From Smart to Intelligent Sensors: A Case Study

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Volume 14-1, Special Issue March 2012

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Editors-in-Chief: Sergey Y. Yurish, tel.: +34 93 413 7941, e-mail: [email protected]

Editors for Western Europe Meijer, Gerard C.M., Delft University of Technology, The Netherlands Ferrari, Vittorio, Universitá di Brescia, Italy

Editor for Eastern Europe Sachenko, Anatoly, Ternopil State Economic University, Ukraine

Editors for North America Datskos, Panos G., Oak Ridge National Laboratory, USA Fabien, J. Josse, Marquette University, USA Katz, Evgeny, Clarkson University, USA

Editor South America Costa-Felix, Rodrigo, Inmetro, Brazil

Editor for Africa Maki K.Habib, American University in Cairo, Egypt Editor for Asia Ohyama, Shinji, Tokyo Institute of Technology, Japan

Editor for Asia-Pacific Mukhopadhyay, Subhas, Massey University, New Zealand

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Sensors & Transducers Journal (ISSN 1726-5479) is a peer review international journal published monthly online by International Frequency Sensor Association (IFSA).

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Volume 14-1 Special Issue March 2012

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Research Articles

Physical and Chemical Sensors & Wireless Sensor Networks (Foreword) Sergey Y. Yurish, Petre Dini............................................................................................................... I From Smart to Intelligent Sensors: A Case Study Vincenzo Di Lecce, Marco Calabrese ................................................................................................ 1 Smart Optoelectronic Sensors and Intelligent Sensor Systems Sergey Y. Yurish................................................................................................................................. 18 Accelerometer and Magnetometer Based Gyroscope Emulation on Smart Sensor for a Virtual Reality Application Baptiste Delporte, Laurent Perroton, Thierry Grandpierre and Jacques Trichet................................ 32 Top-Level Simulation of a Smart-Bolometer Using VHDL Modeling Matthieu Denoual and �Patrick Attia ................................................................................................. 48 A Novel Liquid Level Sensor Design Using Laser Optics Technology Mehmet Emre Erdem and Doğan Güneş ........................................................................................... 65 Recognition of Simple Gestures Using a PIR Sensor Array Piotr Wojtczuk, Alistair Armitage, T. David Binnie, Tim Chamberlain ................................................ 83 Sinusoidal Calibration of Force Transducers Using Electrodynamic Shaker Systems Christian Schlegel, Gabriela Kiekenap, Bernd Glöckner, Rolf Kumme.............................................. 95 Experimental Validation of a Sensor Monitoring Ice Formation over a Road Surface Amedeo Troiano, Eros Pasero, Luca Mesin....................................................................................... 112 Acoustic Emission Sensing of Structures under Stretch Irinela Chilibon, Marian Mogildea, George Mogildea ......................................................................... 122 Differential Search Coils Based Magnetometers: Conditioning, Magnetic Sensitivity, Spatial Resolution Timofeeva Maria, Allegre Gilles, Robbes Didier, Flament Stéphane................................................. 134 Silicon Photomultipliers: Dark Current and its Statistical Spread Roberto Pagano, Sebania Libertino, Giusy Valvo, Alfio Russo, Delfo Nunzio Sanfilippo, Giovanni Condorelli, Clarice Di Martino, Beatrice Carbone, Giorgio Fallica and Salvatore Lombardo............. 151 An Integrated Multimodal Sensor for the On-site Monitoring of the Water Content and Nutrient Concentration of SoilbyMeasuring the Phase and Electrical Conductivity Masato Futagawa, Md. Iqramul Hussain, Keita Kamado, Fumihiro Dasai, Makoto Ishida, Kazuaki Sawada............................................................................................................................................... 160 Design and Evaluation of Impedance Based Sensors for Micro-condensation Measurement under Field and Climate Chamber Conditions Geert Brokmann, Michael Hintz, Barbara March and Arndt Steinke.................................................. 174


A Parallel Sensing Technique for Automatic Bilayer Lipid Membrane Arrays Monitoring Michele Rossi, Federico Thei and Marco Tartagni............................................................................. 185 Development of Acoustic Devices Functionalized with Cobalt Corroles or Metalloporphyrines for the Detection of Carbon Monoxide at Low Concentration Meddy Vanotti, Virginie Blondeau-Patissier, David Rabus, Jean-Yves Rauch, Jean-Michel Barbe, Sylvain Ballandras .............................................................................................................................. 197 Group IV Materials for High Performance Methane Sensing in Novel Slot Optical Waveguides at 2.883 μm and 3.39 μm Vittorio M. N. Passaro, Benedetto Troia and Francesco De Leonardis ............................................. 212 The Impact of High Dielectric Permittivity on SOI Double-Gate Mosfet Using Nextnano Simulator Samia Slimani, Bouaza Djellouli......................................................................................................... 231 A Novel Sensor for VOCs Using Nanostructured ZnO and MEMS Technologies H. J. Pandya, Sudhir Chandra and A. L. Vyas ................................................................................... 244 La0.7Sr0.3MnO3 Thin Films for Magnetic and Temperature Sensors at Room Temperature Sheng Wu, Dalal Fadil, Shuang Liu, Ammar Aryan, Benoit Renault, Jean-Marc Routoure, Bruno Guillet, Stéphane Flament, Pierre Langlois and Laurence Méchin.................................................... 253 Cell-Culture Real Time Monitoring Based on Bio-Impedance Measurements Paula Daza, Daniel Cañete, Alberto Olmo, Juan A. García and Alberto Yúfera................................ 266

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From Smart to Intelligent Sensors: A Case Study

1 Vincenzo Di Lecce, 2 Marco Calabrese

1 Politecnico di Bari, DEE, Via E. Orabona, 4, 70125, Bari (Italy) 2 Holsys soc. coop., Via Polesine 10/B, 74121, Taranto (Italy)

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Received: 14 November 2011 /Accepted: 20 December 2011 /Published: 12 March 2012 Abstract: This paper showcases the opportunity of embedding intelligence in smart sensor devices with particular reference to air quality monitoring applications. The work bases upon recent findings attained and published by authors in the field of information extraction from measurements signals and smart sensor research. Smart sensors are commonly conceived as hardware/software transducers able to lift the source physical signal(s) to the application target level. This entails an intricate twist of physical measurements and application-level bits of information. When measures are noisy or ambiguous, information extraction is demanding and thus requires artificial intelligence to intervene in the data interpretation process. Experience gained with handcrafted prototypes allowed us to harness the complexity of bringing artificial intelligence inside physical measurements. To provide a complete picture of the encountered criticalities, the chosen semantic model, the carried out and the obtained results are reported and discussed. Copyright © 2012 IFSA. Keywords: Smart sensors, Intelligent sensors, Cross-sensitivity, Semantic model, Holonic modeling. 1. Introduction In the latest years, smart sensor technologies have witnessed a great interest both on the scientific and OEMs side. The enthusiasm is motivated by the awareness that these technologies are progressively replacing traditional sensors in the consumer market such as in smart phones [1] and industrial automotive applications [2].



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The reasons endorsing the adoption of smart sensors on a wide scale are at least three-fold [3]: reduction of the data communication with the main applications processor for some preset functions

with a specific value; lower system power consumption since some data is filtered and not all of the processing needs to

be done by the main processor; easier integration due to standard digital interface and pre-defined functions, avoiding developing

all applications from raw data. Yet, to overcome limitations related to effective customization, data fusion, self-calibration (to cite a few), a wider use of ‘intelligence’ has to come into play. As a result, the original concept of sensor is progressively broadening in the direction of intelligent agents [4]. In the vast chaos of legacy, incompatible, and often proprietary industrial solutions, the IEEE-1451 family of smart transducer interface standards [5] offers a unifying perspective (in particular from an architectural viewpoint) by addressing two significant engineering aspects: connection transparency and sensor self-description ability. The aim is to aid transducer manufacturers in developing smart devices and to interface those devices to networks, systems, and instruments by incorporating existing and emerging sensor and networking technologies. Notwithstanding, when viewed through the lens of artificial intelligence, sensor ‘smartness’ appears to stay in between merely transduction and complex post-processing, with the boundary purposely left hazy. Yet, it remains quite undetermined to what extent IEEE-1451 compliant sensors can be considered intelligent entities since they share aspects related to the pure physical world with others related to data acquisition, information processing and communication. This work capitalizes on our experience gained in the fields of smart sensors research and computational intelligence. The case study is obtained from a couple of Italian projects named “Ecourb” and “Slim-Port” having the ultimate goal of introducing an innovative approach to environmental sensor-based applications. With reference to the Ecourb project (grant P-047- Apulia region - research projects Ecourb), the focus was on low-cost oxide-based resistive sensors used to discriminate among volatile organic compounds (VOCs). These sensors are prone to show imprecise and inaccurate responses as they are sensitive to multiple contaminants at once. Hence, a disambiguation strategy to enhance sensor selectivity was due. As far as the Slim-Port project (European VII Framework Program for Research and Sustainable Development SLIMPORT project) was concerned, the aim was addressing a safety policy in seaport environments to eventually raise alarms to people and workers when unhealthy and unsafe conditions are detected. This objective required sensors to discern the operational context, being able to discriminate between local and global phenomena and then taking appropriate actions to deliver the right information to the right person. In both cases, experiments have been carried out with handcrafted intelligent sensor prototypes. This work is centered on explaining the conceptual model used for the design and implementation of the intelligent sensor and on presenting experiments done and obtained results. Paper layout is as follows: Section 2 explores the recent trends in the evolution of smart sensor technologies towards ever more intelligent solutions and proposes the employed semantic model; Section 3 describes our case study; in Section 4, our intelligent sensor implementations are presented along with the introduced intelligent features and the carried out experiments; Section 5 briefly comments on possible future developments of sensor-based intelligent-driven technologies and concludes.


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2. From Smart to Intelligent Sensor Research: Recent Trends In the last couple of years, there has been a growing debate on the appropriateness of using the term “smart sensor” when referring to functionalities typical of information processing and artificial intelligence [6]. Far from being it resolved, two clustered positions can be identified: one can be defined as hardware-centered and supports the view of the IEEE 1451 family of standards; the other is intelligence-centered and probably is yet to be formalized at the moment. On the hardware-centered side, the roadmap seems to be well defined. The progress in silicon technology coupled with the need to deal with local connectivity and interfacing problems is apparently paving the way towards micromechanical system-in-package (SIP) solutions. On the intelligence-centered side, the direction is not so straightforward. In fact, when viewed through the lens of intelligence, sensor ‘smartness’ appears to stay in between merely transduction and complex post-processing, with the boundary purposely left blurry and undetermined [7]. As addressed in [8], even the language gap between practitioners in the field of artificial intelligence and those working with measurements is a major point of concern. Beyond smart sensor prescriptions defined by standards, a new family of intelligent sensor capable to deal with the increasing complexity of modern applications is needed. Significant upgrades from smart to intelligent sensor are the possibility to host self-correction on board, performing data integration and fusion, managing local alarms to reduce the network and the host load. As of the latest couple of years, a new class of devices referred to as intelligent sensor hubs is attracting the focus of the market and the academia (the first IEEE/ACM international workshop on intelligent sensor hubs has been recently held in Nice, France, in August 2011). Intelligent sensor hubs can be viewed as sensor platforms endowed with a microcontroller unit that pre-process and aggregate external sensor data. An example of this new sensor generation is the MMA9550L motion sensing platform from Freescale, housing a 14-bit 3-axis accelerometer together with a 32-bit CPU, I2C, SPI and other GPIOs. The low-power and small size enable applications in mobile phones, portables devices and also medical and industrial applications. The enhancement of sensor platforms with a microcontroller unit derives from the need to overcome the limits of traditional smart sensor technologies, which cannot be customized to any specific application since the embedded logic is fixed. However, the bare availability of an algorithm on a microprocessor does not suffice to provide an intelligent behavior alone. In fact, information processing brings sensory data gathering at a higher level: this realm is governed by the problems of data interpretation and dictates the shift from the measurement field to the artificial intelligence field. 2.1. Employed Intelligent Sensor Semantic Model In the worst (but common) case, information processing deals with semantic models and hence with knowledge and ontologies [9]. In knowledge-driven problems, transductions continue to occur but at the ontological level, i.e., between models and meta-models. As reported in [10], information processing can be viewed at multiple nested granularity levels. This means, in practice, that more precise bits of information can be obtained as long as a more detailed view of the underneath transduction model is available. The ontological model is in fact based on a recursive representation of all the underlying self-nested transduction models. The last transduction level regards the measured physical system. The employed semantic model for smart/intelligent sensor


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modeling, recently presented in [7], bases on such a recursive model that complements the hardware-centered with the intelligence-centered view. The model is made of two layered parts: The Transduction Layer and The Ontological layer. The Transduction Layer accounts for the physical processing. It consists in all the transduction functions necessary to map a measured input signal into a digital value. Since these data are provided by the sensor manufacturer by means of the datasheet and correction curves, this layer is taken for granted and is not considered henceforth; The Ontological Layer regards the interpretation (i.e., the semantics) of the values obtained from the subordinate physical layer and is dependent on the kind of information processing required by that layer. The Ontological Layer is enabled to communicate towards higher or lower level modules, depending whether it is informing the upper modules (which contain it) or it is commanding an action to the lower modules (which are contained in it). To avoid the propagation of semantic ambiguities between modules and the consequent raise of false alarms or wrong actions, the Ontological Layer has to share part of its ontology with its upper modules. The model is considered holonic with reference to the concept of “holon” [11], a recursive conceptual entity with interesting computational aspects such as self-modularity, self-organization and the ability to process information at multiple granularity levels [10]. As a result, the holonic view is highly suitable for modeling complex problems. In this paper we are not concerned with both the technicalities and the broad scope of holonic modeling: the interested reader can refer to [10] for an overview. The focus is indeed on how data interpretation is handled with the two goals of enhancing selectivity in the chosen low-cost setups (EcoUrb project) and managing information disambiguation in different operational contexts (Slim-Port project). The employed intelligent sensor semantic model stays in the middle between field measurements and application-level processes as depicted in Fig. 1. Notice that the intelligent sensor can have multiple input sources, while the output provided is generally the result of some kind of data fusion performed by means of a computational intelligence technique. In the following, it will be shown how this conceptual model has been put into practice in the case study of air quality monitoring applications. 3. Case Study The growing interest of the industrial research activity in the implementation of smart monitoring systems for air quality is motivated by some observations. Modern and authoritative scientific studies have demonstrated the close relationship between air, environment and human health. In this respect there are many laws promulgated in several countries in the latest years. However, the vast majority of currently used monitoring systems: o have a slow dynamics since they are based on sophisticated chemical methods to provide hourly

average of the pollutant concentrations (hence they are very expensive, thus limiting the number of monitoring stations deployed);

o are not flexible with respect to the type of information provided (e.g., the same alarm means different things and requires different actions depending on the section of the organization involved).


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Fig. 1. The employed semantic model of an intelligent sensor-based system [7] laying in between field measurements and the application layer.

The two projects pursued the objective to engineer a new kind of monitoring stations that, with respect to traditional solutions, showed higher dynamics, had cheaper setups and offered more flexible implementations. In particular: o the Ecourb project had to address the problem of high cross-sensitivity in low-cost gas sensors

setups with particular reference to hospital environments; o the Slim-Port project required the employed sensor network to implement an intelligent policy in

managing safety alarms to different actors, hence adapting the type of information provided to the role of the person receiving it.

3.1. Ecourb Project Challenge: Cross-Sensitivity in Low-Cost Sensor Setups Nowadays, gas sensors are being massively employed for different purposes in a number of commercial components such as alarms, solvent detectors, alcohol testers and so on, covering a wide range of application scenarios from healthcare [12] and food industry [13], to air quality monitoring [14]. Gas sensors are available on the market at various prices and measurement performances. Low-cost solutions are generally based on simple electrical circuits where an integrated heater maintains the sensing element (generally a metal oxide semiconductor layer) within a specific temperature range defined by the manufacturer. Although these sensors have long life and offer moderate or low power consumption, they are sensitive to multiple contaminants at once. For example, the TGS2602 from the Figaro company has high sensitivity to low concentrations of odorous gases such as ammonia and H2S but also to small part-per-million (ppm) quantities of VOCs. Datasheets are provided in terms of resistance values against ppm concentrations, one curve for each sensed gas. Curves are also significantly affected by variations due to temperature and humidity conditions. It is useful noticing (Fig. 2) that if a certain resistance value is measured, then multiple curves are intercepted thus producing a range of possible (ppm values, gas) couples.


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Fig. 2. TGS2602 Figaro sensor datasheet, correction curves and basic sensor circuitry.

Notice that, each Rs/R0 value (leftmost image) generally intercepts more than one gas curve. Furthermore, the response of metal oxide sensitive layer is nonlinear in gas concentration [15], thus a simple superposition of the responses for different gases is problematic [16]. To be properly assessed, pre-calibration should be carried out not only for single gases but also for their mixtures. This would mean exploiting all the possible concentration combinations, which is in apparent contrast with the manufacturer’s objective of an affordable sensor price. Metal oxide VOC sensors seem then unable to produce true quantitative information of gaseous concentrations, offering low selectivity [17]. The problem of correctly attributing low-cost sensor response to exactly one gas can be considered as a classification problem [18] and in fact a number of reference works in the literature can be found applying neural networks as classifiers for gas discrimination [19-23]. Unfortunately, classification is hindered by a number of factors. For example: provided timesheets often show an underestimation of the number and the range of sensed gases; furthermore, sensor drift determines non-steady behavior even when similar emission stimuli are supplied, thus invalidating the calibration phase. In [24], the problem of cross-sensitivity has been accounted from a different viewpoint. It has been considered as a disambiguation process driven by algorithmic rules that come from the observation of the sensor datasheets and simple hypotheses on sensor behavior. The basic idea grounds on the hypothesis that, if the same gas is actually measured by two or more sensors, then their estimated concentrations will be similar, with accuracy related to the number of concordant sensors. The same consideration can be drawn for every possible gas detected by the sensors so that a simple ranking strategy is applied. If the level of agreement among sensors about a supposed measured gas is above a certain confidence threshold, then the gas is considered to be a good candidate for the disambiguation process. The main stance of signal disambiguation is a variant of the mainstream of classification-based approaches. The difference is in that the output class is represented by a rule (or a combination of them) describing sensor responses in terms of inference patterns. These represent a simple means to characterize sensor behaviors in intuitive linguistic terms, as it happens with fuzzy sets [25]. This “linguistic” approach has been pursued in very recent papers [26, 27] and encompasses both separability (i.e. classification) and semantics (i.e. disambiguation) issues.


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3.2. Slim-Port Project Challenge: Adapting Sensor Semantics to Different Operational Contexts As the complexity of modern working environments progressively increases, context-aware alarm systems become an interesting case study for researchers [28, 29] and industry [30]. Following this trend, Slim-Port project focused on seaport workplaces. Seaport workplaces are characterized by strong heterogeneities (e.g., different rooms, polluted areas around different machineries, etc.) [31]. For monitoring purposes, a dense sensor network can be accustomed to analyze each micro-climatic area and raise appropriate alarms triggered by potentially hazardous situations. When safety-critical events occur, several organization roles are committed in producing a prompt response. Certainly, the raised alarm has to be kept by the people directly exposed to the risky situation, by the area manager and by the personnel responsible for delivering emergency first aid. Hence, the raised alarm changes its meaning depending on the receiving person. Locally, semantics is related to emergency lights and sirens; at the office level, it means safety procedures to implement; for a first-aid worker it regards the kind of treatment to apply, the number of casualties, precaution measures and so on. Generally, for dealing with safety issues, the employed monitoring devices are duplicated for guaranteeing redundant detection of hazardous situations. This entails potential communication nuisance. An intelligent monitoring device should be capable of producing unambiguous alarm message contents that self-adapt in relation to the context where the message is delivered. The Slim-Port project exploited the recursiveness and self-adaptation mechanism of the holonic semantic model presented above. More details are given in the following section. 4. Implemented Prototypes In this section, the prototypes delivered from two abovementioned research projects Ecourb and Slim-Port are discussed. In both cases, the design of the air quality monitoring device starts from a number of factors such as the definition of the local connectivity, the locally available memory and the physical quantities to monitor. It has also to be considered that the whole definition process gets increasingly troublesome as long as the number and the type of sensors to integrate within the smart device grow. In order to respond to such requirements, the basic prototype e-nose is composed of an acquisition module comprising the sensors and the signal conditioning circuitry, an ADC, an embedded control system with sufficient storage capabilities, multimodal connectivity (depending on the network constraints). Fig. 3 depicts the general architecture. For brevity reasons, after an overview of the hardware and electronics related to the implemented prototypes, our focus will be on the intelligent features provided and the obtained results. Henceforth, the section is divided into two subparts, one for each prototype, introducing some technicalities first and then discussing the intelligent behavior.


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network GSM/UMTS

Wi‐FI

Eth/USB port

Embedded control

system

(u‐processor based)

Sensor data and

acquired data buffer

ADC

sensor

sensor

sensor

sensor

Fig. 3. General architecture used to implement the employed prototypes. 4.1. EcoUrb Project Implementation: a Wireless E-nose Node As a test case for the EcoUrb project, the hospital environment was chosen. The choice was motivated by the need to constantly monitor safety-critical conditions to guarantee the maximum degree of comfort to patients and doctors. After an analysis on the possible contaminants that were worth monitoring, the chosen low-cost sensors were TGS2620, TGS2602, MQ811, MQ137, MQ131, and MQ135. The LM 335 and HIH-4000-001 were used for temperature and humidity calibration. All the employed low-cost sensors are widely used for detecting inflammable gases and certain toxic gases in air. The sensing element is tin dioxide (SnO2), which has low conductivity in clean air. In the presence of a detectable gas, sensor resistance decreases depending on the gas concentration in the air. An electrical circuit converts the change in resistance into an output signal and operates the preliminary corrections as function of temperature and humidity. A very simple and low-cost constant voltage power supply has been used with the main feature of 5V DC, 1 A (high current supply for heating gas sensor headers). The circuit is designed around ON’s NCP1014 integrated controller with internal mosfet in a discontinuous mode flyback topology. A low drop serial regulator IC has been used to reduce 5 Volts outputted to 3.3 volts for the ZigBee transponder supply. Due to low current used by the transponder, the dissipated thermal power is about 170 mW. In order to assess the features of powerline communications, an OFDM-based modem is also present on board. The monitoring device is enhanced with ZigBee technology, based on the IEEE 802.15.4 standard and characterized by low cost, low power consumption and miniaturization. The ZigBee stack architecture defines two layers, namely, the physical layer and the medium access control sub-layer. The ZigBee alliance provides the network layer and the framework in the application layer. In the test-bed a commercial ZigBee module has been used (XTR-ZB1-xHE from Aurel). From specifications, the line of sight distance of this module can extend up to 1000 feet (open air) with power consumption of 350 mW. ASCII strings commands are used to configure the module (PAN ID, channel scan function, time to join the network, destination address, hopping parameters, data baud rate, sleep mode function etc). This ZigBee module uses a frequency band at 2.4 GHz and 128 bits cryptography. We have employed a stylus external antenna in order to work also in reinforced concrete buildings.


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As alternative to direct sending of data towards server-side application-level components, it is possible to use a data-logger to store all the data in a local Secure Digital (SD) memory or any other kind of module for network communication. Data processing is in charge of a server-side component. See Fig. 4 for an illustration of the handcrafted prototype.

Fig. 4. Wireless e-nose node for environmental emergency application (left). It is possible to notice the power supply modules with OFDM modem, the ZigBee transponder antenna and the gas sensors. The architecture of the electronic node is displayed on the right; dot line highlights an additional feature currently under test [32].

4.1.1. Characterizing the Intelligent Sensor Behavior: Disambiguating Sensor Response In our experiments, sensor smart behavior consists in proposing to the application level a set of inference statements of the type:

IF <premise> THEN <consequence> with k credibility

In the chosen application scenario, <premise> is made of numerical hypotheses over sensor data (e.g., that the measured Rs/R0 value of the TGS2602 sensor is more than a given empirically-found threshold θ) and <consequence> is the name of a gas the intelligent sensor is supposed to have correctly found (e.g., carbon monoxide). Furthermore, IF THEN statements are weighted by a certain fuzzy constraint (e.g., ‘high’ or ‘low’) which is to be interpreted as a qualitative estimate that the intelligent sensor does about the truthfulness of the inference provided. This index is computed after a training phase as a fuzzy measure of the ratio between the numbers of events related to a given pattern against the number of recorded emissive events for the particular sensed gas. At the very base of the whole inference extraction process lays down a heuristic able to obtain logical rules arranged in the form of IF THEN propositions (presented in [33]). Since the methodology is general with respect to the nature of the considered signals, it has been applied to the first step of the heuristics for gas sensors selectivity enhancement. Considering N sensor signals as input, the technique produces rules of this type:

IF hyp(sensor1) AND hyp(sensor2) … AND hyp(sensori) THEN hyp(sensork)


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with k ≠ i. hyp() is a function defined as follows:

otherwise0

sensor for verifiedis hyp if1 xsensorhyp x

IF THEN rules account for logical relationships found within the available dataset. Depending on the type of the hypotheses applied and on the nature of the events affecting the gaseous context of the monitored environment, the obtained patterns can cover the entire dataset or part of it. The whole inference extraction process is based on a 2-step heuristics [27]. First step is devoted to finding, for each possible inference, a logical pattern identifying the <premise> of the inference statements. Second step assigns the pattern to a possible gas emissions identifying the <consequence> of the inference statement, thus producing an inference rule with a certain credibility index attached. Supposing to use three low-cost VOC sensors such as Hanwei MQ136, Hanwei MQ131, Figaro TGS2602, an example output of our intelligent sensor can be:

IF MQ131 < θ AND TGS2602 > θ AND MQ136 < θ THEN Ammonia is being spotted with high credibility

Unfortunately, it often happens that the <consequence> does account only for a range of possible gases with an acceptable high credibility index, thus having statements like:

IF MQ131 < θ AND TGS2602 < θ AND MQ136 > θ THEN Sulfure dioxide is being spotted with high credibility OR

Ammonia is being spotted with low credibility This problem has been addressed in [27] as a pure logical inference problem. In summary, the whole number of IF THEN statements (actually, the knowledge base) is investigated and statements are combined so that expression like the following are found:

IF Statement 1 and 4 have high credibility AND Statements 2 and 3 have negligible credibility THEN Sulfure dioxide is likely to have been spotted

Notice that this latter expression can be used as high-level information for a number of complex application-level actions, such as raising alarm in hazardous situations which threaten human life. 4.1.2. Obtained Results First proof-of-concepts preliminary experiments have been carried out by directly exposing the device acquisition unit to small quantities of different gaseous contaminants. Emissions have been produced in sequence to stimulate subgroups of the chosen sensor triplet. An example emission is the following: first, carbon monoxide (CO); second, sulfure dioxide (SO2); third, a mix of the first two (CO+SO2). The inference rules obtained from the intelligent sensing device have been compared with the output of high-cost chemical sensors. In particular, two SensoriC sensors for CO and SO2 detection respectively have been used for this aim. These two sensors are approximately between one and two


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orders of magnitude more costly than the low-cost ones. Their response to the events of CO and SO2 is depicted in Fig. 5.

Fig. 5. Output of high-cost chemical sensors (image A and C) along with the activation patterns of the inference

rules obtained as in [27] discriminating CO (white lines in image B) and SO2 (image D).

After the preliminary test phase, the wireless e-nose was tested in a public hospital in three different operating conditions. The first test has been realized in an office on the 4th floor with a courtyard window left open. The office belongs to a Head Physician. Raw data are displayed in Fig. 6A, while data after humidity/temperature correction and disambiguation are shown in Fig. 6B. Another test was setup in the hall of the hospital with the access to Emergency Room. The hall is open and exposed to an urban traffic-congested street, with private cars, ambulances and buses stationing around. There are at least 30 persons including patients, relatives, staff and waiting persons. Gas concentrations in ppm are reported in Fig. 6C. The last test has been made in a surgical room (Fig. 6D). All operating rooms are equipped with continuous monitoring stations and they have air conditioning and ventilation systems with HEPA filters. The volume of each room is 25 m2. The presented data are related to a plastic surgery facility (specifically in relation to mastectomy operation). The smell of alcohol was perceived by the operators, probably due to use of Betadine (alcohol disinfectant), Sevoflurane (vaporized) and Propoform. During the surgery operation, the door of the operating room is often open or ajar and there is continuous passing by of personnel (doctors, nurses and auxiliary ones). An electrosurgical device was used. Gas concentrations of the monitoring performed in operating rooms on the 1st floor in 1 hour of acquisition, are shown in Fig. 7 and, after 25 minutes, a sensor saturation can be seen, due to the use of electrosurgical devices.


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Fig. 6. Experimental data collected in different hospital operating conditions. Raw data (A) and processed data

(B) in a room at the 4th floor; processed data in the hall room (C) and during a mastectomy operation (D)

Fig. 7. A fully assembled prototype of WEGES (left) and its basic block structure (right).


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4.2. Slim-Port Project Implementation: the WEGES Prototype WEGES is a modular and flexible air quality monitoring device employed in the Slim-Port project. It consists of: o an acquisition module composed of high-cost chemical sensors (for high-quality measures) and the

signal conditioning circuitry; o a Linux based Single Board Computer (SBC); o a GSM-EDGE module for network communication. A single board computer (Ph-1072 produced by Phidget Inc.) is used according to the project specifications. Data acquisition is continuous. Data are periodically read by an ever-running task, which compresses and sends them to the application server using a client/server socket application on a TCP/IP connection. The system is able to communicate using various network modules such as GPRS/UMTS/EDGE modem or Wi-Fi. When the signal quality is too low or the network is temporary unavailable, the system uses a high-capacity flash memory for buffering data. When the network connection comes up again, the device sends all the buffered data to the server. The system is designed to work without connection up to three days. A very simple and low cost constant voltage power supply has been used. A rechargeable battery is used to overcome power failures for about 24 hours. Before sending or storing the sampled data, the system performs two relevant operations: sensors output is corrected for temperature and humidity dependence and the ambiguity is reduced by means of the previously referenced techniques. A snapshot of the WEGES prototype and its architecture is displayed in Fig. 7. The intelligent behavior of the prototype is described in the next subsection. 4.2.1. Characterizing the Intelligent Sensor Behavior: Managing Alarms

at Multiple Semantic Levels One typical aspect of monitoring systems processes is the need to raise alarms involving multiple persons and roles in a complex organization. In the test case of the Slim-Port project, three main operational contexts have been identified: o Context 1: in the immediate proximity of the sensor. Here, the semantic level required by the alarm

management system is low. Local workers are supposed to physically perceive the alarm, e.g., siren signaling an intrusion or a liquid gas leakage.

o Context 2: in the control room. Personnel are physically distant from the area where the alarm has been raised but they can monitor the situation by means of remote terminals. The semantics here regards the plant layout, the safety procedures to eventually trigger and so on.

o Context 3: in the emergency department. Physicians have to prepare for delivering emergency medical services in places they do not know before the aid request is received. They have to decide on which action to take depending on the patient conditions and on the available resources (ambulances, squads, medical devices, etc.).

In order to manage such different contexts, information management has to be specialized and prompt for each single scenario. Considering that redundancy is a requirement for safety-critical issues, the whole alarm management system tends to be expensive and prone to high complexity. For this reason, a holonic approach has been pursued [34]. In particular, WEGES prototype has been endowed with a model of the monitored parameters in the region under scope. The model consisted in a set of IF THEN rules similar to those previously presented. The difference here is that in the


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<premise> there are hypotheses on high-cost sensor outputs and in the <consequence> there are one or more possible alarm scenarios (e.g., a gas leakage) weighted by a credibility index. Result is something like:

IF sensor1 is high AND sensor2 is low THEN

we are likely to be in the k–th Scenario with high credibility Scenarios have been defined by domain experts. When a WEGES device verifies that more scenarios are likely to have been spotted, a communication is run towards the other neighboring WEGES devices for disambiguation purposes in a way similar to that of low-cost sensor output discrimination. At the moment, software on WEGES prototypes has been engineered to communicate directly with a server unit where the disambiguation step is performed. We are currently working on a new software version that completely eliminates server in order to implement a fully holonic architecture. An object-oriented design of such architecture is reported in Fig. 8.

Fig. 8. Object-oriented reference architecture to use for a fully holonic implementation of a flexible air quality monitoring system (adapted from [34]).

4.2.2. Obtained Results The experimental campaign carried out with the deployment of a couple of WEGES prototypes (others are in production at the moment) is still ongoing. By now, the main result is the possibility to draw some basic considerations on air quality measurements and, more generally, on the complex nature of uncertainty measures in a broad sense. These considerations are briefly reported here and will be the subject of future research and experiments. It is plain to say that all measurements are affected by some uncertainty. In fact, concepts such as accuracy and precision have been purposely introduced for dealing with uncertain measures. In the physical and technical world, the goodness of a physical transducer (lower part of the semantic model in Fig. 1) relies on tabular information provided by the manufacturer (sensor datasheets) with a resolution level required by applications (e.g., 2 °C in a cooking thermometer and 0.1 °C in a fever thermometer).


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In the information world, measures are primitive elements of computation necessary to take decision at the application level by means of intermediate functions or algorithms. Fig. 9 provides an illustration of this. It is useful reminding that the tree structure has to be explored and interpreted recursively (e.g., function F.5 needs F.2 and F.3 to be first computed). The more complex the information processing structure, the higher the number of nested recursions required by the parse tree, the more critical the communication among intermediate units before decision is taken.

Fig. 9. The information processing structure of complex sensor-based applications viewed as an automaton. Sensor data have to pass through intermediate computation functions until an application-level decision is taken. Notice that the tree-based structure can be folded into the more compact holonic model depicted in Fig. 8. In modern applications where complexity and high-level interfaces [35] plays a pivotal role, information processing requires several intermediate steps to achieve the desired action or goal. In this sense, the use of our holonic modeling strategy is a first attempt to deal with complexity in a modular and scalable way. As far as the EcoUrb and Slim-Port projects were concerned, the case study of air quality monitoring with the criticalities related to low-cost implementations and heterogeneity of the operational contexts proved to be an interesting point of departure for future speculation and research on this matter. 5. Conclusions and Future Perspectives In this work, the problem of embedding intelligence in smart sensor devices has been harnessed in the case study of air quality monitoring applications. Handcrafted prototypes have been engineered as outputs of two research projects aiming at introducing an innovative approach to environmental sensor-based applications. When operational contexts are complex, cluttered and ambiguous, in addition to the relevant features of connection transparency and pre-processing functions, it is useful to endow devices also with the ability to interpret data and showing an intelligent behavior towards the application layer. For this aim,


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a recently presented holonic semantic model has been applied to our prototypes in different experimental scenarios. In sum, our experience gained from the research projects enlightened that information processing will occupy a primary position in future sensor-driven application scenarios. Acknowledgements The authors would like to explicitly thank their colleagues Jessica Uva, Rita Dario, Alessandro Quarto, Domenico Soldo and Claudio Martines for the useful material provided in writing this paper. References [1]. N. D. Lane, E. Miluzzo, Lu Hong, D. Peebles, T. Choudhury, A. T. Campbell, A survey of mobile phone

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Sensors & Transducers Journal

Guide for Contributors

Aims and Scope Sensors & Transducers Journal (ISSN 1726-5479) provides an advanced forum for the science and technology of physical, chemical sensors and biosensors. It publishes state-of-the-art reviews, regular research and application specific papers, short notes, letters to Editor and sensors related books reviews as well as academic, practical and commercial information of interest to its readership. Because of it is a peer reviewed international journal, papers rapidly published in Sensors & Transducers Journal will receive a very high publicity. The journal is published monthly as twelve issues per year by International Frequency Sensor Association (IFSA). In additional, some special sponsored and conference issues published annually. Sensors & Transducers Journal is indexed and abstracted very quickly by Chemical Abstracts, IndexCopernicus Journals Master List, Open J-Gate, Google Scholar, etc. Since 2011 the journal is covered and indexed (including a Scopus, Embase, Engineering Village and Reaxys) in Elsevier products. Topics Covered Contributions are invited on all aspects of research, development and application of the science and technology of sensors, transducers and sensor instrumentations. Topics include, but are not restricted to:

Physical, chemical and biosensors; Digital, frequency, period, duty-cycle, time interval, PWM, pulse number output sensors and

transducers; Theory, principles, effects, design, standardization and modeling; Smart sensors and systems; Sensor instrumentation; Virtual instruments; Sensors interfaces, buses and networks; Signal processing; Frequency (period, duty-cycle)-to-digital converters, ADC; Technologies and materials; Nanosensors; Microsystems; Applications.

Submission of papers Articles should be written in English. Authors are invited to submit by e-mail [email protected] 8-14 pages article (including abstract, illustrations (color or grayscale), photos and references) in both: MS Word (doc) and Acrobat (pdf) formats. Detailed preparation instructions, paper example and template of manuscript are available from the journal’s webpage: http://www.sensorsportal.com/HTML/DIGEST/Submition.htm Authors must follow the instructions strictly when submitting their manuscripts. Advertising Information Advertising orders and enquires may be sent to [email protected] Please download also our media kit: http://www.sensorsportal.com/DOWNLOADS/Media_Kit_2012.pdf


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