Method of Wireless Sensor Network Data Fusion

Transcription

1 Method of Wreless Sensor Network Data Fuson L-l Ma!! ", Jang-png Lu Inner Mongola Agrcultural Unversty, Hohhot, Inner Mongola, Chna J-dong Luo Zte Corp, Hohhot Cty Offce, Inner Mongola, Chna Abstract In order to better deal wth large data nformaton n computer networks, a large data fuson method based on wreless sensor networks s desgned. Based on the analyss of the structure and learnng algorthm of RBF neural networks, a heterogeneous RBF neural network nformaton fuson algorthm n wreless sensor networks s presented. The effectveness of nformaton fuson processng methods s tested by RBF nformaton fuson algorthm. The proposed algorthm s appled to heterogeneous nformaton fuson of cluster heads or snk nodes n wreless sensor networks. The smulaton results show the effectveness of the proposed algorthm. Based on the above fndng, t s concluded that the RBF neural network has good real-tme performance and small network delay. In addton, ths method can reduce the amount of nformaton transmsson and the network conflcts and congeston. Keywords Wreless sensor networks, nformaton fuson, RBF neural networks, bg data 1 Introducton In recent years, computer technology and mcroelectroncs have made great progress. The prce of varous sensors and communcaton tools s gettng lower and smaller, and the volume s smaller and smaller [1]. The computng performance has been greatly mproved, and the functons can be mplemented more and more strongly [2]. There are dfferent types of sensors for dfferent montorng tasks. All knds of sensors cooperate wth each other to form an nfnte sensor network (WSN), whch can perform the task well [3]. The defnton of nformaton fuson can be summarzed as follows: The computer processng system makes full use of mult-sensor nformaton resources of dfferent tme and space. Under certan crtera, computer technology s used to automatcally analyze the observatons of a number of sensors obtaned by tme seres [4]. Ths nformaton process s carred out after optmzaton and synthess to complete the reured decson-makng and estmaton tasks [5]. Sensors are the bass of nformaton fuson. Mult-source nformaton s the processng object of nformaton fuson. Coordnaton, optmzaton and ntegrated processng are the core of nformaton fuson [6]

2 Generally, any sngle aspect of nformaton s ncomplete and naccurate n the process of nformaton montorng. Heterogeneous nformaton processng needs to be carred out n a tmely manner n order to utlze the nformaton to obtan the exact state of the observaton target or the complete real-tme evaluaton results. As a smplfcaton and Smulaton of human bran, neural network has powerful parallel processng ablty [8]. In computer networks, the most commonly used are BP neural networks and RBF neural networks, and an mproved model based on these two networks s also adopted [9]. In theory, both BP neural networks and RBF neural networks can approxmate any nonlnear functon wth arbtrary precson. However, ther approxmaton performance s dfferent due to ther dfferent ncentve functons [7]. 2 Lterature revew Powell proposed a radal bass functon (RBF) method for multvarable nterpolaton [10]. Broomhead and Lowe frstly appled RBF to the neural network desgn, and consttuted the radal bass functon neural network, that s RBF neural network [11]. Poggo and Gros have proved that RBF neural network s the best approxmaton of contnuous functon, and BP network s not the best choce [12]. The RBF network wth local exctaton functon can overcome the defects of the nherent property brought by BP algorthm that t s easy to get nto local mnmum. And ts convergence s also easer to guarantee than the BP network, so the optmal soluton can be obtaned. Based on the analyss of the structure and learnng algorthm of RBF neural networks, a heterogeneous RBF neural network nformaton fuson algorthm n wreless sensor networks s presented [13]. When t s used n heterogeneous nformaton fuson of cluster head or snk node n wreless sensor networks, wreless transmsson only needs to transfer the fuson results. Ths reduces the amount of data traffc, network conflcts and congeston [14]. 3 Method 3.1 RBF nformaton fuson algorthm for wreless sensor networks RBF neural network n wreless sensor networks adopts herarchcal nformaton fuson structure. Raw data or processed data collected by each sensor node s transmtted to the cluster head node (or snk node) n wreless communcaton mode. After the nformaton fuson, the result s transmtted to the remote database system va the network and base staton. The key problem of RBF neural network s to desgn the mnmum structure neural network to meet the reurement of precson, so as to guarantee the generalzaton ablty of the network. When the network works, when each nput s sent to the network, each neuron n the RBF layer outputs ts value accordng to the degree to whch the nput vector s close to the weght vector of each neuron. The result s the nput JOE Vol. 13, No. 9,

3 vector and the output weghts are far away from the phase RBF layer close to 0. The effects of these small outputs on the underlyng lnear layer can be gnored. For any weght that s very close to the nput vector, the RBF layer outputs a value close to 1. Ths value s weghted wth the sum of the weghts of the second layers as the output of the network, and the output layer s the sum of the weghted outputs of the RBF layer. MATLAB Neural Network Toolbox provdes a lot of toolbox functons for radal bass functon neural networks. Ths has an rreplaceable functon for the desgn, analyss and practcal applcaton of RBF neural networks usng MATLAB. The nput and output structures of the hdden layer neurons n the RBF neural network are shown n fgure 1: x 1 wl l x 2 x m... wl m Wl -X bl r 1 Fg. 1. Confguraton of RBF nerve cell s nput and output The nput of the neuron to the hdden layer correspondng to the nput of the nput layer s k : 2 k = "(wl j-x j )! bl f After the transformaton of the Gauss functon, the nput output of the I neuron n r the hdden layer produces an output value of : j (1) r " ( wl j -x j! bl 2 2 ( k f - wl-x! bl - = e = e = e (2) The bas value bl of the radal bass functon can adjust the senstvty of the functon, but another parameter C (called the extended constant) s often used n practcal work. The relatonshp between BL and C has many ways of determnng n practcal applcatons. In the MATLAB neural network functon, the relatonshp between bl and C s set to bl=0.8326/c. At ths pont, the output of the hdden layer neuron s: 116

4 Wl-X! C r = e = e Wl 2 -X C 2 (3) For the output layer, the output s the sum of weghts for each hdden layer neuron output. The exctaton functon uses pure lnear functon. Correspondng to the nput of the nput layer Q, the output layer neuron output y s: y = n! = 1 r " w 2 (4) BF network tranng s dvded nto 2 steps. The frst step s unsupervsed learnng to tran the weghts between the nput layer and the hdden layer w1. The second step s to tran the weghts between the hdden layer and the output layer for supervsed learnng w2. n the tranng of RBF networks, the determnaton of the number of neurons n the hdden layer s a key ssue. In MATLAB, 0 neurons are traned, and the output s automatcally ncreased by checkng the output error. Each tme the loop s used, the maxmum error s generated by the network, and the correspondng nput vector s used as the weght vector to generate a new hdden layer neuron. The error of the new network s then checked, and the process s repeated untl the error reurement or the maxmum number of hdden neurons s reached. 3.2 Applcaton characterstcs of RBF nformaton fuson n sensor network n envronmental montorng The researchers placed sensor nodes n the montored area of nterest, allowng sensors to form networks autonomously. Each node collects nformaton about temperature, humdty, lght, etc. Because the sensor nodes have computng power and communcaton ablty, they can do some data processng n the sensor network, especally the nformaton fuson process. Ths can greatly reduce the amount of data traffc and the forwardng burden of the sensor nodes near the gateway, thereby savng the energy of sensor nodes. Compared wth tradtonal envronmental montorng methods, there are three dstnct advantages by usng sensor networks for envronmental montorng. Frst, the sensor nodes are small n sze, and the entre network needs to be deployed only once. Second, the sensor nodes are large and the dstrbuton densty s hgh. Each node can detect the detals of the local envronment and summarze t to the base staton. Therefore, the sensor network has the advantages of large data acuston and hgh accuracy. Thrd, the sensor node tself has a certan computng power and storage capacty. It can react to the physcal envronment and the network tself n a tmely manner, or carry out more complex montorng. JOE Vol. 13, No. 9,

5 4 Result and dscusson In ths paper, MATLAB smulaton software s used to smulate the RBF neural network. The smulated tranng data are selected from the values of the groundwater level, channel flow, ar temperature, saturaton dfference, precptaton and evaporaton measured n the 24 months of a nearby rver test well n a mountan area. The relevant groundwater dynamc records were selected as samples, and the samples contaned 24 sets of data. The frst 19 sets of data are used as tranng samples, and the sets of data are used as test samples. The number of neurons n the nput layer of the RBF network depends on the number of factors affectng the groundwater table. There are fve factors accordng to the problem, so these factors were determned for 5. There s only one uantty of output, that s, the depth of the water level, so the number of neurons n the output layer s set to 1. The NEWRB functon n MATLAB s used to tran the network and automatcally determne the number of hdden layer unts reured. The exctaton functon of the hdden layer unt s Radbas, the weghted functon s Dst and the nput functon s NetProd. The exctaton functon of the output layer neuron s pure lnear functon Pureln and the weghted functon s DotProd. The nput functon s Netsum. The NEWRB functon n MATLAB can be used to create a radal bass functon neural network. Its call format s: [Net tr]=newrb(p T GOAL SPREAD MN DF) Among them, P: t s a RXQ dmensonal matrx consstng of Q nput vectors; T: t s a SXQ dmensonal matrx consstng of Q group target classfcaton vectors; GOAL: mean suare error, default to 0; SPREAD: t s the extenson speed of the radal bass functon, and s assumed to be 1; MN: t s the maxmum number of neurons and defaults to Q; DF: t s the number of neurons added between two dsplays, and s default to 25; Net: t represents the return value and t s a radal bass network; Tr: t s a return value that s used for tranng records. The larger the extenson speed of the radal bass functon s, the smoother the SPREAD functon s ftted. However, excessve SPREAD means that very large numbers of neurons are reured to accommodate fast changes n functon. If the SPREAD s too small, t means that many neurons are reured to adapt to the slow changes n the functon. The network performance s not very good. Therefore, n the network desgn process, t s necessary to use dfferent SPREAD values to try to determne an optmal value. The tranng algorthm mplemented by NEWRB functon can adaptvely determne the structure of radal bass network and do not need to determne the ntal weghts of the network artfcally, thus reducng the randomness of network tranng. In the process of creatng RBF neural networks, the NEWRB functon can automatcally ncrease the number of neurons n the hdden layer untl the suare error 118

6 satsfes the reurement. Therefore, the network creaton process s the tranng process, the tranng process s as follows (among them spread=3.2). NEWRB,neurons=0,SSE= NEWRB,neurons=2,SSE= NEWRB,neurons=3,SSE= NEWRB,neurons=4,SSE= NEWRB,neurons=5,SSE= NEWRB,neurons=6,SSE= NEWRB,neurons=7,SSE= NEWRB,neurons=8,SSE= NEWRB,neurons=9,SSE= NEWRB,neurons=10,SSE= NEWRB,neurons=11,SSE= NEWRB,neurons=12,SSE= NEWRB,neurons=13,SSE= NEWRB,neurons=14,SSE= NEWRB,neurons=15,SSE= NEWRB,neurons=16,SSE= NEWRB,neurons=17,SSE= NEWRB,neurons=18,SSE= Performence s ,Goal s 0,0005 Tranng-Blue Goal-Black Epoches Fg. 2. Tranng of RBF neural network JOE Vol. 13, No. 9,

7 The tranng result of RBF neural network s shown n fgure 2: When the nput varables of the network are determned, normalzaton s needed. In the calculaton process, the transformaton s set n the range of [-1,1], and the normalzed data s easer to tran and learn for the neural network. The normalzed data s obtaned by calculatng the devaton between the measured value and the reference value of the correspondng no fault case, and then dvdng the absolute value of the maxmum devaton to obtan the nput varable. The smulated data of the test samples and the RBF neural network are shown n table 1. After the normalzaton, the comparson of output data and actual data of RBF networks s shown n table 2. Table 1. Compare of the detecton data Test sample RBF output data Table 2. Compare of the detecton data of the RBF neural network and real data Actual data RBF output data Absolute error (%) Relatve error (%) When adjustng the Spread value of the NEWRB functon n MATLAB, dfferent results can be obtaned, and the results are very close to the measured values. From the above smulaton results, RBF neural network can predct the data well. After fusng cluster heads or snk nodes n wreless sensor networks, the data concerned can be transmtted drectly, such as envronmental assessment, envronmental temperature, humdty, ar ualty, and real-tme RBF neural network. And the RBF neural network has good real-tme performance and small network delay. Ths feature can save the energy of sensor nodes and prolong the lfetme of the whole sensor network. 5 Concluson In ths secton, the structure model and learnng algorthm of RBF neural network are analyzed. Based on herarchcal nformaton fuson structure model, a heterogeneous RBF neural network nformaton fuson algorthm n wreless sensor networks s presented. When t s used to fuse heterogeneous nformaton of cluster head nodes or snk nodes n wreless sensor networks, wreless transmsson only needs to transfer the fuson results. Ths method can reduce the amount of nformaton transmsson and the network conflcts and congeston. And the RBF neural network has good realtme performance and small network delay. Ths feature can save the energy of sensor nodes and prolong the lfetme of the whole sensor network. Smulaton results show the effectveness of the proposed method

8 In the wreless sensor network system of nformaton fuson contract, t s mportant to study the combnaton of multple protocol layers. For example, n applcaton layer, t s necessary to dscuss how to select data gradually through dstrbuted database technology to acheve the effect of fuson. At the network layer, the most mportant s to study how to ntroduce the fuson mechansm nto the routng protocol to reduce the amount of data transmsson. 6 References [1] Tan, L., & Jng, Z. (2015). A data fuson algorthm based on neural network research n buldng envronment of wreless sensor network. Internatonal Journal of Future Generaton Communcaton & Networkng, 8(78), [2] Bo, L., Hu, Y., L, T., & Huang, D. (2016). Study on food nformaton onlne montorng system utlzed wreless sensor network. Advance Journal of Food Scence & Technology, 11(2), [3] Lu, L., Luo, G., Qn, K., & Zhang, X. (2017). An algorthm based on logstc regresson wth data fuson n wreless sensor networks. Eurasp Journal on Wreless Communcatons & Networkng, 2017(1), [4] He, H., Zhu, Z., & Mäknen, E. (2015). Task-orented dstrbuted data fuson n autonomous wreless sensor networks. Soft Computng, 19(8), s [5] G. Santh, S., & R. Ramya, R. R. (2015). Clusterng based data collecton usng data fuson n wreless sensor networks. Internatonal Journal of Computer Applcatons, 116(9), [6] Yuan, K., Xao, F., Fe, L., Kang, B., & Yong, D. (2016). Conflct management based on belef functon entropy n sensor fuson:. Sprngerplus, 5(1), s [7] Luo, X., & Chang, X. (2015). A novel data fuson scheme usng grey model and extreme learnng machne n wreless sensor networks. Internatonal Journal of Control Automaton & Systems, 13(3), [8] Abbass, M. A. E., Jlbab, A., & Bourouhou, A. (2016). A robust model of mult-sensor data fuson appled n wreless sensor networks for fre detecton., 9(3), [9] Yu, J., & Zhang, X. (2015). A cross-layer wreless sensor network energy-effcent communcaton protocol for real-tme montorng of the long-dstance electrc transmsson lnes. Journal of Sensors, 2015(5), [10] Sun, B. (2015). Desgn and testng of hybrd antenna cluster networkng model research based on turntable snk node. Journal of Informaton & Computatonal Scence, 12(7), [11] Wu, Y. I., Wang, H., & Zheng, X. (2016). Wsn localzaton usng rss n three-dmensonal space a geometrc method wth closed-form soluton. IEEE Sensors Journal, 16(11), [12] Wang, H. (2015). Usng mult-sensor data fuson n outler detecton of buldng stran montorng data. Journal of Informaton & Computatonal Scence, 12(11), JOE Vol. 13, No. 9,

9 [13] Sorber, L., Barel, M. V., & Lathauwer, L. D. (2015). Structured data fuson. IEEE Journal of Selected Topcs n Sgnal Processng, 9(4), [14] Lahat, D., Adal, T., & Jutten, C. (2015). Multmodal data fuson: an overvew of methods, challenges, and prospects. Proceedngs of the IEEE, 103(9), Authors L-l Ma (correspondng author) s wth the College of Computer and Informaton Engneerng of the Inner Mongola Agrcultural Unversty, Hohhot , Inner Mongola, Chna (lujangpng@mau.edu.cn). Jang-png Lu s wth the College of Computer and Informaton Engneerng of the Inner Mongola Agrcultural Unversty, Hohhot , Inner Mongola, Chna J-dong Luo s wth Zte Corp, Hohhot Cty Offce, Hohhot, Inner Mongola, Chna. Artcle submtted 14 August Publshed as resubmtted by the authors 20 September