A Survey on Trust Models for Secure Data Transmission in Wireless Sensor Networks (WSN)

IJSRD - International Journal for Scientific Research & Development| Vol. 3, Issue 08, 2015 | ISSN (online): 2321-0613

A Survey on Trust Models for Secure Data Transmission in Wireless Sensor Networks (WSN) A. Divya Preetha1 P. Prittopaul2 1 P.G. Scholar 2Professor 1,2 Department of Computer Science & Engineering 1,2 Velammal Engineering College Chennai, India Abstract—Security is the most questing issue in Wireless Sensor Networks (WSN) now-a-days because of the various attacks on mobile nodes. Although a number of proposals have been reported concerning security in Wireless Sensor Networks, provisioning security remains a critical and challenging task. Trust is an important aspect of Wireless Sensor Networks that enables sensor nodes to cope with uncertainty and uncontrollability caused by the free will of other nodes. However due to the computational complexity constraints and excessive energy consumption, trust computation and management is a highly challenging task.

Analyzing the trust level of a node has a positive influence on the confidence with which two sensor nodes communicate since an untrustworthy node can adversely affect the quality and reliability of data. A detailed survey on various trust based routing protocols and various trust computation models is presented in this paper. To boot our work, we have analyzed various works on trust dynamics like trust propagation, prediction and aggregation models, and the impact of trust on various security services and the influence of network dynamics on trust dynamics.

Key words: Wireless Sensor Network, Trust Threshold, Trust Computation, Propagation, Aggregation, Trust Dynamics I. INTRODUCTION Sensor Networks are the key to the creation of smart spaces, which embed information technology in everyday home and work environment. Wireless Sensor Network (WSN) is a network that consist of multiple battery operated sensor devices with sensing, computation, and wireless communications capabilities. The main aim of Wireless Sensor Network (WSN) is to collect data from the environment [1]. WSN includes many different types of sensors such as seismic, low sampling rate magnetic, thermal, visual, infrared, acoustic and radar, which are able to monitor a wide variety of ambient conditions [2]. WSN finds its applications in military, environment, health, home and other commercial areas.

Wireless Sensor Network (WSN) is employed in harsh environments where pure human access and monitoring cannot be easily scheduled or efficiently managed [3]. Wireless sensor networks are employed mostly in public and uncontrolled areas; hence security is a major challenge [4]. The traditional security mechanisms like cryptography, authentication, confidentiality and message integrity can resist external attacks like eavesdropping, message replay and fabrication of messages.

But it cannot resolve internal attacks which are caused by the captured nodes. To facilitate secure communication, we need to ensure that all communicating nodes are trusted.

Trust is defined as the belief level that one sensor node puts on another node for a specific action based on the previous observation of behaviour [5]. A trust model

supports decision making in a Wireless Sensor Network (WSN) such as pre key-distribution, data aggregation, sink node selection and self-reconfiguration of these sensor nodes. Trust model encourages trustworthy nodes to communicate bit it discourages untrustworthy nodes to even participate in the network [6]. Trust also increases network lifetime, throughput and resilience in a WSN. Trust may be subjective or objective depending on the task. In general, trust is classified as behavioural or computational trust based on where it is used. Behavioural trust defines trust relations among people and organizations. Computational trust defines trust relation among devices, computers, and networks. Depending on the observation, trust may be direct trust or indirect trust. Direct trust specifies the direct observations and called as first hand information. Indirect trust specifies the indirect observation and called as second hand information.

The three primary aspects associated with evaluating trust in distributed networks are as follows. First, trust offers an incentive for good behaviour. Creating an expectation that sensor nodes will “remember” one’s behaviour will cause sensor nodes to act responsibly.

Second, trust evaluation provides a prediction of one’s future behaviour. This prediction protects trusted nodes from communicating with less trustworthy nodes. Third, the trust evaluation results can be directly applied to detect selfish and malicious nodes in the network.

The rest of the paper is organized as follows.

Section II provides a summary of the various trust based routing protocols. Section III describes the fundamental elements in trust evaluation system that includes trust definition, trust metrics and trust models. In Section IV, the various attacks and protection techniques for trust evaluation system is presented. The conclusion is drawn in Section V II. TRUSTED ROUTING IN WIRELESS SENSOR NETWORKS (WSN) Various routing, power management, and data dissemination protocols have been specifically designed for WSNs where energy consumption is an essential design issue.

A. Classification of Routing Protocols

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Fig. 1: Classification of Routing Protocols in WSN The three main categories of routing protocols are datacentric, hierarchical and location-based. Fig. 1 shows the classification of the routing protocols.

B. Structure of Trust Model In a network, trust helps a node to determine whether its neighbour node is uncooperative or malicious [27]. In an Ad-hoc network, trust plays a major role in detecting misbehaviours, routing and resource sharing. The advantages and disadvantages of a trust model are given in Table I. Structure Pros Cons Less computation Communication Centralized overhead and overhead, less reliable memory usage and no scalability Reliable and Distributed Computation overhead scalable Large computation Less overhead and memory communication requirements than Hybrid overhead than centralized; less reliable centralized and scalable than distributed Table 1: The Structure of a Trust Model- Pros and Cons C. Various Trust Models In WSN, trust plays a major role in detecting a malicious node [36]. Table II shows various techniques that can be used to build a trust model in sensor networks.

Trust Trust Purpose Structure Model Computation To monitor Distributed Beta RFSN fraudulence in and distribution [7] the network cooperative approach Hierarchical ATRM To minimize Certificate Agent based [8] overhead based To monitor Distributed Quorum DRBTS fraudulence in and voting [9] the network cooperative approach AEMP To support Weighted Distributed [10] secure routing approach To improve the RDAT reliability of Beta Distributed [11] data distribution aggregation Distributed To build TRMand collaboration Fuzzy IoT [12] Behaviour among nodes based To pick out BTRM trustworthy Distributed Ant colony [13] nodes To discover DTLSRP trust based Distributed Weight [14] routing Behaviour To minimize and TMA communication Certificate Weight based [15] and storage based overhead Hierarchical

RSDA [16]

LDTS [17]

HTMW [18]

To amend Beta accuracy of Distributed probability aggregated data Density To help collaborative processing by Weight based Hierarchical detecting Approach malicious behaviour To execute routing and Stochastic Hierarchical intrusion Petri net detection Table 2: Trust Models in WSN III. TRUST EVALUATION BASICS

In order to compute the trust level on nodes, we need to understand trust definition, metrics and various trust properties that are employed in trust computations [37].

A. Definition of Trust In literature, several definitions are given to trust. Trust is always defined by reliability, utility, availability, risk, quality of services and other concepts.

1) Trust as a Belief: Trust is defined as the belief level that one sensor node puts on another node for a specific action based on the previous observation of behaviour [5].

2) Trust as a Probability: Trust is a particular level of the subjective probability with which one party assesses that other party will perform a particular action [19].

3) Trust as a Transitive Relationship: Trust is a weighted binary relationship between two sensor nodes.

4) Trust as a Risk Factor: [20] states that trusting behaviour occurs when an individual node perceives an ambiguous path, the result of which could be good or bad, and the occurrence of the good or bad result is contingent on the actions of another node. [21], [22] defines trust as a bet about the future contingent action of other nodes.

B. Trust Metrics The various metrics that are used in trust evaluation are categorized as follows 1) Trust Scale: The level of trust can be measured using continuous or discrete data. Trust can be measured by a continuous value in [0,1] or as a discrete value in [-1,1]. Trust can also be measured using threshold based approaches. A node is considered to be trustworthy if the trust value of that node exceeds a particular threshold.

2) Trust Facets: In [23], a confidence value in the interval [0,1] and a trust value in the interval [0,1] denote the trustworthiness of a node. Trust can also be measured as a triplet (b,d,u) Ń” [0,1]3 b+d+u=1, where b,d and u denote belief, disbelief and uncertainty respectively.

3) Trust Logics: Probability can also be used as a trust metric. The ratio between the numbers of packets forwarded correctly to the

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A Survey on Trust Models for Secure Data Transmission in Wireless Sensor Networks (WSN) (IJSRD/Vol. 3/Issue 08/2015/148)

total number of packets received can also be used as a trust metric. The bad and good experiences are used in Beta distribution to obtain the trust value.

Trust is always established between two parties for s specific action. The first party can be called as subject and the second party can be called as object. A trust relationship is represented by {subject: agent, action}.

D. Trust Blocks

C. Trust Properties The three main properties of trust [5] are as follows: 1) Asymmetry: Asymmetry implies that if node A trusts node B, it does not necessarily mean that node B trusts node A. 2) Transitivity: Transitivity implies that trust value can be passed along a Fig. 2: Relationship among various trust blocks path of trusted nodes. If node A trusts node B and node B First, the trust value of all the nodes will be computed based trusts node C, it can be inferred that node A trusts node C at on certain metrics and recommendations. The trust a certain level. computation can be centralized or distributed.

3) Composability: Then the computed trust values will be propagated Composability implies that trust values received from in the network in order to establish trust between nodes that multiple available paths can be composed together to obtain are not in immediate contact. an integrated value.

Title & Perspective Trust & Advantages Complexity Limitations Year Employed Performance Metric Direct Trust Enhancing Additional Trust Aware overhead in Fails when a Routing by Trust is categorized as .Improved network maintaining Finite State critical node fails False Alarm {T,S,U}. S is sublifetime, routing & Trust Threshold Machine (FSM) and no alternate Detection categorized as SU and false alarm recovery for Suspicious Model path can be and ST performance nodes and found Recovery Untrustworthy (2014) [31] nodes A Novel Approach to Trust When threshold Managemen is small, more Difficulty in t in Resilient to major correct trust Subjective Logic Trust is measured as a setting up a Unattended attacks like ADV-Del, opinions are Framework triplet T={B,D,U} suitable threshold Wireless Noise, Homo, Hybrid considered as value Sensor outliers and vice Networks versa (July 2014) [30] HYBRID TRUST Will not give Trust Additional .Trust values and good results if Managemen maintenance of recommendations are the number of t in MANET Auxiliary Trust Trust is measured in not propagated to nonrecommending using a Recommendatio Table (ATT) to [0,1]. Relationship neighbours. neighbours is Scalable n Exchange supply nodes Maturity is used as a .This saves energy and less Maturity Protocol (REP) with additional performance metric minimizes the Can tolerate only Based information that probability of false 35% false Model (Sept improves trust recommendations recommendation 2010) level evaluation s Building a Significantly improves Energy When black hole Trust Aware Trust is measured in network throughput, consumption is nodes positioned Trust Aware Dynamic [0,1]. Trustworthiness reliability even under high since trust in a straight line Dynamic Routing is derived from attacks like message value is collude, nodes in Routing Solution for Reputation (RE) and tampering/modificatio calculated based the middle may Framework Wireless Risk(RI) n & selective on historical not be detected Sensor forwarding data, recent short due to lack of

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Networks (2011)

ReTrust : AttackResistant and Lightweight Trust Managemen t for Medical Sensor Networks (July 2012) Trust Managemen t for Defending On-Off Attacks (April 2015)

term information, self-observation and recommendation

Collection Tree Protocol

Trust is considered to be an integer in [0,lam]. Here lam=100. Trust value is denoted by T(subject:agent,action )

.Identify malicious behaviour & exclude it .Improve network performance .Defend on-off attack & bad mouthing attack

The usage of checking method to isolate bad recommendation s

redundancy check

Network throughput decreases as the number of malicious nodes increases

Since almost all As the size of the nodes are trusted, Bad Behaviour Trust is measured in the hop count Window(BBW) [0,1]. Predictability increases thereby increases, the trust & Redemption consuming more trust value factor energy for decreases communication automatically Table 3: Comparison of Different Distributed Trust Computing Mechanisms During trust propagation, the trust value from the trustee node. Trust factor is determined based on the multiple paths can be aggregated to get a combined trust amount of deviation between the observation reports. value that can be stored in history. This can be used in trust Trust of a particular node x is calculated [32] by a prediction which in turn can be used in the applications that node y as follows: need security.

T = W(Rp)×Rp+W(Rq)×Rq+W(Re)×Re+W(D)×D (1) where W(.) is the weight assigned to a particular event, Rp, Rq, Re, D are normalized route reply misbehaviour IV. TRUST COMPUTATIONS factor, route request misbehaviour factor, route error The three components of trust computation include misbehaviour factor and data delivery misbehaviour factor ‘experience’, ‘recommendation’ and ‘knowledge’ [24]. respectively.

Experience is directly measured by immediate neighbours Rp = Rps − Rpf Rps + Rpf and kept updated at regular intervals in the trust table. The Rq = Rqs − Rqf Rqs + Rqf existing trust table is propagated among the network as Re = Res − Ref Res + Ref ‘recommendation’. Knowledge component includes the D = D s − Df D s + Df previously evaluated trust at a regular interval [37]. Trust where R ps, Rqs, Res and Ds are the number of computation can be either centralized or distributed. successful: route reply acknowledgement packets, route A. Distributed Trust Computation request acknowledgement packets, route error acknowledgement packets and data delivery Distributed trust computation is classified as direct, indirect acknowledgement packets, respectively. Similarly Rpf, Rqf, and hybrid trust.

Ref and Df are the number of failed packets.

1) Direct Trust: Every node measures the trust of other nodes by Every node observes their neighbours for their event reports observing their behaviour. Over the time, these experiences and stores the reports in their knowledge cache. A trustor will become stale. Therefore, x will assign some weight node (trust measuring node) compares its own observation value to the past history. report on event with the observation report it received from Trust & Context In Performance & Title & Year Performance Advantages Complexity Use Limitations Metric .Less communication Trust vale is an As the number Group Based Trust overhead by usage integer between 0 of clusters Management of trust state.

More suitable only Sliding and 100. Trust is increases, the Scheme for .Each for large scale Window quantized into memory Clustered Wireless communication WSN with small Scheme three states: consumption Sensor Networks don’t require extra size of clusters trusted, untrusted, requirement at (2009) communication to uncertain the CH increases calculate the trust vale Communication Trust Based Watchdog .CH sends the key Updating sensor Will not give good Trust Redemption Scheme & Dynamic Sliding Window Scheme

.Badly reputed nodes have an opportunity to regain trust .Average throughput is high

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Scheme in Trusted Sensor Network (2010)

Hierarchical Trust Management for WSN & its Applications to Trust-Based Routing & Intrusion Detection (June 2012)

Dynamic Key Management Scheme

Cluster Based Hierarchical Trust Management Protocol

mechanism Trust vale of CH is updated by BS and SN is updated

by CH

Trust is measured in [0,1]. Social and QoS trust are measured

required for two nodes of same cluster to communicate .Saves energy and reduces traffic Peer to peer trust evaluation is enhanced by adding components like intimacy, honesty, energy & unselfishness

node’s trust value consumes most of the energy

results if the CH fails and the BS did not find a new CH in time

Identifying the best way to aggregate and propagate trust

As the minimum trust threshold increases, the false negative probability decreases & the false positive probability increases

Table 4: Comparison of Different Centralized Trust Computing Mechanisms 2) Recommendation Based Trust: and the average of them determines the final trust value.

Trust relationships on nodes are established based on This is given as trust certificate to all nodes in the cluster. recommendations alone. Every node will monitor its one An agent-based trust and reputation scheme is hop neighbour nodes and generate a “trust report” based on proposed is [25], [26] for MANET. It is assumed that there the neighbour nodes’ behaviour. Initially all nodes will have are n number of reputation assistants. To evaluate the trust a random unknown trust level on other nodes. Once the trust value of a neighbour node x, the node C has to query its report is generated it will be either broadcasted to all nodes reputation assistants about this neighbouring node x. Once or it can be flooded controllably in the network. node C receives the trust value from its reputation assistants, Recommendation reliability and recommendation familiarity it uses the weighted means to measure the node’s final trust [5] are used to filter false recommendations and to detect thereby making the corresponding decision. The final trust malicious nodes. of C on X(T) is given by 3) Hybrid Method: TrustAVG =(∑ni=1 TrustRAi , Xn)/n Trust on a node is computed based on direct experience and wi = TrustRAi,X / TrustAVG also recommendations from other nodes. Node x’s trust [33] T = (TrustC,X + ∑ni=1 wi × TrustRAi,X)/(n+1) on node y is given by Where TrustAVG is the average agent (reputation Tx,y = αTs + βTo assistant) trust on node X, TrustRAi,X is the trust of reputation where α and β are such that α+β=1. Ts is computed assistant i on X, wi is the weight given to trust value by monitoring node y for total packets dropped by y, packet obtained from assistant i and TrustC,X is the self-measured forwarding delay by y, packets misrouted by y and the trust of node C on X. packets wrongly injected by y. To is the collective trust Comparison of various centralized trust computing evaluation by all nodes on y. schemes is provided in Table IV. Comparison of various distributed trust computing C. Attacks in Trust Model schemes is provided in Table III. A list of the possible trust based attacks in Wireless Sensor B. Centralized Trust Establishment Network is given below: Centralized trust establishment scheme assumes a trust agent 1) Bad Mouthing Attack: (TA) that can be accessible by all nodes in the group. Trust Bad Mouthing attack involves a node giving bad Agent can either compute trust for the whole network or can recommendation about other nodes intentionally. assist nodes in their trust computation by providing initial 2) Denial of Service Attack (DoS): trust value on target nodes. A centralized cluster head based The attackers send as much trust recommendations as trust computation [34] works as follows. The cluster head possible to consume the resources of the trust calculating provides every node in the cluster with the initial trust value nodes. on every other node. Each node will combine its own 3) On-off Attack: calculated trust value based on experience with the initial The malicious nodes behave good and bad intentionally trust value obtained from the cluster head. Node i evaluates based on the importance of the situation. the trust value of node j as follows: 4) Sybil Attack: φ(i, j) = T (i, j) × α + T (H, j) × (1 − α) × β A malicious node creates several fake IDs. The fake IDs where T(i,j) is the trust value of node j calculated take the blame that should be given to the actual malicious by node i based on successful data delivery rate and node experience rate. T(H,j) is the node j’s initial trust value 5) Conflicting Behaviour Attack: obtained from the cluster head. β is the malicious factor The malicious node behaves differently towards different (β=0 denotes malicious and β=0 denotes non-malicious). nodes. That is, it can give good recommendation about a The trust value of the target node will be evaluated by all node to one group of nodes and bad recommendation about other nodes and sent to the cluster head. The cluster head the same node to the other set of nodes. will multiply each value with the trust value of the provider

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6) Camouflage Attack: The dishonest users attempt to build up trust by always reporting as per the observed majority. After earning enough trust value, they behave dishonestly for specific occasions.

7) New Comer Attack: The attacker just leaves the system and joins again hoping to flush out the previous bad history and to accumulate new and good trust value.

8) Collusion Attack: This attack is launched by more than one malicious node collaborating and providing false recommendations about other nodes.

D. Trust Dynamics: Trust is a dynamic phenomenon. It changes with time, experience and the state of various sources it is based on.

Evolution of trust over time is called trust dynamics and is characterized by the following components: trust propagation, prediction and aggregation.

1) Trust Propagation: Energy consumption of nodes can be reduced if the computed trust value gets propagated in the network.

Recommendation is the simplest form of trust propagation.

Recommendation is usually from a single-hop neighbour while trust propagation can be multi-hop. Trust propagation is based on the transitivity property of trust.

Trust Aggregation Trust & Performance & Performance Advantages Complexity Limitations Metric

Title & Year

Context In Use

Secure Trust Reputation with Multi-Criteria Decision Making for WSN Data Aggregation (2011) [29]

Byzantine decision making is employed to aggregate data

Trust level is calculated based on Gaussian pdf paired with a Byzantine decision making

Data Aggregation reduces communication and power consumption

Calculation of the reputation of neighbour nodes based on Gaussian pdf

Integrity and authenticity can be easily attacked by misbehaving nodes

Trust Aware InNetwork Aggregation for Wireless Sensor Networks(2009)

Trust Aware Aggregation facilitated with a penalty or award to a node’s trust value based on the partial aggregation result it provides

Trust is measured in [-1,1]. Multi-parent tree topology is employed where trust value is used to detect and flag a malicious node

.Robust to collusive attacks .No additional messages .Resource efficient .Returns the highest fidelity possible response

Aggregation for a single node is done by multiple parents that consumes more energy and time

It can resist collusive attacks only to a certain level.

Trust-Based Secure Aggregation in Wireless Sensor Networks [28]

Aggregation is done based on three main schemes: average, median & trustbased median

Trust is measured in [-1,1]. Trust Evaluation Matrix with several factors is included to improve the accuracy in trust evaluation

.Additional maintenance of anchor nodes .Localization algorithms increases complexity

There may be redundant communication between the aggregator node and the sink node

An Improved Intrusion Detection Scheme Based on Weighted Trust Evaluation for WSN (2010)

Weighted Trust Application (WTA) is employed to propagate trust among three layers : SN, FN & BS Layer

Trust is measured in [0,1]. Each node is assigned a weight value to improve reliability and as a reference to FN

.Shorter response time .Higher Detection Rate .Lower Misdetection Rate

Overhead in updating the mistaken ratio for every node

If the mistaken ratio of a node increases, the weight of that node decreases

Trust Based Intrusion Detection in Wireless Sensor Networks (2011)

Hierarchical Trust Management Protocol to propagate trust from SN to CH & CH to BS

Trust is measured in [0,1]. Trust metric considers both social trust and QoS trust

Accuracy of trust value is improved by using honesty, energy and cooperativeness trust components

Maintenance of trust threshold to detect false positive and false negative

LDTS : A Lightweight and Dependable Trust

Dependability Enhanced Trust Evaluation to

Trust is measured as an unsigned integer in the

.Less memory overhead .Less transmission

Each CH has to maintain an additional table

.A maximum difference function preserves consistency of sensed data .�Failure count� value for a node if it fails to sense an event Trust Propagation

Optimal trust threshold to detect false positive and false negative decreases as the network lifetime increases More suitable only for large-scale clustered WSN

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System for Clustered Wireless Sensor Network (June 2013) [17]

propagate trust from CM to CH & CH to BS

interval between 0 and 10

overhead .Improves system efficiency

to store the feedback matrix

with a large size of clusters

Trust Prediction Trust is measured Markov Chain as a five-tuple .Resists bad Complex trust Based Trust Markov model mouthing attack computation Adaptive memory Applicable only Management Ω=(R,V,Q,^,π). .95% accurate since sensor factor is used to for a Wireless Scheme for Markov chain trust value nodes are given predict trust Sensor Network Wireless Sensor reduces the impact .Transaction punishment for values with static nodes Networks (Dec of environment on success rate is malicious 2014) evaluating node’s 90% behaviour state Table 5: Various Trust Aggregation, Propagation and Prediction Techniques 2) Trust Aggregation: When trust values are propagated through multiple paths, REFERENCES multiple values of this value are received at the destination.

[1] Du, X.; Chen and H.H, “Security in Wireless Sensor Aggregation can be done at the destination to combine these Networks”, IEEE Wireless Communication, 2008, values into a single trust value. This trust aggregation is Vol.15, 60-66. based on the composability property of trust. Aggregation [2] Shivangi Raman, Amar Prakash and Shveta plays a major role in suppressing some malicious activities.

Singh,”Wireless Sensor Networks: A Survey of Trust Aggregation problem consists of aggregating n-tuples Intrusions and their Explored Remedies”, International of observed trust values, all belonging to a given set (x1, Journal of Engineering Science and Technology, Vol. x2,...,xn), into a single value of the same set (y) as follows: 2(5), 2010, 962-969. y = Aggre (x1, x2,...,xn) [3] A.A. Abbasi and M. Younis,“ A Survey on Clustering The important conditions [35] for aggregation Algorithms for Wireless Sensor Networks”, Computer operator ⊕ are as follows: Communications, 30, 2826–2841, 2007. a) Boundary Condition: [4] Giruka, V.C. Singhal, M.Royalty and Aggre(0, 0,..., 0) = 0, Aggre(1, 1,..., 1) = 1 Varanasi.S,”Security in Wireless Sensor Networks”, b) Non Decreasing Condition: Wireless Communications and Mobile Computing, If yi > xi ∀i 8(1), 1-24.

Aggre(x1, x2,...,yi,...,xn) > Aggre(x1, x2,...,xn) [5] Jinfang Jiang, Guangjie Han, Feng Wang, Lei Shu and 3) Trust Prediction: Mohsen Guizani,”An Efficient Distributed Trust Trust Prediction is a method of predicting unknown trust Model for Wireless Sensor Networks”, IEEE value between two nodes using the present and past Transactions on Parallel and Distributed Systems, behaviour of nodes and also the recommendation received Vol.26, No.5, May 2015. from other nodes.

[6] V.Umarani and K.Soma Sundaram, “Survey of V. CONCLUSIONS AND FUTURE WORK Trust is an important area of research. The goal of this paper is to provide multiple perspectives on the concept of trust, an understanding of the properties to be considered in developing a trust metric, and how trust should be computed. We then presented a survey of various trust computing metrics. We then analyzed trust dynamics such as trust propagation, aggregation and prediction. We hope that in the near future we will bring a set of fundamental principles for building trust and its issues. These will be realized in practical and commercial applications too. We have also proposed a trust model that uses only direct trust based on three factors like Channel State Information, Link Residual Life and energy of the receiver node to calculate the trust value of a sensor node. This proposed Self Evaluating Trust Model in WirelessHART Standard saves energy, improves network lifetime and improves performance of the network. Trust computation and performance metric of this proposed work can be found better in our next paper.

Various Trust Models and their Behaviour in Wireless Sensor Networks”, International Journal of Emerging Technology and Advanced Engineering, Vol.3, Issue 10, Oct 2013.

[7] S. Ganeriwal, L. K. Balzano, M. B. Srivastava, ”Reputation Based Framework for High Integrity Sensor Networks in ACM Transactions on Sensor Networks pp.1-37, 2008.

[8] Boukerch, L. Xu, K. EL-Khatib, ”Trust-Based Security for Wireless Ad hoc and Sensor Networks” in Computer Communications, pp. 2413–2427, 2007.

[9] A. Srinivasan, J. Teitelbaum and J. Wu, 2,”DRBTS: Distributed Reputation-based Beacon Trust System in the 2nd IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC’06), Indianapolis, USA, 2006.

[10] Gu Xiang, Qiu Jianlina, Wang Jina, “Research on Trust Model of Sensor Nodes” in WSNs in International Workshop on Information and Electronics Engineering (IWIEE), pp.45-57, 2012.

[11] Suat Ozdemir,”Functional Reputation Based Reliable Data Aggregation and Transmission for Wireless Sensor Networks in Computer Communications, pp 3941–3953, 2008.

All rights reserved by www.ijsrd.com

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[12] Dong Chen, Guiran Chang, Dawei Sun, Jiajia Li, Jie Jia, and Xingwei Wang, “TRM-IoT: A Trust Management Model Based on Fuzzy Reputation for Internet of Things” in ComSIS Vol. 8, No. 4, Special Issue, pp. 1208-1228, 2011.

[13] Félix Gómez Mármol, Gregorio Martínez Pérez, “Providing Trust in Wireless Sensor Networks Using a Bio-Inspired Technique” in Journal on communication, pp.86-94, 2008.

[14] Shaik Sahil Babu, Arnab Raha, Mrinal Kanti Naskar , “A Direct Trust Dependent Link State Routing Protocol Using Route Trusts for WSNs (DTLSRP)” in Journal of Scientific Research Wireless Sensor Network, 3,pp 125-134, 2010.

[15] Junqi Zhang, Rajan Shankaran, Mehmet A. Orgun Vijay Varadharajan and Abdul Sattar , “A Trust Management Architecture for Hierarchical Wireless Sensor Networks , 35th annual IEEE conference on local computer networks.LCN ,pp. 268-273, 2010.

[16] Alzaid, Hani and Foo, Ernest and Gonzalez Nieto, “RSDA: Reputation-based Secure Data Aggregation in Wireless Sensor Networks” in Proceedings 1st International Workshop on Sensor Networks and Ambient Intelligence (SeNAmI), Dunedin, New Zealand, 2008.

[17] Xiaoyong Li, Feng Zhou and Junping Du, “LDTS: A Lightweight and Dependable Trust System for Clustered Wireless Sensor Networks” in IEEE transactions on information forensics and security, pp 451-551, 2013.

[18] Fenye Bao, Ing-Ray Chen, MoonJeong Chang, and JinHee Cho, “Hierarchical Trust Management for Wireless Sensor Networks and its Applications to Trust-Based Routing and Intrusion Detection” in IEEE Transactions on network and service management, Vol. 9, no. 2, pp.169-183, 2012.

[19] D. Gambetta, “Can we trust trust?,” Gambetta, Diego (ed.) Trust: Making and breaking cooperative relations, electronic edition, Department of Sociology, University of Oxford, pp. 213-237, 2000.

[20] M. Deutch, “Cooperation and Trust: Some Theoretical Notes,” Nebraska Symposium on Motivation, Nebraska University Press, pp. 275–319, 1962.

[21] P. Sztompka, “Trust: A Sociological Theory,” in Cambridge: Cambridge University Press, 1999.

[22] R. C. Mayer, J. H. Davis and F. D. Schoorman, “An Integrative Model of Organizational Trust,” Academy of Management Review, vol. 20, pp. 709–734, 1995.

[23] G. Theodorakopoulos and J. S. Baras, “Trust Evaluation in Ad-hoc Networks,” in the 3rd ACM workshop on Wireless security, WiSe ’04, pp. 1–10, 2004.

[24] S. Choudhury, S. D. Roy and S. A. Singh, “Trust Management in Ad hoc Network for Secure DSR Routing,” Novel Algorithms and Techniques In Telecommunications, Automation and Industrial Electronics, pp. 496– 500, 2008.

[25] A. Boukerche and Y. Ren, “A Security Management Scheme using a Novel Computational Reputation Model for Wireless and Mobile Ad hoc Networks,” in Proc. 5th ACM symposium on Performance evaluation

of wireless ad hoc, sensor, and ubiquitous networks, pp. 88–95, 2008.

[26] Y. Ren and A. Boukerche, “Modeling and Managing the Trust for Wireless and Mobile Ad hoc Networks,” in IEEE International Conference on Communications, ICC ’08, pp. 2129 – 2133, 19-23 May 2008.

[27] P.Prittopaul, A.Divya Preetha, M.Usha, “WirelessHART Standard for Security and Energy Efficiency in Wireless Sensor Networks”, in Proc. of Fourth National Conference on Recent Trends in Information Technology, Feb 2015, pp. 31-37.

[28] Junbeom Hur, Yoonho Lee, Seongmin Hong, Hyunsoo Yoon,” Trust Based Secure Aggregation in Wireless Sensor Networks”, Korea Advanced Institute of Science and Technology, 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Rep. of Korea.

[29] Bjorn Stelte and Andreas Matheus, ”Secure Trust Reputation with Multi-Criteria Decision Making for Wireless Sensor Networks Data Aggregation” Institut f¨ur Technische Informatik Universit¨at der Bundeswehr M¨unchen Neubiberg, Germany.

[30] Yi Ren, Vladimir I. Zadorozhny, Vladimir A. Oleshchuk and Frank Y. Li,”A Novel Approach to Trust Management in Unattended Wireless Sensor Networks”, IEEE Transactions on Mobile Computing, Vol 13, No 7, July 2014.

[31] Youngho Cho and Gang Quo, “Enhancing Trust-aware Routing by False Alarm Detection and Recovery” IEEE Military Communication Conference, 2014.

[32] A. A. Pirzada and C. McDonald, “Trust Establishment in Pure Ad-hoc Networks,” Wireless Personal Communications, vol. 37(1-2), pp. 139– 168, 2006.

[33] M. Virendra, M. Jadliwala, M. Chandrasekaran, and S. Upadhyaya, “Quantifying Trust in Mobile Ad-hoc Networks,” in International Conference on Integration of Knowledge Intensive Multi-Agent Systems, pp. 65– 70, April 18-21, 2005.

[34] S. S. Park, J. H. Lee, and T. M. Chung, “Cluster-based Trust Model Against Attacks in Ad-hoc Networks,” in Third International Conference on Convergence and Hybrid Information Technology, pp. 526–532, 2008.

[35] M. Detyniecki, “Mathematical Aggregation Operators and their Application to Video Querying,” in PhD thesis, Computer Science Department of the University Pierre and Marie Curie (UPMC) - Paris, France, 2000.

[36] P.Pritto Paul, Dr.N.Sankar Ram and A.Divya Preetha, “Trusted WirelessHART Communication Protocol (TWHCP) for Secure Communication in Wireless Sensor Networks (WSN)”, Proc. the International Conference on Networks, Electronics, Communication and Control, Mar 2015.

[37] Kannan Govindan and Prasant Mohapatra,”Trust Computations and Trust Dynamics in Mobile Adhoc Networks: A Survey” Proc IEEE Communications Surveys and Tutorials, Vol 14, No. 2, pp 279-298, Second Quarter 2012.

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