Normal Distribution Based Dynamical Recommendation Trust Model

Shao, Kun; Luo, Fei; Mei, Niao-Xiong; Liu, Zongtian

doi:10.3724/sp.j.1001.2012.04204

Cited by 22 publications

(9 citation statements)

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“…The perceptual data of nodes always follows certain distribution, e.g. Gaussian distribution [28], [29]. For simplicity, the perceptual data of nodes in this paper is modeled as a Gaussian distribution.…”

Section: A Direct Trust Calculationmentioning

confidence: 99%

BLTM: Beta and LQI Based Trust Model for Wireless Sensor Networks

Huang

Ling

et al. 2019

IEEE Access

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To defend against internal attacks in wireless sensor networks (WSNs), building a trust model between sensors nodes has been proved to be an effective way in this paper. The most current trust models only consider communication behavior when calculating direct trust, which is directly calculated based on the interactions between sensor nodes. However, this is not enough because of the various types of attacks. Furthermore, the adverse effect of poor-quality links on the trust value of normal nodes is not discussed in the current trust models. In this paper, we propose a beta and link quality indicator (LQI)-based trust model (BLTM) for the WSNs. First, communication trust, energy trust, and data trust are considered when calculating direct trust. Then, the weight of communication trust, energy trust, and data trust are discussed. Finally, an LQI analysis mechanism is proposed to maintain the accuracy and stability of the trust value of normal nodes in a network with poor-quality links. Compared with other models, e.g., beta-based trust and reputation evaluation system (BTRES), the simulation results show that the BLTM can defend against internal attacks, e.g., DoS attack and data tampering attack which the BTRES cannot resist and can reduce the adverse effect of poor-quality links on the trust value of normal nodes effectively. INDEX TERMS Wireless sensor networks, beta distribution, link quality indicator, trust model.

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Section: A Direct Trust Calculationmentioning

confidence: 99%

BLTM: Beta and LQI Based Trust Model for Wireless Sensor Networks

Huang

Ling

et al. 2019

IEEE Access

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“…Utilizing users' historical task records, it computes similarities of users or extracts features from task information through statistics and machine learning methods to establish a recommendation model. There are many classical content-based recommendation methods, such as the TF-IDF method Chum et al (2015) and model-based recommendation methods (Bayesian classification Yang et al (2013); Shao et al (2013), clustering, decision tree and neural network, etc.). Such recommendation methods work well for structured information (news, articles, etc.…”

Section: Related Workmentioning

confidence: 99%

A novel task recommendation model for mobile crowdsourcing systems

Wang

Tong

Wang

et al. 2018

IJSNET

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With the developments of sensors in mobile devices, mobile crowdsourcing systems are attracting more and more attention. How to recommend user-preferred and trustful tasks for users is an important issue to improve efficiency of mobile crowdsourcing systems. This paper proposes a novel task recommendation model for mobile crowdsourcing systems. Considering both user similarity and task similarity, the recommendation probabilities of tasks are derived. Based on dwell-time, the latent recommendation probability of tasks can be predicted. In addition, trust of tasks is obtained based on their reputations and participation frequencies. Finally, we perform comprehensive experiments towards the Amazon metadata and YOOCHOOSE data sets to verify the effectiveness of the proposed recommendation model.

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“…Many scholars put forward a variety of dynamic trust relationship prediction models in the open environment based on the mathematical methods and tools used to describe the uncertainty phenomenon [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. These models reflect the dynamics, complexity and uncertainty of trust relationship from different aspects, making people understand the nature of the dynamic trust relationship, and promoting the research and the development of related theory of trust relationship effectively.…”

Section: Research Backgroundmentioning

confidence: 99%

“…Dimitri proposed the model based on the Bayesian network model and used the Kalman information filter mechanism [12]. Claudiu proposed the dynamic trust model based on the reinforcement learning method of machine learning [13]. Also, the dynamical recommend trust model based on the normal distribution of the Bayesian formula was proposed by Shao et al [14].…”

Section: Related Research Workmentioning

confidence: 99%

A Direct Trust Aggregation Algorithm Based on the Minimum Variance Time Sequence Weight

Long¹,

Wensheng²

2015

TOCSJ

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Direct trust relationship is the foundation of research on the indirect recommendation trust between agents and the global trust degree. In view of the lack of analysis and attention on the direct trust in the current research of dynamic trust relationship, this paper proposed a minimum variance time sequence weight based on direct trust aggregation algorithm which is constrained by the fuzzy integration operator orness measurement level. The algorithm introduces the concept of agent reputation, elaborates the evolution process and the law of the trust in the process of direct interaction, and further characterizes the complexity and stability of direct trust relationship. The simulation experimental results show that the model has a good dynamic adaptability and scalability compared with the conventional model.

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Normal Distribution Based Dynamical Recommendation Trust Model

Cited by 22 publications

References 0 publications

BLTM: Beta and LQI Based Trust Model for Wireless Sensor Networks

BLTM: Beta and LQI Based Trust Model for Wireless Sensor Networks

A novel task recommendation model for mobile crowdsourcing systems

A Direct Trust Aggregation Algorithm Based on the Minimum Variance Time Sequence Weight

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