Trust Evaluation Model Research Articles

Wireless Sensor Network Technology Based on Security Trust Evaluation Model

Because the current research on wireless sensor networks is not thorough enough, with such shortcomings as lack of objectivity and insufficient accuracy of the trust evaluation model of network nodes, this paper proposes a trust evaluation model with multi-factor comprehensive consideration and Bayesian model, an improved trust evaluation model, by combining trust management mechanism, trust factor, fuzzy set and D-S evidence theory, solving factor singleness and imprecise evaluation in the original model. The simulation results show that the proposed model has good dynamic adaptability, higher detection rate and lower detection error rate, and it consumes less energy and improves the security of the network.

D-S evidence theory based trust ant colony routing in WSN

This paper proposes a trust ant colony routing algorithm by introducing a node trust evaluation model based on the D-S evidence theory into the ant colony routing protocol to improve the security of wireless sensor networks. To reduce the influence of conflict evidences caused by malicious nods, the consistent intensity is introduced to preprocess conflict evidences before using the D-S combination rule to improve the reliability of the D-S based trust evaluation. The nodes with high trust values will be selected as the routing nodes to insure the routing security, and the trust values are used as heuristic functions of the ant colony routing algorithm. The simulation tests are conducted by using the network simulator NS2 to observe the outcomes of performance metrics of packets loss rate and average end-to-end delay etc. to indirectly evaluate the security issue under the attack of inside malicious nodes. The simulation results show that the proposed trust routing algorithm can efficiently resist malicious attacks in terms of keeping performances of the average end-to-end delay, the throughtput and the routing packet overhead under attacking from malicious nodes.

A Customer-Centric Trust Evaluation Model for Personalized Service Selection

Trust is a very important criterion when service customers select desired Web services from a cluster of Web services with the same function. Most existing trust models cannot effectively implement personalized service selection with regard to consumer preferences and expectations. This paper designs a novel trust management method based on peer-to-peer network and presents a customer-centric trust evaluation model for personalized service selection. The trust evaluation model firstly maintains consumer-to-consumer trust values that are calculated according to preference similarity between customers, secondly gathers ratings on services submitted by other consumers, then synthesizes customer-to-customer trust and these ratings to generate personalized consumer-to-service trust, and finally selects the desired services according to the expected trust levels presented by customers. This paper conducts some experiments to demonstrate the details of service selection. Experimental results show that this model has good applicability to implement personalized service selection. The proposed model well simulates the reality.

A service oriented adaptive trust evaluation model for ubiquitous computing environment

The increased participation of various devices and networks, involvement of heterogeneous system, and context-awareness raises a significance of trust and trust evaluation in the ubiqutioius computing environment. Consequently, the trust evaluation based on the context and service requirements is adapted to decide the applicable level of security to the offered services. Therefore, this paper presents a service-oriented adaptive trust evaluation model (SOATEM) in the ubiquitous computing network. The proposed trust evaluation model computes direct trust based on various interaction properties and recommendation trust by filtering the dishonest recommenders based on the time context. A balanced blending of direct and recommendation trust are done with trust weights to make them relevant to current application scenarios. The results are compared with some of the available schemes and found to be a consistently good in performance for the today's ubiquitous network.

A secure routing of wireless sensor networks based on trust evaluation model

In order to counter the malicious attacks from internal nodes in Wireless Sensor Network(WSN), this paper proposes an Energy-optimized Secure Routing(EOSR) based on distributed trust evaluation model to identify and isolate Malicious node. EOSR routing protocol designed a multi-factor routing strategy, taking into account the node’s trust level, the remaining energy and path length. This strategy not only ensures that data is transmitted through the trusted nodes, but also balances energy consumption among the trusted nodes. By simulating the existing routing algorithms, the EOSR protocol shows better performance in packet delivery rate, network throughput and node average energy consumption.

Trust Evaluation Model for P2P Networks based on Time and Interaction

The traditional centralized trusting mechanism does not meet the requirements of the modern P2P network, so it is necessary to establish a distributed trusting mechanism to strengthen the system’s reliability. Factors including time attenuation factor, interactive frequency factor, interactive size factor and average online time factor markedly influence the trust of the nodes in a P2P network. This paper defines the precise effects of these factors on trust and proposes a comprehensive trust model and global trust model based on direct trust and indirect trust.

Fog-Based Evaluation Approach for Trustworthy Communication in Sensor-Cloud System

A sensor-cloud system is a combination of wireless sensor networks and cloud computing that is equipped with ubiquitous physical sensing ability, high-speed computation, huge storage, and so on. However, sensor-cloud systems suffer from various types of malicious attacks that can cause sensor communications to become unreliable. Establishing a trust evaluation method to ensure members’ reliability in sensor cloud is an effective way to resist malicious attacks. However, most current trust evaluation methods are constrained to specific attacks or applications, and they lack compatibility, verifiability, and scalability. To solve these problems, we formulated the trust evaluation issue as a multiple linear regression problem. Considering energy restrictions, we adopt fog nodes to assist in the trust computation. Moreover, the least squares algorithm is used to find the fitting function between the communication feature and the trust value. The experimental results show that our approach can find the best trust evaluation model and improve the compatibility, verifiability, and accuracy of trust evaluation.

An approach based on the transferrable belief model for trust evaluation in web services

Considering the existence of a wide range of web services and their diversity, selecting a service with the best performance among similar services remains a challenge for the demanding users. However, sometimes it is necessary that web services invoke each other and become combined to create a more complex composite web service. Evaluation of the trust degree based on the trust of individual services forming composite ones can be an effective factor for choosing the best combination of web services. In this paper, by using the fundamental concepts of belief combination and transferability in transferable belief model (TBM), the ratings of a customer for different interactions are combined to constitute the direct trust value. This creates a requirement to take the uncertainty of beliefs into account as well. In direct trust evaluation, the time of ratings determines the impact of the ratings in the final combination. By using the generalized Bayesian theorem in TBM, the trust of the composite services is modeled through time by considering the structure of the individual components within the service. Subsequently, it is shown that the proposed model provides a flexible mechanism for composite trust evaluation, it provides more accurate calculations compared to other existing models, and it is capable of taking into account more complex parameters in trust calculations such as the consistency of opinions.

TSTE: A Time-variant Stochastic Trust Evaluation Model in Social Networks

Building appropriate trust evaluation models is an important research issue for security guarantee in social networks. Most of the existing works usually consider the trust values at the current time slot, and model trust as the stochastic variable. However, in fact, trust evolves over time, and trust is a stochastic process. In this paper, we propose a novel time-variant stochastic trust evaluation (TSTE) model, which models trust over time and captures trust evolution by a stochastic process. Based on the proposed model, we derive the time-variant bound of untrustworthy probability, which provides stochastic trust guarantee. On one hand, the time-variant trust level of each node can be measured by our model. Meanwhile, by tolerating nodes with relatively poor performance, our model can effectively improve the node resource utilization rate. Numerical simulations are conducted to verify the accuracy and consistency of the analytical bounds on distinguishing misbehaved nodes from normal ones. Moreover, simulation results on social network dataset show the tradeoff between trust level and resource utilization rate, and verify that the successful transmission rate can be improved by our model.

Toward a Trust Evaluation Mechanism in the Social Internet of Things.

In the blooming era of the Internet of Things (IoT), trust has been accepted as a vital factor for provisioning secure, reliable, seamless communications and services. However, a large number of challenges still remain unsolved due to the ambiguity of the concept of trust as well as the variety of divergent trust models in different contexts. In this research, we augment the trust concept, the trust definition and provide a general conceptual model in the context of the Social IoT (SIoT) environment by breaking down all attributes influencing trust. Then, we propose a trust evaluation model called REK, comprised of the triad of trust indicators (TIs) Reputation, Experience and Knowledge. The REK model covers multi-dimensional aspects of trust by incorporating heterogeneous information from direct observation (as Knowledge TI), personal experiences (as Experience TI) to global opinions (as Reputation TI). The associated evaluation models for the three TIs are also proposed and provisioned. We then come up with an aggregation mechanism for deriving trust values as the final outcome of the REK evaluation model. We believe this article offers better understandings on trust as well as provides several prospective approaches for the trust evaluation in the SIoT environment.

Integrated trust based control system for multirobot systems: Development and experimentation in real environment

Abstract In a multirobot system, cooperation among the robots is important to successfully achieve mission objectives which requires the robots to work together. Under such circumstances, the cooperation between the robots is akin to the cooperation between humans where trust among each other plays a vital role in ensuring the effectiveness for achieving given objectives. This paper presents the development of a trust based control system for a multirobot framework aimed to improve the cooperation efficiency of the robots by identifying and collaborating only with trustworthy robots in the system. The developed control system is embedded with a trust evaluation model, designed using a novel adaptation of Markov Games concept into temporal difference learning to compute the direct trust and using a novel heuristic method to integrate the indirect trust. Simulation and real world experiments are carried out to compare and evaluate the performance of the developed model. Evidence from the experiments strongly suggest that the developed trust based control system can effectively distinguish between trustworthy and untrustworthy robots leading a more efficient task completion.

Trust Model of Wireless Sensor Networks and Its Application in Data Fusion.

In order to ensure the reliability and credibility of the data in wireless sensor networks (WSNs), this paper proposes a trust evaluation model and data fusion mechanism based on trust. First of all, it gives the model structure. Then, the calculation rules of trust are given. In the trust evaluation model, comprehensive trust consists of three parts: behavior trust, data trust, and historical trust. Data trust can be calculated by processing the sensor data. Based on the behavior of nodes in sensing and forwarding, the behavior trust is obtained. The initial value of historical trust is set to the maximum and updated with comprehensive trust. Comprehensive trust can be obtained by weighted calculation, and then the model is used to construct the trust list and guide the process of data fusion. Using the trust model, simulation results indicate that energy consumption can be reduced by an average of 15%. The detection rate of abnormal nodes is at least 10% higher than that of the lightweight and dependable trust system (LDTS) model. Therefore, this model has good performance in ensuring the reliability and credibility of the data. Moreover, the energy consumption of transmitting was greatly reduced.

An Efficient Dynamic Trust Evaluation Model for Wireless Sensor Networks

Trust evaluation is an effective method to detect malicious nodes and ensure security in wireless sensor networks (WSNs). In this paper, an efficient dynamic trust evaluation model (DTEM) for WSNs is proposed, which implements accurate, efficient, and dynamic trust evaluation by dynamically adjusting the weights of direct trust and indirect trust and the parameters of the update mechanism. To achieve accurate trust evaluation, the direct trust is calculated considering multitrust including communication trust, data trust, and energy trust with the punishment factor and regulating function. The indirect trust is evaluated conditionally by the trusted recommendations from a third party. Moreover, the integrated trust is measured by assigning dynamic weights for direct trust and indirect trust and combining them. Finally, we propose an update mechanism by a sliding window based on induced ordered weighted averaging operator to enhance flexibility. We can dynamically adapt the parameters and the interactive history windows number according to the actual needs of the network to realize dynamic update of direct trust value. Simulation results indicate that the proposed dynamic trust model is an efficient dynamic and attack-resistant trust evaluation model. Compared with existing approaches, the proposed dynamic trust model performs better in defending multiple malicious attacks.

CAT: Context-Aware Trust-Oriented Worker Selection in Social Crowd

Finding trustworthy worker is a longstanding issue in crowdsourcing systems. On traditional crowdsouring platforms, like Amazon Mechanical Turk1, the trustworthiness of a worker is usually based on the contextual information, like different types of tasks and different reward amounts of tasks. However, with the combination of OSNs and the crowdsourcing applications in social crowd, in addition to the above mentioned task based contexts, the social contexts like the social relationships and the social positions of participants can greatly assist requestors to select trustworthy workers. In order to select the trustworthy workers in social crowd, in this paper, we first present a contextual social network structure which contains complex social contexts. Then we propose a trust evaluation model taking both contexts information and the requirements of requestors into consideration, which leads to the trust worker selection in social crowd as a classical NP-Complete Multi-Constrained Optimal Path (MCOP) selection problem. For solving this challenging problem, we propose a new efficient and effective approximation Context-Aware Trust-Oriented Worker Selection algorithm CAT. The results of our experiments conducted on four real OSN datasets illustrate the superiority of our method in trustworthy worker selection.

A rough set-based association rule approach implemented on a brand trust evaluation model

In commerce, businesses use branding to differentiate their product and service offerings from those of their competitors. The brand incorporates a set of product or service features that are associated with that particular brand name and identifies the product/service segmentation in the market. This study proposes a new data mining approach, a rough set-based association rule induction, implemented on a brand trust evaluation model. In addition, it presents as one way to deal with data uncertainty to analyse ratio scale data, while creating predictive if–then rules that generalise data values to the retail region. As such, this study uses the analysis of algorithms to find alcoholic beverages brand trust recall. Finally, discussions and conclusion are presented for further managerial implications.

Study of Continuity Trust on Adoption and Design of e-Education and Student Development

Trust or trustworthiness is an important issue in e-education and development of students’ capabilities. Factors to calculate trust level have been studied to provide valuable information to students to avoid invalid choices. While some education branches provider and fraudulent services online still widely exist. A typical one is to attract students with ideal price or service but fail to deliver a solid item. This behavior is often carried out by a newly registered e-education. This paper proposes a new trust evaluation model to indicate to some extent the trust level of an e-education firm or service provider at different registered time. We describe our proposed approach for continuity trust evaluation, which evaluated empirically in the paper.

A novel membership cloud model‐based trust evaluation model for vehicular ad hoc network of T‐CPS

AbstractIt is generally known that the trust relationships among mobile nodes of vehicular ad hoc network (VANET) are uncertain in transportation cyber‐physical system (T‐CPS). However, the existing researches could not accurately describe the uncertainty in their trust evaluation models. To solve this problem, we propose a novel membership cloud‐based trust evaluation model for VANET of T‐CPS. The proposed model considers the trust uncertainty of fuzziness and randomness in the interactions among vehicles and uses membership cloud to describe the uncertainty in unified formats. Besides, we give the detail description of trustworthiness as well as algorithm to calculate the cloud droplets and the aggregated trust evaluation values. An experiment of study case demonstrates that our trust model can perfectly describe trust relationship among vehicles from the quantitative data to the qualitative information, and also vice versa. Thus, our trust model can accurately describe the uncertainty of fuzziness and randomness of the trust relationships. Meanwhile, two typical scenarios simulation and comparison with other up to date models demonstrate the validity and practicality of our trust model among vehicles for VANET of T‐CPS. Copyright © 2017 John Wiley & Sons, Ltd.

Collusion detector based on G-N algorithm for trust model

In the open network environment, malicious attacks to the trust model have become increasingly serious. Compared with single node attacks, collusion attacks do more harm to the trust model. To solve this problem, a collusion detector based on the G-N algorithm for the trust evaluation model is proposed in the open Internet environment. By analyzing the behavioral characteristics of collusion groups, the concept of flatting is defined and the G-N community mining algorithm is used to divide suspicious communities. On this basis, a collusion community detector method is proposed based on the breaking strength of suspicious communities. Simulation results show that the model has high recognition accuracy in identifying collusion nodes, so as to effectively defend against malicious attacks of collusion nodes.

A dynamic trust evaluation mechanism based on affective intensity computing

A complete description of trust relationship is key to construct a high precision trust model. But most of existing models not only miss the negative information and the hesitation information of trust, but also ignore the discordance between text comments and ratings. To solve the problems, a dynamic trust evaluation model based on the affect intensity is proposed. In the model, the intensity of sentimental polarities are calculated from words in comments. The corresponding relation between evaluations of trust property and the emotional intensity vector can be also described. Time series weights to distinguish the importance of transaction at different time are determined by the inverse form of exponential distribution. So the dynamic attenuation of trust can be described. To improve the polymerization ability of trust information, the local trust, the feedback trust and the overall trust are calculated by operators of fuzzy logic. The experimental results show the proposed model can effectively describe the trust relationship between nodes to identify and eliminate various malicious attacks significantly. Copyright © 2016 John Wiley & Sons, Ltd.

An efficient trust estimation model for multi-agent systems using temporal difference learning

In multi-agent system (MAS) applications, teamwork among the agents is essential as the agents are required to collaborate and pool resources to execute the given tasks and complete the objectives successfully. A vital part of the collaboration is sharing of information and resources in order to optimize their efforts in achieving the given objectives. Under such collaborative environment, trust among the agents plays a critical role to ensure efficient cooperation. This study looks into developing a trust evaluation model that can empirically evaluate the trust of one agent on the other. The proposed model is developed using temporal difference learning method, incorporating experience gained through interactions into trust evaluation. Simulation experiments are conducted to evaluate the performance of the developed model against some of the most recent models reported in the literature. The results of the simulation experiments indicate that the proposed model performs better than the comparison models in estimating trust more effectively.