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Sensors & Transducers Volume 18, Special Issue January 2013
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ISSN 1726-5479
Editors-in-Chief: professor Sergey Y. Yurish, Tel.: +34 696067716, e-mail:
[email protected] Editors for Western Europe
Editor South America
Meijer, Gerard C.M., Delft Univ. of Technology, The Netherlands Ferrari, Vittorio, Universitá di Brescia, Italy
Costa-Felix, Rodrigo, Inmetro, Brazil
Editor for Asia Editor for Eastern Europe Sachenko, Anatoly, Ternopil National Economic University, Ukraine
Ohyama, Shinji, Tokyo Institute of Technology, Japan Zhengbing, Hu, Huazhong Univ. of Science and Technol., China
Editor for Asia-Pacific Editors for North America
Mukhopadhyay, Subhas, Massey University, New Zealand
Katz, Evgeny, Clarkson University, USA Datskos, Panos G., Oak Ridge National Laboratory, USA Fabien, J. Josse, Marquette University, USA
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Editorial Board Abdul Rahim, Ruzairi, Universiti Teknologi, Malaysia Abramchuk, George, Measur. Tech. & Advanced Applications, Canada Ascoli, Giorgio, George Mason University, USA Atalay, Selcuk, Inonu University, Turkey Atghiaee, Ahmad, University of Tehran, Iran Ayesh, Aladdin, De Montfort University, UK Baliga, Shankar, B., General Monitors, USA Basu, Sukumar, Jadavpur University, India Bouvet, Marcel, University of Burgundy, France Campanella, Luigi, University La Sapienza, Italy Carvalho, Vitor, Minho University, Portugal Changhai, Ru, Harbin Engineering University, China Chen, Wei, Hefei University of Technology, China Cheng-Ta, Chiang, National Chia-Yi University, Taiwan Chung, Wen-Yaw, Chung Yuan Christian University, Taiwan Cortes, Camilo A., Universidad Nacional de Colombia, Colombia D'Amico, Arnaldo, Università di Tor Vergata, Italy De Stefano, Luca, Institute for Microelectronics and Microsystem, Italy Ding, Jianning, Changzhou University, China Djordjevich, Alexandar, City University of Hong Kong, Hong Kong Donato, Nicola, University of Messina, Italy Dong, Feng, Tianjin University, China Erkmen, Aydan M., Middle East Technical University, Turkey Gaura, Elena, Coventry University, UK Gole, James, Georgia Institute of Technology, USA Gong, Hao, National University of Singapore, Singapore Gonzalez de la Rosa, Juan Jose, University of Cadiz, Spain Guillet, Bruno, University of Caen, France Hadjiloucas, Sillas, The University of Reading, UK Hui, David, University of New Orleans, USA Jaffrezic-Renault, Nicole, Ecole Centrale de Lyon, France Jamil, Mohammad, Qatar University, Qatar Kaniusas, Eugenijus, Vienna University of Technology, Austria Kim, Min Young, Kyungpook National University, Korea Kumar, Arun, University of Delaware, USA Lay-Ekuakille, Aime, University of Lecce, Italy Lin, Paul, Cleveland State University, USA Liu, Aihua, Chinese Academy of Sciences, China
Mansor, Muhammad Naufal, University Malaysia Perlis, Malaysia Marquez, Alfredo, Centro de Investigacion en Materiales Avanzados, Mexico Mishra, Vivekanand, National Institute of Technology, India Moghavvemi, Mahmoud, University of Malaya, Malaysia Morello, Rosario, University "Mediterranea" of Reggio Calabria, Italy Mulla, Imtiaz Sirajuddin, National Chemical Laboratory, Pune, India Nabok, Aleksey, Sheffield Hallam University, UK Neshkova, Milka, Bulgarian Academy of Sciences, Bulgaria Passaro, Vittorio M. N., Politecnico di Bari, Italy Penza, Michele, ENEA, Italy Pereira, Jose Miguel, Instituto Politecnico de Setebal, Portugal Pogacnik, Lea, University of Ljubljana, Slovenia Pullini, Daniele, Centro Ricerche FIAT, Italy Reig, Candid, University of Valencia, Spain Restivo, Maria Teresa, University of Porto, Portugal Rodríguez Martínez, Angel, Universidad Politécnica de Cataluña, Spain Sadana, Ajit, University of Mississippi, USA Sadeghian Marnani, Hamed, TU Delft, The Netherlands Sapozhnikova, Ksenia, D. I. Mendeleyev Institute for Metrology, Russia Singhal, Subodh Kumar, National Physical Laboratory, India Shah, Kriyang, La Trobe University, Australia Shi, Wendian, California Institute of Technology, USA Shmaliy, Yuriy, Guanajuato University, Mexico Song, Xu, An Yang Normal University, China Srivastava, Arvind K., LightField, Corp, USA Stefanescu, Dan Mihai, Romanian Measurement Society, Romania Sumriddetchkajorn, Sarun, Nat. Electr. & Comp. Tech. Center, Thailand Sun, Zhiqiang, Central South University, China Sysoev, Victor, Saratov State Technical University, Russia Thirunavukkarasu, I., Manipal University Karnataka, India Vazquez, Carmen, Universidad Carlos III Madrid, Spain Xue, Ning, Agiltron, Inc., USA Yang, Dongfang, National Research Council, Canada Yang, Shuang-Hua, Loughborough University, UK Yaping Dan, Harvard University, USA Zakaria, Zulkarnay, University Malaysia Perlis, Malaysia Zhang, Weiping, Shanghai Jiao Tong University, China Zhang, Wenming, Shanghai Jiao Tong University, China
Sensors & Transducers Journal (ISSN 1726-5479) is a peer review international journal published monthly online by International Frequency Sensor Association (IFSA). Available in both: print and electronic (printable pdf) formats. Copyright © 2013 by International Frequency Sensor Association. All rights reserved.
Sensors & Transducers Journal
Contents Volume 18 Special Issue January 2013
www.sensorsportal.com
ISSN 1726-5479
Research Articles Editorial Sergey Y. Yurish .........................................................................................................................
I
10 Top Reasons to Include SENSORDEVICES Conference in your Calendar of Events Sergey Y. Yurish ......................................................................................................................... 1 From Sensors to Applications: A Proposal to Fill the Gap Vincenzo Di Lecce, Marco Calabrese, Claudio Martines............................................................ 5 Smart Sensors and Actuators: A Question of Discipline Hoel Iris, François Pacull ............................................................................................................ 14 Atmospheric Icing Sensors - Capacitive Techniques Umair N. Mughal, Muhammad S. Virk ........................................................................................ 24 Design of a Sigma-Delta Interface for Heat Balanced Bolometer Matthieu Denoual, Damien Brouard, Arthur Veith, Mathieu Pouliquen, Olivier de Sagazan, Patrick Attia, Gilles Allegre.......................................................................................................... 33 Implementation of a Shoe-Embedded Human Interface and Collaborative Supplementation of Service Requirements on Smartphone System Kaname Takaochi, Kazuhiro Watanabe, Kazumasa Takami ..................................................... 47 Module with Piezoelectric Sensor for Acoustic Emission Applications Irinela Chilibon, Marian Mogildea, George Mogildea.................................................................. 59 Performance Improvement of High Frequency Aluminum Nitride Ultrasonic Transducers Yangjie Wei, Thomas Herzog and Henning Heuer..................................................................... 66 Development of Specialty Optical Fiber Incorporated with Au Nano-particles in Cladding for Surface Plasmon Resonance Sensors Seongmin Ju, Seongmook Jeong, Youngwoong Kim, Poram Jeon, Won-Taek Han, Seongjae Boo, Pramod R. Watekar............................................................................................ 76 Selective Detection of Hydrogen with Surface Acoustic Wave Devices Using Palladium Layer Properties Meddy Vanotti, Virginie Blondeau-Patissier, David Rabus, Jean-Yves Rauch, Sylvain Ballandras ................................................................................................................................... 84 High Performance Sensor Nodes for Wireless Sensor Networks Applications Sergey Y. Yurish and Javier Cañete........................................................................................... 92 Intelligent Parking Management System Based on Wireless Sensor Network Technology Nikos Larisis, Leonidas Perlepes, George Stamoulis, Panayiotis Kikiras .................................. 100
Wireless Underwater Monitoring Systems Based on Energy Harvestings Sea-Hee Hwangbo, Jun-Ho Jeon and Sung-Joon Park ............................................................. 113 Assessing the Impact of Wind on Detecting Fire Using a Wireless Sensor Network Ronald Beaubrun and Yacine Kraimia........................................................................................ 120 All Organic Flexible Lightweight BL-Film Sensor Systems with Wireless Data Transmission Raphael Pfattner, Victor Lebedev, Bahareh Moradi, Elena Laukhina, Vladimir Laukhin, Concepció Rovira, Jaume Veciana............................................................................................. 128 A Medical Wireless Measurement System for Hip Prosthesis Loosening Detection Based on Vibration Analysis Sebastian Sauer, Sabine Kirsten, Florian Storck, Hagen Grätz, Uwe Marschner, Dietmar Ruwisch and Wolf-Joachim Fischer............................................................................................ 134 Wireless Sensor Network Simulation: The Current State and Simulation Tools Fayez Al-Fayez, Abdelrahman Abuarqoub, Mohammad Hammoudeh, Andrew Nisbet ............ 145 Low Power Consumption Wireless Sensor Communication System Integrated with an Energy Harvesting Power Source Vlad Marsic, Alessandro Giuliano and Meiling Zhu .................................................................... 156 Power Saving Algorithm for Monitoring Extreme Values in Sensor Networks Pei-Hsuan Tsai, Chun-Lung Lin, Jau-Wu Huang, Jia-Shung Wang........................................... 166 Faults in Sensory Readings: Classification and Model Learning Valentina Baljak, Tei Kenji, Shinichi Honiden ............................................................................. 177 On QoS Guarantees of Error Control Schemes for Data Dissemination in a Chainbased Wireless Sensor Networks Zahra Taghikhaki, Nirvana Meratnia, Paul J. M. Havinga .......................................................... 188 Authors are encouraged to submit article in MS Word (doc) and Acrobat (pdf) formats by e-mail:
[email protected] Please visit journal’s webpage with preparation instructions: http://www.sensorsportal.com/HTML/DIGEST/Submition.htm International Frequency Sensor Association (IFSA).
Sensors & Transducers, Vol. 18, Special Issue, January 2013, pp. 92-99
Sensors & Transducers ISSN 1726-5479 © 2013 by IFSA http://www.sensorsportal.com
High Performance Sensor Nodes for Wireless Sensor Networks Applications 1, 2
Sergey Y. Yurish and 1, 3 Javier Cañete 1
Technology Assistance BCNA 2010, S. L. International Frequency Sensor Association (IFSA), 3 Universitat Politècnica de Catalunya (UPC, Barcelona) Barcelona, Spain E-mails:
[email protected],
[email protected] 2
Received: 15 November 2012 Accepted: 14 December 2012 Published: 22 January 2013 Abstract: Cost reduction in wireless sensor networks becomes an important requirement to extend their application in fields where a great amount of sensors is needed. Traditional approach to use multichannel analog-to-digital converter and/or analog multiplexers for analog sensors will not give any reduction in price. Moreover, the analog multiplexer introduces additional measuring error. This article describes in details the developed advanced, robust but cost-effective sensor nodes' architectures suitable for further integration in a node-on-chip. Such sensor nodes can work with any analog and quasi-digital sensors and transducers, and its sensing sub-system lets achieve the best metrological performances. A comprehensive comparative study of sensor node's architectures and sensing sub-systems are presented. Copyright © 2013 IFSA. Keywords: Sensor nodes, Wireless sensor networks, Frequency-to-digital converter, Universal sensors and transducers interface, Node-on-chip.
1. Introduction Wireless sensors and sensor networks can be deployed almost anywhere at a far lower cost than can a wired system. With the recent advances in embedded systems and wireless technology, the hardware used is becoming more inexpensive and more widely available. Wireless sensor devices connect sensors wirelessly among each other as well as to monitoring and management setups. According to the MarketResearch.com the global market for wireless sensor devices used in end vertical applications totaled $ US 790 million in 2011 and expected to increase at a 43.1 % compound annual growth rate (CAGR) and reach an estimated $ US 4.7 billion by 2016 [1]. The Asia-Pacific (APAC) region is unsurprisingly the fastest growing market for
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wireless sensors. This region was worth $225 million in 2011 and is projected to reach $1.6 billion by 2016, a CAGR of 48.6 % between 2011 and 2016. Japan has a top market for wireless sensors in the APAC region. This segment was worth $77 million in 2011 and is expected to reach $392 million by 2016, a CAGR of 38.5 % between 2011 and 2016 [1]. Because wireless sensor networking is built around low-power radios, the nodes that make up the network play a key role in wireless communication. From a physical perspective, the deployment of nodes may take several forms depending on the sensor application and the desired pattern of communication. Deployment may also be a one-time activity, where the installation and use of a sensor network are strictly separate activities. It can also be a continuous process where more nodes are deployed over the lifetime of
Sensors & Transducers, Vol. 18, Special Issue, January 2013, pp. 92-99 the network [2]. The application can vary from a single sensor node to multiple sensor nodes. A wireless sensor net is made up of a group of sensor nodes. Wireless sensor nodes are the essential building blocks in a wireless sensor network. Each sensor node possesses the ability to monitor some aspect of its environment, and each is able to communicate its observations through other nodes to a destination where data from the network is gathered and processed. Recent developments in wireless technologies and the semiconductor fabrication of miniature sensors are making wireless sensor networks (WSNs) smaller and more cost-effective for a growing number of uses [3]. Cost reduction in wireless sensor networks becomes a requirement to extend their application in fields where a great amount of sensors is needed [4], for example, industrial applications. In this case it should be a good solution to connect many existing low-cost sensors both: analog and quasi-digital to one sensor node to reduce the cost of nodes. Traditional approach to use multichannel analog-to-digital converter (ADC) and/or analog multiplexers for analog sensors will not give any reduction in price. Moreover, the analog multiplexer introduces additional measuring error. Hence, the analog signal must be preliminary converted to the quasi-digital signal (frequency, period, duty-cycle, time interval, phase-shift, pulse number or pulse-width modulated (PWM) output). The described in [4] sensor interface transforms the voltage provided by various sensors with different output ranges to a pulse signal, which frequency will depend proportionally on the input voltage. The conversion of the sensor signal to a frequency value will bear much less sensitivity to interferences. The further frequency-to-digital conversion is performed by using the classical direct counting method. This technique counts the number of pulses Nx of a signal of unknown period Tx=1/fx during a gate time window T0 determined by n periods of a signal of known, reference frequency f0. The unknown frequency fx is calculated by the number of pulses into the counting window:
Nx n
T0 Nx fx . Tx n T0
(1)
Such classical method has two well known disadvantages: the dependence of relative quantization error x on frequency fx, and redundant conversion time determined by the constant gate time T0. In order to achieve acceptable performance in the conversion to digital values of the frequency signal, it is necessary to the different sensor output ranges are converted into the same frequency range. For this, the conditioning electronics must be able to change sensor’s gain and offset voltage depending on the sensor signal characteristics. The circuit [4] consists of two operational amplifiers, analog multiplexer, two programmable potentiometers and voltage-controlled oscillator, which performs the transformation to the frequency range. The low resolution serial digital-to-analog
converter is used for self-calibration purposes. When a node of the network is activated, the microcontroller selects the sensor to be read by means of the control lines of the analogue multiplexer and carry out the conditioning and the conversion. However, such sensor interface has some disadvantages. It introduces additional error, because of mainly based on analog electronics components. Mantracourt Electronics Ltd. (UK) manufactures Wireless Telemetry Pulse Acquisition Module (T24PA) for quasi-digital sensors and transducers with frequency range from 0.5 Hz to 3 kHz and relative error 0.15 ... 0.25 % [5]. It can not be used with various quasi-digital sensors and transducers, which as rule have very broad dynamic frequency range: form part of Hz to some tens MHz, and low relative error 0.01 % and better [6]. Form the other side, the price for T24-PA module is high: 262 Euro. In order to get the optimal trade-off between metrological performances and price for the sensor node it is expediently to use other, universal, advanced solutions based on the novel frequency-to-digital conversion method. In order to design cost-effective sensor nodes, which satisfy to modern requirements, the following measures must be realized. Instead of voltage or current sensors, so-called quasi-digital sensors with frequency, period, time interval, phase-shift, pulse number or PWM output must be used. The frequencyto-digital converter should be based on the advanced method for frequency measurements, which have not disadvantages, mentioned above. In case of analog output sensors, the intermediate voltage-to-frequency converter(s) should be used. The aim of this article is to describe in details the developed advanced but cost-effective sensor nodes' architectures suitable for further integration in a nodeon-chip for wireless sensor networks. The paper consists of four parts and is organized as follows. The first part includes state-of-the-art review and task definition. The second part describes a design approach for sensor nodes architectures based on a Universal Sensors and Transducers Interface circuit (USTI) and are suitable for any quasi-digital sensors and analog sensors, and the third part is devoted to the further design of a node-on-chip based on the USTIWSN IC. The forth part includes results of experimental investigation of sensing sub-system based on the USTI IC. The last part of the article provides conclusions and future research directions. This article is an extension version of paper, presented and published in proceedings of the 6th International Conference on Sensor Technologies and Applications (SENSORCOMM' 2013), 19-24 August 2012, Rome, Italy [7].
2. Sensor Nodes' Architectures A sensor node in a wireless sensor network is capable of performing some processing, gathering sensory information and communicating with other connected nodes in the network. Its architecture consists of the following components: sensing subsystem, processing sub-system, communication sub-
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Sensors & Transducers, Vol. 18, Special Issue, January 2013, pp. 92-99 and one sensing element (resistive, capacitance or resistive bridge) can be also connected to this integrated circuit with minimal number of addition external components [14]. 1
0,8
Relative error, %
system and power management sub-system. The sensing sub-system directly influences on metrological performances on the whole. However, during the last years a lot of publications have been devoted mainly to communication and power management sub-systems. This article will be focused on the sensing sub-system design, which satisfies to the modern requirements of relatively low cost, expansibility and power-aware [8]. The low cost means that sensor node should be cheap since wireless sensor network may have hundreds or thousands of sensor nodes. The expansibility signifies that hardware design must be expandable with a number of different quasi-digital and analog sensors to support a variety of applications. The power-aware means that hardware supports intelligent function, which allows algorithms to adapt themselves to the available power. Let consider several advanced sensor node architectures for analog and quasi-digital sensors and transducers. All these architectures are based on novel USTI ICs developed by authors [9].
0,6
0,4
0,2
0 0,0002 0,0003 0,0006 0,0016 0,0032 0,0064 0,016 0,032 0,064
0,16
0,32
Time, s
Fig. 2. Relative error vs. conversion time.
2.1. Sensor Node Architecture with Analog Multiplexer A simple sensor node for analog and quasi-digital sensors with analog multiplexer and time division channeling is shown in Fig. 1.
2.2. Sensor Node Architecture with Digital Multiplexer The analog multiplexer and VFC introduce additional measurement errors. To eliminate the error due to an analog multiplexer, it is possible to convert a voltage to frequency for each of analog output sensors before the multiplexer, and use a digital multiplexor (MX), instead of analog multiplexer. Such sensor node with the digital multiplexer and combined (time for digital signal domain and space for analog signal domain) channeling is shown in Fig. 3.
Fig. 1. Sensor node with analog multiplexer.
A sensing sub-system in such architecture contains an analog multiplexes (MUX), voltage-to-frequency converter (VFC), USTI integrated circuit and microcontroller with embedded transceiver. A processing sub-system, communication sub-system and power management sub-system are realized on a separate microcontroller. The USTI is a core component of the sensing sub-system. It is based on advanced, modified patented frequency (period)-todigital conversion method of the dependent count with a constant quantization error in all broad frequency range and non-redundant conversion time [9-11]. The dependence of relative error on conversion time is shown in Fig. 2. In this case it is not necessary to convert the different sensor output ranges into the same frequency range, as it was proposed in [4]. Only one voltage-tofrequency converter is used to convert sensor's output to frequency. As usually, integrated VFCs have broad frequency ranges and good metrological performances [12, 13]. In addition, one quasi-digital sensor can be directly connected to the second channel of USTI IC,
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Fig. 3. Sensor node with digital multiplexer.
For the time-division channeling, the cycle polling time can be calculated according to the following equation [15]:
n (Tmeas delay 1 delay 2 ) ,
(2)
where delay1 is the time delay between the ending of the conversion in the previous sensor and the command to poll the next sensor; delay2 is the time delay of the frequency conversion starting after the sensor connection; n is the number of sensors in the sensor node; Tmeas is the measurement time. The measurement time Tmeas for the USTI includes three main components: conversion rate (tconv),
Sensors & Transducers, Vol. 18, Special Issue, January 2013, pp. 92-99 communication time (tcomm) and calculations time (tcalc) [14]. The digital multiplexer does not introduce any additional error. However, the VFCs still do it. So, the solution with minimum possible hardware and high metrological performance is possible if instead of analog sensors to use quasi-digital sensors.
2.3. Sensor Node Architecture for Quasidigital Sensors A low-cost sensor node with high metrological performance for quasi-digital sensors and transducers is shown in Fig. 4, which addresses the challenges of metrological performance improvement and node's cost reduction [4, 8, 14]. In this sensor node architecture no any VFC is necessary.
Fig. 4. Low cost sensor node for quasi-digital sensors.
By this way it is possible to decrease the total measurement error, for example, from 0.14 % to 0.08 % (for the numerical example, described in [14]), and reduce the sensor node's price. For example, at the same price for analog and quasi-digital sensors, the core of sensor node - the 24-bit resolution, 8-channel ADC ADS1278 [16] costs 23.95 $ US (in quantities of 1,000) while the USTI IC with significantly better metrological performance and a digital 8-channel multiplexer costs only 18.95 $ US in the same quantities. The space division and combining channeling also can be realized in this sensor node. In such sensor node instead of one USTI and the n-channel digital multiplexer, n ICs (according to the number of channels) and a microprocessor system with n inputs are used. That is, for simultaneous measurement of several frequencies, there is an independent channel with the USTI. The microprocessor simultaneously starts all converters and at the end of the measurement processes reads results. Quasi-digital sensors and transducers can be also connected in pairs to one USTI. In addition, one resistive, capacitive of resistive bridge sensing element can be also connected directly to the USTI. For one's turn, all USTI can be connected to a master microcontroller or microprocessor with the help of SPI or I2C buses. Each USTI IC can serves up to 3 channels by itself in a sensor node. The cycle polling time for the space-division channeling is decreased approximately in k times in comparison with the time-division channeling and should be calculated as:
= Tmeas + treadout,
(3)
where treadout, is the time for result reading by a microprocessor. In the case of analog sensors, an addition VFC in each channel should be also used. Such solution lets achieve maximum possible speed at a little bit increased cost for a sensor node. Another benefit to use quasi-digital sensors and transducers instead of analog sensors in WSN is a possibility to transmit frequency-time signals without preliminary conversion to digital. Two examples are described below. The RF transmitter using pulse width modulation (PWM) method is reported in [17]. It does not use an analog to digital converter for the RF transmission of analog data. The transmitter consists of a pulse width modulator, a voltage-controlled oscillator (VCO) and on-chip antenna. The PWM method digitally encodes analog signal levels, but the PWM signal remains quasi-digital. By keeping the signal quasi--digital, noise effects are minimized. The modulated signals are inputted into the VCO using PWM. If the voltage of the modulated signal is the high level, the VCO is oscillated, and the RF carrier waves for transmission can be obtained. Then, the output of the VCO is transmitted by the on-chip antenna [17]. The transmitter was possible to transmit with low power dissipation of 0.75 mW the carrier of 315 MHz. An interdigital capacitor based battery-free wireless pressure sensor is described in [18]. It consists of an interdigital capacitor (IDC) that serves as a pressure sensing element and an inductor, which works as a passive power source and data communication element. These two components work together as an LC resonator to realize the wireless pressure sensing and remote power to eliminate the need for wire connection in conventional pressure sensor. The sensing element is comprised of a set of linear parallel electrodes coated with Polyvinylidene Fluoride pressure sensing material on the top. The change of capacitance in the IDC is a function of the geometry of the electrodes and the electric properties of the sensitive layer. The sensor prototype demonstrated that it performs well in the range of 0 psi to 60 psi with an average pressure sensitivity of 25 kHz/psi [18].
3. Node-on-Chip The future step to reduce hardware expenses is to use the recently designed IC USTI-WSN of node-onchip instead of the USTI IC and C [14]. The USTIWSN IC contains all sensor node's sub-systems (see Fig. 5). The USTI-WSN IC prototype is shown in Fig. 6. The prototype can work with two various quasidigital sensors or transducers and one sensing element at the same time. It has a high speed embedded transmitter with high data rate transceiver for the 2.4 GHz ISM band. The radio transceiver provides high data rates from 250 kb/s up to 2 Mb/s for wireless communications and provides frame
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Sensors & Transducers, Vol. 18, Special Issue, January 2013, pp. 92-99 handling, outstanding receiver sensitivity and high transmit output power enabling a very robust wireless communication. High performance RF-CMOS 2.4 GHz radio transceiver designed for industrial and consumer applications targeted for IEEE 802.15.4, ZigBee, IPv6/6LoWPAN, RF4CE, SP100, WirelessHART and ISM applications. Supply voltage is 3.6 V. Power consumption is less than 18.8 mA in active mode, and < 250 nA in sleep mode. The operation temperature range is -40 ºC to +85 ºC.
Fig. 5. Block-diagram of node-on-chip.
with an embedded transceiver, the single USTI-WSN IC should be used.
4. Experimental Results and Future Research The USTI IC has been tested with various quasidigital temperature, humidity, acceleration, light, displacement, etc. sensors with frequency, period, duty-cycle and PWM outputs, and sensing elements such as resistive, capacitive and resistive bridges. The maximal possible input frequency of a square waveform pulse signal for the USTI was 9.1 MHz without prescaling, the minimal possible frequency was 0.04 Hz. The IC was programmed to measure frequency with the minimal possible relative error x=0.0005 %. Experimental results of measurements for 9 MHz and 0.05 Hz frequencies square waveform pulse signals are shown in Fig. 8 (a) and 8 (b) respectively. fx, Hz 9000040 9000039 9000038 9000037 9000036 9000035 9000034 9000033 9000032 9000031 9000030 9000029 1
5
9
13 17 21 25 29 33 37 41 45 49 53 57 61 65 N
Fig. 6. USTI-WSN IC prototype in 64-pad QFN package.
A sensor node based on the USTI-WSN IC with high metrological performance for quasi-digital sensors and transducers, which addresses the challenges of metrological performance improvement, smart system integration and node's cost reduction is shown in Fig. 7. The IC combines two functional blocks: USTI and transceiver.
(a) fx, Hz
0,04999966 0,04999964 0,04999962 0,0499996 0,04999958 0,04999956 0,04999954 0,04999952 1
5
9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69
N
(b)
Fig. 8. Experimental results for frequency measurements: 9 MHz (a), and 0.05 Hz (b).
Fig. 7. Sensor node for quasi-digital sensors based on the USTI-WSN ICs.
A sensor node for analog sensors can have the similar architectures, as it is shown in Fig. 1 and Fig. 3, but instead of two ICs of USTI and microcontroller
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Before measurements, the USTI was calibrated in the working temperature range: +23.5 oC … + 25.4 oC with the purpose to compensate the quartz oscillator’s systematic error [19]. The statistical characteristics are presented in Table 1. As it is visible from the table, the maximal relative error does not exceed the programmable x < 0.0005% in all frequency range including high and infralow frequencies. For 0.05 Hz frequency the relative error does not exceed x = 0.00009 % < 0.0005 %. The absolute and relative errors for infralow frequency measurements (fx=0.05 Hz) are shown in
Sensors & Transducers, Vol. 18, Special Issue, January 2013, pp. 92-99 Fig. 9 (a) and 9 (b) respectively. The 2 test for goodness of fit test was applied to investigate the significance of the differences between observed data in the histograms and the theoretical frequency distribution for data from the Gaussian distribution law. Table 1. Statistical Characteristics. Value fx
Parameter
9 MHz
Number of measurements, N Minimum fx (min), Hz Maximum fx (max), Hz Sampling Range, fx (max)- fx (min), Hz Median Arithmetic Mean, Hz Variance Standard Deviation Coefficient of Variation Relative error, %
0,00000005
0.05 Hz
65
70
9000032.48 9000038.73
0.049999576263 0.049999652243
6.2515
7.6E-0008
0 9000035.42 2.405 1.5508 5803428.66 0.00039 < 0.00050
0 0.04999962 3.4E-0016 1.8E-0008 2709716.49 0.00009 < 0.0005
hypothesis of normal (Gaussian) distribution can be accepted. Experimental results confirmed high metrological performances and justified, that the USTI IC can be used with all quasi-digital sensors existing on the modern market [6]. The comparative analyze of proposed solution for sensor nodes and convenient solution described in [4] is shown in Table 2. The comparative metrological and technical performance of proposed solution and wireless module T24-PA available on the market [5] are shown in Table 3. Table 2. Comparison Results of Sensor Node Designs. Traditional Solution [4]
Proposed Solution
1.
Analog sensors
Quasi-digital sensors
More robust and cheaper; less sensitive to interferences and noises
2.
Analog multiplexor
Digital multiplexor
No addition error
3.
Analog and mixed IC design
Digital IC design
Easy integration in standard CMOS technological processes
Low metrological performances Need a frequency range unification
High metrological performances Does not need any frequncy range unification
6.
Classical direct counting method
Modified method of the dependent count
7.
Adaptation features: no
Adaptation features: yes
No.
x, Hz
0,00000004 0,00000003 0,00000002
4.
0,00000001 0 -0,00000001 1
5
9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69
-0,00000002
5.
-0,00000003 -0,00000004 -0,00000005
N
(a) x, % 0,0001 0,00009 0,00008 0,00007 0,00006 0,00005 0,00004 0,00003 0,00002 0,00001 0
Benefits
Wide applications
Lower hardware expenses Constant programmable quantization error; Non-redundant conversion time; Broad range of input frequencies Self-adaptation; wide applications
Table 3. Comparative performances.
1
5
9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69
N
(b)
Fig. 9. Absolute error (a), and relative error (b) at 0.05 Hz frequency measurements.
The number of equidistant classes was calculated according to the following equation [20]:
k = 1.9 N0.4,
(4)
where N is the number of measurements. At probability P = 97 %, and 5 equidistant classes (k=5), the hypothesis of Gaussian distribution law can be accepted for all sets of measurement data because of S < 2max, where S = 1.7593 is the sum of deviations between the dataset and the assumed distribution; S < 2max = 7 is the maximum possible allowable deviation in the 2 distribution. Hence, the
Relative error, % Frequency Range, Hz Time range, s RPM range (presuming 1 pulse / rev), rpm Power Supply Current, mA
T24-PA 0.15 ... 0.25 0.5 ... 3 000 333E-06 ... 2
USTI IC 0.0005 0.04 ... 9 000 000 1.5E-06 ... 250
30 ... 180 000
3 ... unlimited
35
9.5
As it is visible from this table the wireless sensor node based on the USTI IC has significantly better metrological performance (accuracy (in 500 times better) and measuring ranges (in 200 times wider for time interval, in 3000 wider for frequency and unlimited for rpm), wider functionality (26 measuring modes vs. 3 measuring modes), lower power consumption (in 4 times less). All these can be achieved at significantly less cost (production price). In addition, the USTI IC has much wider functionalities and can work not only with frequency and period output sensors but also with any dutycycle, pulse-width modulated, phase-shift, time
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Sensors & Transducers, Vol. 18, Special Issue, January 2013, pp. 92-99 interval, pulse number output sensors and transducers. The price comparison for two sensor nodes is shown in Table 4. Table 4. Price comparison. IC
Manufacturers
Price, $ US (in quantities of 1, 000)
ADS1278, 24-bit, 8 channels, SPI
Texas Instruments
23.95
USTI, 3 channels, SPI, I2C, RS232 + any digital 8-channel multiplexer
Technology Assistance BCNA, 2010, S.L.
19.95
Saving:
23.95-18.95 = 5.00
The further reduction of power consumption will be able due to the use of advanced method for frequency-to-digital converter with non-redundant, programmable reference frequency [21]. It will allow to change accuracy for power consumption and opposite dependent on a sensor node's activity, measuring algorithm and available power.
5. Conclusions The proposed sensor node architectures and design approach are suitable for any quasi-digital sensors and transducers. It is based on the designed USTI IC, which lets to achieve high metrological performances at relatively low cost, and get robust solution, less sensitive for various interferences, noises and distortions. Due to the USTI's broad frequency range of input signals and constant quantization error, it is not necessary to convert the different sensor output ranges into the same frequency range. The proposed sensor node architectures can also work with analog sensors. In this case the output voltage must be preliminary converted to the frequency with the help of voltage-to-frequency converter. In addition, the advanced, modified method of the dependent count for frequency measurements provides the best tradeoff between accuracy and operation time, giving a relative error less than ±0.0005 % at 0.32 s conversion time or ±1 % at 0.00016 s conversion time respectively. The USTI IC is available on the market since 2011 from the Technology Assistance BCNA 2010 S. L., Spain. The USTI-WSN IC will be introduced on the market in 2013-2014.
Acknowledgment This research and development were supported by the International Frequency Sensor Association (IFSA).
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References [1]. Market Research Projects Wireless Sensors Market Growth at 43 % CAGR Through 2016, MarketResearch. com, 29.02.12. [2]. R. Thusu, Wireless Sensor Use is Expanding in Industrial Applications, Sensors Magazine, 1 June 2010. [3]. Comprehensive Analysis of Wireless Sensor Systems Market, Research and Markets, April 2006. [4]. Bayo, N. Medrano, B. Calvo and S. Celma, A Programmable Sensor Conditioning Interface for Low-Power Applications, in Proceedings of the Eurosensors XXIV, 5-8 September, 2010, Linz, Austria, Procedia Engineering, Vol. 5, 2010, pp. 53–56. [5]. Wireless Telemetry Pulse Acquisition Module T24PA, Product Sheet, Mantracourt, UK, issue 1.0, 21.12.2011. [6]. http://www.sensorsportal.com/ (last access: 26.12.12). [7]. S. Y. Yurish, J. Cañete and F. Puerta, Cost-effective Sensor Nodes for Wireless Sensor Networks, in Proceedings of The 6th International Conference on Sensor Technologies and Applications (SENSORCOMM' 2012), Rome, Italy, 19-24 August 2012, pp. 89-94. [8]. M. A. M. Vieira, A. B. da Cunha, D. C. da Silva Jr., Designing Wireless Sensor Nodes, SAMOS 2006, pp. 99-108. [9]. Universal Sensors and Transducers Interface (USTI), Specification and Application Note, Technology Assistance BCNA 2010, S. L., 2011. [10]. Patent No. 81851 (UA), Method of Frequency and Period Measurement of Harmonic Signal and Device for its Realization, Kirianaki N. V., Yurish S. Y., G01R 23/00, 2006. [11]. S. Y. Yurish, Novel Modified Method of the Dependent Count for High Precision and Fast Measurements of Frequency-Time Parameters of Electric Signals, in Proceedings of the IEEE International Instrumentation & Measurement Technology Conference - I2MTC, Victoria, Vancouver Island, British Columbia, Canada, 12-15 May 2008, pp. 876-881. [12]. Manufacturers of Voltage-to-Frequency Converters at http://www.sensorsportal.com (last access 26.12.12). [13]. J. Williams, Designs for High Performance Voltageto-Frequency Converters, Application Note 14, Linear Technology, March 1986. [14]. S. Y. Yurish, Digital Sensors and Sensor Systems: Practical Design, IFSA Publishing, 2011. [15]. N. V. Kirianaki, S. Y. Yurish, N. O. Shpak and V. P. Deynega, Data Acquisition and Signal Processing for Smart Sensors, Chichester, UK, John Wiley & Sons, 2002. [16]. Presision Data Converters Guide, D011012, SLYT475, Texas Instruments, USA, 2012. [17]. [W. Lee, Y. Nishida, K. Sawada and M. Ishida, CMOS RF Transmitter using Pulse Width Modulation for Wireless Smart Sensors, submitted for IEEE Transaction for Components, Packaging and Manufacturing Technology, 2011. [18]. J. G. Villalobos, Z. Xu, and Y. Jia, IDC Based Battery-free Wireless Pressure Sensor, Sensors & Transducers, Vol. 121, issue 10, October 2010, pp. 121-132.
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Aims and Scope Sensors & Transducers is a peer reviewed international, interdisciplinary journal that provides an advanced forum for the science and technology of physical, chemical sensors and biosensors. It publishes original research articles, timely state-of-the-art reviews and application specific articles with the following devices areas:
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