CCIS 95 - Aerial Vehicle Based Sensing Framework for ... - Springer Link

Aerial Vehicle Based Sensing Framework for Structural Health Monitoring Ashish Tanwer, Muzahid Hussain, and Parminder Singh Reel Electronics and Communication Engineering Department Thapar University, Patiala – 147001, India {Ashishtanwer,parminder.reel}@gmail.com, [email protected]

Abstract. A novel approach to implement wireless active sensing framework for Structural Health Monitoring is presented using an Unmanned Aerial Vehicle as a Mobile Agent. This approach has unique features of critical activity detection, wireless power delivery and measurement and data collection options from the sensor nodes through Zigbee. The active sensing is initiated by wirelessly triggering the sensor node from UAV. UAV provides unrestricted accessibility in various applications for SHM. It carries Beagle Board payload for real-time calculations to check the health of structures. The onboard video camera provides improved analysis of Critical activities like abnormal vibration and structure tilt. It captures video of the area of interest and data measured by any sensor node network. This paper is intended to give sufficient details of the implementation of our approach. Keywords: Structural Health Monitoring, Wireless Sensing Framework, Wireless Energy Transmission, Mobile Agent, UAV, Mulle Platform.

1 Introduction Structural health monitoring (SHM) is the process of detecting damages in civil, military, aerospace structures such as bridges, buildings, aerial vehicles, oil and water tanks etc. before it reaches critical stage. It is a field of public interest aiming to improve the safety and reliability of costly infrastructure by detecting damage at early stage of its evolution. Wireless Sensor Networks (WSN) are employed for monitoring process of these structures in which the dynamic response of the deployed sensors are measured and damage sensitive features of the signals are analyzed. WSN provide an agile and effective solution over the convectional wired sensors system in terms of multipath transfer, distributive computing and lack of extensive wiring for every individual sensor node. However, wireless sensor nodes have energy constraints which require periodic replacement of battery in sensor nodes. In order to overcome these constraints, an energy harvesting system such as that of use of solar cells in sensor node is developed. Wireless sensor nodes having energy harvesting feature are already been developed for SHM applications [1] [2]. In an alternative approach, mobile agent is used for wireless powering and triggering of sensor nodes on as needed basis [3]. This approach involves the use of an unmanned S. Ranka et al. (Eds.): IC3 2010, Part II, CCIS 95, pp. 71–83, 2010. © Springer-Verlag Berlin Heidelberg 2010

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A. Tanwer, M. Hussain, and P.S. Reel

mobile agent which collaborates the concept of wireless energy transmission along with the distant monitoring platform. The previous work in this context includes a lot of theoretical investigations but a hardheaded demonstration of mobile agent based approach is done. The use of the Unmanned Ground Robot (UGV) as a mobile agent node is proposed but it severely restricts the application of mobile agent in the remote areas like huge buildings, containers and military structures [4]. In this paper, a new approach of using UAV as mobile agent node is presented and implemented. The UAV here is a mobile WSN member providing wireless power delivery and data collection options from the sensor nodes. The UAV node provides a radio-frequency (RF) signal to the receiving antenna of the sensor node that has been deployed on the structure. The sensors measure the desired response at critical areas on the structure and transmit the signal back to the mobile-agent again via the wireless communication.

2 Previous Work WSN have been thoroughly inquired for the structural health monitoring applications. The current wireless sensing system used for SHM varies from decentralized processing hopping protocol based sensors system to that of mobile based sensing networks. Various researchers have proposed collaborating robots/UAVs as mobile agents in WSN. Tong [3] and Tirta [5] did a significant work in this regard. Tong investigated the use of mobile sensor node for executing computational activities and come to the conclusion that the energy required by an ad-hoc network exponentially increases as the density of the deployment of WSNs increases. Tirta proposed the concept of mobile agent as data collector which collects the data from the local data storage nodes in the group of various small subnets. A very few illustrations of mobile agent based WSN used for SHM are found. Esser [6], Huston [7] and Ma [8] have proposed their research dealing with the use of mobile agents for inspecting structural integrity as well as performing power line inspections respectively. The hardheaded demonstration of the mobile based WSN used for structural health monitoring rarely exists. The use of unmanned ground robot as mobile agent in WSNs for providing wireless energy transmission and data collection have been thoroughly investigated and practically demonstrated in the work of Taylor [4]. However, to the author best knowledge, the use of UAV as mobile agent and its field demonstrations does not exist up to this date.

3 System Architecture The system used for SHM is 3-tier architecture consisting of WSN, UAV as mobile agent and the base station. There are 4 mode of communication between Mobile Agent, WSN and UAV as follow: Mode I: Data Communication between Gateway Node of WSN and UAV for data transfer through Zigbee (IEEE 802.15.4) @ 2.4 GHz.

Aerial Vehicle Based Sensing Framework for Structural Health Monitoring

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Mode II: Data Communication between UAV and Base station at RF of 1.2 GHz Mode III: RC Control of UAV from the Base Station Mode IV: Energy Transfer from UAV to Sensor nodes by reflector grid antenna of 5.8 GHz

Mobile Agent (UAV)

Wireless Data Transfer @ 1.2 Ghz

Wireless Energy Transfer

Zigbee Communication

Gateway RC Control

Base Station

Sensor Network

Fig. 1. Tier Architecture of SHM System

4 Wireless Sensor Network-Tier I Performance of WSN is primarily dependent on the performance of sensor node.SHM through WSN is critical task. For SHM the sensor node must have following characteristics: • Long range communication (range>100m) so that less number of sensor node can cover whole structure. • Low power consumption so that the battery replacement period is large • Wireless power transfer to remote nodes in case batteries are down • High precision 3- degree accelerometers • High data rate communication protocols like IEEE 802.15.4 Zigbee standard • Large data storing capacity on sensor nodes like ROM or flash memory • Time stamp. The system should be capable to time stamp events in real time A number of commercial wireless sensor platforms are capable to perform SHM. For testing, improved Mulle Platform, an open-source wireless sensor platform was adopted. Mulle EIS (Embedded Internet System) is based on the Renesas M16C/62P

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CPU having Core clock frequency of 24 MHz. The size of the Mulle platform is only 26 x 24 x 5 mm3. The Mulle platform is a complete standalone sensor node aimed at adhoc sensor. It has 10-bit A/D Converter consisting of 26 channels for sensor interface. The node has 3-axis accelerometer ADXLxxx (v5.2) with. Zigbee range 150 m. This enables the same sister nodes to be used on 10/100 m. The platform is equipped with AT86RF230, a low-power 2.4 GHz transceiver specially designed for IEEE 802.15.4, ZigBee and 6LoWPAN applications and high data rate 2.4 GHz ISM band applications. Node battery is single Lishen Lithium polymer (25x25 x 5 mm) rechargeable battery with life of 2 years and capacity of 130 mAh.

Renesas M16C/62P

ZigBee Module

Main Connector

Fig. 2. Mulle Embedded Internet System

The operating system running on sensor nodes is TinyOS. Renesas M16C/62P CPU has 31K CPU RAM, 384K CPU Flash and 2 MB serial flash (sufficient for TinyOS and data storage). Piezoelectric (PZT) sensors along with the impedance convertor chip of AD5933 is used .The change in the mechanical impedance shows the degree of damage done in the structure which is measured by the PZT sensor. The equivalent electric impedance signal is converted into digital signal by AD5933 chip embedded on the node which further fed its digitalized output to the microprocessor of the node. Each sensor node records data into local flash memory. Bonjour Service Discovery Protocol and Mulle Public Server (MPS) are used for application level communication. Mulle has open Application Programming Interface (API) in the C programming language that simplifies code reuse and improves performance. All programming work is done in nesC (subset of C) using GCC compiler. Table 1. Overview of Mulle Architecture Purpose Processor Tran-receiver Sensors Battery OS Programming

Component Renesas M16C/62P @ 24 MHz AT86RF230 @ 2.4 GHz for IEEE 802.15.4, Zigbee, 6LoWPAN 3-axis accelerometer (ADXLxxx), Piezoelectric Sensor with AD5933 Impedance chip Lishen Lithium polymer (25x25x5 mm) rechargeable battery TinyOS nesC using GCC Compiler


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The Mulle power Management Architecture (PMA) consists of a number of components that allow the Mulle to monitor and control its power supply. Various power saving modes adopted are • Passive mode: Transceiver on in listening mode. MCU is in stop mode and unused components are powered down in order to conserve energy. • Active mode: Mulle initiates outgoing connections. Once a connection is established, the Mulle starts to stream sensor data to a user or database server. The power consumption usually depends on the specific type of sensor(s) attached to the Mulle • Time-synchronous mode: Combines the two previous modes using an activation schedule, is a form of distributed duty-cycling and allows the Mulle to conserve a considerate amount of energy. The activation schedule can be modified dynamically, and allows users to make trade-offs between system life-time and end to-end delay. The Mulle spends most of its time, typically 95-99 %, in sleep mode where it consumes less than 10μW. Periodically it wakes up to either: listen for incoming connections or establish its own outgoing connections. The energy consumption is calculated as:

E = T ×(Psleep+ facq × Eacq + fcon× Econ)

(1)

where Psleep is Power Consumption while sleeping, Eacq is Energy Consumption while acquiring data, Econ is Energy Consumption for a connection, facq Frequency of acquisition, and fcon is Frequency of connection. An implementation of the wireless energy transfer system using mobile agent has been demonstrated. The system is designed in such a way that sensor nodes accept the wireless energy from the UAV (mobile agent). The energy received from the rectenna at receiver end is fed to a 0.1 F capacitor for storage shown in Fig 3. There is a provision of voltage trigger switch which provides regulated voltage to the processor. The sensor node acquires the data from accelerometer and piezo-electric sensors when triggered by UAV.

Fig. 3. Working Diagram of Sensor Node

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When the data from the sensors has been acquired, sensor node sends back this data to the computational load of the UAV with the help of its on-board Zigbee module. The mobile agent then stores the data, move to the next node if required performing the similar operation and send the stored data back to the base station.

5 The Mobile Agent (UAV)-Tier II The mobile-agent used for testing purpose is RC (radio controlled) unmanned aerial vehicle (UAV). The UAV weighs approximately 8 kg and carries components needed to implement the mobile-agent based SHM process as shown in Fig 4. Payloads are RC receiver for motion control, High frequency Radiator to make Nodes harness energy, Zigbee receiver for WSN access, Data transmitter unit, Seagull Telemetry system and EagleTree GPS. Data transmitter unit operates 500 mW, 1.2 GHz and delivers data received from WSN to the base station. 5.8 GHz reflector grid antenna for energy transmission

Wireless antenna

Data Recorder, Data Transmitter, GPS Receiver, Camera

Fig. 4. UAV developed at Thapar University in assistance of DRDO

5.1 Telemetry System The payloads seagull telemetry system has 3 main components: Seagull dashboard telemetry receiver with USB interface (displays telemetry data and sounds alarms when problems occur), onboard data recorder (collects and logs the data/sessions) and onboard telemetry transmitter (sends the data from the recorder to the dashboard).

Fig. 5. Dashboard, data recorder and GPS expander


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The basic building blocks of Video Transmitter unit are Modulator, Oscillator, Multiplier and RF Amplifier.

Fig. 6. Block Diagram of Video Transmitting Unit

5.2 Beagle Board Payload Beagle Board payload provides wireless interface platform with 2.4 GHz Zigbee devices through a dynamic mapping framework. It communicates and downloads data from Gateway Sensor Node through Zigbee link. Beagle board has POP Processor TI OMAP3530 (600MHz ARM Cortex-A8 core, HD capable' TMS320C64x+ core (75MHz up to 720p @30fps), Imagination Technologies PowerVR SGX 2D/3D graphics processor, 256MB LPDDR RAM memory and 256MB NAND Flash memory. Angstrom Linux is installed on SD/MMC card which boots from NAND memory. Zigbee USB Dongle is attached to Beagle Board via USB hug plugged in Enhanced Host Controller Interface (EHCI) port [Refer Fig 7].

Fig. 7. Interfacing of Beagle Board’s EHCI port with USB hub and Zigbee Bluetooth Dongle

5.3 Wireless Energy Transfer A Mobile Agent based wireless energy transmission to the sensor nodes is one of the major challenging problem since microwave transmission is always associated with some sort of attenuation which is governed by the Friis equation, all symbols having their usual meaning:

PR =

GTG Rλ2 PT (4π R )2

(2)

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Mobile agent acts as data storage and processing unit, transmits energy to the node when needed and triggers it for data extraction from the nodes. An antenna with rectification circuit called rectenna is used which have the capability to provide an efficiency up to 80% for DC conversion. An antenna at the receiver end captures the transmitted microwave energy and converts it into DC power with the help of rectifying circuit used and finally stored it in a storage medium (capacitors/battery). From the experiments, it was concluded that grid antenna of 5.8 GHz was used as transmitting antenna and rectenna was used at receiver end. Table 2 shows the power requirements of a sensor node when the circuit is operated on 3.3 V. Table 2. Power Requirements of Sensor Node Sensor Node Component Renesas M16C/62P Processor AT86RF230 Trans-receiver AD5933 Impedance chip

Voltage (V) 3.3 3.3 3.3 Total

Current (mA) 14 16.5 15 45.5

Power (mW) 46.2 54.45 49.5 150.15

6 The Base Station-Tier III The radio controller used here is Futaba 6ex-pcm model kit. It works on 72 MHz frequency. The kit consists of the transmitter T6exap, receiver 136hp and servos s3004 and s3151. T6exap transmits in both FM (PPM) and PCM by selecting modulation/cycling. It requires receiver of proper modulation. The LCD on the face of the compact transmitter enables easy reading and allows rapid data input. The system also holds independent memories for six different models. It has landing gear, trainer cord and buddy box capabilities. It also includes servo reversing, dual rates, exponentials and programmable mixing.

Fig. 8. Futuba Transmitter


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6.1 Video Receiver Video Receiver is used to receive the video signals transmitted by transmitter place in the aircraft. It works at 1.2GHz frequency. When the critical activity is detected by any of the sensor node the on board video camera captures the detailed pictures of the critical area and GPS locates the location of that area and return back these data to base station.

Fig. 9. Block Diagram of Video Receiver

The block diagram of typical video receiver is shown above. It is super- heterodyne receiver. It is less prone to noise and gives better quality of images. The different blocks of video receiver are: 1. Antenna: High gain directional 1.2GHz patch antenna is used to receive signal transmitted by the video transmitter placed in the aircraft. 2. RF Amplifier: It is a type of electronic amplifier used to convert a low-power radio-frequency signal into a larger signal of significant power, typically for driving the antenna of a transmitter. It is usually optimized to have high efficiency, high output Power (P1dB) compression, good return loss on the input and output, good gain, and optimum heat dissipation. It receives the low power signal from the antenna and selects the desired frequency and amplifies it. It gives at the output frequency component fs. 3. Mixer: Mixer is a nonlinear or time-varying circuit or device that accepts as its input two different frequencies and produces four frequencies at the output fo, fs, fo- fs, fo+fs out of these four frequency fo- fs is the desired one and is fed to the IF amplifier. 4. Local Oscillator: A local oscillator is an electronic device used to generate a signal normally for the purpose of converting a signal of interest to a different frequency using a mixer. This process of frequency conversion also referred to as heterodyning, produces the sum and difference frequencies of the frequency of the local oscillator and frequency of the input signal of interest. These are the beat frequencies. Normally the beat frequency is associated with the lower sideband, the difference between the two. The frequency component produced by it is denoted by fo. The local oscillator frequency is always greater than signal frequency. 5. IF Amplifier: It selects the intermediate frequency from the mixer output and amplifies it. The IF frequency used here is 480MHz. It removes the image frequency i.e. improves the image frequency rejection. It plays very important role in removing noise or unwanted signal from the received signal.

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6. Demodulator: Demodulation is the act of extracting the original informationbearing signal from a modulated carrier wave. A demodulator is an electronic circuit used to recover the information content from the modulated carrier wave. It receives the signal from the IF amplifier and demodulates it i.e. converts the modulated signal into the original signal and fed it to the modulating amplifier. 7. Amplifier: It amplifies the weak demodulated signal received from the demodulator. It provides enough strength to the signal that it can be derive USB video card effectively. The USB video input to the laptop is used for further processing (such as object detection). 6.2 Computing Platform The base station consists of laptop computer running windows XP. The SHM software is installed on this computer to make a real-time assessment on the structural Condition. Data received from the sensor node via Zigbee attached to Beagle Board is send to the base station. The UAV trigger the sensor node wirelessly using a low Frequency RF triggering antenna installed on the side of the vehicle.

7 Simulation Results The planning of WSN implant and positioning of sensor nodes for the proper functioning of WSN and optimization planning are done with WSN simulator. The sensor nodes are shown as yellow dots, gateway nodes as red dots and sinks as small receiver towers.

Fig. 10. WSN Analysis with WSNSim


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The simulation and optimization of WSN is done by Prowler, a MATLAB based Probabilistic Wireless Network Simulator is used to simulate the communication scheme: local OS services including the network protocol stack, and also the radio transmission phenomena (signal power vs. distance, fading, collision, disturbances). The figure shows results radio-channel simulation of span tree application on Prowler GUI.

Fig. 11. Simulation results of WSN on Prowler

The critical activity detection of a structure is done by measuring the vibrations for different buildings in the mentioned places (mentioned in the table 3) for the frequency range 1-80 Hz. The values in the block represent critical ones for a vibration of a building. Table 3. Intermittent vibrations values of a building

Place Residences Offices Critical areas

Intermittent vibrations(m/s2 ) .0072 .0063 .0013 .013 .0036 .0028

.0058 .014 .009

.030 .021 .0031

Table 4 and 5 shows the maximum values of impedance measured from various sensor sub-networks integrated on the bridge. Table 5 reveals the damage identified in the structure.

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Table 4. Impedance (Max) Values –No Damage

Sensor sub-nets

S1 S2 S3

Impedance(max)

1356.04 1319.80 1322.30

Table 5. Impedance (max) values-damage identified Sensor sub-nets

A1 A2 A3

Impedance(max)

1340.3 2234.67 1234.8

8 Conclusion The work of this paper represents the first hardheaded demonstration of using an Unmanned Aerial Vehicle (UAV) as a mobile agent for WSN in SHM applications. It is so designed to overcome the limitations of existing mobile agent based sensing techniques in WSN based SHM applications. The system developed consists of a group of structural monitoring sensors, wireless sensor nodes and an AUV mobile agent which delivers the wireless energy for the node for active sensing. The system measures the structure monitoring parameters like mechanical strain to analyze the degree of on effectiveness of the structure. UAV is capable of associating itself with any node and wirelessly collects the data from the node. The performance of the system has been verified. This work gives the feasibility and applicability of UAV in WSNs based applications. The future work may includes the development of power receiving efficient sensors system improving the efficiency of wireless energy delivery and further investigate the methods of developing the agility of WSNs using mobile agent based approach. The work should also be done on investigating the applications of UAV based WSNs in emergency situations like earthquakes, terrorist attacks etc.

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6. Esser, B., Pelczarski, N., Huston, D.: Wireless inductive robotic inspection of structures. In: Proc. IASTED Int. Conf. Robot. Appl., Honolulu, HI, August 14-16 (2000) 7. Huston, D., Esser, B., Gaida, G., Arms, S., Townsend, C., Chase, S.B., Aktan, A.E. (eds.): Wireless inspection of structures aided by robots. In: Proc. SPIE Health Monitoring and Management of Civil Infrastructure Syst., August 2001, vol. 4337, pp. 147–154 (2001) 8. Ma, L., Chen, Y.: Aerial Surveillance system for overhead power line inspection. Center for Self-Organizing and Intelligent Systems (CSOIS), Utah State Univ., Logan, Tech. Rep. USU-CSOIS-TR-04-08 (September 2000)