Parallel Processing for Large-scale Fault Tree in Wireless Sensor Networks

Transcription

1 1160 JOURNAL OF NETWORKS, VOL. 8, NO. 5, MAY 2013 Parallel Processng for Large-scale Fault Tree n Wreless Sensor Networks Xnyan Wang, Ruxn Zhang School of Mechancal Electronc & Informaton Engneerng, Chna Unversty of Mnng & Technology (Beng), Beng , Chna Emal: wxy041801@163.com Abstract Wreless sensor networks (WSN) covers many knds of technologes, such as technology of sensor, embedded system, wreless communcaton, etc. WSN s dfferent from the tradtonal networks n sze, communcaton dstance and energy-constraned so as to develop new topology, protocol, qualty of servce (QoS), and so on. In order to solve the problem of self-organzng n the topology, ths paper proposes a novel strategy whch s based on communcaton delay between sensors. Frstly, the gateway selects some boundary nodes to connect. Secondly, the boundary nodes choose nner nodes. The rest may be deduced by analogy. Fnally, a net-tree topology wth multpath routng s developed. The analyses of the topology show that net-tree has strong ablty n self-organzng and extensble. However, the scale of system s usually very large and complexty so that t s hard to detect the falure nodes when the nodes fal. To solve the greater challenge, the paper proposes to adopt fault tree analyss. Fault tree s a commonly used method to analyze the relablty of a network or system. Based on the fault tree analyss, a parallel computng algorthm s represented to these faults n the net-tree. Frstly, two models for parallel processng are came up and we focus on the parallel processng algorthm based on the cut sets. Then, the speedup rato s studed. Compare wth the seral algorthm, the results of the experment shows that the effcency has been greatly mproved. Index Terms wreless sensor networks, cut sets, fault tree, mult-threadng, parallel processng I. INTRODUCTION A wreless sensor network (WSN) [1, 2] s composed of sensors and aggregate nodes (e.g., gateway and wreless router). The sensors are responsble for collectng data, and they are transferred to external networks or hosts by the aggregate nodes. In the mltary felds, WSN can undertake montorng, searchng and defendng, etc. In the cvl felds, t can be used to montor and operate n dangerous envronment, to supervse n producton process, or to track and sense obects. So WSN s becomng ncreasngly hot topc, especally wth developng of the Internet of thngs [3, 4]. Manuscrpt receved January 10, 2013; revsed Aprl 12, 2013; accepted Aprl 22, Proect supported by the Natonal Nature Scence Foundaton of Chna (No ). Correspondng author: Xnyan Wang, wxy041801@163.com If the sensors are treated as nodes, and wreless lnks as edges, then the graph whch s consttuted by them s regarded as topology of the WSN. When the sensor network nodes are faled [5], t s hard to fnd the faled nodes snce the system generally s a large scale. The fault tree analyss (FTA) [6-8] s an mportant method to deal wth ths problem. Fault tree analyss, whch s sutable for large and complex network or system relablty analyss, s a method wth graphcal nterpretaton. It was frst conceved n 1961 by H.A. Watson of the Amercan Bell Telephone Laboratores. The varous factors that may cause a system falure are decomposed and analyzed layer by layer. All factors whch are possble lead to faults are analyzed, and to compute ther logcal relatonshp between each other untl the basc reason of the accdent wll be found out. Based on these varous combnatons, one can to determne the causes of system falure and the probablty of occurrence. Then one can take correspondng measures to mprove the relablty of the system. FTA s now wdely used n many areas, such as Electrcal System [9], Chemcal Processng System [10, 11], Nuclear Reactor Safety Study [12, 13], product safety, and so on. Wth the ncreasng of system functonalty, system performance also be enhanced on a sngle platform. A comprehensve varety of automated nformaton processng system s more and more commonly. Fault tree analyss s often used to analyze the large and complex systems. Wth the ncrease n the number of system components, the mnmal cut sets wll be ncreasng wth exponental because the large scale prone to the combnatoral exploson or state space exploson problem, and these problems make that these systems s dffcult to be analyss by synthess [14, 15]. In ths paper, we propose a Net-Tree structure for sensor network to solve the layout and nterconnecton problem. Base on the communcaton delay, the strategy teratve choose downstream nodes to construct nterconnecton path. Its basc structure s a tree wth mult-path that a gateway node s regarded as the tree root. From the formal pont of vew, a net-tree topology s smlar to a fat-tree whch s used to the hgh performance computng network. To solve the fault dagnoss problem n sensor network, we also desgn a parallel algorthm based on fault tree analyss. The algorthm dvdes the whole logcal expresson of fault nodes nto multply do: /nw

2 JOURNAL OF NETWORKS, VOL. 8, NO. 5, MAY subsets whch are assgned to every subroutne. Then, the subroutnes are responsble for computng the local truth table. As a result, the local truth tables can be computed, respectvely. Lastly, the man subroutne gathers the local results whch are merged nto the global result. Generally, the system maybe contans a large number of basc events even f the expresson can be smplfed. As a result, a large number of ndependent varables need to be defned. Hence, n the concrete computng proceeds, the algorthm adopts the strng matchng method to compute the sngle tem. The strng matchng method can effectvely reduce the number of ndependent varables. In summary, the contrbutons of ths paper are as follows: 1) We propose a Net-Tree structure for sensor network to solve the layout and nterconnecton problem. 2) We present a parallel algorthm based on faulttree analyss to solve the fault dagnoss problem n sensor network. The rest of the paper s organzed as follows. In the secton II, we descrbe the related works about the topology structure of sensor network. Secton III and secton IV represent our scheme whch ncludes a novel topology structure and a parallel computng algorthm based on fault-tree analyss to solve the fault dagnoss problem n sensor network. Performance evaluaton and conclusons are presented n secton V and secton VI, respectvely. II. RELATED WORKS A. Wreless Sensors Network Wth the lmtatons of energy, communcaton and layout [16], etc., the WSN usually cannot form rregular topology. Accordng to ts network structure [17], there are flat, herarchc and mxed. In the flat topology, all nodes have equvalent functons. As shown n the Fg.1a and Fg.1b, the topology can be dvded nto mesh [18] and chan. All adacent nodes are connected together n the mesh network. Whle the chan network's nodes are strng-shaped connecton n many chans, and the chan tal s connected wth aggregate node. Herarchc topologes [19] n general are conssted of backbones and nodes. The backbones whch generally have strong ablty (a) Net (b) Chan to aggregate nodes, and the nodes whch are n the lower have weakly communcaton ablty to responsble for collectng data. Each node n the backbones connects each node n the lower to compose a subnet whch usually called as cluster. A hybrd networks are combned by the above two, as shown Fg.1d. In addton, a clustertree [20, 21] topology s presented to mprove the herarchc. In the lower, the nodes are connected wth each other to construct a tree, and the backbones stll s same structure wth a herarchc. Wth the PulsAR rados from AFAR Communcatons Inc, WSN can be deployed a wreless mesh network n a tree topology where any rado n the mesh network can serve as an access pont to other rados. In the wreless mesh tree topology, there are three node types: one root node, multple branch and leaf nodes. B. Parallel Computng Parallel program s a parallel algorthm or concurrent algorthm, whch can be executed on many dfferent processng unts at the same tme. The most common method of parallel computng s PCAM (parttonng, communcaton, agglomeraton and mappng) [22, 23]. Parttonng s to dvde the problem nto some ndependent sub-problems as far as possble, and then the sub-problems are solved respectvely on multple computng unts at the same tme. Lastly, the man program gather these results to form the fnally answer. In ths procedure, each subtasks need to exchange data and results so that the communcaton also s a key step. The agglomeraton means to combne multply smaller subtasks so as to ncrease the performance and reduce the system overhead. Obvously the advantage of parallel computng s to dvde a large problem nto multply smaller problems and to perform at the same tme. However, how to map these sub-problems to dfferent computng unts s a very mportant task. Ths task s wrtten as mappng. As opposed to the seral computng, the parallel computng can be dvded nto two parallel models, whch are space model and tme model. The parallel tme model refers to ppelne technology but the parallel space model adopts multple computng unts. Furthermore, the parallel space model can be dvded nto data parallelzaton and task parallelzaton. Wth the development of Very Large Scale Integraton (VLSI), the processors wth mult-cores, whch denote to ntegrate multply complete calculaton engne n a processor, are becomng the manstream archtecture of the compute archtecture. Furthermore, f there are multply processors, whch can be heterogeneous or somorphc archtecture, are deployed on the same motherboard, then the archtecture s named as multprocessor. (c) Backbone (d) Layers Fgure 1. Topologes of WSN n communcaton are n the upper for transformng data Fgure 2. Compute node wth two-processor four-core

3 1162 JOURNAL OF NETWORKS, VOL. 8, NO. 5, MAY 2013 The Fg. 2 shows the typcal mult-cores multply processor archtecture [24]. In the archtecture descrbed above, the program can be performed based on the dvde and conquer strategy. The subtasks are assgned to two processors or four cores so that multply tasks can be executed at the same tme. The programmng model s named as mult-processors or mult-threadng, where the programs s performed at the thread-level parallel or at the task-level parallel. In ths paper, the follow-up expermental works are based on a dual-core compute node. III. THE PROPOSED SCHEME Base on the communcaton delay, the strategy teratve choose downstream nodes to construct nterconnecton path. Its basc structure s a tree wth mult-path that a gateway node s regarded as the tree root. From the formal pont of vew, a net-tree topology s smlar to a fat-tree whch s used to the hgh performance computng network. A. Topology of Net-Tree Ths paper strategy orented the applcaton envronment of sensor networks as follows: 1) Sensor nodes are densely and random deployed; 2) Sensor nodes are prone to falures; 3) Sensor nodes manly use a broadcast shortdstance communcaton; 4) Sensor nodes are lmted n power, computatonal capactes, and memory; 5) No centre nodes. Defne of net-tree topology as below. Let Γ denote the set of sensors, and Φ denote the set of gateway or router. F( x, y) denotes the functon of communcaton ablty. x s capable to send data to y when F( x, y ) s bgger than 0. {,,, }, where 0 Γ= Γ Γ Γ 0 1 n n, Γ = { s, s,, s }, where [0, n], m 0, 0 1 m Φ= { g, g,..., g } 0 1 r, where r 0, F = { F, F}, 0 1 F (, s g ), where s Γ, g Φ, 0 0 F ( s, s ), where 1 x y x y s Γ, s Γ. + 1 Fgure 3. Net-Tree topology wth sngle root Fgure 4. Net-Tree topology wth mult-root For Γ and Φ, there exsts a path between them when F 0 s bgger than 0. When F s bgger than 0, there exsts a 1 path between s and s x y. The defne shows that a net-tree s a herarchc mesh tree wth mult-path, no center nodes, no nterconnect path n the nodes whch are at the same level dfferent nodes, but they may have some publc chld nodes. The Fg.3 s a typcal net- tree topology. In Fg.3 and Fg.4, there are two node types: one root node and multple nner nodes. The root node s a gateway or router, and other nodes are sensor nodes whch undertake both collectng data and routng at the same tme. If there are many roots, then the topology looks more lke fat-tree than mesh as shown below. When the sensors n the net-tree network are falure, t needs to deal wth t by fault tree analyss method. B. Self-organzng Strategy Accordng to the above defne, the self-organzng process s conssts of two steps: the frst step s to connect gateways to sensors, and the second step s to nterconnect every sensor by layers. To any node, the number of downstream nodes s depends on ts communcaton capablty. The pseudo-code of the algorthm s shown n the Algorthm.1. Algorthm 1. Self-organzng strategy 1: //Watng for a call from parent nodes. 2: whle (parentnode=node.watngparent ()) do 3: //Regster parent node 4: node.addparent (parentnode); 5: //Feedback to the parent node 6: node. Feedback (); 7: end whle 8: //Search chldren 9: node.searchchldren (); 10: //Watng for a feedback from chldren 11: whle chldnode=node.watngchldren () do 12: //Regster chldren 13: node.addchld (chldnode); 14: end whle 15: node.notfychldren (); The algorthm s dvded nto three processes. Frst, watng for father nodes, and establsh nterconnecton. Then, search chld nodes, and connect wth each other. At last, the node notfes ts chldren that the path has already been establshed. If no new nodes send package to the node, then the WatngParent functon wll return NULL

4 JOURNAL OF NETWORKS, VOL. 8, NO. 5, MAY so as to termnate the whle loop. The same s true about the watngchldren functon. C. Dynamc Adustment When falures occur or the battery s depleted, the node had to wthdraw from the net-tree. As a result, ts chldren wll also be dsconnected from the net-tree so as to rebuld the net-tree. On the other hand, when a new node need to be nserted nto the net-tree, the same s true. Both cases can be handled by nsertng new nodes. In both cases, the chld broadcast a call and then watng for feedback. When receve a message from parent nodes, the routng table need to be clean to remove useless nformaton before regster the parent nodes. The adustment process s shown n the Algorthm.2. Algorthm 2. Dynamc adustment 1: //Insert new node 2: newnode.insert (); 3: whle parentnode=newnode.watngparent() do 4: //Clean routng table 5: newnode.cleanroutngtable(parentnode); 6: //Regster parent node 7: newnode.addparent(parentnode); 8: end whle IV. PARALLEL PROCESSING MODEL OF FAULT-TREE ANALYSIS A. Fault-tree Analyss Fault tree analyss contans qualtatve analyss and quanttatve analyss. Qualtatve analyss s to calculate the combnaton of basc mnmum ncdent whch may lead to the top of events, whch s the fault tree (mnmum) cut sets; quanttatve analyss s to be used the known basc event probablty to calculate the probablty of occurrence of the top-level event. Qualtatve analyss of the fault tree s manly based on the theory of Boolean algebra and the theory of structure functon. In partcular, the computaton and analyss of fault tree requres the structure functon searches the whole expresson but the expresson s generally very complex. Usually, a large number of smplfcaton operatons need to be executed. Therefore, the analyss work s lower effcency generally. As a result, the man work of qualtatve analyss s to study how to mnmze the cut sets. The sngle pont refers falure s a sngle falure pont. One of the man tasks of fault tree analyss s to fnd all the falure modes that are composed by all sngle pont falures. Every mode s a cut set of the system expresson. The maxmum probablty of falure modes for the system s the probablty of mnmal cut set, that s to say t s the most weaknesses pont of the system. The top event s the sumof-product form of all basc events. Therefore, the works that search the falure mode s very mportant to take effectve measures to reduce systemc rsk and to mprove the relablty of the system. Defnton 1. Cut sets are a collecton of basc events n fault tree tops event. Formal descrpton of cut sets s shown below: Assume that there are X, X,, X 0 1 n, where n denotes 1 the number of basc events n fault-tree T. Let Q s the set of T and Q = { p, p,, p } 0 1 m, where 1 The value of θ s Then T p = [ x, x,, x ] [ θ, θ,, θ ] T 0 1 n n 1 θ 1, ff x p = (1) 0, else. 1, ff p = 1, p Q = (2) 0, else. Assume that there exsts p ' make T = 1, where p ' Q, and then Q s called as the cut sets of T. Defnton 2. Mnmal cut set s a smallest combnaton of component falures whch wll cause the top event occurs. Formal descrpton of the mnmal cut set: If p, p Q and there exsts ' p ' Q make p + p = p, then Q s not a mnmal cut set. Otherwse, Q s the mnmal cut set of T. B. Modular-based Parallel Processng Let T be a fault tree, ts recursve dvson shown below: 1) T s a fault tree; 2) T, T,, T 0 1 k 1, where T, means the th block of T are also fault tree. Assume to perform r rounds dvdng and there are r sub-modules n the th round, where [0, r], then the dstrbuton algorthm, whch allocate sub-modules between parallel programs, s shown n Algorthm.3. Algorthm 3. Recursve dstrbuton algorthm of modules 1: Assgn( T,k) 2: Dvde( T, k) 3: for =0;<k;++ do 4: Q.Add( T ) % s, 5: Assgn( T,h), 6: end for In Algorthm.3, Dvde( T,k) means that the fault tree T s dvded nto k sub-modules and Assgn( T,k) denotes that the fault tree T s recursvely dvded nto k submodules to assgn to each program. Therefore, the parallel processng method can be desgned as shown n Algorthm.4. In Algorthm.4, ReceveMsgFromChld( ) means to receve the computng results from the submodule, Compute( ) means to calculate the cut sets of sub-modules and SendMsgToParent( ) denotes to send the local results to the parent module. Algorthm 4. Parallel processng 1: ReceveMsgFromChld(); 2: Compute(); 3: SendMsgTOParent();

5 1164 JOURNAL OF NETWORKS, VOL. 8, NO. 5, MAY 2013 C. Parallel Processng based on Cut Sets Let Q satsfy Q Q, where 0 < r m, s a set of p Q Q, Q r 1 = = 0Q, and Q Q = ϕ,, then cut-set Q s dvded nto r subsets where the value of T can be determned ndependently. Therefore, the parallel process n subsets can be performed to accelerate computng. The dvson algorthm of cut sets can be shown n Algorthm.5. Algorthm 5. The dvson algorthm of cut sets 1: =0; 2: whle p=q.getitem() do 3: Q.Add(p); % r 4: ++; 5: end whle In Algorthm 5, the cut set Q s assgned to the sub-cut set Q based on the number of tems and Q = mn Q / r, Q = max Q / r. Therefore, the processng tme T Q satsfy TQ = mn( TQ ), [0, 2]. The parallel processng of Q s to assgn r subsets to r subroutnes, respectvely. When the result of th subroutne wll have been calculated, the subroutne wll notfy the man program and be termnated by the man program's notfcaton. In the subsequent chapters, the paper wll show the specfc mplementaton detals and analyze ts performance through experments. D. Cut Sets Calculatons based on Strng Matchng In the secton IV-B, the paper dscusses two parallel processng mode of large-scale fault tree, ths secton wll desgn the parallel processng algorthm based on the fault tree cut sets. Algorthm 6. Calculaton of cut sets 1: char cs; 2: Bool rtn=true; 3: V=OpenFle(Value.Txt); 4: h=converttocomplementary(v); 5: =0; 6: whle <k & (!rtn) do 7: Str=cs.GetItem(); 8: =0; 9: whle (<h)& rtn do 10: f Str.fnd(V.GetItem()) then 11: rtn=false; 12: ++; 13: end f 14: end whle 15: ++; 16: end whle 17: k=dvdestr( CS,cs); Generally, there are two method, whch are the logcal expresson and the structure functon, can be used to compute the fault tree. However, the system stll contans a large number of basc events even f the expresson can be smplfed. As a result, a large number of ndependent varables need to be defned. Therefore, the paper adopts the strng matchng algorthm to solve the result based on logcal expresson and the specfc matchng algorthm s shown n Algorthm.6. E. Speedup The speedup rato, whch ndcates the rate of the parallel processng tme relatve to the tme of the seral processng, s one of the key ndexes of parallel programs. It used to measure the performance of parallel computng. The speedup rato can be defned as the followng formula. α = T / T s p (3) In the Equaton.3, α denotes the speedup rato, T s ndcates the executon tme of the sequental algorthm and T p means the executon tme of the parallel algorthm n p processors. Wth the development of computer scence and technology, the hgh performance computers s becomng faster and faster so that the dfference n computng tme to process the sngle tem can be neglgble. Therefore, assume that each tem consume the same computng tme. Let the number of logcal expresson be n terms and the number of parallel subroutnes s m. Let have K, where [1, n], truth value tems, and then the frst K s ust be found snce the logcal expresson s the sum of products. Therefore, the speedup rato satsfy Theorem 1. The speedup rato can be descrbed as α = K / K %( n / m). (3) 1 mn And the value scope of the speedup rato s α [1,1 + n ( n/ m)]. Proof: Suppose that the frst truth value tems located n the n/ [ mm ( 1)] + rposton, then n/ [ mm ( 1)] + r n α =, r [1, ], r m r =, nm ( 1) n α = 1+ = 1+ n ; max m m Obvously α s a decreasng functon. When 1 when the truth value located the th poston, = ( n/ m) + 1, [0, m 1]. Furthermore, when = 0, α = 1. As a result, the value mn range of the speedup s [1,1 n n/ m] α +. V. EXPERIMENTAL ANALYSIS The proposed scheme wll be analyzed by two experments n ths secton. A. The Procedure of Topology Constructng Net-Tree s a mxture topology of mesh and tree. It s somewhat smlar to the wreless mesh network, but the topology s wth mult-path, and no specal center nodes. The nterconnecton relatons among nodes are stored n a routng table. The man features of net-tree as descrbed below: 1) It s a tree wth mult-branch; 2) No path among those nodes whch locate n the same level, but dfferent nodes can have publc chld nodes;

6 JOURNAL OF NETWORKS, VOL. 8, NO. 5, MAY ) The mnmum degree of a node s one, whle the maxmum degree s determned by the communcaton capablty of nodes and the layout; 4) The dameter of net - tree s the dstance from the root to the farthest node. In the subsequent part, an example, whch ncludes one gateway and ffteen sensors, s gven to analyze the constructng procedure of net-tree. In the Fg. 5, ths paper presents the processes to construct net-tree network. The Fg.5a s the ntal state of net-tree. In the Fg.5b, the gateway search downstream nodes, but all sensor nodes keep slent untl they receve a call from the gateway. Then, those nodes whch are at the frst level start to search ther downstream nodes. As a result, two nodes establsh nterconnecton wth three downstream nodes whch there s a publc node among, but another has no the second layer node. From the Fg.5b to the Fg.5d, all sensors are nterconnected wth each other. At last, the Fg.5e presents a whole net-tree topology B. Fault Tree Analyss on The net-tree Network The experments are performed on a mult-core computer and the mult-threadng lbrary adopts Pthread. The samples are dvded nto three groups and execute many tmes. The Tab.I shows the expermental results n the dfferent experment desgn. The average values are plotted n the Fg.6 and Fg.7. In the Fg.6, the runnng tme n the dfferent sze of cut sets s plotted based on the dfferent number of threads. The horzontal axs denotes the sze of cut sets and the vertcal axs ndcates the runnng tme. It can be seen that the computng tme of sngle threadng s growng wth the scale of cut sets. When two threads are adopted, the curve s concded wth the curve of sngle threadng at the early stage. The phenomenon shows that the parallel computng cannot shows hs advantages for the small scale cut sets because the tme consumed at the data allocatng and the thread swtchng cannot be gnored. (a) (b) (c) (e) (d) Fgure 5: The Procedure of Topology Constructng TABLE I. EXPERIMENTAL RESULTS tems 1 thread 2 thread 4 thread m=2 m= Fgure 6: Elapsed Tme However, n the Fg.7, wth the ncreasng n the scale of cut sets, the computng tme of mult-threadng s sgnfcantly better than the sngle threadng. That's to demonstrate the parallel algorthm s more sutable to deal wth large scale data. When the number of threads s more than the number of processor cores, threads must be swtched for performng so that the computng tme of four threadng s concded wth the tme of two threadng. In addton, the paper also fnd that the dfferent locaton of true value tem also affects the computng tme when the truth value table s dfferent. Fgure 7: Speedup Tme

7 1166 JOURNAL OF NETWORKS, VOL. 8, NO. 5, MAY 2013 VI. CONCLUSIONS Amng at the shortages of the exstng sensor networks topology structure, ths paper presents a novel structure: net-tree topology wth mult-path. It s composed of mesh and tree, and layout nodes by layer, but no center nodes. The purpose of ths topology s to support self-organzng layout and mult-path routng. To solve the problem whch s to detect the fault nodes n a large scale sensor network, the paper also propose a parallel algorthm based on fault tree analyss. The parallel algorthm s to dvde a large logcal expresson nto multply subsets and compute based on strng matchng. Through the experment analyss, we can see that the method n ths paper can effectvely shorten computng tme. Furthermore, we also deduce the upper and lower lmt value of the speedup. However, the shortage of ths topology structure s no nterconnecton between those nodes whch locate n the same layer. Therefore, t s not sutable to the stuatons n whch many nodes collaborate to do a task. The followup work s to analyss ts topology propertes n depth to optmze the self-organzng strategy. Then, desgn a communcaton protocol base on TCP/IP to evaluate ts routng performance. In addtonal, we also wll contnue to study the modular-based parallel processng, and focus on how to quck dvde a large task nto many modules. In addton, a message passng mechansm among modules s need to be desgned. ACKNOWLEDGMENT The authors are grateful to the anonymous referees for ther valuable comments and suggestons to mprove the presentaton of ths paper. REFERENCES [1] B. M. Yck, Jennfer and D. Ghosal, Wreless sensor network survey, Computer networks, vol. 52, no. 12, pp , [2] L. Janzhong and G. Hong, Survey on sensor network research, Journal of Computer Research and Development, vol. 45, no. 1, pp , [3] A. I. Lug Atzor and G. 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8 JOURNAL OF NETWORKS, VOL. 8, NO. 5, MAY Xnyan Wang (1979-) receved hs M.S. degree n Software Engneerng from Northeastern Unversty, Shenyang, Chna n 2007.She s currently a Ph.D. canddate n school of mechancal electronc and nformaton engneerng n Chna Unversty of Mnng and Technology, Beng, Chna. Her research nterests nclude relable analyses and data processng. Ruxn Zhang (1964-) receved hs Ph.D. degree n mnng engneerng from Chna Unversty of Mnng & Technology, Xuzhou, Chna n 1994.He s a professor n school of mechancal electronc and nformaton engneerng n Chna Unversty of Mnng and Technology, Beng, Chna. Hs research nterests nclude relable analyses, data processng, and nternet of thngs.