CSE237d: Embedded System Design Junjie Su May 8, 2008

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1 Jamie Steck CSE237d: Embedded System Design Junjie Su May 8, 2008 Project Progress Report: Efficient Energy Management and Task Scheduling of a Solar-Powered System Background Every two years, a team of engineers and technicians inspects all United States governmentowned bridges in order to determine the "health" of the structure. However, as seen in the recent crash of a bridge in Minneapolis, these inspections do not always accurately reflect the condition of the bridge. Due to the need for better bridge inspection methods and in response to advances in sensor technology, sensors networks are being employed to enhance this two-year inspection requirement. These sensor networks can measure qualities of a bridge such as strain, temperature, and seismic activity, and using data processing, these measurements can indicate possible failure or need for repair. Monitoring certain features of a structure over time and evaluating these features to determine the health of a structure is referred to as Structural Health Monitoring (SHM). Structural Health Monitoring is a popular area of research in the fields of embedded systems and structural engineering and encompasses not only bridges, but also other structures such as buildings, aircraft, spacecraft, and oilrigs. Currently, most deployed SHM systems are wired, and thus take a significant amount of time to install and are usually expensive. Current research, such as the work of Wisden and Shimmer, as well as government initiatives, are working to develop wireless SHM systems in order to reduce cost, time of installation, and maintenance requirements. Over the years, many methods have been developed to identify and detect damage in a structure. Some of these methods include: natural frequency observations, fiber optic sensing systems, impedance-based methods, and lamb waves. First, natural frequency observations exploit the fact that a change in a structure affects the natural frequency of the structure. Natural frequency changes can be used to identify structure vibrations, changes in structural stiffness, and cracks. Unfortunately, natural frequency methods require a high level of damage and may not be effective in detecting deterioration over time or more subtle failure identification. Fiber optic sensing systems can also be used to determine the health of a structure. Fiber optic sensing systems use characteristics of propagating light to measure properties such as strain, moisture, radiation, and motion. These systems can be either passive or active. One of the most promising methods for SHM is the integration of smart materials into the structures and utilization of these smart materials as sensors and actuators. For example, due to its chemical makeup, Lead-Zirconate-Titanate (PZT) can be used to both generate and sense signals. Because the electrical impedance of PZT sensor/actuator is directly related to the structure's mechanical impedance, this impedance-based method can use high frequency vibrations to monitor the structure's mechanical impedance in order to detect and locate the damage of a structure. In addition, lamb waves are a type of elastic perturbation that can propagate in and reveal certain characteristics about a solid. The speed of a lamb wave is dependent on the frequency of the solid and can be generated by an Electromagneticacoustic transducer (EMAT) or a smart material such as PZT transducers. The types of SHM systems focused on for this project typically consist of an external agent,

2 such as a base station or UAV, and one or more sensor nodes. Each sensor node is composed of many hardware components. The sensor node needs one or more processors, such as a microcontroller, DSP, or FPGA, to control the node, the data acquisition and processing, and transmittance to an external agent. The node needs memory, both RAM and ROM, to store the processor code as well as data. The node needs a sensor and an A to D converter. If it is active, it will also need an actuator and a D to A converter. The node must communicate with an external agent via a wire or, if it is wireless, a radio, such as Bluetooth or Zigbee. Last, the node will need a source of energy. This energy can be obtained through a wired power source or an energy efficient power source, such as the sun, and must be stored using a battery or a super capacitor. Some SHM systems only require nodes to actuate, sense, and transmit data, but for this project, the sensor node will also process the data. In order to effectively control the sensor node, acquire, process, and transmit the sampled data or results, the SHM node requires several software components. While an entire operating system can be used, such as TinyOS, only certain OS features are needed to adequately control the node. Software is needed to turn the node on and off, communicate with the external agent (via radio or the like), as well as monitor the power and current energy supply. In addition, software should be used to control the sensors and/or actuators to acquire the data (through sampling) and then process the data. Data can either be directly sent to the external agent or analyzed on the node itself. Analysis involves storing the data, transforming the data using a Fourier transform or the like, and then performing analysis to determine if damage exists in the structure. Some issues to consider when designing an SHM system include energy, maintenance, installation, cost, and reliability. While it is possible to create a wired SHM system, a wireless system is much more attractive due to the difficulties of installing these systems on structures. Wireless systems, however, are limited by power, and thus must conserve energy in every part of the system. Using batteries increases the maintenance requirements, and on a bridge, accessing sensor nodes may be dangerous and difficult. In addition, it is essential that an SHM system is reliable and accurate. Citizens depend on these systems to provide correct and constant results for the safety purposes. This project focuses on an area of current research here at UCSD. Shimmer, a wireless SHM system that uses solar power, super-capacitors, and on-node data processing, has been designed by several UCSD students. Shimmer monitors the structure, processes the results, and transmits to an Unmanned Aerial Vehicle (UAV). The Shimmer project employs the impedance-based technique and lamb waves technique mentioned above to perform the structure health monitoring. Both of these techniques are non-destructive evaluation methods that utilize PZTs to actuate and sense, and both techniques have advantages and disadvantages. Under observations, the lamb waves technique is more effective for thin plates, while the impedance-based technique is more suitable for detecting damage near structure joints and connections. Therefore, the Shimmer project combines these two techniques to provide a better structure health monitoring method. The diagram of the sensor node used by SHIMMER is shown below in Figure 1. The tasks for the node are 1) communicate with the UAV, 2) control the actuators and sensors to collect data, and 3) perform analysis on the collected data. The microcontroller is connected to a passive radio trigger circuit, which can wake up the microcontroller if the UAV sends the signal. The microcontroller (ATMega128L) controls the power of the rest of the functional units by a CMOS switch network. Once the microcontroller is woken up, it communicates with the UAV via the radio

3 transceiver (CC1100). Based on the instruction received from the UAV, the microcontroller then fetches the instructions for the DSP (TMS320C2811) that stored in the EEPROM (Microchip 25AA256) via the SPI interface. The DSP is interfaced to 1Mb of SRAM (CY7C1021), a DAC (DAC902), and two signal conditioning stages (actuation and sensing). As mentioned before, PZT piezoelectric transducers are the sensors and actuators. The DAC takes the signals from the DSP and generates the actuation waves to the PZTs. The SRAM stores the samples that generated by the ADC integrated in the DSP. Also, the DSP controls a multiplexer that selects different PZT as sensor or actuator from a group of 16 PZTs. Figure 1. SHiMmer Schematic In order to provide sufficient energy for the sensor node to perform and reduce the maintenance cost, the SHiMmer sensor node employs an energy harvesting circuit, which is collecting the solar power and store it into super-capacitors. Comparing to other energy harvest method, the solar power is the most efficient method so far. The super-capacitor provides a much higher durability (20 years) than other rechargeable batteries, and yet it also has slower performance degradation than other batteries. Currently, the SHiMmer sensor node is still under development and is facing significant challenges. The energy harvesting system of the node cannot collect enough energy to provide the node the ability to function properly during cloudy days. Because the solar panel collects energy based on the sun light density, it collects very little energy during cloudy days as compared to sunny days. If the node continues to perform tasks when the energy stored in the super-capacitor is low, the system will consume all the energy and eventually die. Currently, this energy problem is the most challenging part of the Shimmer sensor node platform. Furthermore, another challenge is how to install the sensor nodes properly. Many structures have different shapes. It is very challenged to place all the sensor nodes in a location that can absorb sunlight. When nodes are placed in shady areas, such as underneath a bridge, it is imperative that the node can absorb enough energy to stay alive. A final challenge is the implementation of the algorithms used to process the sampled data on the DSP. Because of the energy constraints highlighted above, the algorithms must be extremely efficient, using fixed-point arithmetic and minimal lines of code. Most recently, Joaquin Recas and Carlo Bergonzini, under the instruction of Dr. Tajana Rosing, have designed an energy prediction and management scheme for SHiMmer. Their design uses an energy prediction algorithm combined with an energy management unit to schedule tasks

4 such as data actuation and acquisition (Act./Acq.), data processing (Proc.), and radio communication (Comm.) according to their corresponding energy requirements. The relationship between the energy management unit and the energy prediction algorithm is shown in Figure 2, and the energy management scheme is shown in Figure 3. Recas has implemented these algorithms in MatLab and tested them using sample solar panel data. Figure 2. System Design Figure 3. Energy Management Unit Project Goal The goal of this project is to implement efficient energy management and task scheduling for the SHiMmer platform. The prediction algorithm and energy management unit will be implemented on the ATmega128L micro-controller of a wireless sensor node that relies on an irregular solarpower source. Additionally, power requirements for the different types of tasks will be measured to feed into the energy management unit. These measurements will require programming the DSP to sense and actuate, perform data processing, and transmit this data via Zigbee. Approach The project can be separated into three parts. The first part involves rewriting the current algorithms (implemented in MatLab) in C code to run on the micro-controller, while the second part involves creating an energy simulation model circuit and the full implementation of the energy management scheme on the micro-controller. A monitoring circuit will be set up to verify the energy management scheme. The third part is to implement the three types of tasks (data actuation and acquisition, data processing, and radio communication) and then measure the power requirements of each of these tasks. The first part of the project involves implementing the current MatLab algorithms and simulation in C and running them on the micro-controller. To do this, a basic operating system needs to be loaded onto the micro-controller. Because the ATmega128L uses the Atmel SDK with WinAVR compiler, Free RTOS, a simple open source operating system, can be ported to the microcontroller using the Atmel AVR (MegaAVR) / WinAVR Port [5]. After the OS is running on the micro-controller, the current algorithms need to be written in C and ran on the micro-controller. It is possible that some changes will need to be made to the current simulation design to adjust and optimize the algorithms. In order to compare the prediction algorithm to the actual energy data,a simulation circuit can be constructed to accurately model the behavior of the solar panel and the super capacitor (voltages, currents etc.). Before applying the simulation circuit to verify the prediction algorithm, we

5 have to make sure the simulation circuit can model the behaviors accurately. Once we connect the simulation circuit to the micro-controller, we can verify how reliable the prediction algorithm is. Lastly, a monitoring LED on the Atmel AVR STK500 development platform can be used to verify the energy management scheme. Based on the waveform that feeds into the microcontroller, and thus, the expected ordering of tasks, the LED lighting pattern can be used for hardware debugging purposes. The final part of this project involves implementing the three tasks of for the SHiMmer platform, shown below in Figure 4. First, the DSP will be programmed to actuate and sense using the PZTs, and the power of the actuation and sensing will be measured. Second, the microcontroller will be programmed to send and receive packets using Zigbee. Specifically, send and receive functions will be implemented to enable packet communication, and the power consumption of transmitting data via the radio will be measured. Third, three types of data processing will be implemented on the DSP, including finding the maximum voltage and variance, the maximum time, and the Fourier transform. The power required for the data processing will also be measured for various data sizes. Progress Figure 4. SHiMmer Platform So far, we have become familiar with the Atmel AVR STK500 developing platform (shown below in Figure 5). We ran a sample program on the platform, using AVR studio, and read parts of the developing platform manual and microcontroller data sheets. We also began to port Free RTOS to the microcontroller; however, Joaquin Recas finished it before us. We plan to continue to work on it ourselves, but depending on SHiMmer needs, we may just use the work he has done.

6 Figure 5. Atmel AVR STK500 with extension board In addition, we have implemented functions to calculate the maximum voltage, variance, and the time of the maximum voltage using fixed-point data representation and arithmetic to be used in the DSP implementation for data processing. We have also studied the FFT algorithm and began implementation, but are not finished yet. Following the simulation circuit in [4], we constructed a PSPICE model in order to find the parameters that can accurately simulate the solar panel behavior. In the paper, the authors mention that the estimation of the circuit s resistance through an approximated measured current-voltage relation curve fitting, which are not clearly explained. We tried to connect the authors, and hopefully will get some feedback later. Since our resistance value is not accurate, the circuit model is not working well. We will continue to tune the parameter to achieve a better result. Given the collected data of the open circuit voltage of a solar panel, we transferred the data into an excel.csv file and loaded it into the Agilent waveform editor. The open circuit voltage data was collected every 15 minutes over a 15-day period. Because the waveform is not regular, we need to use the arbitrary waveform function to generate it. We connected the computer to the Agilent 3320A waveform generator through a USB cable and adjusted the waveform s frequency and amplitude to adapt our simulation environment. This waveform will be used as input to the prediction algorithm inside microcontroller because the open circuit voltage is the indicator for sun light condition. Future Work First, we need to translate the power management algorithm from Matlab code to C code, and implement it into the Free RTOS on the microcontroller. We should understand how our power management algorithm can be adapted utilizing the OS s queuing function. Second, we will finalize the parameters for the simulation circuit for the solar panel and create a real circuit to setup the simulation environment for our energy prediction and power management scheme. Lastly, we will try to configure the Zigbee radio chip to communicate with other devices, combine the microcontroller with the DSP running our data processing algorithms, and program the DSP to measure the current and voltage along the actuation path. We believe these features will significantly improve the current version of SHiMmer platform.

7 References [1] SHiMer Overview, [2] D. Musiani, K. Lin, T. Simunic Rosing, An active sensing platform for structural health monitoring, IPSN-SPOTS 07. [3] SHM Article, [4] Dondi, D.; Brunelli, D.; Benini, L.; Pavan, P.; Bertacchini, A.; Larcher, L., "Photovoltaic cell modeling for solar energy powered sensor networks," Advances in Sensors and Interface, IWASI nd International Workshop on, vol., no., pp.1-6, June [5] Free RTOS Atmel AVR Port instructions,