Warning System of Dangerous Chemical Gas in Factory Based on Wireless Sensor Network

Transcription

1 565 A publication of CHEMICAL ENGINEERING TRANSACTIONS VOL. 59, 07 Guest Editors: Zhuo Yang, Junie Ba, Jing Pan Copyright 07, AIDIC Servizi S.r.l. ISBN ; ISSN The Italian Association of Cheical Engineering Online at DOI: /CET Warning Syste of Dangerous Cheical Gas in Factory Based on Wireless Sensor Network Manzeng Ma, Bo Zhang School of Coputer Science & Engineering, Cangzhou Noral University, Cangzhou 0600, China 957a@63.co Wireless sensor network (WSN) is a dynaic network topology which is fored in a self-organizing way. In the field of WSN, the perception of inforation is realized by cooperative work between sensors. The wireless sensor network has the characteristics of siple structure, low cost and high reliability. Usually, ost of the energy of sensor nodes is consued in data transission. So, reducing the data transission between sensor nodes becoes an effective way to reduce the energy consuption of sensor networks. Data fusion technology can effectively reduce the aount of data transission in the sensor network, which can reduce the energy consuption and get ore accurate inforation. In this paper, we propose a hybrid data fusion algorith based on adaptive weighting algorith and Calan filter algorith. In the hybrid algorith, the Pearson siilarity algorith is used to filter the sensor nodes which do not eet the requireents, and then the average values of the two algoriths are obtained. Firstly, this paper introduces the principle of adaptive weighting algorith and Calan filter algorith. Secondly, we propose the iproved hybrid algorith by extracting soe nodes with low siilarity, and then we get the result by weighted calculation. Finally, the experiental results show that the proposed algorith has higher accuracy and lower energy consuption.. Introduction Data fusion technology is widely used, and it can be used to create a coplete view based on the fragented inforation collected fro each node. Through data fusion, the sensor network can avoid redundant inforation transission and greatly reduce the network counication load. Many scientists regard data fusion technology as one of their ain research directions and they put forward their own solutions through theoretical analysis and experients. Lin et al., (008) propose the CHEP ethod, which can balance the energy load effectively. Xiang et al., (00) proposes a clustering algorith by optiizing the position inforation and the residual energy. Li et al., (0) proposes a LEACH-New algorith, which effectively reduces the network energy consuption and ensures the network load balance. Zhang et al., (00) proposes an optiized Bayesian data fusion ethod which effectively solves the proble of uncertainty and inconsistency. Qiu et al., (04) proposes the SAEMDA algorith based on deep learning and it iproves the perforance of data fusion in the field of WSN. Yanger et al., (00) abandons probabilistic ethod by calculating the support function of each sensor data. Ding Lei proposes SVR algorith and the experiental results show that the ethod is superior to the data fusion ethod of artificial neural network (Ding et al., 0). In this paper, we propose a hybrid data fusion algorith based on adaptive weighting algorith and Calan filter algorith. In the hybrid algorith, the Pearson siilarity algorith is used to filter the sensor nodes, and then the average values of the two algoriths are obtained. Firstly, this paper introduces the principle of adaptive weighting algorith and Calan filter algorith. Secondly, the Pearson siilarity algorith is used to extract soe nodes with high siilarity, and then the average values of the two algoriths are obtained. Finally, the experiental results show that the proposed algorith has higher accuracy and lower energy consuption. Please cite this article as: Manzeng Ma, Bo Zhang, 07, Warning syste of dangerous cheical gas in factory based on wireless sensor network, Cheical Engineering Transactions, 59, DOI:0.3303/CET759095

2 566. Basic theory and ethod.adaptive weighting algorith Adaptive weighting algorith is a flexible ethod in the field of data fusion. In this algorith, the onitoring values collected by each sensor node are used to deterine the weight coefficient, so that the fused result will be close to the true value. Copared with other traditional data fusion algoriths, we can get a better fusion value, which can provide ore accurate data for advanced fusion (Qiu, 0)). Assuing that in a wireless sensor area, is the total nuber of sensor nodes, n is the nuber of obects which need to be onitored, and t is the onitoring inforation of the obect fro sensor i. Where,,, 3, =,, 3 n. The atrix T can be expressed as: t t t n T = ti t in t t tn () Then, the final onitoring inforation of the sensor network to the obect is calculated as follows: t = WT = wt i i In the above forula, w i represents the weight of a sensor node, and the weights of all nodes satisfy the following forula: () W = w = i i (3) The easureent variance of each sensor node is defined as σ, σ, σ 3, σ, and the total variance is calculated as follows: σ =E[(T-T) ]= wiσi Through the forula, we can get that the total ean square error σ is a quadratic function. According to the principle of calculating iniu value of variance function, we can get the partial derivative of the above forula. (4) σ = 0 wi w = i (i =,,3n) The weight under the condition of iniu ean square error is as follows: wi =,,3n σ i l= σ l Finally, we get the iniu ean square error by the following forula. (5) (6) σ in = σ l= l The advantage of adaptive weighted data fusion algorith is that it does not need the prior probability distribution of the data and it can quickly deterine the weights of each sensor according to their variance. What is ore, it has a strong fault tolerance and anti-interference ability (7)

3 567. Calan filtering algorith Calan filtering algorith is a ethod of valuation based on iniu variance. This ethod is suitable for the redundant data processing under the condition that the environent around the sensor node changes at any tie. The optial estiate of the present tie is obtained based on the optial estiate of the syste state at the last oent and the observed value of the present tie (Liu, 04). The discrete state equation of linear tie-varying syste is presented: St () = F St ( ) + T Ut () + Wt () (8) In the above forula, S(t) and W(t) represent respectively the state vector and easureent process of oent t. F represents the state transition atrix, U(t) represents the dynaic noise of oent t and T represents the syste control atrix. The corresponding observation equation is: Yt () = H Xt () + Nt () (9) Where, Y(t) represents the observation vector, N(t) represents the and the observation noise and H represents the observation atrix at oent t. We assue that they are consistent with the gauss distribution noise forula, and the covariance is respectively Q and R. In this paper, we assue that the ean square error does not change with the change of the syste state. Based on the assuption above, we can get the vector estiate of the next oent. S( t+δ t t) = F S( t t) + T U( t) (0) In the above forula, S(t+Δtt) is the estiated value which is calculated based on the vector value of oent t, U(t) is the state control vector of oents t. When the control vector does not exist, the value of U(t) will be 0. Using C(t+Δtt) to represent the covariance of S(t+Δtt). Ct ( +Δ t t) = F Ct ( t) F + Q () We use C(tt) to represent the covariance of S(tt) and F to represent the covariance of atrix F. So we can get the best estiate value of oent t+δt: S( t+δ t t+δ t) = S( t+δ t t) + Kg( t+δt) ( Y( t+δt) F S( t+δt t)) () Where, Kg(t+Δt) represents the Calan gain. Ct ( +Δt t) H Kg( t +Δ t) = H C( t +Δt t) H + R (3) Therefore, we can get the updated covariance: Ct ( +Δ t t+δ t) = ( M Kgt ( ) H) Ct ( +Δt t) (4) For a single odel: M = (5) In Calan filtering algorith, the noise contained in easureent data is required to obey the Gauss white noise distribution. Otherwise, it will bring greater distortion when they do not obey the Gauss distribution.

4 Iproved hybrid data fusion algorith The hybrid data fusion algorith in this paper is based on adaptive weighting algorith and Calan filtering algorith. By aking Pearson siilarity analysis on the data atrix of the two algoriths, we can get the siilarity atrix. Then, we extract the nodes with high siilarity by setting a threshold. When the siilarity of a node is less than the threshold value, the node is abandoned. Conversely, the onitoring inforation of the corresponding node will be saved for reference. Finally, the effective nodes are re-calculation to get the results. Assuing that W is the result atrix of adaptive weighting algorith, and K is the result atrix of Calan filtering algorith. w w w n W = wi w in w wn wn k k k n K = ki k in k kn kn According to Pearson siilarity algorith, we can get the siilarity of the two algoriths: si( W, K) = i Twk i, Twk ( W W)( K K) ( W W) ( K K) (6) (7) (8) Where, T wk is the intersection of W and K. That is to say, T wk=w K. Assuing that the nuber of sensor nodes which eet the threshold condition is l, and we can get the estiated value of the latest tie. H W + K = (9) Therefore, the final result can be get by the following forula: h = WH = wh i i l (0) 4. Realization of warning syste in factory The early warning syste of dangerous cheical gas is coposed of three parts. They are data acquisition section, wireless counication sectionand reote onitoring section.the syste frae diagra is shown in Figure Figure : Early warning syste of dangerous cheical gas

5 The data acquisition part is coposed of a large nuber of sensors, which are deployed in every corner of the cheical plant, and they are the sources of onitoring data. The distribution of these sensors can be regular, and the distribution can also be stochastic. The better the perforance of the sensor, the ore accurate the onitoring data in the later stage. Wireless counication ainly includes wireless sensor network and network inforation transission.in wireless sensor networks, wireless counication is used to transit between sensor nodes. Wired counication network is used to transit data between sensor network and onitoring center. The reote onitoring section receives the signal and sends it to the onitoring platfor through network. Three diensional visualization technology is used to odel the factory buildings, which can realize the 3D siulation environent. In addition, the data processing algorith directly deterines the perforance of the whole syste. 5. Experient and result analysis In order to test the perforance of the algorith, in this paper we copare the three algoriths in ters of tie consuing, average energy consuption and precision. The experients were carried out in the region of 000*000, and the nubers of saples are respectively 80, 60, 80, 440, 640, 880, 00. Assuing that T ˆ is the actual onitoring value and T is the estiated value of the hybrid algorith, then we can get the accuracy of the onitoring results. T ˆ T ε = () Tˆ Thus, the onitoring accuracy of the whole sensor network can be get as follows 569 T Tˆ ε = = Tˆ n ( T Tˆ ) = n = Tˆ () As can be seen in the Figure, with the increase of sensor nodes, the accuracy of the three algoriths is iproved. When the nuber of nodes reaches a certain threshold value, the change of precision tends to be stable. However, generally speaking, the accuracy of the hybrid algorith is higher than the first two algoriths Figure : Coparison of accuracy Figure 3 respectively represents the coparison of energy consuption. With the increase of nodes, the energy consuption of sensor network is gradually iproved. As a result of abandoning soe nodes with saller weights, the energy consuption of the hybrid algorith is lower than the other two algoriths. Figure 4 respectively represents the coparison of tie-consuing. Because the coputational coplexity of the hybrid algorith is ore coplex, it is ore tie-consuing than other algoriths. Nevertheless, the tie is still in the acceptable range.

6 570 Figure 3: Coparison of energy consuption Figure 4: Coparison of tie-consuing In general, the iproved hybrid algorith proposed in this paper has ore advantages in energy consuption and precision. Although the coplexity of coputational logic leads to long coputation tie, the overall tie is still within acceptable liits. 6. Conclusion Cheical enterprises play an iportant role in the econoy of our country, and the products of cheical enterprises bring great convenience to huan life. However, there is a great safety risk in the production of cheical enterprises, which need to attract enough attention. In view of the potential danger of cheical industry production, this paper proposes an early warning syste of dangerous cheical gas based on wireless sensor network, which can onitor the production process in real tie by sensors in all corners of the plant. In the aspect of sensor data fusion technology, a hybrid fusion algorith based on adaptive weighting algorith and Calan filtering algorith is proposed, which not only iproves the onitoring accuracy, but also reduces the energy consuption. References Ding L., Liao T.Q., Tao L., 0, The Method of Sensors Data Fusion Based on SVR. Chinese Journal of Sensors and Actuators, 4(5), 70-7 Li D., Liu T.J., Lin N., Huang C.Z., 04, Data Aggregation in Wireless Sensor Network Based on Deep Learning Model. Chinese Journal of Sensors and Actuators, 7(), Li L., Wang L., Zhang F.G., Wang X.Z., 0, Lower energy adaptive clustering hierarchy routing protocol for wireless sensor network. Journal of Coputer Applications, 3(0), Lin K., Zhao H., Yin Z.Y., Luo D.D., 008, A Clustering Hierarchy Arithetic Based on Energy Prediction for Wireless Sensor Networks. Acta Electronica Sinica, 36(4), Qiu C., 0, Research on data fusion algoriths in wireless sensor network for indoor environent onitoring syste. 3-7 Liu X.R., 04, Research of The Data Fusion Technology in Wireless Sensor Networks. 7-9 Xiang M., Shi W.R., Jiang C.J., Zhang Y., 00, Energy efficient clustering algorith for axiizing life tie of wireless sensor networks. AEU-International Journal of Electronic & Counication, 64(4), Zhang P., Dong W.J., Gao D.D. 04, An Optial Method of Data Fusion for Multi-Sensors Based on Bayesian Estiation. Chinese Journal of Sensors and Actuators, 7(5):44-47 Yager R.R., 00, The Power Average Operator. IEEE Transactions on Systes, Man, and Cybernetics, 3(6),