Trusted cognitive radio networking

WIRELESS COMMUNICATIONS AND MOBILE COMPUTINGWirel. Commun. Mob. Comput. (2009)Published online in Wiley InterScience(www.interscience.wiley.com) DOI: 10.1002/wcm.777

Trusted cognitive radio networking

Kwang-Cheng Chen1∗, Peng-Yu Chen2, Neeli Prasad2, Ying-Chang Liang3 and Sumei Sun3

1Institute of Communications Engineering and Department of Electrical Engineering, National TaiwanUniversity, Taipei, Taiwan, ROC2Center for TeleInFrastruktur, Aalborg University, Aalborg, Denmark3Institute for Infocom Research, A-STAR, Singapore

Summary

Networking cognitive radios and nodes from primary system (PS) results in a heterogeneous coexisting multi-radio wireless network, so that significant network throughput gain can be achieved. However, by investigatingcognitive radio network (CRN) architecture, the links in CRNs are unlikely to support complete security check dueto link dynamics, opportunistic availability, and uni-directional in available time window. We therefore introducetrusted cognitive radio networking (TCRN) concept to facilitate network functions such as association in dynamicspectrum access and routing. First of all, we explore the mathematical framework for trust in CRNs. We then showsuccessful association of node to CRN based on the mathematical structure of trust from statistical decision theory.Furthermore, we modify the machine-learning algorithm to update the trust measure for each node, and developrules of thumbs to facilitate TCRN with learning capability, based on numerical simulations. Trusted CRN cangreatly alleviate heterogeneous challenge for CRN operation. Copyright © 2009 John Wiley & Sons, Ltd.

KEY WORDS: cognitive radio; cognitive radio networks; trust; trusted networking; cognitive radio networkarchitecture; statistical decision

1. Introduction

The concept of cognitive radio (CR) was pioneeredby J. Mitola, III, and has been widely discussed inliteratures [1–3]. Its fundamental idea is that radiocommunications are possible by using the spectrumholes of primary system(s) at a given frequency band.However, after such a radio link construction to trans-port packets from one transmitter to another receiver,methodology to transport these packets to destinationthrough appropriate routing protocols and networking

∗Correspondence to: Kwang-Cheng Chen, Institute of Communications Engineering and Department of Electrical Engineering,National Taiwan University, Taipei, Taiwan, ROC.

†E-mail: [email protected]

functions to meet quality of services remains a wide-open research area, where we call such scenario ascognitive radio networking to form a cognitive radionetwork (CRN) by considering nodes of both pri-mary system(s) and cognitive radios. It is obvious thatCRN is a dynamically and stochastically organized net-work with both ad hoc networking and heterogeneousnetworking features, except much more dynamicsin CRN topology and link availability to makestatic security and traditional networking functionsinfeasible.

Copyright © 2009 John Wiley & Sons, Ltd.

K.-C. CHEN ET AL.

1.1. The Need of Trust in Cognitive RadioNetworks

It is obvious that CRN is a temporarily organized net-work with both ad hoc networking and heterogeneousnetworking features, except much more dynamics inCRN topology and link availability to make static secu-rity and traditional networking functions infeasible.CRN surely can be considered through 7-layer OSImodel like all wireless networks and Internet appli-cations. From the chapters regarding network layerfunctions, trust plays one of the key parameters in facil-itation of CRN, due to its role to glue the nature ofheterogeneous (wireless) networks in CRN.

There are two important steps that need trust in CRNoperations: association that makes dynamic spectrumaccess realistic, and routing, as the major focusingissues hereafter. When a CR initially tries to connecta node to join an existing (cognitive radio) network orto form a CRN, it is practically not possible to executeconventional security functions, as

� Such an action may create security holes.� It is not wise to consume huge computation power

for security without making sure it is a valid requestto form CRN.

� There is not enough available time for opportunisticCRN link to exist, for a complicated hand-shakingsecurity protocol.

Trusted mechanism is therefore needed, whileauthentication is a part of trust along with other tech-nical or non-technical factors. The next challengehappens when a node in CRN routes the traffic throughanother node or some part of another network. Typi-cal ad hoc networks and sensor networks using publickey infrastructure (PKI) scheme achieve secure routingand other purposes in literatures [4–9]. Such a CRNnode under the request of routing packet(s) may not beable to execute security like typical PKI scheme in adhoc networks or sensor networks, due to not practicalto perform checking under limited communication andcomputation resources and due to increasing possibil-ity of being attacked. Consequently, trusted networkingand update of trust measure can be very useful to reacha compromise facing these technical challenges. Oncea node passing trust evaluation that must be quick andsomewhat reliable, it can function as a node in CRNto transport/relay packets, though other security andservice mechanisms are still executing at later appro-priate timings, which acts as the fundamental spiritbehind the CRN network functions by treating trusted

cognitive radio networking (TCRN) as allowing quickexecution of network functions in such heterogeneouswireless networking environment without completingentire secure encryption procedure. Nodes in CRN withthe same level of trust can therefore operate similar toad hoc networks, except opportunistic available linksand likely uni-directional links as given in Section 2and Reference [10].

1.2. Organization of the Paper

The rest of this paper is organized as follows. Section 2describes the CRN architecture and the links in CRN.Section 3 explores the framework of trust in CRN. Weexplain the operation of trusted association and trustedrouting in Section 4. Update of trust based on machinelearning is introduced in Section 5. Section 6 concludesthis paper.

2. CRN Architecture

It has been widely recognized that the cognitive radiocan efficiently improve spectrum utilization at linklevel. We also demonstrate that cooperative relayamong CRs and nodes in PS can greatly enhance thenetwork capacity by constructing a general sense CRN[11]. It suggests that a cognitive radio shall sense avail-able networks and communication systems around it, tocomplete networking functions beyond utilizing spec-trum hole at link level. Thus, the CRNs are not justanother network with interconnecting cognitive radios.The CRNs are composed of various kinds of co-existingmulti-radio communication systems, including cog-nitive radio systems. CRNs can be viewed as somesort of heterogeneous networks composed of variouscommunication systems. The heterogeneity exists inwireless access technologies, networks, user terminals,applications, service providers and so on. The designof cognitive radio network architecture is toward theobjective of improving network utilization. From theusers’ perspective, the network utilization means thatthey can always fulfill their demands anytime and any-where through accessing CRNs. From the operators’perspective, they cannot only provide better servicesto mobile users, but also allocate radio and networkresources in a more efficient way.

2.1. CRN Structure

Following the efforts in References [12–14] and abovediscussions, we may conclude that CRN is a sort of

Copyright © 2009 John Wiley & Sons, Ltd. Wirel. Commun. Mob. Comput. (2009)

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TRUSTED COGNITIVE RADIO NETWORKING

Fig. 1. Infrastructure architecture of CRN.

cooperative (relay) networking and heterogeneous net-working. Although CRN can be generally consideredas a multi-hop wireless network, CRN, heterogeneousin nature, is fundamentally different from a homoge-neous wireless ad hoc network. CRN can be deployedin network-centric, distributed, ad hoc and mesh archi-tectures, and serve the needs of both licensed and unli-censed applications. The basic components of CRNsare mobile station (MS), base station/access point(BSs/APs) and backbone/core networks. These threebasic components compose three kinds of networkarchitectures in the CRNs: infrastructure, ad hoc andmesh architectures, which are introduced as follows.

2.1.1. Infrastructure architecture

In the infrastructure architecture (Figure 1), an MScan only access a BS/AP in the one-hop manner.MSs under the transmission range of the same BS/APshall communicate with each other through the BS/AP.Communications between different cells are routedthrough backbone/core networks. The BS/AP maybe able to run one or multiple communication stan-dards/protocols to fulfill different demands from MSs.A cognitive radio terminal can also access various kindsof communication systems through their BS/AP.

2.1.2. Ad hoc architecture

There is no infrastructure support in ad hoc architec-ture (Figure 2). The network is set up on the fly. If anMS recognizes that there are some other MS nearbyand are connectable through certain communicationstandards/protocols, they can set up a link and thusform an ad hoc network. Note that this links betweennodes may be set up by different communication tech-nology. In addition, two cognitive radio terminals caneither communicate with each other by using exist-ing communication protocols (e.g., WiFi, Bluetooth)or dynamically using spectrum holes.

Fig. 2. Ad hoc architecture of CRN.

Fig. 3. Mesh architecture of CRN.

2.1.3. Mesh architecture

This architecture (Figure 3) is a combination of infras-tructure and ad hoc architectures plus enabling thewireless connections between BSs/APs. This networkarchitecture is similar to the Hybrid Wireless MeshNetworks. In this architecture, BSs/APs work as wire-less routers and form wireless backbones. MSs caneither access the BSs/APs directly or use other MSsas multi-hop relay nodes. Some BSs/APs may connectto the wired backbone/core networks and function asgateways. Since BSs/APs can be deployed without nec-essarily connecting to wired backbone/core networks,it is more flexible and less cost in planning the locationsof BSs/APs. If the BSs/APs have cognitive radio capa-bilities, they may use spectrum holes to communicatewith each other. Due to the inefficiency of current spec-trum utilization, there may be lots of spectrum holesdetected. So the capacity of wireless communicationlinks between cognitive radio BSs/APs may be largeand it makes the wireless backbone feasible to servemore traffics.

2.2. Links in CRN

We may recall two kinds of wireless communicationsystems in CRNs: Primary System (PS) and CognitiveRadio (CR) System, which are classified by their prior-ities on frequency bands. A PS is referred as an existingsystem which operates in one or many fixed frequency


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K.-C. CHEN ET AL.

bands. Various kinds of primary systems work either inlicensed or unlicensed bands, either in the same geo-graphical location or in the same frequency band (orthe same set of frequency bands) and are described asfollows:

� Primary system in licensed bands: A primary sys-tem operated in the licensed band has the highestpriority to use that frequency band (e.g., 2G/3Gcellular, digital TV broadcast). Other unlicensedusers/systems can neither interfere with the primarysystem in an intolerable way nor occupy the licenseband.

� Primary system in unlicensed bands: A primarysystem operating in the unlicensed band (e.g.,ISM band) called unlicensed band primary system.Various primary systems should use the band com-patibly. Specifically, primary systems operating inthe same unlicensed band shall coexist with eachother while considering that the interference to eachother. These primary systems may have differentlevels of priorities which may depend on some reg-ulations.

A cognitive radio system does not have privilegeto access certain frequency band. Entities of CR sys-tem must communicate with each other by dynamicallyusing spectrum holes and opportunistic access. Thereare two components in CR systems: cognitive radiobase station (CR-BS) and cognitive radio mobile sta-tion (CR-MS).

� Cognitive radio base station (CR-BS): A CR-BSis a fixed component in the cognitive radio systemand has cognitive radio capabilities. It represents theinfrastructure side of the CR system and providessupports (e.g., spectrum holes management, mobil-ity management, security management) to CR-MSs.It provides a gateway for CR-MSs to access thebackbone networks (e.g., Internet). CR-BSs can alsoform a mesh wireless backbone network by enablingwireless communications between them, and someof them act as gateway routers if they are connectedwith wired backbone networks. If a CR-BS can runPR system protocols, it can provide access networkservices to PR-MSs.

� Cognitive radio mobile station (CR-MS): A CR-MSis a portable device with cognitive radio capabilities.It can reconfigure itself in order to connect to differ-ent communication systems. It can sense spectrumholes and dynamically use them to communicatewith CR-MS or CR-BS.

Table I. Summary of Links in CRN.

Rx\ Tx CR-MS CR-BS PR MS PR-BS

CR-MS • • • •CR-BS • • • •PR-MS • • • •PR-BS • • •

•Possible link.

Fig. 4. Links in CRNs.

Since the cognitive radio system can provide inter-operability among different communication systems,some inter-system connections should be enabled. Welist all possibilities in Table I and illustrate them inFigure 4.

(i) CR-MS←→CR-MS: A CR-MS can communi-cate with other CR-MSs in direct links. They maycooperatively sense spectrum holes at differentfrequency bands which may be licensed or unli-censed and utilize it as their operating frequencyband.

(ii) CR-MS←→CR-BS: A CR-BS can dynamicallysense available frequency band around it andgather other MSs’ sensing results and provideone-hop access to CR-MSs under its coveragearea. This may need cooperative sensing tech-nique. Under the coordination of CR-BS, theCR-MS can either access the backbone networksor communicate with other communication sys-tems.

(iii) CR-MS←→PR-BS: If there is a need for a CR-MS to connect to a PR-BS, it will reconfigureitself and become one part of the primary system(i.e., PR-MS). In this case, it can be treated as aprimary user on that band.

(iv) CR-BS←→CR-BS: While enabling direct wire-less links between CR-BSs, they can form a meshwireless backbone network. Because of theircognitive radio capability, they can dynamically


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choose operating frequency band and communi-cate with each other and may reduce deploymentcost.

(v) PS-MS←→PS-BS: It is the typical one-hopconnection between mobile stations and base sta-tions. The PR-BS is responsible for coordinatingcommunications in its coverage and providingbackbone network access to the PR-MS. Thislink is bi-directional all the time, which is fun-damentally different from other links.

(vi) PS-MS←→CR-MS: In order to provide inter-operability between different communicationsystems, this kind of link may be necessary. Inthis case, the CR-MS shall reconfigure itself tobe one part of the primary system.

(vii) PS-MS←→CR-BS: In order to provide inter-operability between different communicationsystems, this kind of link may be necessary. If theCR-BS can run the protocol of primary system,it can provide access service to the PR-MS.

(viii) PS-MS←→PS-MS: This type of communica-tion may exist in PS as a sort of ad hoc networkin wireless networking systems. However, it maybe prohibited in the infrastructure mode of somesystems. However, if both nodes are transformedinto CRs and this case folds back to CR-MS←→CR-MS.

Note a special feature of CR links in the above list.Except the link between PS-MS and PS-BS that war-rantee bi-directional, each of the other seven types oflinks is available in only one direction, during an oppor-tunity window of spectrum access. It is not hard tounderstand, as the opportunity window in time mightbe too short to warrantee bi-directional exchange ofpackets and the next opportunity available time isnot warranted either. This unidirectional link propertyplays a more critical role if we consider more net-work operations such as network security, and we canmodel link availability as a Markov chain [10,15] as anopportunistic link.

3. Framework of Trust in CRN

Trust has been studied in literatures for a long timefrom social science to computer science, as in Refer-ences [16–20]. The immediate challenge to study trustin CRN is the mathematical definition of trust, whilewe want to keep in mind that distrust may be equallyimportant as trust.

Consequently, to ensure smooth operation of CRNto support ubiquitous computing, trust forms the foun-dation of CRN. Trust has been widely mentionedin literatures regarding trusted computing and Inter-net/web computing, ad hoc network, and even socialscience. However, trust for CRN is quite different fromthese scenarios. Trusted computing deals with compo-nents inside a set or a territory. Internet/web computingtreats trust as a kind of reputation/credits given by amechanism (such as other’s scoring). We therefore needto develop a mathematical framework to model trust inCRN for quantitatively apply trust in CRN design andoperation.

Trust is critical in CRN operation and beyond secu-rity design, as security usually needs communicationoverhead in advance. We can use the following exam-ples to explain the need of trust other than security:

(a) A cognitive radio senses a spectrum hole or oppor-tunity and dynamically accesses the spectrum fortransmission requires ‘trust’ from originally exist-ing system (i.e., primary system) and regulator,even without creating interference to PS.

(b) A cognitive radio may want to leverage anotherexisting cognitive radio to route its packets, eventhough another CR is not the targeted recipientterminal. It requires ‘trust’ from another CR.

(c) A cognitive radio can even leverage PS to forwardits packets to realize the goal of packet switchingnetworks. It needs ‘trust’ from the PS, not onlyat network level but also in service provider (ornetwork operator).

3.1. Mathematical Structure of Trust

If we temporarily ignore operator side, we just considerthe trust in (CR) network. Trust must be measurable sothat networks can operate based on it, such as routingand association to make dynamic spectrum access pos-sible and practical. Intuitive measure of trust might beas follows.

Definition 1. Trust is a measure in [−1, 1].

Remark. τ(i, j) denotes the trust measure for node jto handle (receive and/or forward) a packet from nodei. It is actually measured over (–∞,∞) and can be nor-malized as τ(i, j) ∈ (−1, 1). 1 as the normalized valuemeans trust in full (confidence); 0 means no informa-tion regarding trust or not; −1 means no trust at all.For a decision or a policy in CRN, it is obvious thatnegative trust and zero trust are under the same action


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(rejecting the packets). We can modify Definition 3.1as follows.

Corollary 2. Trust in CRN is a measure in [0,1].

Remark. It is just like the probability measure, andenables our mathematical framework using probabilis-tic development and statistical decision theory. Pleasenote that this degeneration from Definition 3.1 mayresult in an equivalent probabilistic atom for the trustmeasure at zero (i.e.

∫ 0−1 1τ(i,j)∈[−1,0]dτ).

Remark. The trust measure at zero means distrust.Any node in CRN being identified with zero trust shallbe rejected by any action of CRN. That is, any possiblelink to such a node should be removed from CRN. Itdoes not make any sense to be measured as zero ornegative.

Lemma 3. Trust in CRN is generally irreversible.That is,

τ(i, j) �= τ(j, i) (1)

Remark. It is generally true for all consequences toadopt the concept of trust. The degree of Alice trustingBob is not equal to the degree of Bob trusting Alice.

Definition 4. (Metric Space [16]). Every normedspace can be regarded as a metric space, while distanced(x,y) between x and y, with the following property:

(i) 0 ≤ d(x, y) < ∞∀x, y

(ii) d(x, y) = 0 ifandonlyif x = y

(iii) d(x, y) = d(y, x)∀x, y

(iv) d(x, y) ≤ d(x, y) + d(y, z) ∀x, y, z

Lemma 5. Trust in CRN is not a metric.

Proof. To form a metric space,τ(i, j) ≥ 0, whichcan be resolved by introducing a bias. However,τ(i, j) + τ(j, k) ≤ τ(i, k)violates the requirement ofmetric space. This equation means that the trust throughan intermediate node is not higher than the trust directlyfrom the originating node. Furthermore, trust is irre-versible, that is,τ(i, j) �= τ(j, i). This can be explainedby the case when node i is a mobile CR and node j isthe base station of a cellular network. �

Remark. This lemma is usually ignored in most lit-eratures to model trust in all research areas. However,it is critical as many trust measuring systems are con-structed based on the assumption of trust measure being

a metric, and such a fundamental assumption might bein jeopardy during further development.

Remark. However, if we define ‘distrust’ instead oftrust (i.e., D as distrust measure), the triangular inequal-ity actually holds.

D(i, j) + D(j, k) ≥ D(i, k) (2)

We would like to point out, as many literatures note,modeling distrust might not be less important thanmodeling trust in networking research (either Internetor CRN).

Definition 6. Trust in CRN is contributed fromreputation (trust measured by other nodes) and col-laboration (behaviors observed by targeting node andpossibly other nodes). Any zero-trust implies distrust.

Remark. Reputation is a terminology borrowedfrom trust in e-commerce. A CR node can increase itsreputation by executing more actions under the oper-ation rules. Both reputation and collaboration followDefinition 10.1. However, any of reputation or collab-oration being zero results in zero/no trust. Definition10.6 is different from common additive definition in lit-eratures, and suggests ‘multiplication’ (or semi-group)to be more suitable.

Proposition 7 (Trust-Path Theorem). Trust in CRNis a function of routing path. The overall trust is themultiplication of trust in each segment. That is,

τ (n0, n1, . . . , nL) =L−1∏l=0

τ (nl, nl+1) (3)

and

τ(n0, n1, . . . , nL) �= τ(n0, nL) (4)

Remark. For 3-node (2-segment) packetforwardingτ(i, j, k) = τ(i, j)τ(j, k) �= τ(i, k).

Remark. In literatures, many researchers note thattrust relies on its past history, which coincides with thisproposition. However, this proposition suggests that itis more than just history; the order of historical pathmakes sense.

Proposition 8. (Trust Processing Theorem) Moreprocessing (i.e., packet/traffic transportation) in CRNcannot increase trust.


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Remark. This is a direct result from Definition 2 andProposition 7. It is easy to understand in CRN. For anypacket from the originating node, after more segmentsof transportation, we have no more trust.

Definition 9. (Semi-group [16]). Let X be a Banachspace, and suppose that to every t ∈ [0, ∞)is associ-ated an operator Q(t) ∈ B(X) in such a way that

(i) Q(0) = I

(ii) Q(s + t) = Q(s)Q(t) ∀s, t ≥ 0

Proposition 10. Trust in a homogeneous ad hoc net-work is degenerated into a semi-group as Reference[21].

Remark. Note that we require homogeneous ad hocnetwork condition such that ad hoc network impliesthe reversible (or exchange) property of trust holdsas Equation (1). However, CRN, in general, is nei-ther homogeneous nor ad hoc (CRN is likely to haveinfrastructure in some parts). From this mathematicalproperty, we can also easily tell the difference of trustin CRN and trust in an ad hoc network.It might be disputable to identify mathematical mea-sure for trust in CRN. However, it is widely agreed thattrust measure has the property like probability mea-sure, and as Corollary 2 suggests. Let us start from CoxAxiom proposed in 1946, from a branch of artificialintelligence, mathematical reasoning.

Lemma 11 (Cox Axiom). If we want to measureany “certainty” that is consistent with the followingconditions:

(a) Degree of certainty can be ordered.(b) There exists a function to map certainty of a state-

ment to its negation/complement.(c) Degree of reasoning R(A∀B) is related to the

conditional reasoning R(A|B) and R(B) by somefunction g

R(A ∨ B) = g(R(A|B), R(B))

Then, this reasoning system must be equivalent tothe measure of probability.

Remark. Trust model in CRN indeed applies. There-fore, we subsequently treat trust to be measured just likeprobability and to develop as statistical decision theory[22].

3.2. Trust Model

The primary objective of trust model in CRN is to pro-vide us a kind of mathematical framework to sense,measure, analyze, and learn the topology variation andthe behavior of neighbors in such heterogeneous envi-ronment. In such heterogeneous network, trust modelplays an important role among entities belonging todifferent systems and it will provide a mechanism fornodes to establish trust association. After trust asso-ciation, cooperation between entities is possible and,therefore, they can communicate with each other towork together such as relay traffic, partner selection,and trusted routing. In this chapter, we try to build upthe trust model for CRN and the trust model in CRNshould provide two major components for nodes:

� Trusted association: It is the initial decision for anode to accept or to reject the trusted associationfrom a neighboring CR node. To conduct network-ing of minimum risk is the central concept for thisinitial decision.

� Learning algorithms: Each node in CRN shouldkeep the records of others and employ a properlearning algorithm to adapt the long-term trend ofprobability/trust measure of packet forwarding, sothat timely judgment to hold on the trusted route orto retract it could be made.

We depict the basic components of our proposedtrust model in Figure 5, which illustrates the inter-action between these two functions and provides thewhole scenario of the trust model. In wireless net-work, channel condition is always changing and trustis thus dynamic in CRN. We should develop proper

Fig. 5. Flowchart of the trust model in CRN.


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K.-C. CHEN ET AL.

Fig. 6. Neyman–Pearson theorem in trust decision.

learning algorithm to adapt the tendency of coopera-tion from neighbors in all kinds of environments. Eachnode in CRN could determine its trust policy about itsneighbors according to the existing environment. Thealgorithm must catch the bad behaviors and punish thenodes for this attempt to deteriorate the throughput inwireless network. On the other hand, this mechanismneeds to give the nodes opportunity for recovering theterrible isolation situations.

4. Trusted Association and Routing

One of the most challenging tasks for CRN is that a CR(transmitting node) initially requests association witha cooperative PS node or another CR (called receivingnode) to conduct cognitive radio networking functions.Similar to cooperative networks, the receiving node inCRN can possibly conduct: (i) reject association, (ii)amplify-and-forward (AF) mode, (iii) compress-and-forward (CF) mode. The difference between AF andCF in routing lies that AF just executes physical layerfunction and we do not need to worry attacks and CFactually decodes the packets to upper layers under thethreat of security threats. In other words, the cost for AFmode is simply communication bandwidth (and possi-ble battery energy) waste even a wrong decision, butthe cost for CF of a wrong decision might jeopardizethe entire network, which suggests a security check likePKI after association before CF.

4.1. Trusted Association

We illustrate the association of CRN as Figure 6 andadopt the Neyman–Pearson criterion (since no a pri-ori probability nor cost function is available in such adecision) as follows.

Proposition 12. There are only two possible deci-sions for association in CRN based on trust measure,that is, to accept association and to reject association.

Let X denote the trust measure with distribution Fθ(x)representing the information inside the associationrequest from CR-MS to PS-MS. Let � = �0 ∪ �1bea disjoint covering of the trust space, and Hi denotethe hypothesis that the parameter θ belongs to the trustspace, θ ∈ �i. Then, the decision problem is now todistinguish between the two hypotheses by consideringCRN architecture as given in Section 2:

(i) H0 : θ ∈ �0: It means that CR-MS would not beworth being trusted. The probability density func-tion for CR-MS isfτ|0 = fτ(x |0 ), where x meansthe trust measure of CR-MS.

(ii) H1 : θ ∈ �1: It means that the CR-MS would beworth being trusted. The probability density func-tion for CR-MS isfτ|1 = fτ(x |1), where x meansthe trust measure of CR-MS.

The PS-MS decides to trust CR-MS if the probabil-ity density function of trust measure, x, from CR-MSunder trust space,θ ∈ �1, is larger than that under trustspace,θ ∈ �0.

Remark. For AF, once accept association, thepacket(s) is relayed. For CF, once accept association,the transmitting node has to go through security check,and then the receiving node compresses and relays thepacket(s).

Proposition 13. When a node in CRN (primarysystem or secondary systems) receives a request ofassociation from a new node (i.e., to join this CRN),it forms a statistical decision as

Based on the trust measure τ associated with thisnode, decision δ(x) = ai can be formed, while a1 means‘accept’ the association and a0 means ‘reject’ the asso-ciation.

The PS-MS will decide to trust CR-MS if the prob-ability density function for trust measure, x, fromCR-MS under trust spaceθ ∈ �1 is larger than thatunder trust spaceθ ∈ �0. Therefore, with these twohypotheses, we can form the decision rule for the trustmeasure:

δ (x) ={ 1 , fτ|1 (x |1) > γ · fτ|0 (x |0 )

k , fτ|1 (x |1) = γ · fτ|0 (x |0 ) , for some γ ≥ 0, 0 ≤ k ≤ 10 , fτ|1 (x |1) < γ · fτ|0 (x |0 )

(5)

The decision that maximizes the probability ofdetection for a given probability of false alarmis the likelihood ratio test as specified by the


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Neyman–Pearson theorem. In order to maximize PD

for a givenPF ≤ α, PS-MS decides to trust CR-MS if

l (x) = fτ|1 (x |1)

fτ|0 (x |0 )> γ (6)

In the same way, PS-MS decides not to trust CR-MSif

l (x) = fτ|1 (x |1)

fτ|0 (x |0 )< γ (7)

The likelihood ratio,l (x) = fτ|1 (x|1 )fτ|0 (x|0 ) , indicates the

likelihood of H1 versus the likelihood of H0 under eachtrust measure. We can transform the decision into

δ (x) ={ 1 , l(x) > γ

k , l(x) = γ for some γ ≥ 0, 0 ≤ k ≤ 10 , l(x) < γ

(8)

Remark. We successfully model the associationprocess as a binary statistical decision problem. It canbe applied to more realistic study of dynamic spectrumaccess to network cognitive radios. Please also note thatrandomized decision rule is still possible here. How-ever, to completely define the binary decision problem,we still need to explore definitions of cost and a prioriprobability (i.e., trust measure) distribution. Since it isdifficult to assign the cost function or a priori proba-bilities before establishing trust association in realisticsituation, we consequently adopt the Neyman–Pearsontheorem to solve the problem.

Definition 14. Following earlier Propositions, wecan define the probability of false alarm and probabilityof detection with the meaningful interpretation for thetrust association. First, we define that the probabilityof PS-MS trusting CR-MS given that CR-MS does nottrust PS-MS as probability of false alarm:

PF =∫

x∈Z1

fτ(x|0)dx (9)

Second, we define that the probability of PS-MS trust-ing CR-MS given that CR-MS also trusts PS-MS asprobability of detection:

PD =∫

x∈Z1

fτ(x|1)dx (10)

Remark. We can easily tell the importance of trustmeasure. The trust measure is composed of true trust

and observation deviation (i.e., like observation noisein common communication theory). Such an observa-tion deviation has tendency to be negatively distributedas more observation cannot increase trust as Propo-sition 10.8 (trust processing theorem). However, formalicious nodes, such a conclusion may not apply andobservation deviation could be two-sided in distribu-tions.What we plan to do with Neyman–Pearson criterion inthis decision problem is to minimize the risk of PS-MS trusting CR-MS given the constraint on CR-MSwould not be worth being trusted. We do not make anyassumptions on the probability density function of trustmeasure here. It could be discrete or continuous and weonly want to derive a general trust decision rule for ourtrust model.

Proposition 15. When a priori information of trustis unknown and the cost function cannot be welldefined, the decision δ(x) = ai can be based on theNeyman–Pearson criterion. That is, given PF ≤ a (0 ≤a ≤ 1), optimize PD.

Remark. The approach that maximizes the proba-bility of detection for a given probability of false alarmis the likelihood ratio test. For a Neyman–Pearson test,the values of PF and PD completely specify the test per-formance. The problem in the trust decision is changedto

maximizePDforagivenPF = a (11)

decide δ (x) ={ 1 , l(x) > γ

k , l(x) = γ for some 0 ≤ k ≤ 10 , l(x) < γ

(12)where the threshold, γ , is specified from the systemconstraint:

PF =∫

{x:l(x)>γ}fτ|0 (x |0 )dx = α (13)

If trust is based on individual perception, it is likelythat different observers observe different situations;therefore, each node in CRN would adopt different sig-nificance level upon their acceptable level of maliciousbehaviors. Increasing γ makes the test less sensitivefor the disturbance and we accept a higher probabilityof detection in return for a lower probability of falsealarm.


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K.-C. CHEN ET AL.

Fig. 7. Execution of association based on Neyman–Pearsoncriterion (using normal distribution).

We will give an example of how nodes in CRN canmake decisions using the Neyman–Pearson criterionwe have described above. We start from the normaldistribution for trust measure to realistically solve thetrust problem here. Consider the case in Figure 7, CR-MS-A independently relays/forwards the packet withprobability p and drops/ignores it with probability 1−pas receiving packets from CR-MS-B. According to thisinformation, PS-MS has to accept or reject the asso-ciation from CR-MS-A by maximizing PD under thesystem constraint, PF .

Let X be the number of packets forwarded by CR-MS-A and we assume that every packet is forwardedor dropped independently. This assumption is reason-able since the amount of the data transferred in thenetwork is large enough. Then, in this situation, X is abinomial distribution with parameters (m + n, p) whichp is the probability of success. As (m + n) is large andby De Moivre–Laplace theorem, we know that for anynumbers a and b, a < b,

limn→∞ P

(a <

X − (m + n) p√(m + n) p (1 − p)

< b

)

= 1√2π

b∫a

e−t2/2dt (14)

where the expected value is

E(X) = (m + n)p (15)

and the variance of probability of success is

σX =√

(m + n)p(1 − p) (16)

Since the probability of packets transmission suc-cessfully in next stage may be impossible to computeanalytically, we could approximate this probability

according to the experience rating:

Pr (cooperation) ≈ number of packets forwarding

number of packets received

(17)

With such approximation, the probability of packetsforwarding successfully by CR-MS-A is m

m+n, where

we define it as probability of trusted cooperation. Wecan model the packet forwarding behaviors of CR-MS-A with normal distribution in this problem. Now, theproblem is turned into the hypothesis problem:

� H0: CR-MS-A would not be worth being trusted.X ∼ N

(µ0, σ

20

)where µ0 = (m + n) · (1 − p)

and σ20 = (m + n) · p (1 − p).

The probability density function of x is

fτ|0 (x |0 ) = 1√2πσ0

e

{− 1

2

(x−µ0

σ0

)2}

(18)

� H1: CR-MS-A would be worth being trusted.X ∼ N

(µ1, σ

21

), where µ1 = (m + n) p and

σ21 = (m + n) p (1 − p).

The probability density function of x is

fτ|1 (x |1) = 1√2πσ1

e

{− 1

2

(x−µ1

σ1

)2}

(19)

We can derive the likelihood ratio by the hypothesisif we decide to choose H1:

l (x) = fτ|1 (x |1)

fτ|0 (x |0 )=

1√2πσ1

e

{− 1

2

(x−µ1

σ1

)2}

1√2πσ0

e

{− 1

2

(x−µ0

σ0

)2} > γ

(20)Finally, it is equivalent to

x >σ1

2

µ1 − µ0ln γ + µ1 + µ0

2= γ ′ (21)

We could determine the threshold γ from the falsealarm constraint

PF = Pr{x > γ ′ |H0

} ≤ α (22)

Then, PS-MS can make the decision by maximizingthe probability of detection,


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PD = {decide H1 given H1}= Pr{x > γ ′ |H1 }

=∞∫

γ ′

fτ|1 (x |1) dx

= Q

γ ′ − µ1√

σ21

(23)

under the system constraint, probability of falsealarm, which also can derive the threshold by

PF = Pr{decide H1 given H0}= Pr{x > γ ′ |H0 }

=∞∫

γ ′

fτ|0 (x |0 ) dx

= Q

γ ′ − µ0√

σ20

(24)

whereQ(x)is the right-tail probability of Gaussianrandom variable with zero mean and unit variance:

Q (x) =∞∫x

1√2π

e−t2/2dt (25)

Using the constraint,PF = α, we can derive the trustthreshold as

γ ′ = σ0 · Q−1(α) + µ0 (26)

and we can obtain the probability of detection as

PD = Q

σ0 · Q−1(α) − (µ1 − µ0)√

σ21

= Q

Q−1(α) − (µ1 − µ0)√

σ21

(27)

If we define the coefficient c2

c2 = (µ1 − µ0)2

σ21

= (m + n) · (2p)2

p(1 − p)(28)

as the trust confidence equaling to the definition ofdeflection coefficient often used to approximate detec-

tion performance evaluation for most of the detectionproblems, we can derive the maximum probability oftrusted cooperation from this system constraint:

PD = Q(Q−1(α) −

√c2

)(29)

Given the constraint on the probability of false alarm,and therefore, the trust threshold, the optimal trust deci-sion is to

� Decide to trust if the probability of trusted cooper-ation from neighbors maximizes PD .

� Decide not to trust if the probability of trusted coop-eration from neighbors does not maximize PD .

4.2. Trusted Routing

Once a node is accepted into CRN after association, astypical multi-hop networks, CRN shall update its net-work topology and routing table. If we treat CRN asa homogeneous amplify-and-forward network, routingis the same as any multi-hop ad hoc networks. How-ever, it is obvious that CRN is not homogeneous. Asunlikely severe security is taken in CRN, it is importantto develop trusted network layer function, especiallytopology and routing. Based on the developed math-ematical framework of trust in CRN, we are ready toderive the fundamental operation of CRN at networklayer, network topology establishment and routing inCRN.

Typical routing algorithms in a homogeneous net-work proceed on the distance measure that accountsavailable bandwidth/capacity and transmission cost.For routing of CRN, we have to consider trust in hetero-geneous networking environments (e.g., PS is a cellularand CR is ad hoc WiFi station).

Proposition 16 (Trusted Routing). Routing metricbetween node i and node j in CRN is defined through astate-machine of state(τ(i, j), d(i, j)), which representstrust measure and distance measure.

To deploy the routing algorithm, say Dijkstraalgorithm, we may simply define a new trusteddistanceD(i, j) = d(i, j)/τ(i, j)to iterate the algorithmto find the route, under the assumption of reversibletrust relationship between any two nodes. For the caseof no trust (i.e.,τ(i, j) = 0), the link is practically elim-inated due to the infinite distance. Figure 8 as anexample depicts both Bell-Ford and Dijkstra routingalgorithms using this new trusted distance measure,with reversible trust relationship.


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K.-C. CHEN ET AL.

Fig. 8. Trusted routing examples.

There exists an open problem to route under generalirreversible trust relationship that invokes unequal dis-tance measure for two directions between two nodes,which is asymmetric nature of uni-directional links inCRN [10].

Definition 17. CRN node (which can be an accesspoint of PS) deciding the trust and taking an action toa packet from another CRN node can form a Markovdecision process (with a decision/policy associatedwith a state-space) [23].

Proposition 10.18 (Markovian Trust Process). Forpacket transportation from node i to node j, the recip-ient node (i.e., node j in this case) can form a binaryhypothesis testing (trust as H1 and non-trust as H0)based on certain decision policy. Trusted routing inProposition 16 becomes a Markov process.

Remark. Trusted routing therefore becomes a kindof Markov decision process. As a matter of fact, ran-domized decision of trust is possible and meaningful inCRN. For example, τ(i, j) = τr, 0 < τr < 1may sug-gest a discount factor for trust status, due to variousreasons such as roaming user/node, robustness against

attacks, or simply insufficient credits in account. Thenumber of states can be finite or infinite. This shall besubject to further study, though some touch in litera-tures.

5. Trust with Learning

The real challenge for trust in CRN is not only toconstruct the measure for trust to take proper actionsbut also to subsequently update the trust ‘distributionfunction’ for each network node to possibly conductcommunication/networking functions such as request-ing relay of packet(s). Each node in PSs supportingCRN or each CR shall be able to update and/or to main-tain a trust table of neighboring nodes for applicationsdescribed in later sections. It might be disputable toidentify mathematical measure for trust in CRN. How-ever, it is suggested that trust measure has the propertylike probability measure. Based on such measure, wemay precede decisions based on trust measure in dif-ferent application scenarios. Hereafter in this section,we focus on ways to update the distribution function oftrust measure based on machine learning [16,24–26].


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5.1. Modified Bayesian Learning

Suppose the probability distribution of trusted cooper-ation is fP(p|αt, βt), 0 ≤ p ≤ 1, where the superscriptt means the discrete-time index with t=0 as the initialdistribution. fP (p |αt, βt ) can be updated recursivelyby fP (p |αt−1, βt−1) based on current trust evidence,observation, and any further information. To resolvethis challenge, we may adopt a learning algorithm fornodes in CRN to adapt the probability distribution ofpackets forwarding from neighbors at each (proper)time instance.

Lemma 19 (Modified Bayesian LearningAlgorithm). Suppose the beta densityfP (p |αt, βt)denote the probability distributionof trusted cooperation at time t in our trust model.The learning algorithm consists of three parts: theprediction of probability, decaying correction, andmeasurement modification

p̂t =∫

p (xt |P = p ) · f (p |αt, βt ) dp (30)

ˆft−1 (p) = f (p |k1αt−1, k2βt−1 ) (31)

and

f (p |αt, βt ) = c · f ((mt, nt) |p ) ·ˆ

ft−1(p) (32)

where t is the discrete-time index, k1 and k2 are thedecaying factors, (mt , nt) is the new trust evidence, c isthe modification factor representing the constant of theintegral, and p̂t is the prediction probability of trustedcooperation.

Remark. The prediction is used to predict the (prob-ability) measure of trusted cooperation in next stageand it is primarily designed for the decision criterion.

Lemma 19 consists of the update probability distri-bution and the update rule. The probability densityfunction,f (p |αt, βt ), for the prediction function incor-porates into new trust evidence and a prior (probability)density function. It includes all the information prior toT = t − 1 where all trust evidence including the initialvalue is decayed as receiving new one

αt = k1αt−1 + mt

= kt1m0 + kt−1

1 m1 + kt−21 m2 + . . . + k1mt−1 + mt

βt = k2βt−1 + nt

= kt2n0 + kt−1

2 n1 + kt−22 n2 + . . . + k2nt−1 + nt

(33)

Then, we carry on the decaying correction of past infor-mation prior to T = t by Equation (31) and incorporatenew trust measurement after T = t in Equation (32). Weuse decaying correction to ‘forget’ the past trust evi-dence to explain the limitation of the period of validitysince the trust model should gradually ignore the old-est record in order to catch the newest one. We use twoconstants k1 and k2 to represent the decaying factor astime goes on and usually k1 is smaller than k2 in order tocatch the bad behaviors of node such like deception orthrow packets away with bad intention. As we receivethe new trust evidence at time T = t, it would be appro-priate to give the latest record more weight in order tosupport the dynamic and fast operation in CRN. There-fore, we can maintain the parameter update for TCRNby this learning mechanism.

Remark. Before deriving the prediction probability,p̂t , we need to describe the details in the correctionequation. At the end of time T = t, we have the new trustevidence (mt, nt) and prior probability distribution andwe can calculate probability density function as

f (p |αt, βt ) = fτ ((mt, nt) |p ) ·ˆ

ft−1(p)∫fτ ((mt, nt) |p ) ·

ˆft−1(p) dp

= p ((mt, nt) |P = p ) · f (p |k1αt−1, k2βt−1 )∫p ((mt, nt) |P = p ) · f (p |k1αt−1, k2βt−1 ) dp

={ (αt + βt)

(αt) (βt)pαt−1(1 − p)βt−1, 0 ≤ p ≤ 1

0 , elsewhere(34)

We give a flowchart for the technological processesin the learning mechanism in Figure 9. It provides two


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K.-C. CHEN ET AL.

Fig. 9. The flowchart of the learning mechanism.

important concepts in our trust model. The first is themeasurement correction on time scale. The past trustevidence must decay to support the immediate interac-tion in the CRN and provide a way to recover from badchannel condition. The second is the prediction. Thetrust decision is made at every stage mainly based onthis prediction value whether to hold or retract it. Theprediction function given in our approach is not the onlysolution for the learning algorithm and it depends onthe system model that one chooses. After receiving newtrust evidence(mt, nt), each node in CRN should pre-dict the probability of trusted cooperation in next stageaccording to past record and latest trust evidence. Whatwe try to do in this learning model is to make inferenceabout the probability of p̂t given the probability dis-tribution of p, fP(p),and new trust evidence(mt , nt).

The probability that next transmission is successdepends on the prediction probability of trusted coop-eration, P = p̂t , and then we could compute theprobability

P (xt |P = p̂t ) ={

p̂t ifxt = 11 − p̂t ifxt = 00 elsewhere

(35)

As we receive the new trust evidence at time t, itwould be appropriate to give the lastly record moreweight in order to support the dynamic and fast oper-ation in CRN. Finally, we can directly calculate theprediction probabilityP(xt = 1)

P(xt = 1) =∫

P (xt = 1 |P = p̂t , (mt, nt) ) f (p |αt−1, βt−1 ) dp

=∫

P (xt+1 = 1 |P = p )f (p |αt, βt ) dp

=∫

p · f (p |αt, βt ) dp

= ((k1αt−1 + mt) + (k2βt−1 + nt))

(k1αt−1 + mt) (k2βt−1 + nt)

×1∫

0

pk1αt−1+mt (1 − p)k2βt−1+nt−1 dp

= k1αt−1 + mt

(k1αt−1 + mt) + (k2βt−1 + nt)(36)

We rewrite the equation in another form to denotethat it is the weighted average of the maximum esti-mate of P = p given (mt, nt) and the mean of the priorinformation

P (xt+1=1) =(

mt + nt

k1αt−1 + k2βt−1 + mt + nt

)mt

mt + nt

+(

k1αt−1 + k2βt−1

k1αt−1 + k2βt−1 + mt + nt

)k1αt−1

k1αt−1 + k2βt−1

(37)

When we obtain the prediction at each stage, wecould make the decision by the decision criterion

δt (mt, nt) ={

0, means‘reject′ifp̂t+1 ≤ γi

1, means‘accept′ifp̂t+1 > γi(38)

If the probability measure of trusted cooperation islarger than the threshold γi, it means it is more probablethat the packet would be delivered successfully at nextstage than dropped, and vice versa.

5.2. Learning Experiments for CRN

We can describe several scenarios to demonstrate thewell-suitable properties of learning algorithm appliedin CRN, and we can further conclude some rule ofthumbs when we build up the trust model for CRN.Since the CRN is a highly dynamic heterogeneous net-work, the nodes leave or join the network dynamicallyand promptly, and the CRN topology may change veryfrequently. The learning algorithm should follow upthe channel variations and user behaviors instantly, tolearn the update (favorable or unfavorable) changes inthe behaviors of packet forwarding.


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Fig. 10. Nodes disconnect from the network underm0 + n0 = 100.

5.2.1. Nodes disconnect and the effect ofinitial value

The disconnection from nodes is frequently encoun-tered in CRN and the learning algorithm should be ableto catch this extreme case as soon as possible in ordernot to deteriorate the trust topology in the network. Aswe show in Figure 10, we accept the trust associationfrom the neighbor with probability of trusted coop-eration p = 0.8 at time t = 0 under k1 = 0.2, k2 = 0.5.At time t = 21, the neighbor disconnects from the net-work and the learning algorithm catches the predictionof probability immediately because the probability oftrusted cooperation drops quickly. We retract the trustassociation at time t = 22 and determine the node hasleft the network at time t = 27. Since nodes in CRBare dynamic, they may leave and join the network for awhile and we have to observe more time before weascertain that it has disconnected from network. InFigure 10, we show the same scenario except that infor-mation inside the association request is different. Thesum of the packets in the past in Figure 10 is 100, andthat of Figure 11 is 1300. We note that the initial valueaffects the subsequent trust decisions at times t = 3 andt = 4. Although we learn the behaviors of node imme-diately, we should make the assumptions on the numberof initial value to avoid such situations in the learningmodel. The simulations later should adopt the value ofinitial value, m0 + n0 = 100, in order to fully catch upthe latest trust evidence of the nodes. Now, we proposethe first rule of thumb from this example.

Proposition 20 (Rule of thumb in different trafficmodel). The learning algorithm in Lemma 19 canquickly learn the new trust evidence under heavy traffic

Fig. 11. Nodes disconnect from the network underm0 + n0 = 1300.

density. Even the initial value is large or the environ-ment is varied frequently, we can still learn quickly tocatch the latest trust evidence if the traffic is heavy andthe decaying factors are small enough.

5.2.2. Nodes leave and join the networksuddenly

The neighbor sent association request including past100 records with probability of trusted cooperationp = 0.8 at time t = 1 under k1 = 0.2, k2 = 0.5. The nodeleaves the network at time t = 21 and comes back attime t = 32. Because the considerable amount of pack-ets dropped at time t = 22, we retract the trusted routeright away and declare that the node has left the net-work. However, when the node comes back, we noticethe considerable probability of trusted cooperation andwe do not re-establish the trusted route immediately.We observe more time to build up the trusted routeagain. It is used to punish for the network performancedrop and make sure that the node does not come backand leave again. We denote that the node may be backat time t = 35 and re-establish the trusted route at timet = 36. In Figure 12, we show the same scenario exceptthat the traffic density in each time slot is different. Thetraffic density of Figure 12 is 100 and that of Figure 13is 30 which represent the heavy and light traffic net-work, respectively. From Figure 12, we observe thatthe initial value significantly affects the learning algo-rithm because the sum of the initial value is 100 andit is larger compared to the traffic. Therefore, at timest = 6 and t = 7, the trust decisions are retracted and re-established in successive order. We are ready to proposeother rules of thumb by the observations of the effectof the initial value problems:


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K.-C. CHEN ET AL.

Fig. 12. Node leaves and joins the network again under heavytraffic density.

Fig. 13. Node leaves and joins the network again under lighttraffic density.

Proposition 21 (Rule of thumb in the importance ofdecaying factor). The learning algorithm of Lemma19 responds to the new trust evidence slowly underlight traffic density. The only way we can adapt to thissituation is to keep decaying factor as small as possibleand making subsequent decisions between longer timeintervals.

Proposition 22 (Rule of thumb in initial value prob-lem). The initial value should be decayed as soon aspossible since it reflects the past information. It con-tradicts the basic concept of learning algorithm whichtrying to respond to latest trust evidence. We can useadaptive decaying factors to ‘forget’ initial value prop-erly and quickly under the realistic network condition.

Fig. 14. Node changes behaviors under light traffic density.

5.2.3. Variation on the behavior of nodes

In this experiment, we consider the probability oftrusted cooperation tending to perform better or worse.The nodes in CRN could incur bad channel conditionand, therefore, cause to alter their behaviors on thetrusted cooperation. The learning algorithm should beable to analyze the possible temporal disconnect andto make further decisions. In Figure 14, we show thatthe node changes its behavior at time t = 16 from prob-ability of trusted cooperation from p = 0.9 to p = 0.7and changes back to p = 0.9 at time t = 36 underk1 =0.2, k2 = 0.5. The learning algorithm detects the vari-ation at t = 20 although the probability of trustedcooperation is still larger than the trust threshold, p =0.7 > 0.6 = γi. The reason is the decaying factor thatwe punish the bad behaviors more than we rewardgood behaviors. Since k1 = 0.2 < k2 = 0.5, at timet = 16–20, the learning algorithm detects the decline inthe prediction of probability and it retracts the trustedroute at time t = 20. From time t = 16 to time t = 35,the learning algorithm also catches the stable pack-ets forwarding from the node though the prediction ofprobability has a notable decline compared to t = 1–15.This may come from many causes and the variationbetween p = 0.9 and p = 0.7 can be possibly resultedfrom the channel condition. The learning algorithm willcatch the probability from p = 0.9 to p = 0.7 immedi-ately at time t = 37 and re-establish the trusted routewhen the channel condition turns better. In Figure 15,we show the same scenario except that the traffic den-sity in each time slot is 30. We note that the predictioncurve in light traffic network triggers the disturbancein the variation of behaviors and we denote the phe-nomenon as ‘temp disconnect’ in Figure 15. They do


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Fig. 15. Node changes behaviors under light traffic density.

not indicate real retraction of trusted route but repre-sent the unstable oscillating across the trust threshold.However, the learning algorithm still works well incapturing the behavior changes, such as those at timest = 16 and t = 36, and detects the re-establishment ofthe trust association finally.

5.2.4. Intentionally drop the traffic

The learning algorithm should not only solve the fre-quent dynamic disconnections from network but alsosome special cases such like drop the traffic intention-ally. In such situation, the nodes do not drop all thetraffic. Instead, they drop some fixed portion of thetraffic. This could result in a great damage to the entireCRN if the dropped portion has important parame-ters to network operation. In Figure 16, we show theremarkable decline in the prediction of probability ofmalicious users at time t = 11 and the usual behaviors

Fig. 16. Node drops the major part of the traffic intentionally.

of nodes. The malicious user is punished by the largerdecaying factor to manifest the intentionally packetdrops.Above examples describe the adaption of trust toallow desirable CRN operation by using the learningalgorithms, so that CRN operation can progress alongthe operating time.

6. Conclusions

To realistically implement CRN, we proposed TCRN,especially for association that enables dynamic spec-trum access of any CR delivering packets. Fromstatistical decision theory and machine learning, wedemonstrated successful realization of CRN function-ing in association and also update of routing parameter,along with operating rules of thumb. As a matter offact, TCRN can allow more homogeneous operationof CRN as a heterogeneous wireless network.UncitedReference[27]

Acknowledgements

This research is supported in part by the National Sci-ence Council, Taiwan, ROC, under the contract NSC95-2923-I-002-001-MY2.

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Authors’ Biographies

Kwang-Cheng Chen received B.S.from the National Taiwan University in1983, M.S. and Ph.D from the Univer-sity of Maryland, College Park, UnitedStates, in 1987 and 1989, all in elec-trical engineering. From 1987 to 1998,he worked with SSE, COMSAT, IBMThomas J. Watson Research Center,and National Tsing Hua University, in

mobile communications and networks. Since 1998, he has

been with the Institute of Communication Engineeringand Department of Electrical Engineering, National TaiwanUniversity, Taipei, Taiwan, ROC, and is the DistinguishedProfessor and Irving T. Ho Chair. He held visiting/guestpositions with Hewlett-Packard Laboratories USA, the DelftUniversity of Technology Netherlands, and Aalborg Uni-versity Denmark. He is actively involved in the technicalorganization of numerous leading IEEE conferences, includ-ing as the Technical Program Committee Chair of 1996 IEEEInternational Symposium on Personal Indoor Mobile RadioCommunications, TPC co-chair for IEEE Globecom 2002,General Co-Chair for 2007 IEEE Mobile WiMAX Sympo-sium in Orlando, for 2009 IEEE Mobile WiMAX Symposiumin Napa Valley, and for IEEE 2010 Spring Vehicular Technol-ogy Conference. He has served editorship with a few IEEEjournals and many international journals, and served variouspositions in IEEE. He is also actively participated in var-ious wireless international standards. He has authored andco-authored over 200 technical papers and 18 granted USpatents. He co-edits (with R. DeMarca) the book MobileWiMAX published by Wiley 2008, and authors a book Prin-ciples of Communications published by River 2009, andco-author (with R.Prasad) another book Cognitive Radio Net-works published by Wiley 2009. He was elected as an IEEEFellow in 2006 and received numerous awards and honors.His research interests include wireless communications andnetworks, nano-computation/communication, and cognitivescience.

Peng-Yu Chen received the B.S. degreein electrical engineering (EE) and M.S.degree in communication engineeringfrom the National Taiwan University,Taipei, Taiwan, in 2006 and 2008,respectively. He has participated inQuanta project cooperated with DataMining Lab in the department ofGraduate Institute of Communication

Engineering (GICE), NTU from 2006 to 2007. His researchinterests include trust computation, trust model in computernetwork and trust model for cognitive radio network.

Neeli Rashmi Prasad, AssociateProfessor and Coordinator of NetworkArchitecture Thematic Group, Centerfor TeleInfrastruktur (CTIF), andHead of Wireless Security and SensorNetworks Group, Aalborg University,Denmark. During her industrial andacademic career for over 13 years,she had lead and coordinated several

projects. At present, she is leading a industry-funded projecton reliable self organizing networks REASON funded byHuawei, Project Coordinator of European Commission (EC)Integrated Project (IP) ASPIRE on RFID and Middlewareand EC Network of Excellence CRUISE on Wireless SensorNetworks. She is coordinating Internet of Things workinggroup for European Commission Future of Internet Assemblyand co-caretaker of real world internet (RWI). She has leadEC Cluster for Mesh and Sensor Networks and Counsellorof IEEE Student Branch, Aalborg. She is Aalborg University


DOI: 10.1002/wcm


project leader for EC funded IST IP e-SENSE on WirelessSensor Networks and NI2S3 on Homeland and Airportsecurity and ISISEMD on telehealth care. Her publicationsrange from top journals, international conferences and chap-ters in books. She has also co-edited and co-authored twobooks titled “WLAN Systems and Wireless IP for Next Gen-eration Communications” and “Wireless LANs and WirelessIP Security, Mobility, QoS and Mobile Network Integration”,published by Artech House, 2001 and 2005. She is memberof IEEE. Her current research interest lies in context-awaresecurity management framework, threat models and attacktrees, mobility, mesh networks, WSN, RFID/NFC, emergingtechnologies and heterogeneous networks.

Ying-Chang Liang is presently a SeniorScientist in the Institute for InfocommResearch (I2R), Singapore, where he hasbeen leading the research activities inthe area of cognitive radio and coop-erative communications. He also holdsadjunct associate professorship positionsin Nanyang Technological University(NTU) and National University of Sin-

gapore (NUS), both in Singapore, and adjunct professorshippositions with Jilin University and University of ElectronicScience & Technology of China (UESTC), both in China.He has been teaching graduate courses in NUS since 2004.From Dec 2002 to Dec 2003, he was a visiting scholar withthe Department of Electrical Engineering, Stanford Univer-sity, CA, USA. His research interest includes cognitive radio,dynamic spectrum access, reconfigurable signal processingfor broadband communications, space-time wireless com-munications, wireless networking, information theory andstatistical signal processing.

He is now an Associate Editor of IEEE Transactions onVehicular Technology, Lead Guest-Editor of ERASIP Journalon Advances in Signal Processing, Special Issue on AdvancedSignal Processing for Cognitive Radio Networks, and LeadGuest-Editor of IEEE Journal on Selected Areas in Com-munications, Special Issue on Advances in Cognitive Radio

Networking and Communications. He was an AssociateEditor of IEEE Transactions on Wireless Communicationsfrom 2002 to 2005, Lead Guest-Editor of IEEE Journal onSelected Areas in Communications, Special Issue on Cog-nitive Radio: Theory and Applications, and Guest-Editor ofCOMPUTER NETWORKS Journal (Elsevier) Special Issueon Cognitive Wireless Networks. He received the Best PaperAwards from IEEE VTC-Fall’1999 and IEEE PIMRC’2005,and 2007 Institute of Engineers Singapore (IES) Presti-gious Engineering Achievement Award. He has served forvarious IEEE conferences as technical program committee(TPC) member. He was Publication Chair of 2001 IEEEWorkshop on Statistical Signal Processing, TPC Co-Chairof 2006 IEEE International Conference on CommunicationSystems (ICCS’2006), Panel Co-Chair of 2008 IEEE Vehicu-lar Technology Conference Spring (VTC’2008-Spring), TPCCo-Chair of 3rd International Conference on Cognitive RadioOriented Wireless Networks and Communications (Crown-Com’2008), TPC Chair of 2010 IEEE Symposium on NewFrontiers in Dynamic Spectrum Access Networks (DyS-PAN’2010), and Co-Chair, Thematic Program on Randommatrix theory and its applications in statistics and wire-less communications, Institute for Mathematical Sciences,National University of Singapore, 2006. He is a Senior Mem-ber of IEEE and holds six granted patents.

Sumei Sun obtained the B.Sc.(Honours)Degree from Peking University, China,the M.Eng Degree from Nanyang Tech-nological University, and Ph.D Degreefrom National University of Singapore.She’s been with Institute for InfocommResearch (formerly Centre for WirelessCommunications) since 1995 and she iscurrently Head of Modulation & Coding

Dept, developing physical layer-related solutions for next-generation communication systems. She is co-recipient ofIEEE PIMRC’2005 Best Paper Award.


DOI: 10.1002/wcm

Trusted cognitive radio networking

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