SAND: a modular application development platform for miniature wireless sensors Martin Ouwerkerk, Frank Pasveer, Nur Engin Philips Research, Eindhoven, The Netherlands
[email protected]
Abstract A modular application development platform for miniature wireless sensor/actuator devices called Small Autonomous Network Devices (SANDs) is described. As an application example a system power breakdown of a real-time ECG analysis is presented. The application development SANDs have a volume of about one cubic centimeter. Based upon testing and optimization the schematics and specifications are obtained for the mass-production SANDs. Utilizing System in Package (SiP) technology the volume can be reduced up to five times. These devices can be used in a truly unnoticeable and unobtrusive way to serve as the smallest components of a personal health care monitoring system or an ambient intelligence system.
Introduction Current state-of-the-art technology in wireless sensor networks has opened the way to miniaturized health monitoring systems, the so-called body sensor networks. They provide the opportunity for continuous monitoring and analysis of physiological parameters. In case of calamities a warning can be wirelessly send to the user and/or doctor. Field testing application opportunities of miniature wireless sensors is usually done with custom made off the shelf electronics. This often results in relatively large and poorly packaged devices. The Mote-series of sensor platforms (see, for example [1]) developed by University of California at Berkeley and sold commercially by Crossbow Inc. and MoteIV [2] have become one of the best-known research tools in sensor networking. Typical design of a mote comprises a small PCB on which a simple micro-controller, various interfaces, and a wireless communication subsystem (predominantly IEEE 802.15.4 in recent designs) is installed. Although these sensors “work well”, they are unpackaged which is a major disadvantage for field testing. The solution to the packaging problem boils down to solving the problem
of stacking and connecting multiple chips in a single sealed package. The best-known manufacturing examples are the Philips, IMEC and VDMA systems. One of the smallest packaged 3D stacked sensor systems is the device developed at IMEC in the HUMAN++ Project [3]. The working group Match-X [4] in the German Engineering Association VDMA developed another small but modular device. It consists of different building blocks, measuring 12.5x12.5 mm, each having a different functionality. Recently, a re-configurable sensor system has been developed at Philips Research called SAND (Small Autonomous Network Devices). The SAND solution has the advantage of providing a packaged system being fully reconfigurable while preserving its hermetic properties, allowing even use for implantable devices. The package target size is to energy be about 1 cm3 and have an average consumption that is low enough to allow field testing of applications. The present article aims at an overview of the technology behind the sensor platform, and will discuss the feasibility of a specific application taking into account all the requirements concerning powermanagement.
Hardware description A reconfigurable modular approach was chosen for SAND, although from a miniaturization point of view it does not yield optimal results. Each module has a specific functionality, such as transceiver, microcontroller, power supply or sensor. With this approach a large number of different configurations becomes possible with a limited number of different modules. A variety of applications both for body sensor networks and for other areas can therefore make use of the same technology. In Figure 1 an exploded view of a typical device is shown. The outer diameter of the modules is 14 mm resulting in a device volume of 1-2 cubic centimeter.
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Figure 1 Exploded view of reconfigurable modular wireless sensor device The SAND modular concept is meant to function as a platform for application development. The modules are designed in such a fashion that a reconfigurable stack can be built, which after capping forms a sealed package. The size and shape are optimized for unobtrusive use and wearability. After sufficient testing of the devices in application specific circumstances an application and/or cost optimized production version can be made based on the obtained data. If System in Package (SiP) technology is used the volume of the device is foreseen to be lowered fivefold while retaining performance and functionality. Available modules are a 802.15.4 enabled transceiver, 3D-accelerometer, an ultra low power Cprogrammable DSP module [5], a power converter module, and a serial flash module. Additional modules are in preparation. Figure 2 shows a number these modules in the injection molded interconnection rings together with a CR1225 coin cell battery and end caps. Two rows of twelve pins electrically interconnect the modules. The antenna of the transceiver is printed onto the printed circuit board. Additionally, skin contacted ECG sensors are available with a conducting pad which does exclude the need of a gel pad.
Figure 2 Some modules of the SAND application development platform
Application example In this section a system-level power breakdown of the SAND sensor node will be presented. As sensor network applications often have a low duty cycle character, the power consumption estimation is only possible for a certain application. An application example for SAND sensor nodes is real-time heart rate variability (HRV) analysis by means of monitoring the ECG. This type of ECG analysis makes use of the socalled cepstrum analysis [6]. Taking the Fourier transform of the last eight seconds of the ECG signal a power spectrum can be obtained. A regular pattern of peaks occurs in this spectrum. Taking an inverse Fourier transform of the logarithm of the magnitude of the Fourier transform, often called cepstrum, yields in that case a strong peak at the R-R peak interval. This peak is supposedly linked with heart coherence [7]. Via biofeedback a person may influence psychophysiological parameters such as HRV to improve or alter their emotional state.
Power analysis The starting point of the power analysis consists of two elements: Firstly, the power consumption model of each system part. This model consists of various operation modes and the corresponding power consumption of each part. The system components of the SAND sensor node with their possible activity modes can be seen in Table 1. Table 1 System components with their activity modes
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Supply Supply Supply Supply curren current current in t in Mode2 Mode 3 Mode1 voltage in mode mode 3 mode (V) 2 (mA) (mA) 1 (mA) Analog parts A/D converter DSP Coolflux Flash memory Samsung IEEE 802.15.4 baseband + RF Chipcon
active
3
0.2466
none
0
none
0
active
3
0.01
powerdown
0
none
0
2
lowpower
0.525
15
standby
0.01
11
idle
0.001
active
1.2
read
1.8
receiv e
3
15.8 low effort 15
erase/ write
19.7 transmit
The second component of the power analysis is the application model. This model consists of all main operating modes of the application, containing information about which system part operates in which power mode (e.g. in standby, low-power or active mode). For deriving the application model for HRV analysis, the cepstrum-based algorithm has been assumed to operate on a 250-samples-per-second ECG signal. The cepstrum analysis used in this application is based on blocks of 2048 ECG samples being processed each 8 seconds. Taking this processing requirement plus the remaining application processing as basis, it has been estimated that the analysis will take less than 0,1 % duty cycle on the Coolflux processor. Other duty cycle figures have been estimated similarly by taking the amount of data to be processed / stored / transmitted as basis. The resulting application model for the HRV analysis is shown in Table 2.
Table 2 Application model for heart coherence measurement, with analog parts in active mode Duty Operating Explanation cycle mode (%)
Standby
Sense
Most modules in 97.663 idle or lowpower mode take an ECG snapshot
listening to the base station for Listen measureme nt/analysis requests Analyze a complete Analyze block of ECG data
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CoolFlash Chipcon flux memory CC2420 DSP
powerdown
lowstandby power
idle
0.2
active
low standby effort
idle
2
powerdown
low standby effort
receive
0.1
powerdown
Active standby
idle
Store
Store a page into memory
0.02
powerdown
low effort
erase/ write
idle
Read
Read a page from memory
0.002
powerdown
low effort
read
idle
Erase a block from 0.005 the memory
powerdown
low effort
erase/ write
idle
powerdown
low standby transmit effort
Erase
Transmit
Transmit data to be logged on other system
0.01
Apart from measuring the ECG signal and evaluating the outcome, the SAND node also can use an IEEE 802.15.4 connection for sending the results to a PC or to another node. This can be required e.g. in situations where another individual must be notified of a status change. The analysis on the DSP takes a very little proportion of the DSP resources. For the rest of the time, the DSP remains in standby or low-power mode. The analyzed data can also be logged in the flash memory of the SAND node. Again, the duty cycle of the memory access has been calculated assuming 250 samples per second ECG signal. The power breakdown analysis brings the power consumption characteristics of the system and the application together. Using the data in the previously given in Table 1and Table 2 the average power consumption is calculated. The corresponding pie chart can be seen in Figure 3. It can be seen that the largest part of the power is used by the DSP and the wireless transmission. Furthermore, the average power is calculated to be 6.64 mW.
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A/D converter
Taking into account the 144 mWh capacity of the CR1225 battery this enables more than 20 hours of operation.
802.15.4 43%
Analog parts 11% A/D 0%
Analog parts A/D DSP
DSP 46%
Memory 802.15.4
Memory 0%
Although the average power consumption figures give an idea about the power consumption of the circuit, some crucial information is missing in this picture. For example, if the average power consumption is quite low but the system draws a relatively large source current during a certain mode, this has implications for the batteries chosen for the system. When very small size is required, the maximum current supplied by the batteries can be limiting. Furthermore, power analysis per operating mode can enable adjusting the application for decreasing the overall system power (e.g. transmitting less bits by doing more processing). In Figure 4, the power breakdown per operating mode is shown for the HRV analysis application. It can be observed that there is a large difference between e.g. listening and transmitting through the 802.15.4 interface. This is true in general for small distances. If the device does not need to listen for input via the wireless connection, then the maximum power during any operating mode remains below 40mW. Power (mW)
70 60 802.15.4
Memory
40
DSP
30
A/D
20
Standby 17% Transmit 27%
Figure 3 Average power breakdown, total average power = 6.64 mW
50
Finally, it is interesting to see which mode contributes most to the overall power consumption. For example, as the device will be in standby mode for a large percentage of the time, the contribution of the standby to the overall consumption will be more significant than Figure 4 suggests. This can be seen in Figure 5. Furthermore, in this breakdown of various operating modes, we get an insight into the power consumption of which modes make the most impact for the application at hand.
Analog parts
10
St an Sedby n L i se s An te al n y St ze o R re ea E d Tr r a an se sm it
0
Operating modes
Figure 4 Power consumption per operating mode.
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Listen 19%
Erase 0% Read 0% Store 0%
Analyze 37%
Sense
Listen
Analyze
Store
Read
Erase
Transmit
Figure 5 Power breakdown per operating mode, total average power = 6.64mW
Discussion Application development of miniature wireless sensor/actuator devices can be greatly facilitated by the reconfigurable modular SAND platform together with a thorough power analysis. The easy to assemble and C-programmable small sized SANDs enable rapid and cost effective field testing System-level power analysis delivers a simple but valuable picture, which can be used to optimize the application to be mapped on the sensor node. The tradeoffs such as storing data versus transmitting, compressing data for decreasing the transmit power can be very easily verified in this model before the final choice is made. Furthermore, boundary conditions such as maximum source current can be useful in choosing the power source for the system. Utilizing the application analysis data a massproduction version may be specified and made.
References [1] J. Hill and D. Culler, “Mica: A Wireless Platform for Deeply Embedded Networks”, IEEE Micro., vol. 22(6), Nov/Dec 2002, pp 12-24. [2] J. Polastre, R. Szewczyk, C. Sharp and D. Culler, “The Mote Revolution: Low Power Wireless Sensor Network Devices”, Proceedings of Hot Chips 16: A Symposium on High Performance Chips. August 22-24, 2004. [3] www.imec.be/human/
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Sense 0%
Standby
[4] www.match-X.org [5] www.coolflux.com [6] Bogert, B.P., Healy, M.J.R., and Tukey, J.W., “The Quefrency Alanysis of Time Series for Echoes: Cepstrum, Pseudo-autocovariance, Cross-Cepstrum, and Saphe Cracking,” Proc. Symposium Time Series Analysis, M. Rosenblatt, Ed., John Wiley and Sons, New York, pp. 209-243, 1963. [7] http://www.heartcoherence.com
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