A Trust Evaluation Model for Wireless Sensor Networks

In most current trust model researches focus on sensor radio communication behaviors. Sensor nodes build node trust model through wireless radio transaction with neighboring nodes. Ganeriwal and Srivastava first proposed a reputation based framework for sensor networks (RFSN) where nodes used reputation to evaluate other's trustworthiness. The framework uses watchdog mechanism to monitor communication behavior of neighboring nodes and represents node reputation distributi"on using Beta distribution. Then the trust value is figured out according to the statistical expectation of the probability reputation distribution. The trust framework is good robustness and very classic. But the recommendation trust is not considered; it cannot resist various internal attacks. In, an agent-based trust model was proposed in WSNs (ATSN); agent node was used to monitor behaviors of sensor nodes and classify the behaviors into good or bad ones. Agent nodes count all the number of good behaviors and malicious behaviors, respectively, and save the results into a three-tuple. ATSN scheme uses agent which can save the computational resources and energy consumption. However, in ATSN, only the direct trust value is calculated while the recommendation trust is ignored. In addition, the updating process of the trust value is not considered. Shaikh et al. proposed a new lightweight group-based trust management scheme (GTMS) for clustered WSNs. The trust value is obtained through the communication behavior of neighboring nodes. It works on trust at three levels: the node level, the cluster head level, and the base station level. The model establishes trust mechanism from the above aspects to resist the attack of malicious nodes, respectively. GTMS can effectively resist the attacks of malicious nodes, and it does not require large data storage and complex computations. However, only observing the number of successful and unsuccessful interactions cannot reflect soundest trust value. Song et al. proposed a dynamic trust evaluation method based on multifactor. The nodes' trustworthiness is measured by combining direct trust with indirect trust dynamically. Besides, both the involved classification standard and dynamic weight assignment are dependent on the interaction times between nodes, which are put forward under the background of Hoeffding's Inequality in Probability Theory. The simulation results show that this method is sensitive to multiple attacks. But the updating process of the trust value is not considered. Li et al. proposed a lightweight and dependable trust system for the clustered WSNs. The trust decision-making scheme is proposed based on the nodes' roles in clustered WSNs. They improve system efficiency by canceling feedback between cluster members or between cluster heads. The trust scheme also defines a self-adaptive weighted method for trust aggregation at cluster head level. This approach surpasses the limitations of traditional weighting methods for trust factors, in which weights are assigned subjectively. In, He et al. defined an attack-resistant and lightweight trust management scheme called ReTrust. This system is oriented to medical sensor network and based on hierarchical architecture, comprised of master nodes and sensor nodes. ReTrust uses sliding time window and aging factor to identify and eliminate the on-off attack. Bad-mouthing attack is avoided by eliminating outliers after collecting recommendations. It is resistant to bad-mouthing and on-off attacks. However, the drawback of this scheme is that master nodes must have abundant storage and energy. Feng et al. proposed a credible Bayesian-based trust management scheme (BTMS). The trust management scheme takes the direct and indirect trust into account. The direct trust is calculated by a modified Bayesian equation with punishment factor and updated by a sliding window using an adaptive forgetting factor. Moreover, the indirect trust computation is invoked from a third party. BTMS performs better in resisting attacks.

While in above-mentioned trust models data security is neglected, in existing trust models many of them focus on the trust of data as the main work. In, Zhan et al. proposed a resilient trust model with a focus on data integrity and sensor node trust for hierarchical WSNs. The sensor node current trust level is evaluated through the past history and recent risk. And then it employs Gaussian model to rate data integrity in a fine-grained style. The model is proven to be resilient against faulty data and malicious data manipulation. But the energy consumption on node is not considered. In, a wireless sensor network based on multiangle trust of node was proposed. The method considers the sensing data and the node's energy in the factors of trust assessment; the integrated trust value is calculated through the average weight of the communication trust, energy trust, and data trust. It is more reliable and effective against Dos attack and data forgery attack. But the trust update mechanism is ignored. Jiang et al. proposed an efficient distributed trust model (EDTM) for wireless sensor networks. In EDTM, the direct trust and recommendation trust are selectively calculated according to the number of packets received by sensor node. The direct trust value is calculated through the average weight of the communication trust, energy trust, and data trust. In addition, trust reliability and trust familiarity are defined to improve recommendation accuracy. EDTM can evaluate trustworthiness of sensor nodes more precisely and identify the malicious nodes more effectively. In, a consensus-aware sociopsychological trust model for WSNs was proposed. The trust model uses the concept of consensus and consistency in understanding the behavior of the sensor nodes for detecting fraudulent nodes in WSN. The factors of ability, benevolence, and integrity are used for the computation of trust. The approach can deal even in the presence of higher number of fraudulent nodes than benevolent nodes. It is more reliable and effective against attacks on data in WSNs. But, communication faults that delay the rate at which packets are sent are not considered.

Recently, several techniques are used in computing the trust of sensor nodes, such as the fuzzy logic approach, the Bayesian network approach, the game theoretic approach, swarm intelligence, and the cloud method. In, Feng et al. first established various trust factors depending on the communication behaviors to evaluate the trustworthiness of sensor nodes. The direct and indirect trust are obtained through calculating weighted average of trust factors. Meanwhile, the fuzzy set method is applied to measure how much the trust value of node belongs to each trust degree. And then the evidence difference is calculated between the direct and indirect trust, which links the revised D-S evidence combination rule to finally synthesize integrated trust value of nodes. Zhang et al. proposed a trust evaluation method for clustered wireless sensor networks based on cloud model. The method considers multifactors including communication factor, message factor, and energy factor to get factor trust cloud. And the trust cloud is calculated by assigning weights for each factor trust cloud and combining them. The final trust cloud is measured by synthesizing the recommendation trust cloud and immediate trust cloud and is converted to trust grade by trust cloud decision-making. The method can detect malicious nodes according to different secure requirements under different WSNs applications, which provides a safe running environment for different applications. Shen et al. studied the trust decision and its dynamics that played a key role in stabilizing the whole network using evolutionary game theory. The evolutionary game theory is used for the area of trust evolution in WSNs. It sets up a WSNs trust game concerning the dynamics of trust evolution during sensor node's decision-making. When sensor nodes are making their decisions to select action trust or mistrust, a WSNs trust game is created to reflect their utilities. It can find out the conditions that will lead sensor nodes to choose action trust as their final behavior to ensure WSNs' security and stability.

Our work is partly motivated by those related works above; however, there are some distinctions compared with them. In our trust model, the trust value is calculated considering multitrust factors including communication trust, data trust, and energy trust. Not like the works in, the trust values are just only based on the communication interaction records, so they cannot be against attacks on data. While other studies combine multifactors to calculate the trust value, they do not consider the following character: "trust is hard to acquire and easy to lose". In our proposed trust model, we add the punishment factor and regulating function to realize the punishment and adjustment of trust value against bad-mouth attack and collusion attack. Then, in most trust models such as in, the direct trust and indirect trust sum up in weighted manner to compute the integrated trust. But the weights are obtained by expert opinion method or average weight method in. In our proposed trust model, we define an adaptive dynamic balance weight function to dynamically adjust the weight of the direct trust and indirect trust. Although the works in have given various computation methods of the dynamic balance weight, the trust decision of our proposed trust model is more scientific and flexible. Furthermore, the most important thing is the dynamic of the trust evaluation model. In many current trust models, the trust value is updated by a sliding time window using forgetting or aging mechanism against on-off attack and other malicious attacks, but the number of sliding time windows is predefined. Once the number of the sliding time windows is confirmed, it is difficult to adapt to the dynamic changes of the network. In our proposed trust model, we centrally focus on setting up a dynamic update mechanism. To our knowledge, there is no literature that can dynamically adjust the number of the sliding time windows and the parameters to achieve a dynamic update mechanism. In our proposed efficient dynamic trust model, an update mechanism is defined by a sliding time window based on induced ordered weighted averaging operator (IOWA) to enhance flexibility. We can dynamically adapt the parameters and the interactive history windows number to change the weight sequence to meet the actual needs of the network. Based on the above analysis, the proposed trust model can be dynamically adjusted according to the environment and requirements to achieve accurate trust evaluation and can realize the identification and defense of various types of malicious attack. It has a powerful capability of the trust estimation for WSNs.