# DDTMS: Dirichlet-Distribution-Based Trust Management Scheme in Internet of Things
## 2 Related Works
### TMS Based on Beta Distribution  
[PERSON] and [PERSON] proposed a framework, namely, the Reputation-based Framework for high integrity Sensor Networks (RFSN) [4]. Furthermore, based on this framework, they proposed the Beta Reputation System for Sensor Networks (BRSN) by using the Bayesian network, combined with the Beta distribution of node reputation. In BRSN, the node's behavior was divided into cooperation and non-cooperation. This is a traditional TMS, however, BRSN cannot defend against the high-reputation internal attacks, which were launchedby malicious nodes. [PERSON] et al. proposed a Beta and link quality indicator (LQI)-based trustmodel (BLTM) for the WSN, in order to defend against internal attacks [5]. In BLTM, they considered communication trust, energy trust, and data trust, and then they discussed the weight of these trusts. Finally, they present a LQI analysis mechanism to maintain the accuracy and stability of the trust valuewith poor-quality links. [PERSON] et al. proposed a Beta distribution-based Trust and Reputation Evaluation System (BTRES) for WSN [3] to solve its security problem, which was vulnerable to be attacked from compromised nodes. Based on the interaction information between the nodes, the system used the reputation of the distributed analog nodes to further calculate the trust value of the obtained nodes. In addition, weights and thresholds were used in combination to complete the construction of BTRES.  
In addition, [PERSON] andBhangwarproposed an improved Weight-basedProbabilistic Trust Evaluation (WPTE) scheme, based on Beta probability distribution, for evaluating the trustworthiness of nodes [6]. [PERSON] et al. carried out Enhanced Beta Trust Model (EBTM) to discover a malicious attack in wireless sensor network [7]. In the EBTM, the malicious behavior could be detected quickly by considering consecutive misbehavior. Meanwhile, the collaboration among sensor nodes could be improved by trust value, and the lifetime of the network could be increased by recovery procedure.Mahmud et al. presented an adaptive neural-fuzzy inference system (ANFIS) and brain-inspired trust management model (TMM) to secure IoT devices and relay nodes [8], and to ensure data reliability. TMM utilized both node's behavior trust and data trust, which were estimated using the Beta reputation distribution and weighted additive methods respectively, to evaluate the nodes' trustworthiness. However, there was often a lack of consideration in terms of energy consumption,which was limited to the ideal conditions of an energy-rich IoT device. [PERSON] et al. proposed a trust value redemption system [9]. In this system, the trust value was punished when the malicious node attacks other nodes, and with the increase of attacks, the strength of punishment for its trust value would also increase. However, its trust value could be redeemed when the node returned to normal, but the weight of redemption must less than punishment. [PERSON] et al. extended their previously designed trust model in wireless sensor networks to include both communication trust and data trust, and then they introduce new Bayesian network trust model and Beta distribution to combine more than one trust component--communication trust and data trust in our case--to produce the overall trust [10].