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This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).

A wireless e-nose network system is developed for the special purpose of monitoring odorant gases and accurately estimating odor strength in and around livestock farms. This system is to simultaneously acquire accurate odor strength values remotely at various locations, where each node is an e-nose that includes four metal-oxide semiconductor (MOS) gas sensors. A modified Kalman filtering technique is proposed for collecting raw data and de-noising based on the output noise characteristics of those gas sensors. The measurement noise variance is obtained in real time by data analysis using the proposed slip windows average method. The optimal system noise variance of the filter is obtained by using the experiments data. The Kalman filter theory on how to acquire MOS gas sensors data is discussed. Simulation results demonstrate that the proposed method can adjust the Kalman filter parameters and significantly reduce the noise from the gas sensors.

The environment is being affected more and more by the release of odorant pollutants in the atmosphere. These odors may discomfort the olfaction system and can even be harmful to human health. Electronic noses (e-noses) have been widely investigated [

In environment monitoring using a wireless e-nose network [

The Kalman filtering algorithm is a recursive algorithm to solve the state estimation problems of known systems based on certain mathematical models and the observation of noisy measurements. Many modified filtering schemes have been developed to tackle the problems in various applications [

In this paper, a wireless e-nose prototype is developed to acquire MOS gas sensor output signals and send them to a remote server. A modified Kalman filtering technique is developed for improving the sensor sensitivity and precision of odor strength measurement. It can adapt in real time to adjust the measurement noise variance of the filter parameters. In addition, the optimal parameter of system noise variance is obtained by using the experimental data. Application of Kalman filter theory to the acquired MOS gas sensors data is discussed.

The block diagram of the proposed e-nose prototype is presented in

The odorant gas measurement chamber unit is shown in

The signal processing and wireless communication unit is shown in

A MOS gas sensor circuit and its interface diagram are shown in _{H}_{s} is the output resistance of the gas sensor, which changes with the variation of odor strength due to the presence of detectable odors. The voltage _{out}_{L}_{S}_{out}_{s}

The odor strength can be obtained from the table of sensor sensitivity characteristics curve by using the calculated _{s}

Noise unavoidably appears at all times in an odor sensing system. The two most common forms of noise are the circuit factor noise and environmental factor noise (see

Circuit noise appears in the odor strength measurement process because the MOS gas sensors must work at the temperature of about 300°C, resulting in high resistor thermoelectric noise. Every semiconductor component of the interface circuit, such as voltage follower and regulated resistor, has its own circuit noise. Random movement of electrons and other charge carriers in resistors and semiconductors variation at random speed will result in random noise. Some noise also comes from factors related to the MOS gas sensors themselves. These factors include MOS gas sensor age, exposure to water and excess voltage, the bulk dissolution of surface atoms, mechanical wear and fatigue, self-heating, poisoning, and oxidation.

In the actual odor strength measurement process, environmental factors such as ambient humidity, pressure variation and ambient temperature can all affect the output signals from the electronic gas sensors. Since MOS type gas sensors rely on the absorption and desorption of the odorant particles on their surface to generate signals, environmental factors can cause obvious changes in the response and the speed of the sensor response by altering the rate of the chemical reactions involved. The resistance of MOS gas sensors falls significantly as the humidity increases, but it will increase as the temperature increases. Furthermore, the impact on various gas sensors from environmental factors is not uniform, therefore, system parameters should be properly adjusted in the de-noise process as the environmental factors.

The Kalman filtering model is based on two sources of uncertainties: measurement noise introduced by the sensor and circuit noise, and the true strength variability; odor strength optimal estimation problem is modeled by a linear stochastic system. The system state vector _{k}_{k}_{k}_{k}_{k}_{k}_{k}_{k}

The noise covariance of _{k}_{k}

The system state prediction at step

The

In practice,
_{k+1} is the actual strength measurement value at step _{k}_{k}

The Kalman gain reflects the relationship between measurement and estimation. It indicates which one would be more reliable and should be “accepted” by the final estimation. The sensor is more reliable and the samples have lower variability, then the measurement error variance _{k}

The _{k}

Tian

The new odor strength measurement equation based on the noise analysis of MOS gas sensors can be modeled by:
_{k}_{k}_{k}

A slip window average algorithm is proposed that is robust in estimating of the measurement average error. Given the window size

From _{k}_{k}_{k}_{k}_{k}_{k}_{k}, and replace _{k}_{k}, then the measurement error can be defined as:

In _{k}_{k} then have:
_{k}

From _{k}

Plugging

Replacing x̂_{k}_{k}

The noise from the sensors and environment will shift dynamically during the odor strength measurement process. The measurement error variance should therefore also be adjusted dynamically in the actual filter implementation process, so an algorithm for adaptive estimation of the measurement error variance is proposed.

From _{k}_{k}_{k}_{k}

From _{k}_{k}

The measurement error variance represents how much noise the sensor is introduced into the measured strength from one measurement to the next one, and the process error covariance represents how much the true strength would vary from one measurement to the next one. In real-time estimation, the measurement error variance and process error covariance vary with the strength changes. Generally, determining the measurement noise variance in the actual implementation of the Kalman filter is possible. It can be determined by using the slip window algorithm proposed in this paper. Determining the system noise variance, however, is more difficult and complicated, as in practice the actual process value can only be estimated and it is impossible to obtain the accurate values. In the proposed algorithm, the process error covariance is estimated by using the error variance ratio factor, and the error variance ratio factor optimal range of each gas sensor is determined in the experiment, as given in Section 6.2. Thus, the error variance ratio can be defined as:

The modified Kalman filter reduced the noise in the strength measurement process by using feedback control. The filter estimates the strength from the odor strength at a previous time step and the sensor measurement with the noise component. The equations for the modified Kalman filter fall into two groups: time updating equations and measurement updating equations. The time updating equations are responsible for projecting forward the time, obtaining an

The time updating equations from time step

The measurement updating equations incorporate a new observation _{k}

The first step is to select an initial _{0}

Subsequently, the sensor performs a strength measurement to obtain _{k}_{k}_{k}_{k} a posteriori_{k}

The developed e-nose prototype was used in a livestock research farm at the University of Guelph in Canada. In the experiments, the four MOS gas sensors in

A series of experiments were conducted with different values of

As shown in

The filtered result when the optimal variance ratio

Continuing the simulations using various

For a more accurate result, the measurement error variance and the ratio need to be properly tuned. The optimal variance ratio

The noise of the gas sensors is reduced by using the proposed adaptive filtering technique. The results are shown in

A standard Kalman filter solves the noise reduction problems based on assumptions that the measurement noise variance is a determined value; however, the measurement noise variance will fluctuate constantly with the filter step prolonged. Therefore, it is improper to assume that the measurement noise variance is a determined value. The measurement noise variances are shown in

In this paper, a wireless e-nose prototype for a wireless sensor network that can accurately measure odorant gases and estimate odor strengths has been designed and implemented. The advantages of the e-nose prototype are its light weight, very small size, and flexibility in applications. Four commercial gas sensors are used. An interface circuit and a MicaZ are used for data acquisition, data analysis, and data transfer. Using the developed wireless e-nose network, remote and real-time odor measurements become possible.

Based on the output noise from gas sensor circuits, a real-time odor strength estimation model and a modified Kalman filter algorithm are proposed, which can improve the prediction capability and the accuracy of measurement. Using the proposed model and algorithm, the direct current component and Gaussian white noise are reduced considerably at the sensor outputs. In addition, even if the sensor noise characteristics is unknown in advance, the variance of measurement error can be changed adaptively. The experiments demonstrate that the modified Kalman algorithm is effective in the measurement of real-time odor strength of livestock farms odors.

This work is supported by Ontario Pork, and Natural Sciences and Engineering Research Council of Canada (NSERC). Jianfeng Qu gratefully acknowledges the financial support from the China Scholarship Council.

Block diagram of the e-nose prototype.

The gas chamber, pump and sensor array in the e-nose.

The interface board and the MicaZ for the e-nose.

Block diagram of the data acquisition circuit.

Block diagram of the inputs and outputs of an MOS gas sensor.

Filter results at different variance ratio factors

Filter results of the four sensors. (a) Sensor 1 at variance ratio factor

Comparison results between the conventional and the modified Kalman filter algorithm. (a) Estimated value of measurement noise variance; (b) The filtered results.

Sensors used for the e-nose prototype.

Butane | Hydrogen-sulfide | Amine compounds | Air contaminants | |

2K-5K ppm | 5-100 ppm | 30-300 ppm | 1-30 ppm of H2 |

The optimal variance ratio factor.

100 | 70 | 65 | 120 |