device for corrosion detection because of its small size and acceptable system accuracy. 6. Various Clustering approaches are applied for data transmission and reception in wireless sensor network for SHM. These approaches are MHop, EGAF, and Cluster Based Data Aggregation.

2.4 PROBLEM DEFINITION Wireless sensor network has emerged as good option for structural health monitoring. WSNs can be used for both global and local damage detection. Corrosion is one of the factors that lead to deterioration of civil structures. Early detection of corrosion can save expenditure of repairing. WSN aimed at corrosion detection requires portable and low power sensor nodes equipped with low power wireless communication techniques. The sensor node should be inexpensive and tolerant towards the environmental conditions in which it is deployed. Since large amount of energy is consumed during transmission and reception of data so data exchange should be targeted to use minimal power. Therefore the proposed work will focus on designing a low power sensor node for corrosion detection and implementing the network used for structural health monitoring by using the designed node.


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Proposed Work and Methodology

CHAPTER 3 PROPOSED WORK AND METHODOLOGY

3.1 OBJECTIVE The proposed work is oriented: 

To design and test wireless sensor network for corrosion detection of structure using ZigBee Protocol.

3.2 DESIGN METHODOLOGY To setup the WSN for SHM, a sensor node is designed for corrosion detection. Considering the requirements of a SHM based sensor node, the sensing, processing and communication units are selected. The various units of the sensor node are configured to serve the dedicated purpose. And finally the network is setup. Therefore, the design methodology consists of two parts. The first part deals with the selection of devices for the sensor node. Second part is oriented towards node design and configuration.

3.3 DEVICE SELECTION Sensor nodes dedicated for SHM are responsible for three tasks which include data sensing, processing and evaluation of the sensed data and wireless transmission of the acquired data in the network. The sensor node should compact size, low power device with easy installation facility.

Data Sensing Unit: The process of corrosion can be modeled electrically, with anode and cathode formation. By detecting the changes in the electrical model, corrosion can be detected. One of the methods for corrosion detection includes Electrochemical Impedance Spectroscopy. The method detects the change in impedance of material due to corrosion. Therefore the sensor unit of node should be capable of performing EIS accurately and contain ADC to convert data to digital form. Since the size and power are crucial parameters, selected device should be small in size and driven by low power source.

Processing Unit: The computational unit of an SHM based sensor node performs collection of data, execution of embedded algorithms and controls flow of data to wireless communication ECE Department, NITTTR Chandigarh

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Proposed Work and Methodology module and data sensing unit. The device should be easy to program and equipped with rewritable RAM with enough space to store sensed data. The ROM memory should offer enough space to accommodate the user designed algorithms for data processing. The size and power requirements of processing should fit into design consideration of a low-powered and small size node.

Wireless Transmission Channel: The selected wireless technology should be reliable with minimum data loss due. Spread spectrum wireless radios distribute data across entire spectrum, by maintaining the data integrity in case of interference; hence the transmission unit should offer this facility. The large size of civil structures require wireless communication ranges of at least 100 m, therefore the selected technology should have a minimum range of 100m. RF means of transmission can be preferred because the size of data packets is small, low data rates are supported. The power consumption of the communication unit should be less.

3.4 SENSOR NODE DESIGN AND CONFIGURATION Based upon the requirements of the sensor node, designed for corrosion detection the following devices are selected. 

C8051F320 (Processing Unit): C8051F320 is 32 pin SoC. It has high speed 8051 microcontroller core with pipelined instruction architecture. The chip has 2304 total bytes of on-chip RAM). 16k bytes of on-chip FLASH memory is also available. It is a low powered device and can be operated by power supply of 2.7 V-to-3.6 V.



AD5933 (Sensing Unit): AD5933 is a miniaturized impedance measuring chip which serves as a replacement for the conventional bulky impedance analyzer. The chip is equipped with ADC which converts the sensed data into digital form. Various parameters required for the measurement of impedance can be programmed using I2C communication.



Xbee S2 (Communication Unit): The module operates up to a range of 40m in indoor conditions and has outdoor RF line-of-sight range up to 120m. It offers RF data rate of the device is 25kbps. The device operates in 2.4 GHz (ISM ) band.


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Proposed Work and Methodology The details and specifications of C8051F320, AD5933 and Xbee S2 are described in Appendix A, B and C.

Figure 3.1 Block Diagram of the proposed sensor node.

3.4.1 SENSOR NODE SCHEMATIC DESIGN. The schematic of the PCB for the proposed sensor node is designed in OrCAD 9.1. The node is driven by a 5 V rechargeable battery. It is equipped with the JTAG port which provides on-chip debugging of C8051F320. The controller chip has an embedded full-speed USB 2.0, so the sensor node has a provision of USB port. Since the node is provided with a limited power resource, therefore monitoring of the battery capacity will help the user to recharge it when value falls below a certain threshold. In order to monitor the battery its output is connected to a voltage divider circuit. The voltage divider consists of two resistors of value 3 KΩ and 2 KΩ. One end of the divider is connected pin 1 of the controller which is grounded internally. The output voltage of the divider circuit is fed to the on-chip ADC of C8051F320. AD5933 has a two pin connector to connect it to the electrodes that will be attached to the unknown impedance. AD5933 chip is connected to the processing unit by SDA and SCL pins. Pull up resistors of value 5 KΩ are used with the SCL and SDA pin. The chip is programmed by I2C communication. DOUT and DIN pins of the Xbee are connected with the pin 29 and 30 of the controller. C8051F320 is programmed using Keil µVision IDE. .


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Figure 3.2 Schematic Diagram of the PCB of sensor node.

3.4.2 INTERFACING AD5933 WITH C8051F320 The RFB pin of AD5933 is connected to 200kΩ. This resistance value is used to calibrate AD5933 to calculate the Gain Factor, which is used to calculate the measured unknown impedance. The AD5933 allows the user to perform a frequency sweep with a user-defined start frequency, frequency resolution, and number of points in the sweep. Since the user can program peak-to-peak output excitation voltage, there are four possible peak-to-peak voltages defined by the device for this purpose. Table 3.1 Output Excitation Voltage Levels for AD5933 at 3.3 V

Range

Output Excitation

Output DC Bias Level

Voltage Amplitude 1

1.98 V p-p

1.48 V

2

0.97 V p-p

0.76 V

3

383 mV p-p

0.31 V

4

198 mV p-p

0.173 V


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Start Frequency: AD5933 needs a 24-bit word start frequency to start frequency that is stored to the on-board RAM at Register Address 0x82, Register Address 0x83, and Register Address 0x84. The code for start frequency is calculated from the given formula. 𝑆𝑡𝑎𝑟𝑡 𝐹𝑟𝑒𝑞𝑢𝑒𝑛𝑐𝑦 𝐶𝑜𝑑𝑒 =

𝑅𝑒𝑞𝑢𝑖𝑟𝑒𝑑 𝑂𝑢𝑡𝑝𝑢𝑡 𝑆𝑡𝑎𝑟𝑡 𝐹𝑟𝑒𝑞𝑢𝑒𝑛𝑐𝑦 𝑀𝐶𝐿𝐾 4

× 227

… . (3.1)

Frequency Increment: Frequency increment is the difference between two successive frequencies in a frequency sweep. This value can be programmed as a 24-bit word and can be stored at Register Address 0x85, Register Address 0x86, and Register Address 0x87. The code for frequency increment is calculated as: 𝐹𝑟𝑒𝑞𝑢𝑒𝑛𝑐𝑦 𝐼𝑛𝑐𝑟𝑒𝑚𝑒𝑛𝑡 𝐶𝑜𝑑𝑒 =

𝑅𝑒𝑞𝑢𝑖𝑟𝑒𝑑 𝐹𝑟𝑒𝑞𝑢𝑒𝑛𝑐𝑦 𝐼𝑛𝑐𝑟𝑒𝑚𝑒𝑛𝑡 𝑀𝐶𝐿𝐾 4

× 227 … … (3.2)

Number of Increments: This is a 9-bit word that represents the number of frequency points in the sweep. The number is programmed to the on-board RAM at Register Address 0x88 and Register Address 0x89. Once the three parameter values have been programmed, the sweep is initiated by issuing a start frequency sweep command to the control register at Register Bit D2 in the status register indicates the completion of the frequency measurement for each sweep point. The measured result is stored in the two register groups: 0x94, 0x95 for real data and 0x96, 0x97 for imaginary data. I2C Communication: In AD5933 all the parameters are programmed through I2C communication (Appendix B) between microcontroller and AD5933. C8051F320 acts as master device and Ad5933 as a slave. The data is sent and received from C8051F320 through SDA and clock is provided to the device by the controller through SCL pin. After programming the start frequency and other parameters, the Gain Factor is calculated. Gain factor is a scaling factor required to calculate impedance To convert the magnitude, obtained from real and imaginary registers of AD5933, into impedance, it must be multiplied the gain factor. The gain factor is calculated during the calibration of the system with known impedance ECE Department, NITTTR Chandigarh

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Proposed Work and Methodology connected between the VOUT and VIN pins. Once the gain factor has been calculated, it can be used in the calculation of any unknown impedance between the VOUT and VIN pins. The formula for gain factor calculation is given below: 𝐺𝑎𝑖𝑛 𝑓𝑎𝑐𝑡𝑜𝑟 =

1 𝐼𝑚𝑝𝑒𝑑𝑎𝑛𝑐𝑒 𝑀𝑎𝑔𝑛𝑖𝑡𝑢𝑑𝑒

… … … (3.3)

In the above equation the magnitude is the value obtained from AD5933 whereas impedance is the known value. The magnitude is calculated from real and imaginary values as: 𝑀𝑎𝑔𝑛𝑖𝑡𝑢𝑑𝑒 = 𝑅 2 + 𝐼 2 ........ (3.4)

To calculate the unknown impedance value its magnitude is obtained and put in the following equation: 𝐼𝑚𝑝𝑒𝑑𝑎𝑛𝑐𝑒 =

1 … … … (3.5) 𝐺𝑎𝑖𝑛 𝐹𝑎𝑐𝑡𝑜𝑟 × 𝑀𝑎𝑔𝑛𝑖𝑡𝑢𝑑𝑒

Figure 3.3 Sensor node PCB.

In the designed sensor node, data is measured at decade frequencies, so the contents of the Frequency increments and Number of increments remain zero. The data is sent to the coordinator only when queried. A general flow of sensor node code for AD5933 is presented in the form of flowchart given in fig. 3.4.


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Start

Initialize Array of start frequencies

Initialize Frequency increments and number of increments to zero

Place AD5933 in Standby Mode

Program Start Frequency Command into Control Register

Program Start Frequency Sweep Command into Control Register

Check if DFT conv. is complete

N

Y Read Real and Imaginary Values

Initialize with next value of start frequency

Figure 3.4 Flowchart for AD5933 Configuration.


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Proposed Work and Methodology 3.4.3 INTERFACING XBEE S2 WITH C8051F320 The Xbee S2 RF module is interfaced with the controller chip through its 29 and 30 pin. Since the data enters the Xbee module through DIN (pin 3), so the TX UART pin (pin 30) of C8051F320 is connected to the DIN pin of the Xbee. Similarly the data is received by the controller through RX UART pin (pin 29). Since data flows out of DOUT pin (pin 2) of Xbee, it is connected with pin 29 of C8051F320.

Figure 1.5 Designed Sensor Node.

UART0 of C8051F320 is an asynchronous, full duplex serial port offering modes 1 and 3 of the standard 8051 UART. UART0 has two associated SFRs: Serial Control Register 0 (SCON0) and Serial Data Buffer 0 (SBUF0). The single SBUF0 location provides access to both transmit and receive registers. With UART0 interrupts enabled, an interrupt is generated each time a transmit is completed (TI0 is set in SCON0), or a data byte has been received (RI0 is set in SCON0). The UART0 interrupt flags are not cleared by hardware when the CPU vectors to the interrupt service routine. They must be cleared manually by software.

The basic programming steps for

initializing the UART0 of C8051F320 are presented in the flowchart given below.


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Start

1. Initialize SCON0 to select 8- bit UART mode and to enable receiver of the UART. 2. Enable Timer 1 in auto-reload mode to set the UART baud rate.

N

If RI0=1

Read the received data from SBUF0 Y

N

If TI0=1

Y

Send array of data to SBUF0

Figure 3.6 Flowchart of UART0 programming steps.


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Testing and Results

CHAPTER 4 TESTING AND RESULTS

4.1 INTRODUCTION The designed node is tested for impedance measurements of resistors and RC networks. This chapter is divided into four sections. The first section deals with the impedance values measured by the single node and the evaluation of these measurements. Second section will describe the setup of the network and its various parameters. The third section includes a query based algorithm for data acquisition in the network. The final section deals with the testing of the proposed algorithm in the network.

4.2 SENSOR NODE IMPEDANCE MEASUREMENT RESULTS Overview: For testing purposes the impedance Z of resistor and capacitors of known value are measured. Resistors of different value are used for calibration and testing. The internal oscillator of frequency 16.776 MHz is used. The AD5933 operates in six ranges from 0.1KΩ-10MΩ in total. In order to reduce the impedance measurement error one of these six ranges should be selected.

Minimizing the impedance range under observation reduces the error. AD5933

datasheet (Rev 0) defines six ranges and percentage error offered in these ranges (Appendix B). The designed node will operate in range 4 since its calibration resistance and feedback resistance above 100 KΩ.

Gain Factor: In order to calculate the unknown impedance value, the obtained magnitude should be multiplied with the Gain Factor. The Gain Factor is calculated by connecting a known impedance of 200 KΩ between the VOUT and VIN pin of the AD5933. The RFB is connected to 200 KΩ. Using equation 3.3, Gain Factor comes out to be 512.453 × 10-12 at a frequency of 30 KHz.

Resistors: All the measurements of resistors are taken at 30 KHz and Range 1 (Table 4.1) is selected for the output excitation peak-to-peak voltage. The resistances measured lies in the range of 100 KΩ to 1 MΩ. The figure 4.1 shows the resistance of different resistors measured ECE Department, NITTTR Chandigarh

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Testing and Results with RFB = 200 KΩ. It is observed that the |Z| value is lesser than the actual values of the resistors. A small error is observed in measured value, since the system accuracy of AD5933 is 0.5%, so small deviations from actual values are seen at single frequency.

1200 1000

Resistor Measurements taken at 30 KHz with G.F 512.453 × 10-12

800 600

Actual Value of Resistance( KΩ)

400

Measured Value of Resistance( KΩ)

200 0 280

330

402

550

674 1000

Figure 1.1 Resistor Measurements taken at 30 KHz with Gain Factor (G.F) 512.453 × 10

-12 .

For corrosion detection of a material, using EIS, the material is excited with the voltage at multiple numbers of frequencies; it implies that there is a greater dynamic interaction over that frequency range. This helps in detecting and locating the corrosion area. In order to test the node for single impedance, a resistor of value 402 KΩ is measured at frequency range of 10 KHz to 60 KHz. 405 400

Impedance in KΩ

395 Actual value of resistance

390 385

Measured value of resistance

380 375 370 365 360 355 10

20

30

40

50

60

Frequency in KHz

Figure 4.2 Measurement of Single Resistance at different frequencies.


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Testing and Results RC Networks: Network with capacitors is complicated to test due to the strong frequency dependent variation in both phase and magnitude. For RC series circuit combination, impedance is calculated as 𝑍

=

𝑠

1 + (𝜔𝑅𝐶)2 … … . (4.1) 𝜔𝐶

An RC series combination of 680 KΩ resistor and 1000pF ceramic capacitor with 10% tolerance are tested from 1 KHz to 50 KHz. The values of gain factor and RFB are same as the previous case. 720 700

Impedance (KΩ)

680 660 640 Actual value of Impedance

620

Measured Value of Impedance

600 580 560 540 1

5

10

20

30

40

50

Frequency (KHz)

Figure 4.3 RC series network measurements at different frequencies.

A parallel RC circuit with same resistor and capacitor is tested at lower frequencies because the effect of parallel capacitor increases at higher frequencies and the measured impedance values falls out of the measurement range. The impedance in parallel RC network is calculated as 𝑍

𝑝

=


𝑅 1 + (𝜔𝑅𝐶)2

… … … (4.2)

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Testing and Results 350

Impedance (KΩ)

300 250 200 150

Measured value of Impedance

100

Actual value of Impedance

50 0 500

600

700

800

900

Frequency (Hz)

Figure 4.4 RC parallel network measurements at different frequencies.

The measurement results show that the deviation of the measured impedance value increases at higher frequencies. Since the tested impedances lies in the range 4 of AD5933 impedance subrange, so the percentage error increases with increase in frequency. The dependence of the gain factor on the temperature and frequency can introduce some error in the output (Appendix B). The typical error variation of gain factor with temperature is 30 ppm/o. The variations in the AD5933 output are also due to the instability of the internal oscillator of the device, caused by temperature variations. The resistors and capacitors used for testing are not pure but have tolerance of 5-10%, this adds some error to the measurements. The impedance of the connector wires used to connect to the resistors and RC network are also a source of error.

4.3 NETWORK SETUP To setup the network of Zigbee based sensor nodes, all the Xbee S2 modules are configured using X-CTU, which is multi-platform application, to interact with Digi International RF modules. The network is deployed in the radial topology. The network will initially consist of three devices configured as coordinator, router and end device. The coordinator is enabled in the API mode (Appendix C) and therefore sends data in structured packets. Coordinator is connected to the PC through a Xbee USB Explorer and acts as a base station. The router and the end device


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Testing and Results are set to operate in the AT mode. The data collected in the network is displayed on the python console of Spyder Software.

Figure 4.5 Network Setup in radial topology.

In order to create a personal area network all the three devices should operate with same PAN ID and Operating Channel. The devices have a unique 64 bit address provided by the manufacturer, but during network setting, 16 bit network address is provided randomly to each device by the coordinator. The 16 bit default address of coordinator is 0000. All three devices communicate with their respective hosts at a baud rate of 9600 bps and serial parity and stop bits for communication are set to zero. The end device and router sends join notification to the coordinator when they joins the network or are powered up. Since there is an option of sleep mode for end devices, in order to conserve the energy. The end device is enabled in the cyclic sleep mode. Table 4.1 describes various configuration parameters of Xbee modules. ECE Department, NITTTR Chandigarh

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Testing and Results Table 4.1 Configuration parameters of Xbee Modules in X-CTU

Coordinator

Router

End Device

64-bit address

0013A20040C1BAB8

0013A20040C1BAB9

0013A20040C1AE4F

Firmware Version

21A7

22A7

28A7

16-bit Network

0000

E596

8697

Mode of operation

API mode

AT mode

AT mode

PAN ID

3322

3322

3322

Operating Channel

C

C

C

Baud Rate

9600 bps

9600 bps

9600 bps

Join Notification

Only for Router and

Enabled

Enabled

Disabled

Cyclic sleep

0 (Set to maximum)

0 (Set to maximum)

Address

End Device Sleep Mode

Only for Router and End Device

Broadcast Hops

0 (Set to maximum)

4.4 DATA ACQUISITION IN THE NETWORK A query-reply mechanism is applied to collect data from sensor nodes in the network. A query is generated by the coordinator and broadcasted in the entire network. A query is a set of characters transmitted as the RF data in the network. The sensor nodes have a user defined ID stored in their memory. The user defined id is a character type value which is different for different nodes. The coordinator broadcasts the queries with these IDs; the node with the matching ID transmits the sensed data back to the coordinator.

Broadcast of the query is based on the broadcast transmission within the ZigBee protocol which is intended to be propagated throughout the entire network such that all nodes receive the ECE Department, NITTTR Chandigarh

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Testing and Results transmission. To accomplish this, the coordinator and all routers that receive a broadcast transmission will retransmit the packet three times. Hence the broadcasting gives the entire network coverage. Each node that transmits the broadcast will also create an entry in a local broadcast transmission table. This entry is used to keep track of each received broadcast packet to ensure the packets are not endlessly transmitted. Each entry persists for 8 seconds. The broadcast transmission table holds 8 entries. Start

Select the User defined node ID from the given set of nodes

Broadcast the query

Wait for the response

N

Response ?

Y Display the sensor node data

Figure 4.6 Query transmission steps for Coordinator.

Query Transmission: Fig. 4.6 shows the steps involved in the transmission of the query in the network. The coordinator selects a node id from the given set of nodes and then broadcast this user defined id into the network. After transmission the coordinator waits for the response of the addressed node. When the response arrives, the sensed data is parsed, to display the real and


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Testing and Results imaginary values of the impedance, at different frequencies. The steps are repeated till all the nodes in the network are covered for data collection. Start sta Measure the real and imaginary values of impedance at different frequencies and remaining battery capacity

N

N

Query ? Y

Node ID matches ? Y

Send Sensed data to Coordinator

Figure 4.7 Response from end device.

Reply from End Device: Fig. 4.7 shows the processes carried out in sensor device. The sensor device senses the data of the impedance under observation, at set of given frequencies and it converts the remaining battery capacity into digital form. The device waits for the query from the coordinator. In case a query arrives, the device check if the node id in the query matches the id stored in the memory of the device. If the match occurs, the device sends the sensed data to the coordinator in the form of an array. The array contains the battery lifetime followed by the real and imaginary values measured in the ascending order of the frequencies. In case the id contained in the query does not match with the stored id, the device waits for the next query and the process continues. ECE Department, NITTTR Chandigarh

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Testing and Results

4.5 TESTING The implemented network is tested for data acquisition using the proposed query-reply algorithm. A python program is run in Spyder to a generate query, on the coordinator side. The coordinator is enabled in the API mode so, the query is sent in the form of an API frame. A query is a Transmit Request API frame generated at the coordinator end that causes the module to send data as an RF packet to the entire network as a broadcast. General structure and types of API frames are explained in Appendix C. Table 4.2 Frame structure of a query originating at Coordinator

Frame Fields

Field value

Start Delimiter

0×7E

Length

0×0F

Description

Number of bytes between Length and Checksum.

Frame type

0×10

This is a default frame id for transmit request

Frame ID

0×00

Set to 0 to disable Acknowledgement.

64 bit Destination

0×000000000000FFFF

64 bit address for broadcast

0×0FFFE

16 bit network address for broadcast.

Options

0×00

Set to default value.

RF data

User defined node id

This field contains a user defined node id

Address 16 bit Network Destination Address.

stored in each node. It is a character value. On receiving this value a particular node will send its sensed data. Checksum

Variable

This field contains checksum value. (0xFF - the 8 bit sum of bytes from offset 3 to this byte.)


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Testing and Results Table 4.2 describes different fields of a query frame. Since the frame is a Transmit Request API frame, the frame starts with a delimiter which remains same for all API frame types. The query is broadcast in the network so the long 64 bit address and short 16 bit address of the destination are set to the broadcast address. The RF data contains the user defined node id as characters. The frame type is set to default value of transmit frame type. The response of the sensor nodes obtained by the coordinator is displayed on the python console. The data acquired from the nodes is displayed in a sequence, in which the queries are sent. The data of end device is displayed as "node a" and the data of the router is displayed as "node b". The response is a ZigBee Receive Packet.

Figure 4.8 Screenshot of python console.

Figure 4.8 displays the different fields of the received packets. The received packet is parsed into the following labels: 

source_add_long: It contains the 64 bit address of the sender node. Since the data of nodes is received one after another this address helps in identifying the module which sends this data.



rf_data: It contains an array of the sensed data, where the first four characters represent the battery life and the next bytes are real and imaginary values of the impedance at 9


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Testing and Results frequencies. The range of frequencies is defined by the user for frequency sweep and the values are known at both receiver and sender side. 

source_addr: It holds the 16-bit network address of the sender. It is a network generated address and can change if the device joins another coordinator.



id: The id value contains the frame type of received packet.



options: This label contains the option value contained in the received frame. This field informs that whether the packet is acknowledged or not or whether the given packet is broadcast packet, encrypted packet or is sent from known end device.

The rf_data field is parsed to obtain the value of battery capacity and the real and imaginary values at different frequencies. The "emf" label contains the remaining battery capacity. The real and imaginary values are displayed in dictionary format using the dictionary type in python. Since the frequencies are known at coordinator end hence only real and imaginary values are displayed in the ascending order of the frequency range.

Summary: The motive of designing hardware for corrosion detection in structures is achieved successfully. The designed sensor node is small size device, driven by a low power source. The node is tested for impedance measurements in laboratory and gave acceptable outputs. Network is setup to collect the impedance data using the designed nodes. The implemented network is tested successfully for data collection from all the nodes. The query-reply approach used is simpler to implement and can be extended for larger networks with minimum changes. Since the approach is based on broadcast transmission, so there is no need to know the 64-bit and 16-bit address of each node in the network.


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Conclusion and Future Scope

CHAPTER 5 CONCLUSION AND FUTURE SCOPE

5.1 CONCLUSION Wireless Sensor Networks are emerging as a reliable technology for detection and advent of corrosion in the civil structures. In contrast to the conventional methods, the WSNs offer Non destructive evaluation techniques for structural health monitoring In order to cover the entire structure, the networks requires compact size devices, driven by battery and capable of implementing various corrosion detection techniques. The wireless mode of communication enables the remote monitoring of the structure.

The main objective of this thesis work is to design sensor node for corrosion detection and implement a wireless network for information exchange. The designed node is a low power, low cost, small size device. It uses AD5933 to detect the change in the impedance of the material using electrochemical impedance spectroscopy. The node is tested to measure impedance of resistors and RC networks at single and multiple frequencies under the laboratory conditions. The measurement results match actual values within a tolerance range of 1-6%. The implemented WSN comprises 3 nodes acting as a coordinator, router and end device in radial topology. The proposed query-reply algorithm used for data collection provides coverage to the entire network. The frame structure used for transmission and reception is simple to implement and will work successfully on expansion of the network. The network is tested in the laboratory for acquiring the sensed data, and results are displayed on the coordinator end. A successful data acquisition is done from the entire network and the results of the collected data are displayed on the python console at the coordinator end.

5.2 FUTURE SCOPE The future scope of this thesis work includes the on-field testing of the sensor nodes and the network and analysis of the collected data for corrosion detection. In order to cover the structure under testing the network should be expanded. The measurement error of AD933 can be reduced further by using external oscillator. The broadcast technique used in the network for query ECE Department, NITTTR Chandigarh

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Conclusion and Future Scope transmission, is based on flooding. Flooding increases the network overhead by increasing the number of rebroadcasts. The rebroadcast problem increases overall energy consumption of the network. To overcome this problem number of rebroadcast nodes should be selected optimally to provide coverage to the entire network and to conserve energy.


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Appendix A

APPENDIX A C8051F320 SPECIFICATIONS C8051F320 C8051F320 device is a 32 pin, fully integrated, mixed-signal System-on-a-Chip MCUs from Silicon Laboratories. It is a low-power device. The C8051F320 utilizes Silicon Labs' proprietary CIP-51 microcontroller core. The CIP-51 is fully compatible with the 8051 instruction set. The CIP-51 core offers all the peripheral included with a standard 8052, including four 16-bit counter/timers, a full-duplex UART with extended baud rate configuration, an enhanced SPI port, 2304 bytes of on-chip RAM, 128 byte Special Function Register (SFR) address space, and25/21 I/O pins. The CIP-51 employs a pipelined architecture that greatly increases its instruction throughput over the standard 8051 architecture. [39].

Figure A.1 Internal blocks of C8051F320 [38].

Features of C8051F320 are: 

It has high speed 8051 microcontroller core with pipelined instruction architecture. The device offers up to 25 MIPS throughput with 25 MHz clock source.



The chip has 2304 total bytes of on-chip RAM (256 + 1k + 1k USB FIFO). 16k bytes of on-chip FLASH memory is also available.


Page 45

Appendix A 

The device is operates in range 2.7 V-to-3.6 V over the industrial temperature, hence is a low powered device.



The SoC supports Supply Voltage Regulator in a range of 5V-to-3V.



C8051F320 has an on-chip 10-bit 200 ksps, 17-channel single-ended/differential ADC with analog multiplexer, On-chip Voltage Reference, Temperature Sensor and Voltage Comparators.



The device has facilities of on-chip Power-On Reset, VDD monitor, Voltage Regulator, Watchdog Timer, and clock oscillator.



The on-chip Silicon Labs 2-Wire (C2) Development Interface allows non-intrusive, full speed, in-circuit debugging. This debug logic supports inspection and modification of memory and registers, setting breakpoints, single stepping, run and halt commands. All analog and digital peripherals are fully functional while debugging using C2.



The chip is equipped with SMBus/I2C, Enhanced UART, and Enhanced SPI serial interfaces implemented in hardware.



The device has 25 I/O pins, four General Purpose 16-Bit Counter/Timers, 16-Bit Programmable Counter Array (PCA) with Five Capture/Compare Modules.



It has a 12 MHz internal oscillator and also facility for using an external oscillator.


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Appendix B

APPENDIX B AD5933 Introduction The AD5933 is a high precision impedance converter system by Analog Devices, that combines an on-board frequency generator with a 12-bit, 1 MSPS, analog-to-digital converter (ADC). The frequency generator allows an external complex impedance to be excited with a known frequency. The response signal from the impedance is sampled by the on-board ADC and a discrete Fourier transform (DFT) is processed by an on-board DSP engine. The DFT algorithm returns a real (R) and imaginary (I) data-word at each output frequency. Once calibrated, the magnitude of the impedance and relative phase of the impedance at each frequency point along the sweep is easily calculated. This is done off chip using the real and imaginary register contents, which can be read from the serial I2C interface [40].Various features of AD5933 are: 

It supports programmable peak-to-peak excitation voltage output

with a maximum

frequency of 100 kHz. 

The device has programmable frequency sweep capability with serial I2C interface.



It can measure impedance from 1 KΩ to 10 MΩ with system accuracy of 0.5%.



The chip has internal temperature sensor and internal system clock option.



The power supply of 2.7 V to 5.5 V is specified for operation.

Figure B.1 Functional Block of AD5933 [39].

[ECE Department, NITTTR Chandigarh]

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Appendix B I2C Communication in AD5933: Control of the AD5933 is carried out via the I2C-compliant serial interface protocol. The AD5933 is connected to this bus as a slave device under the control of a master device. The AD5933 has a 7-bit serial bus slave address. When the device is powered up, it has a default serial bus address, 0001101 (0x0D).

General I2C Timing: The master initiates data transfer by establishing a start condition, defined as a high-to-low transition on the serial data line (SDA), while the serial clock line (SCL) remains high. This indicates that a data stream follows. The slave responds to the start condition and shifts in the next 8 bits, consisting of a 7-bit slave address (MSB first) plus an R/W bit that determines the direction of the data transfer that is, whether data is written to or read from the slave device (0 = write, 1 = read). The slave responds by pulling the data line low during the low period before the ninth clock pulse, known as the acknowledge bit, and holding it low during the high period of this clock pulse. All other devices on the bus remain idle while the selected device waits for data to be read from or written to it. If the R/W bit is 0, then the master writes to the slave device. If the R/W bit is 1, the master reads from the slave device.

Data is sent over the serial bus in sequences of nine clock pulses, eight bits of data followed by an acknowledge bit, which can be from the master or slave device. Data transitions on the data line must occur during the low period of the clock signal and remain stable during the high period, because a low-to-high transition when the clock is high may be interpreted as a stop signal. If the operation is a write operation, the first data byte after the slave address is a command byte. This tells the slave device what to expect next. It may be an instruction telling the slave device to expect a block write, or it may be a register address that tells the slave where subsequent data is to be written. Because data can flow in only one direction as defined by the R/W bit, it is not possible to send a command to a slave device during a read operation. Before performing a read operation, it is sometimes necessary to perform a write operation to tell the slave what sort of read operation to expect and/or the address from which data is to be read. When all data bytes have been read or written, stop conditions are established. In write mode, the master pulls the data line high during the 10th clock pulse to assert a stop condition. In read mode, the master device releases the SDA line during the low period before the ninth clock [ECE Department, NITTTR Chandigarh]

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Appendix B pulse, but the slave device does not pull it low. This is known as a no acknowledge. The master then takes the data line low during the low period before the 10th clock pulse, then high during the 10th clock pulse to assert a stop condition.

2

Figure B.2 I C Timing Diagram for AD5933

Impedance Error in AD5933 Minimizing the impedance range under test optimizes the AD5933 measurement performance. The AD5933 impedance measurements are divided in e six different ranges. The table describes the six impedance ranges, and the value of Calibration resistor and RFB for each range Table B.1 Impedance Measurement ranges for AD5933

Impedance Range

Rcal

RFB

Range 1

0.1 kΩ to 1 kΩ

100 Ω

100 Ω

Range 2

1 kΩ to 10 kΩ)

1 kΩ

1 kΩ

Range 3

10 kΩ to 100 kΩ

10 kΩ

10 kΩ

Range 4

100 kΩ to 1 MΩ

100 kΩ

100 kΩ

Range 5

1 MΩ to 2 MΩ

100 kΩ

100 kΩ

Range 6

9 MΩ to 10 MΩ

9 MΩ

9 MΩ

For range 1 the error varies from 2% to 4% over the frequency range. At higher impedance values of this range, the measurement error increases. Range 2 and range 3 give best results, since error in these ranges is minimum, with 0.8 % to 1.2% for range 2 and 0.2% to -.27% for range 3. For the upper ranges (4-6) the impedance error shows a strong increase with the frequency, up to -7 and -8% for ranges 5 and 6. [ECE Department, NITTTR Chandigarh]

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Appendix B

Figure B.3 The impedance error as function of frequency for ranges 1, 2 [41].




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Appendix B Gain Factor variation with frequency and temperature. Because the AD5933 has a finite frequency response, the gain factor shows a variation with frequency. This results in an error in the impedance calculation over a frequency range. Figure B.6 shows an impedance profile based on a single-point gain factor calculation. To minimize this error, the frequency sweep should be limited to as small a frequency range as possible.

Figure B.6 Single point Gain Factor variation with frequency [41].

The typical impedance error variation with temperature is in the order of 30 ppm/°C. Figure B.7 shows an impedance profile with a variation in temperature for 100 kΩ impedance using a 2point gain factor calibration.

Figure B.7 Impedance Variation with Temperature Using a 2-Point Gain Factor Calculation [41].


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Appendix C

APPENDIX C XBEE S2 Introduction: Xbee S2 is a 20 pin, wireless module based on ZigBee protocol, by Digi International. ZigBee is an open global standard built on the IEEE 802.15.4 MAC/PHY. ZigBee defines a network layer above the 802.15.4 layers to support advanced mesh routing capabilities. The device supports the unique needs of low-cost, low-power wireless sensor networks. The modules require minimal power and provide reliable delivery of data between remote devices. The modules operate within the ISM 2.4 GHz frequency band. The XBee RF Modules interface to a host device through a logic-level asynchronous serial port. Through its serial port, the module can communicate with any logic and voltage compatible UART [42]. XBee modules maintain small buffers to collect received serial data from DIN pin (pin 3). The incoming serial data is held in serial receive buffer until they can be processed. The serial transmit buffer collects data that is received from the host device through DOUT pin (pin 2), which gets transmitted out the UART. Specifications of Xbee S2: 

The module operates up to a range of 40m in indoor conditions and has outdoor RF lineof-sight range up to 120m.



It offers RF data rate of the device is 25kbps.



It is a low power device operating with a power supply in range 2.1-3.6 V.



The operating frequency band for Xbee S2 is ISM 2.4 GHz and operates in industrial range of temperature.

This ZigBee based module supports Point-to-point, Point-to-multipoint, Peer-to-peer and Mesh Network topologies.

Network Topology in Xbee S2 ZigBee defines three different device types: coordinator, router, and end device.

Coordinator: Coordinator selects the channel and PAN ID to start the network. It allows router and end devices to join it and also assists in routing. The coordinator cannot sleep.


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Appendix C Router: Router should join a ZigBee PAN before it can transmit, receive, or route data. After joining it allows other routers and end devices to join the network. Like coordinator, it cannot sleep.

End Device: End device must join a ZigBee PAN before it can transmit or receive data. It cannot allow devices to join the network. It should always transmit and receive RF data through its parent. It cannot route data. It can enter into sleep mode.

Figure C.1 ZigBee Network Topology.

The Xbee S2 operates in two modes: Transparent Mode and API mode. Transparent mode is simpler and acts as a serial line replacement. AT commands are used to configure various module parameters. All received serial data is transmitted unless the module is in command mode. On the other hand API mode frame based method of data transmission and reception. To setup a network using Xbee S2, Coordinator should operate in API mode. This mode is faster in case of data transmission to multiple destinations. The received data frames contain the 64 bit and 16 bit address of the source nodes. In order to setup a network the coordinator should be enabled in API mode.

API Frame Structure: The API specifies how commands, command responses and module status messages are sent and received from the module using a UART Data Frame. There are various types of data frames present oriented for different applications. API specific frames in general have start delimiter field, length field, frame data field and a checksum field added at the last. The start delimiter and length fields have fixed length of one and three bytes respectively. ECE Department, NITTTR Chandigarh

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Appendix C

Figure C.2 API frame structure.

The cmdID frame indicates which API messages will be contained in the cmdData frame. The Xbee supports the following frame types.

FigureC.3 Xbee frame types [40].


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LIST OF PUBLICATIONS 1. Kirandeep Kaur and Amol P. Bhondekar, “A Survey of Routing Protocols for Structural Health Monitoring”, Proceedings of International Conference on Electronics Design Innovations and technologies, NITTTR, pp. 195-197, 2015.

xii

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