TRM-IoT: A Trust Management Model Based on Fuzzy Reputation for Internet of Things

Transcription

1 DOI: /CSIS C TRM-IoT: A Trust Management Model Based on Fuzzy Reputaton for Internet of Thngs Dong Chen 1, Guran Chang 2, Dawe Sun 1, Jaa L 1, Je Ja 1, and Xngwe Wang 1 1 College of Informaton Scence and Engneerng, Northeastern Unversty, Shenyang, Chna chend.2008@gmal.com 2 Computng Center, Northeastern Unversty, Shenyang, Chna chang@neu.edu.cn Abstract. Snce a large scale Wreless Sensor Network (WSN) s to be completely ntegrated nto Internet as a core part of Internet of Thngs (IoT) or Cyber Physcal System (CPS), t s necessary to consder varous securty challenges that come wth IoT/CPS, such as the detecton of malcous attacks. Sensors or sensor embedded thngs may establsh drect communcaton between each other usng 6LoWPAN protocol. A trust and reputaton model s recognzed as an mportant approach to defend a large dstrbuted sensor networks n IoT/CPS aganst malcous node attacks, snce trust establshment mechansms can stmulate collaboraton among dstrbuted computng and communcaton enttes, facltate the detecton of untrustworthy enttes, and assst decson-makng process of varous protocols. In ths paper, based on n-depth understandng of trust establshment process and quanttatve comparson among trust establshment methods, we present a trust and reputaton model TRM-IoT to enforce the cooperaton between thngs n a network of IoT/CPS based on ther behavors. The accuracy, robustness and lghtness of the proposed model s valdated through a wde set of smulatons. Keywords: Internet of Thngs, Cyber Physcal System, Wreless Sensor Network, Trust, Reputaton, Fuzzy Sets. 1. Introducton Cyber-Physcal Systems (CPS) are systems deployed n large geographcal areas and generally consst of a massve number of dstrbuted computng devces tghtly coupled wth ther physcal envronment [1]. CPS and Internet of Thngs (IoT) have always been closely related, snce both of them employ physcal obects and events, ncludng WSNs, RFID-based systems, moble phones, etc. Cyber-Physcal Internet [1], whch can roughly be vewed as a

2 Dong Chen, Guran Chang, Dawe Sun, Jaa L, Je Ja, and Xngwe Wang large-scale unversal network that nterconnects several heterogeneous CPS n order to ensure worldwde nteroperablty of cyber-physcal devces. Therefore, we argue that the proposed fuzzy theory based trust and reputaton model s not only sutable for CPS, but also sutable for IoT. IoT and CPS cannot perceve physcal nformaton from physcal world themselves. Intellgent thngs are usually labeled wth RFID tags or equpped wth sensors and sensors are wdely regarded as the nerve endngs of IoT/CPS [2] [3]. Sensors or sensor embedded thngs can usually form a wreless mult-hoc network-wsn employng ZgBee, W-F, Bluetooth and etc. In a future IoT/CPS, a large number of embedded, possbly moble computng devces wll be nterconnected through WSNs, consttutng varous autonomous subsystems that provde ntellgent servces for end users. IoT/CPS can beneft from WSNs from the perspectve that so far, sensors and RFID readers are the most effcent tools to obtan sensed data from the physcal world, turnng ubqutous computng of IoT/CPS nto a realty. Therefore, Internet connectvty n WSNs of the IoT/CPS s hghly desrable, featurng sensng servces at a global scale all over the world [4]. However, such networks present some new challenges when compared wth tradtonal computer networks, namely n terms of smart node hardware constrants, very lmted computng and energy resources. Unlke other networks usng dedcated nodes to support basc functons lke packet forwardng, routng, and network management, n WSNs of IoT/CPS, those functons are carred out by all avalable nodes. Ths sgnfcant dfference s at the core of the ncreased senstvty to node msbehavor. Due to the wreless nature of ths knd of WSNs, t s also qute possble that a node could be captured by an adversary, whch may lead to ts non-cooperatve behavor or msbehavor wth the rest of the nodes n the network and even become a malcous node. Malcous nodes am at damagng other nodes by causng network outage by parttonng. In order to facltate the detecton of untrustworthy nodes, and assst decson-makng process of varous protocols n a WSN whch s vtal for carryng out specfc tasks as t ads sensors establsh collaboratons, t s necessary to provde a trust and reputaton mechansm for WSNs of IoT/CPS. One strategy to mprove the securty of WSNs s to develop trust mechansms that allow a node to evaluate trustworthness of other nodes [5] [6]. Such trust and reputaton systems not only help n node behavor detecton, but also mprove network performance snce honest nodes can avod workng wth untrustworthy nodes [7]. The measurement and computaton of trust and reputaton to secure nteractons between sensor nodes n IoT/CPS s crucal for the development of trust and reputaton management mechansms. The calculaton and measurement of trust and reputaton n a supervsed ad-hoc envronment nvolves complex aspects such as credble ratng for opnons delvered by a node, the honesty of recommendatons provded by a sensor node, or the assessment of past experences wth the node one wshes to nteract wth. The deployment of sutable algorthms and models mtatng fuzzy logc can help to solve these problems. Therefore, the focus of ths paper s to develop 1208 ComSIS Vol. 8, No. 4, Specal Issue, October 2011

3 TRM-IoT: A Trust Management Model Based on Fuzzy Reputaton for Internet of Thngs a fuzzy theory based trust and reputaton model for IoT/CPS envronment. The proposed theoretcal models are then appled to mprove the performance of routng algorthms and detect node behavors of WSNs n IoT/CPS. The contrbuton of ths paper can be categorzed as follows. (1) Analyss of specal features and unque trust challenges of IoT/CPS; (2) Concepts of trust and reputaton and dscusson of the relatonshp between trust and reputaton n IoT/CPS; (3) A novel fuzzy theory based trust and reputaton management model towards IoT/CPS; (4) Trust evaluaton metrcs, local trust relatonshp evaluaton and global trust relatonshp evaluaton; (5) A wde set of smulatons, performance evaluatons of the proposed fuzzy trust and reputaton management model. The remander of the paper s organzed as follows. We gve an overvew of related nfluental works n Secton 2. In Secton 3, a novel trust and reputaton model for choosng trusted source nodes base on the fuzzy relatonshp theory n fuzzy mathematcs TRM-IoT s proposed and further dscussed n detal n WSNs of IoT/CPS. The smulaton results n Secton 4 show that TRM-IoT model can effectvely prevent malcous and selfsh nodes. TRM-IoT scheme can promote data forwardng and cooperaton between nodes and mprove the performance of the entre network, followed by the concluson and future work of the paper n Secton Related Works Establshng securty communcaton channels based on trust and reputaton models among sensor nodes s an mportant consderaton when desgnng a secure routng soluton n IoT/CPS. ATRM [8] s an agent-based trust and reputaton management scheme for WSNs where trust and reputaton management s carred out locally wth mnmal overhead n terms of extra messages and tme delay. However, snce moble agents are desgned to travel over the entre network and run on remote nodes, they must be launched by trusted enttes. An agent-based trust model for WSN s presented n [9] usng a watchdog scheme to observe the behavor of nodes and broadcast ther trust ratngs. Sensor nodes receve the trust ratngs from the agent nodes, whch are responsble for montorng the former and computng and broadcastng those trust ratngs. In [10], a reputaton-based scheme called DRBTS s proposed to provde a method by whch beacon nodes, BN, can montor each other and provde nformaton so that sensor nodes, SN, can choose who to trust, and based on a quorum votng approach. However, n order to trust a BN s nformaton, a sensor must get votes for ts trustworthness from at least half of ther common neghbors. BTRM-WSN [11] s a bo-nspred trust and reputaton model for WSN amed to acheve to the most trustworthy path leadng to the most reputable node n a WSN offerng a certan servce. Each node must mantan a pheromone trace for each of ts neghbors. CONFIDANT [12] s proposed to extend ComSIS Vol. 8, No. 4, Specal Issue, October

4 Dong Chen, Guran Chang, Dawe Sun, Jaa L, Je Ja, and Xngwe Wang reactve routng protocols wth a reputaton-based system n order to solate msbehavng nodes. Each node montors the behavors of ts next hop neghbors. Trust relatonshps and routng decsons are based on experenced, observed, or reported routng and forwardng behavor of other nodes. SORI [13] scheme s proposed to encourage packet forwardng and dscplne selfsh behavor. The reputaton of a node s quantfed by obectve measures, and the propagaton of reputaton s effcently secured by a oneway-hash-chan-based authentcaton scheme. Watchdog and Pathrater mechansms [14], are ust two extensons to the DSR algorthm. However, not all of the most known works take nto account the strong restrctons about processng, storage or communcaton capabltes. Some of them rely on a watchdog mechansm wth or wthout usng a mult-agent system. IoT/CPS assumes that trllons of thngs whch are used on a daly bass wll eventually be connected to the Internet employng 6LoWPAN [15] protocol and provde ntellgent servce through cooperatng wth each other. Most thngs have the followng sgnfcant characterstcs [16] [17], lmted power capablty, wreless recevers and transmtters wth lmted range facng the use of mult-hop communcaton, moblty (thngs wll move, possbly become dsconnected) and volablty (thngs may be swtched on and off frequently). All the above ssues rase the need for the development of a novel management model, dfferent from those beng n use today. Based on the research of characterstcs of IoT/CPS and n-depth understandng of ATRM [8], ATSN [9], DRBTS [10], BTRM-WSN [11], CONFIDANT [12], SORI [13] and WP [14], we propose a novel trust and reputaton model TRM-IoT to enforce the cooperaton between thngs n a network of IoT/CPS based on ther behavors. 3. TRM-IoT: A Trust Model for IoT/CPS The trust between sensor nodes cannot be set up smply by usng the tradtonal trust mechansms. In a human socal communty, trust between two ndvduals s developed based on the reputaton evaluaton of ther actons over tme. When faced wth uncertanty, ndvduals wll trust and rely on the actons and evaluatons of others who have behaved well n the past. Trust s one of the most fuzzy, dynamc and complex concepts n both socal and busness relatonshps. The dffculty n measurng trust and predctng trustworthness n servce-orented network envronments leads to many problems. These nclude ssues such as how to measure the wllngness and capablty of ndvduals n the trust dynamcs and how to assgn a concrete level of trust to an ndvdual. Wreless networks of IoT/CPS have several salent characterstcs, such as dynamc topologes, bandwdth constrants, varable capacty lnks, energy constraned operaton, and lmted physcal securty. Due to these features, WSNs of IoT/CPS are partcularly vulnerable to all knds of attacks launched through malcous nodes. Unrelable wreless lnks are vulnerable to ammng and 1210 ComSIS Vol. 8, No. 4, Specal Issue, October 2011

5 TRM-IoT: A Trust Management Model Based on Fuzzy Reputaton for Internet of Thngs eavesdroppng. Constrants n bandwdth, computng power, and battery power n moble devces may lead to ther trade-offs between securty and resource consumpton. Dynamcs make t hard to evaluate node behavors, because routes n ths knd of networks change frequently. Sensors or sensor embedded thngs are more lkely to form a wreless mult-hoc network. Therefore, they cannot rely on central authortes and nfrastructures for key management. In ths paper, we propose a generalzed and unfed mechansm to address the trust and reputaton ssue by developng a communty of sensor nodes n the WSNs of IoT/CPS. Our motvaton s to develop a smlar behavor and fuzzy theory-based trust and reputaton model for sensor nodes or sensorembedded nodes, where each node develops a drect reputaton for each other node by makng drect observatons and ndrect reputaton between ndvduals set up upon recommendatons of other ndvduals about these other nodes n the neghborhood. The two knds of reputatons are used together to help a node evaluate the trustworthness of other sensor nodes, detect the malcous nodes, and assst decson-makng wthn the wreless network. The proposed scheme can be employed n any WSNs routng protocol to enforce cooperaton among nodes and counter wth noncooperatve nodes n IoT/CPS nfrastructure. The resource constrants of WSNs such as lmted battery lfetme, memory space and computng capablty n IoT/CPS make t easy to attack and farly hard to protect. Therefore, t s farly crtcal to detect the compromsed nodes n order to avod beng msled by those compromsed or malcous nodes. However, malcous nodes are so dffcult to detect even a cryptography mechansm s appled, snce most low-cost tny sensor nodes are not tamperresstant and easy to be cracked by the adversary. Therefore, n ths paper we argue that behavors-based trust and reputaton mechansm can be used to resolve ths problem effcently. Based on ths motvaton, ths paper proposes a novel behavor-based trust and reputaton for IoT/CPS. The management model of trust and reputaton s related to the creaton, update and deleton of trust and reputaton degree. Frst, an effectve lghtweght authentcaton mechansm must exst to ensure all the denttes are trustworthy [18] [19] [20]. That means the dentty of each sensor node s unque and trustworthy, on the bass of cryptographc prmtves [21] [22] [23]. In fact, we have proposed a novel lghtweght parwse key management scheme towards IoT/CPS n a prevous paper before ths one. Second, the task evaluaton component evaluates the performance of the nodes, ncludng sensors nodes and sensor-embedded devce nodes. The tasks here nclude data processng and routng. Thrd, evaluaton combnaton component s n charge of the result combnaton of the old trust degree and the ndrect nformaton from the thrd node n order to form the new trust degree whch s used n future task allocaton and evaluaton. Throughout ths paper we assume a scenaro where a WSN of IoT/CPS s composed of hundreds of sensor nodes wth relatvely hgh sensor actvty. Wthout loss of generalty, we consder some sensor nodes requestng lghtweght common servces and some nodes provdng these servces. We ComSIS Vol. 8, No. 4, Specal Issue, October

6 Dong Chen, Guran Chang, Dawe Sun, Jaa L, Je Ja, and Xngwe Wang also assume that every sensor node n the WSN only knows ts neghbors and nothng else about the whole topology of the WSN. Addtonally, the topology s consdered to be relatvely hghly dynamc, wth many nodes onng or leavng the communty. The contrbuton of the proposed model s amed to help a sensor node requestng a specfc servce to fnd the most trustworthy route leadng to another sensor node provdng the correspondng servce. An untrustworthy node n ths paper can be consdered ether because t ntentonally provdes a fraudulent servce or because t provdes a wrong servce due to hardware falures or performance deteroraton. In ths paper, takng costly decsons depends on the expectatons created accordng to past behavor of others. Usually, ths knd of nformaton s called reputaton and t s one of the most sgnfcant factors to trust merchants and recommenders towards IoT/CPS Defntons of Trust and Reputaton Although we experence and rely on trust n everyday lfe, t s so challengng to defne trust accurately. The lterature on trust s also qute confusng, snce t manfests tself n farly dfferent forms. In ths paper, we adopt the followng defntons for trust and reputaton. Defnton 1. Trust s the subectve probablty by whch an ndvdual, A, expects that another ndvdual, B, performs a gven acton on whch ts welfare depends [24]. Defnton 2. Reputaton s what s generally sad or beleved about a thng s character or standng [25]. Reputaton exsts only n a communty whch s observng ts members n one way or the other. Accordngly, reputaton s the collected and processed nformaton about one partner s former behavor as experenced by others. Josanga [25] gves a survey of trust and reputaton systems and ponts out that there s sgnfcant dfference between trust and reputaton. Trust s a subectve phenomenon whch s based on varous factors or evdences. In fact, frsthand experence always carres more weght than the secondhand trust recommendaton or reputaton. Snce the nodes n the data collecton layer of IoT/CPS usually are heterogeneous and moble, trust establshment model can sgnfcantly stmulate collaboraton among dstrbuted computng and communcaton enttes, facltate the detecton of untrustworthy enttes, and assst decson-makng process of varous protocols. Based on [24] and [25], we try to gve the followng more detaled trust and reputaton defntons towards IoT/CPS ComSIS Vol. 8, No. 4, Specal Issue, October 2011

7 TRM-IoT: A Trust Management Model Based on Fuzzy Reputaton for Internet of Thngs Defnton 3. In a wreless network of IoT/CPS, a node S s trust n another node P s the subectve expectaton of node S recevng postve outcomes through the transactons wth node P. Defnton 4. In IoT/CPS, a node S s reputaton s the global percepton of ts trustworthness n the wreless network. Furthermore, the trustworthness can be evaluated from ts past and current behavors. Trust n ths paper descrbes the relyng node s trust n a servce or resource provder node and t s relevant when the relyng party s a user seekng protecton from malcous or unrelable servce provders Relatonshp between Trust and Reputaton The term trust and reputaton have strongly lnked meanngs. Especally n WSNs of IoT/CPS, trust s often defned as an abstract acqured attrbute relatve to some sensor nodes whch s due to the amount of reputaton held by such sensor nodes. By makng full use of observng good long-term behavor, reputaton ratngs can be mproved; therefore, trust relatonshps wll be easly establshed [5]. In real-lfe communtes, trust s the consequence of the satsfacton of certan desred propertes [26]. As dscussed n [25], the concept of reputaton s closely lnked to that of trust; however, there s a clear and sgnfcant dfference. A node S can trust n another node P because of ts good reputaton. Lkewse, node S can also trust n node P n spte of ts bad reputaton. Reputaton s usually nspred by the past behavors observed. Trust reflects the relyng party s subectve vew of an entty s trustworthness, whereas reputaton s a score whch can be seen by the whole communty. Note that, n ths paper, trust s consdered as a subectve probablty value whle reputaton s regarded as an obectve and acknowledged value n a specfc communty context Fuzzy Trust Model Descrpton An entty s trustworthness s the qualty ndcator of the entty s servces, whch s used to predct the future behavor of the entty (stored n sensors or sensor-embedded thngs). Intutvely, f t s trustworthy enough, the entty wll provde good servces for future transactons. In most trust models, the doman of trustworthness s assumed to be [0, 1]. Snce the key ssue n nvestgatng fuzzy problems s to establsh membershp functons (membershp degrees) by employng the fuzzy set theory, we have to create the mathematcal model of fuzzy trust frstly [27]. ComSIS Vol. 8, No. 4, Specal Issue, October

8 Dong Chen, Guran Chang, Dawe Sun, Jaa L, Je Ja, and Xngwe Wang Suppose that SN { SN1, SN2,, SN n } s a problem doman of the fuzzy trust model. Note that, SN ( 1,2,, n) s a subset n the correspondng doman. Then we can get the followng mappng, MappngFucton : SN SN [0,1], (1) ( SN, SN ) ( SN, SN ) [0,1]. where ( SN, SN ) represents the degree of trust relatonshp between SN and SN. MappngFuctons a fuzzy relaton mappng from SN SN to [0,1]. In the proposed scheme, a neghbor montorng process s used to collect nformaton of the package forwardng behavors of the neghbors. Each sensor node n the network mantans a data forwardng transacton table as follows, DFT Souce, Destnaton, RF, F, TTL (2),,, where Souce s the trust and evaluaton evaluatng nodes, Destnaton s the evaluated destnaton nodes, RF, denotes the tmes of successful transactons whch node postve transactons. SN has made wth node F SN, and, denotes the 3.4. Trust Evaluaton Metrcs Wthn the realm of IoT/CPS securty, we nterpret the concept of trust as a relaton between enttes stored n sensor nodes that partcpate n varous protocols. Trust relatons are based on evdence or reputaton created by the prevous nteractons of enttes wthn a protocol. Each node employs a neghbor montorng process n order to collect nformaton about the packet forwardng behavors of the neghbor nodes. Furthermore, each node s capable of overhearng the transmssons of ts neghbors n the promscuous mode. Each node ndependently overhears ts neghborng nodes packet forwardng actvtes. Ths overhearng s related to the proporton of correctly forwarded packets wth respect to the total number of packets to be forwarded durng a fxed tme wndow. Then, each node n the network mantans a data forwardng nformaton table. The table ncludes only the data forwardng transacton nformaton by overhearng ts neghborng nodes. In the proposed model, we consder the followng trust evaluaton metrcs for the establshment and valdaton of the proposed trust management model. (1) End-to-end packet forwardng rato (EPFR). EPFR s defned as the rato between the numbers of packets receved by the applcaton layer of destnaton nodes to the numbers of packets sent by the applcaton layer of the source node. The EPFR can be calculated by k RECV EPFR,0 k n. n SEND (3) 1214 ComSIS Vol. 8, No. 4, Specal Issue, October 2011

9 where TRM-IoT: A Trust Management Model Based on Fuzzy Reputaton for Internet of Thngs RECV and SEND denote the packages receved and sent by the -th destnaton node and the -th source node, respectvely. And k denotes the successful recevng tmes, whle n denotes the total tmes of packages sendng. (2) AEC. The key crteron for the desgn of a WSN n IoT/CPS Infrastructure s the energy consumpton. In order to research and analyze the energy consumpton of our TRM-IoT model, we defne the energy consumpton metrc as follows, AEC n 1 consume send recv n 1 where send and recv denotes the energy consumpton when the th sensor node sendng and recevng messages, respectvely. consume denotes the total energy cost of consumpton the trust and reputaton values of the correspondng sensor node. And represents the other energy consumng whch s used to mantan the normal runnng of the node tself. (3) PDR. In fact, the package delvery rato (PDR) s affected by the packet loss and packet retransmssons. Packet loss may occur for many reasons. In ths paper we only focus on the behavor that an ntermedate node ntentonally drops the receved data packets nstead of forwardng them to the next hop node. (4) 3.5. Reputaton Evaluaton Node SN evaluates the reputaton of node SN wth whch t tres to make transactons by ratng each package forwardng process as ether postve or negatve, dependng on whether SN has completely done the transacton correctly. As dscussed above, we use Con to descrbe the evaluaton of the whole metrcs n order to udge whether ths transacton s successful. The Con can be computed by Con EPFR, AEC, PDR EPFR AEC PDR (5) where,, represent the correspondng aspect weghts of the dfferent resources. We also defne a parameter Sat to descrbe the satsfacton Threshold degree. That means, f Con Sat, then t ndcates that node SN get a Threshold negatve reputaton evaluaton to nodesn ; f Con Sat, t ndcates that Threshold node SN gets a postve reputaton evaluaton to node SN. The reputaton evaluaton of all nteractons from node defned as follows, SN to node SN s ComSIS Vol. 8, No. 4, Specal Issue, October

10 Dong Chen, Guran Chang, Dawe Sun, Jaa L, Je Ja, and Xngwe Wang F, [0,1]. (6) RF, Reputaton evaluaton s the bass of trust management. In our trust model, the reputaton s evaluated consderng three metrcs, EPFR, AEC and PDR. Compared wth other reputaton evaluaton methods, we consder more factors whch can more accurately evaluate the behavors of nodes accordng to specfc characterstcs of IoT/CPS Local Trust Evaluaton From dfferent ponts of vew, trust can usually be categorzed nto dfferent classes: drect trust and ndrect trust. When we say node SN s trustworthy or untrustworthy for the node SN, t means that there must be a trust and reputaton model between node SN and node SN. If a trust relatonshp statement s based on the reputaton of drect observatons on nodesn, the correspondng model mentoned above s the drect trust model. Snce the drect trust relatonshp also has some sgnfcant fuzzy propertes, we can descrbe the drect trust model employng the fuzzy theory. Accordng to the data forwardng transacton table, fuzzy reputaton membershp based drect trust model can be defned as d T (7), (1 ) RF, where denotes the weght of the past negatve behavor that can be regulated to punsh the malcous node acton. represents the uncertanty trust for the weght value. Snce the behavor of a node s not always constant but often changes n tme and volatlty, t s sgnfcant that the recent events are more credble d than the hstorcal events. Let T ( t 1) be the most recent trust evaluaton, and d T ( t ) be the past trust evaluaton durng a tme nterval t. We, d combne the recent events and hstorcal events to update T, () t : d d d T ( t) T ( t 1) T ( t ),, 1, 2, 1 1, [0,1] Therefore, the new trust of T () t s dependent on the three factors,, d d T ( t 1), T ( t) and. Then we can get the local trust updatng equaton,,, (8) 1216 ComSIS Vol. 8, No. 4, Specal Issue, October 2011

11 TRM-IoT: A Trust Management Model Based on Fuzzy Reputaton for Internet of Thngs d 1 d 1 d T, ( t) (1 ) T, ( t 1) T, ( t ). (9) 2 2 However, t s arbtrary and dffcult to decde whether a moble sensor node behavor s good or bad only based on a few nteractons. Therefore, we must have an nteracton threshold value of nteracton tmes C threshold. Consequently, the fuzzy drect trust evaluaton can be computed by 1 (1 ), RF C, threshold 2 Cthreshold d T, (10), RF C, threshold (1 ) RF, When node SN and node SN has no drect relatonshp and cannot establsh drect communcaton channel to exchange data, node SN can evaluate the trust of node SN based on the recommendaton trust of a thrd party node SN. k As s dscussed n [28], the recommendaton trust and reputaton model can be dvded nto two categores, transtvty and consensus recommendaton trust and reputaton management models. The fuzzy transtvty recommendaton trust and reputaton model defnes a degree of recommendng relatonshp between node SN and node SN. RR denotes the number of request recommendatons, and HR,, represents the number of the postve recommendatons. CR threshold s defned as threshold value of the recommendaton tmes. Therefore, the membershp functon for fuzzy recommendaton trust model s defned as: 1 (1 ), RR, CRthreshold 2 CRthreshold r Tk, (11), RR, CRthreshold (1 ) RR, HR, where [0,1]. RR, The dfferent sensor nodes may provde dverse recommendatons on the same nodes. That means, dfferent nodes may have the dfferent or even opposte trust evaluatons towards the same sensor node. Assume that node r SNk gves the recommendaton trust evaluaton of Tk, and nodesnt provdes r the recommendaton trust evaluaton of T t, to node SN. Also there have two drect trust relatonshps between node SN and node SN k, node SN and node SN, respectvely. t ComSIS Vol. 8, No. 4, Specal Issue, October

12 Dong Chen, Guran Chang, Dawe Sun, Jaa L, Je Ja, and Xngwe Wang Here we combne the two recommendaton trust evaluaton and the two drect trust evaluatons to make a relatvely obectve assessment for node SN, T, ( D( SN, SNk ) R( SNk, SN )) ( D( SN, SNt ) R( SN, SN )), SN, SN SN. t k t (12) Therefore, n a smlar way, the fuzzy membershp functon of n-level fuzzy consensus recommendaton trust and reputaton model can be defned as T ( R D) ( R D) ( R D) (13), n In concluson, the fuzzy local trust relatonshp can be calculated through the combnaton based on drect and ndrect trust evaluaton by d d r T X T Y,, T T,,,1 Y X 0 k k k (14) X Y 1 where X, Y denotes the weght of drect trust value and ndrect trust value n the whole fuzzy local trust value, respectvely. Note that, 1 Y X 0 means that compared wth the ndrect recommendatons, our fuzzy local trust evaluaton s more focused on the drect observatons. Snce nodes n IoT/CPS may dynamcally on n the WSNs and qut the WSNs, t stands to reason that the long hstorcal recommendatons should have relatvely small weghts n the Equaton (14) Global Trust Evaluaton In fact, node SN may have not only the drect observaton on the node SN, but also ndrect experences by askng ts acquantances. Therefore, there are two fuzzy trust models between node SN and node SN, fuzzy drect trust model and fuzzy ndrect trust model. Obvously, f a node wants to obtan more accurate trust value wth another node, t must ntegrate more drect and ndrect experences. Note that, the drect trust may vary wth tme. In order to get the most accurate trust value, we must dscover the most wde ndrect trust set. In ths paper, the fuzzy global trust relaton s defned as a unon of fuzzy drect trust relaton, 1-level fuzzy ndrect trust relatonshp, 2-levels ndrect trust relatonshp, and n- levels fuzzy ndrect trust relaton ( n ). Let us consder an example of the fuzzy trust relatonshp evaluaton between node SN and node SN n a communty of (n+16) nodes, as shown n Fg.1. In the example the source node SN has fve routes to the destnaton node SN. If we want to obtan the most accurate trust evaluaton between them, all of the fve routes must be contaned and evaluated. Therefore, the fuzzy global trust relatonshp evaluaton can be calculated by 1218 ComSIS Vol. 8, No. 4, Specal Issue, October 2011

13 TRM-IoT: A Trust Management Model Based on Fuzzy Reputaton for Internet of Thngs 2 3 n T D R D R D R D R D, 2 3 n ( SN R R R R ) D. (15) D Source Node D R a D R b c Destnaton Node D R R 2 R 3 d e f R 2 D R g h n +1 Nodes Fg. 1. Illustraton of the fuzzy global trust relatonshp evaluaton between node SN and node SN Obvously, node SN can make the fuzzy global trust evaluaton to node SN whch s computed as, 2 n T lm ( SN R R R ) D., n (16) WSNs of IoT/CPS have dynamc topologes, bandwdth constrants, varable capacty lnks, energy constraned operaton, and lmted physcal securty. Dynamcs make t hard to evaluate behavors, because routes n ths knd of network change frequently. In ths case, fuzzy global trust evaluaton reflects the past nteractons of the communty wth the correspondng node beng evaluated. Ths evaluaton s globally avalable to all member nodes of the communty and updated each tme a member node ssues a new evaluaton of a sensor node. R n 4. Smulaton and Dscusson 4.1. NS-3 Setup In ths paper, we perform our smulaton on a NS-3 smulator [29]. Every plot s taken as an average of ten dfferent runs. And each run s executed wth source and destnaton pars selected randomly from the WSN. Snce we rely on TCP acknowledgments and retransmsson as ndcatons of successful and faled package delvery events, respectvely, we employ AODV protocol [30] as the communcaton protocol n our smulaton. The ComSIS Vol. 8, No. 4, Specal Issue, October

14 Dong Chen, Guran Chang, Dawe Sun, Jaa L, Je Ja, and Xngwe Wang NS-3 setup parameters and model confguraton parameters are lsted n Table 1 and Table 2. Table 1. NS-3 Setup Parameters. Parameter Value Smulator NS-3 MAC Layer IEEE Nodes Number 300 Node Placement Random, unform Package Sze 512 bytes Maxmum Connecton 30 Transmsson Range 250 Applcaton Traffc CBR Table 2. Model Confguraton Parameters. Parameter Value t 0.5 C threshold 12 CR threshold 12 Reply Delay 60ms Note that, snce the maxmum connecton number of a servce node s no more than 30, C threshold and CR threshold have to be ntalzed as a value whch s no more than 0.5 (0 MAX _ Connectons ) 15. Hgher value of the two parameters wll reduce the success rate of recommendatons from neghbor nodes. In ths smulaton experment, we dvde the sensor nodes nto two types, good nodes and malcous nodes. Moreover, accordng to the behavor n route dscovery, route mantenance and data forwardng, malcous nodes can be dvded nto two categores further. For the frst type (Type 1): the malcous nodes do not perform the package forwardng functon; for the second type (Type 2), the malcous nodes do not partcpate n the route dscovery phase. Those malcous nodes are selected randomly n each run accordng to the setup percentage, as shown n Fg.2. The trust and reputaton relatonshp s ntalzed randomly at the very begnnng of smulaton. Therefore, after several rounds, we establsh a smlar behavor and fuzzy theory-based trust and reputaton model for WSNs of IoT/CPS, where each node develops a drect reputaton for each other node by makng drect observatons and ndrect reputaton between 1220 ComSIS Vol. 8, No. 4, Specal Issue, October 2011

15 TRM-IoT: A Trust Management Model Based on Fuzzy Reputaton for Internet of Thngs ndvduals whch are set up on recommendatons of other ndvduals about these other nodes n the neghborhood. Fg. 2. The random dstrbuton of malcous and msbehavor nodes n the smulatons EPFR End-to-end packet forwardng rato (EPFR) s defned as the rato between the number of packets receved by the applcaton layer of destnaton nodes to the number of packets sent by the applcaton layer of the source node. As dscussed n [28], ths parameter sgnfcantly reflects the effect on the drop rato, the path nterrupton repar, sendng buffer overflow, nterface queue overflow, the conflct MAC packet and end-to-end packet n the process of data packet. The lost packets cover all packet losses due to drops, route falures, congeston and wreless channel losses. As shown n Fg. 1, some sensor nodes are set to be malcous nodes randomly. The percentage of malcous sensor nodes s ncreased and taken values from 10% to 60%, whle other nodes of the network behave benevolently. The results ndcate that some ndvdual selfsh nodes obvously result n the lnear regresson of EPFR. Therefore, the secure mechansms manly focus on Type 1 to correctly perform the packet forwardng functon. When 60% of the nodes follow Type 1 and Type 2, EPFR degrades by 53% and 82%, respectvely. However, when the number of normal nodes becomes so smaller to a certan degree, such as 50%, the correspondng EPFR wll decrease sgnfcantly. ComSIS Vol. 8, No. 4, Specal Issue, October

16 Dong Chen, Guran Chang, Dawe Sun, Jaa L, Je Ja, and Xngwe Wang Fg. 3. The relatonshp between EPFR and dfferent percentage of malcous nodes. As shown n Fg. 3, EPFR can be degraded by malcous nodes. Through employng the proposed behavor-based trust and reputaton model, the WSNs of IoT/CPS performance can be enhanced, snce t enables the best loyal route selecton process to avod askng the less trustworthy nodes to forward messages. By examnaton of EPFR, we can see mprovements by BRM-IoT under attacks of type 1 and 2, compared to the orgnal AODV protocol. Moreover, as the percentage of malcous nodes ncreases, Type 2 has a less obvous nfluence on EPFR than Type I AEC As shown n Fg. 4, we make malcous nodes whch change between 10% and 60% of the sensor nodes n the network, ncreasng 10% for each runnng of the experment, whle the other nodes of the network behave vrtuously. Snce any malcous node does not partcpate n the route dscovery phase of the AODV protocol or t not be honestly execute data packets forwardng, AEC of malcous nodes s less than that of other normal nodes. The expermental results show that even f ndvdual malcous nodes of type 1 serously affect the network performance, the trust and reputaton mechansm, whch prompts the tmes of nodes transmttng data packets, s the basc securty need for the non-malcous routng n WSNs. TRM-IoT model effectvely cubes the malcous nodes, and sgnfcantly reduces the energy consumpton of good sensor nodes ComSIS Vol. 8, No. 4, Specal Issue, October 2011

17 TRM-IoT: A Trust Management Model Based on Fuzzy Reputaton for Internet of Thngs Fg.4. The relatonshp between AEC and dfferent percentages of malcous nodes Package Delvery Rato Fg. 5 shows a comparson between the proposed trust and reputaton scheme for IoT/CPS, TRM-IoT, and two exstng trust models based on reputaton mechansms, namely DRBTS [10] and BRTM-WSN [11], n terms of PDR. In fact, package delvery rato (PDR) s affected by packet loss and packet retransmssons. Packet loss may occur for many reasons. In ths paper, we focus on the behavor that an ntermedate node ntentonally drops receved data packets nstead of forwardng them to the next hop node. From Fg. 5, we can see that the proposed trust and reputaton model outperforms the other two schemes especally at hgher loads on the network. Fg. 5. The relatonshp between load and package delvery rato. ComSIS Vol. 8, No. 4, Specal Issue, October

18 Dong Chen, Guran Chang, Dawe Sun, Jaa L, Je Ja, and Xngwe Wang 4.5. Convergence Speed Convergence speed (CS) s defned as the least number of cycles requred makng the number of the faled data forwardng transacton. That s, the greater of the CS, the more unfar represents that f a trust model works, the good nodes can be dfferentated from the msbehavor nodes by ther trust values after a few transacton cycles [31]. At the begnnng, all sensor nodes have the same ntal trust value, and the source sensor nodes randomly select a node for data packet forwardng. After a small numbers of transactons, the good nodes can get the hgher trust value than the other bad malcous nodes. Fg. 6. The relatonshp between cycles and convergence speed. The falure numbers of all data forwardng packets of the normal nodes reflect CS wth the change of the smulaton cycles. Snce nodes always select the nodes wth the hgher trust values, the fewer cycles the faster the convergence of the model. Fg. 6 descrbe TRM-IoT almost completely elmnates the falure of data packet forwardng after the frst eght cycles n WSNs. However, selfsh nodes of Type 1 ntentonally drop the receved packets nstead of forwardng them, and ncrease n the falure rato of the normal packet forwardng ncreasng. The system s not the very good convergence, and has slow convergence speed n comparson wth the selfsh nodes of Type Detecton Probablty Detecton Probablty (DP) ndcates that whether a trust and reputaton model can better handle ncorrect recommendatons from the thrd party. In Fg. 7, BRTM-WSN [11] model performs better than DRBTS [10] model. Ths s 1224 ComSIS Vol. 8, No. 4, Specal Issue, October 2011

19 TRM-IoT: A Trust Management Model Based on Fuzzy Reputaton for Internet of Thngs because BRTM-WSN model can better handle ncorrect recommendatons from the thrd party. Moreover, TRM-IoT model performs well than the other two exstng models. Ths s manly because TRM-IoT model consders the possble estmaton error when evaluatng the trust and reputaton values. Therefore, compared wth the two other exstng models, our model, TRM-IoT, has better performance. Fg. 7. The relatonshp between false probablty and detecton probablty. 5. Concluson and Future Works Snce WSNs are to be completely ntegrated nto Internet or Next Generaton Internet as a core part of IoT/CPS, t s necessary to consder varous securty challenges that come wth IoT/CPS, such as the detecton of malcous attacks. A trust and reputaton model s recognzed as an mportant approach to defend a large dstrbuted sensor networks n IoT/CPS aganst malcous node attacks, snce trust establshment mechansms can stmulate collaboraton among dstrbuted computng and communcaton enttes, facltate the detecton of untrustworthy enttes, and assst the decson-makng process of varous protocols. Based on n-depth understandng of trust establshment process and quanttatve comparson among trust establshment methods, ths paper present a trust and reputaton model TRM-IoT to enforce thngs cooperaton n a WSN of IoT/CPS based on ther behavors. The potental benefts of employng fuzzy sets to manage trust and reputaton relatonshps are analyzed accordng to the excellent NS-3 smulatons. Although the proposed model TRM-IoT has better performance compared wth two other exstng models, we have ncreasngly aware of the necessty ComSIS Vol. 8, No. 4, Specal Issue, October

20 Dong Chen, Guran Chang, Dawe Sun, Jaa L, Je Ja, and Xngwe Wang of elmnatng the nfluence upon the evaluaton results affected by malcous recommendaton and defamaton behavors of the thrd party. The mechansm by whch global trust s updated whle local trust changes can be mproved n order to be more effcent n future works. Acknowledgement. Ths work s supported by the Natonal Natural Scence Foundaton of Chna under Grant No and the Fundamental Research Funds for the Central Unverstes under Grant No. N References 1. Wolf, W.: Cyber-Physcal Systems. Computer, Vol. 42, No. 3, (2009) 2. Zhu, Q., Wang, R. C., Chen Q., Lu Y., Qn W. J.: IOT Gateway: Brdgng Wreless Sensor Networks nto Internet of Thngs. In Proc. of 2010 IEEE/IFIP Internatonal Conference on Embedded and Ubqutous Computng, USA, CA, Los Alamtos, (2010) 3. Khoo, B.: RFID- from Trackng to the Internet of Thngs: A Revew of Developments. In Proc. of 2010 IEEE/ACM Int'l Conference on Green Computng and Communcatons & Int'l Conference on Cyber, Physcal and Socal Computng, Hangzhou, (2010) 4. Joel, J. P. C. Rodrgues, Paulo, A. C. S. Neves.: A survey on IP-based wreless sensor network solutons. Internatonal Journal of Communcaton Systems, Vol. 23, Issue 8, (2010) 5. Sun, Y. L., Yang, Y.: Trust establshment n dstrbuted networks: Analyss and modelng. In Proc. of 2007 IEEE Internatonal Conference on Communcatons, ICC 07, Unted Kngdom, Glasgow, (2007) 6. Sun, Y., Yu, W., Han, Z., Lu, K.: Trust modelng and evaluaton n ad-hoc networks. In Proc. of Global Telecommuncatons Conference 2005 GLOBECOM 05, Vol. 3, (2005) 7. Buttyan, L., Hubaux, J. P.: Stmulatng cooperaton n self-organzng moble ad hoc networks. Moble Networks and Applcatons, Vol. 8, No. 5, (2003) 8. Boukercha, A., Xua, L., EL-Khatbb, K.: Trust-based securty for wreless ad hoc and sensor networks. Computer Communcatons Vol. 30, Issues 11-12, (2007) 9. Chen, H. G., Wu, H. F., Zhou, X. and Gao, C. S.: Agent-based Trust Model n Wreless Sensor Networks. In Proc. of the Eghth ACIS Internatonal Conference on Software Engneerng, Artfcal Intellgence, Networkng and Parallel/Dstrbuted Computng, (2007) 10. Srnvasan, A., Tetelbaum, J. and Wu, J.: DRBTS: Dstrbuted Reputaton-based Beacon Trust System. In Proc. of 2nd IEEE Internatonal Symposum on Dependable, Autonomc and Secure Computng (DASC'06), (2006) 11. Marmol, G., Perez, M.: Provdng trust n wreless sensor networks usng a bonspred technque. Telecommuncaton Systems, Vol. 46, Number 2, pp (2010) 12. Buchegger, S., Boudec, J. Y. L.: Performance analyss of the confdant protocol. In Proc. of MobHoc 02: Proceedngs of the 3rd ACM nternatonal symposum on Moble Ad-hoc networkng & computng, ACM, New York, NY, USA, (2002) 1226 ComSIS Vol. 8, No. 4, Specal Issue, October 2011

21 TRM-IoT: A Trust Management Model Based on Fuzzy Reputaton for Internet of Thngs 13. He Q., Wu D., Khosla P.: Sor: A secure and obectve reputaton-based ncentve scheme for ad-hoc networks. In Proc. of 2004 Wreless Communcatons and Networkng Conference, Vol. 2, 21-25, (2004) 14. Zhong, S., Chen, J., Yang, Y.: Sprte: a smple, cheat-proof, credt-based system for moble ad-hoc networks. In Proc. of INFOCOM 2003, Twenty- Second Annual Jont Conference of the IEEE Computer and Communcatons Socetes, Vol. 3, (2003) 15. Hu, J. W., Culler, D. E.: Extendng IP to Low-Power Wreless Personal Area Networks. IEEE Internet Computng, Vol. 12, No. 4, (2008) 16. Lekenbrock, D.: The Internet of Thngs State-of-the-Art and Perspectves for Future Research. Communcatons n Computer and Informaton Scence, Vol. 32, No. 2, (2009) 17. Lug, A., Antono, I., Gacomo, M.: The Internet of Thngs: A survey. Computer Networks, Vol. 54, No. 15, (2010) 18. Eshenauer, L., Glgor, V. D.: A Key-Management Scheme for Dstrbuted Sensor Network. In Proc. of 9th ACM Conf. Computer and Comm. Securty (CCS 02), Unted states, Washngton, DC, (2002) 19. Lu, D., Nng, P., Rongfang, L. I.: Establshng Parwse Keys n Dstrbuted Sensor Networks. ACM Transactons on Informaton and System Securty, Vol. 8, No. 1, (2005) 20. Zhu, S., Sea S., Jaoda S.: LEAP+: Effcent Securty Machansms for Large- Scale Dstrbuted Sensor Networks. ACM Transactons on Sensor Networks, Vol. 2, (2006) 21. Lu, F., Cheng, X., Ma, L., Xng, K.: SBK: A Self-Confgurng Framework for Bootstrappng Keys n Sensor Network. IEEE Transactons on Moble Computng, Vol. 7, No. 7, (2008) 22. Loree, P., Nygard, K., Du, X. J.: An Effcent Post-Deployment Key Establshment Scheme for Heterogeneous Sensor Networks. In Proc of 2009 Global Telecommuncatons Conference, GLOBECOM 2009, Unted states, HI, Honolulu, 1-6. (2009) 23. Sun, Y., Trappe, W., Lu, K. J. R.: A scalable multcast key management scheme for heterogeneous wreless networks. IEEE/ACM Transactons on Networkng, Vol. 12, No. 4, (2004) 24. Gambetta, T.: Can we trust trust? In: D. Gambetta (Ed.), Trust: makng and Breakng Cooperatve Relatons, Basl Blackwell, Oxford, (1990) 25. Josang, A., Ismal, R., Boyd, C.: A survey of trust and reputaton systems for onlne servce provson. Decs. Support Syst, Vol. 43, No. 2, (2007) 26. Better Busnes Bureau. [Onlne]. Avalable: Azzedn, F., Rdha, A., Rzv, A.: Fuzzy trust for peer-to-peer based systems. In Proc. of World Academy of Scence, Engneerng and Technology, Vol. 21, (2007) 28. Luo, J. H., Lu, X., Fan, M. Y.: A trust model based on fuzzy recommendaton for moble ad-hoc networks. Computer Networks, Vol. 53, (2009) 29. NS-3. [Onlne]. Avalable: Perkns, C., Beldng-Royer, E., Das, S.: Ad hoc on-demand dstance vector (AODV) routng, IETF RFC 3561, July Grffths, N., Chao, K. M., Younas, M.: Fuzzy trust for peer-to-peer systems. In Proc. of 26th IEEE Internatonal Conference on Dstrbuted Computng Systems Workshop, Portugal, Lsboa, (2006) ComSIS Vol. 8, No. 4, Specal Issue, October

22 Dong Chen, Guran Chang, Dawe Sun, Jaa L, Je Ja, and Xngwe Wang Dong Chen s a PhD canddate at the school of Informaton Scence and Engneerng, Northeastern Unversty, Shenyang, Chna. He receved hs MSc n Computer Scence from Northeastern Unversty n Hs current researches nterests nclude Internet of Thngs, Cyber Physcal System. Guran Chang receved hs Ph.D. degree n electrcal engneerng from the Unversty of Ten-nessee, Knoxvlle, Tennessee n He s currently a Professor at the computng center of Northeastern Unversty, Chna. Hs current research nterests nclude computer networks, Internet of Thngs and nformaton securty. Dawe Sun s a PhD canddate at the school of Informaton Scence and Engneerng, North-eastern Unversty, Chna. He receved hs MSc n Computer Scence from Northeastern Unversty n Hs current researches nterests nclude cloud computng and vrtualzaton technology. Jaa L s a PhD canddate at the school of Informaton Scence and Engneerng, Northeastern Unversty, Chna. He receved hs MSc n Computer Scence from Northeastern Unversty n Hs current researches nterests nclude Spatal-Temporal Database and XML Database. Je Ja receved her Ph.D degree n computer scence from Northeastern Unversty, Chna. She s currently an Assocate Professor at the School of Informaton Scence and Engneerng, Northeastern Unversty, Chna. Her research nterests are manly on RFID systems and Wreless Sensor Network. Xngwe Wang receved hs Ph.D degree n computer scence from Northeastern Unversty, Chna n He s currently a Professor at the School of Informaton Scence and Engneerng, Northeastern Unversty. Hs research nterests are manly on routng algorthms and protocols, moblty management n NGI. Receved: March 3, 2011; Accepted: Aprl 22, ComSIS Vol. 8, No. 4, Specal Issue, October 2011