A Trust Evaluation Model for Wireless Sensor Networks
1. Introduction
Nowadays, technology development in the fields of microelectromechanical system (MEMS) and wireless communication has facilitated the extensive distribution of WSNs. WSNs are composed of a large number of sensor nodes. In general, sensor nodes are reliable, accurate, flexible, inexpensive, and easy to deploy. Some areas and industries that are subject to environmental constraints rely on WSNs for data collection and monitoring. They are widely used in many applications such as emergency response, healthcare monitoring, military, agriculture, environmental monitoring, and smart power grid. However, due to the characteristics of working environments (usually deployed in remote and unattended) and the way of wireless communication, WSNs are prone to sudden accidents failures and suffer from attacks of malicious nodes. Once a node is compromised, the availability and integrity of the network can be destroyed. In addition, it is difficult to predict the malicious attacks. Hence, network security is a vital issue, which needs to be addressed to guarantee correct operation of the whole network.
Recently, in the security field of wireless network, a great deal of research has been carried out commonly using cryptography, authentication, and hash functions to improve the security of network. Undoubtedly, the present achievements have greatly promoted related research in improving security of network, especially the confidentially, integrity, authentication, availability, and no-repudiation of data in the network. But in the security field of WSNs, the above traditional security mechanisms such as cryptography and authentication are not mostly suitable for processing capability constrained and energy limited WSNs due to the complexity and huge computing memory. Furthermore, the traditional security mechanisms are widely and availably used to deal with external attacks but cannot solve insider or node misbehavior attacks effectively which are caused by the captured nodes. In pursuit of the security of WSNs, trust and reputation mechanisms have proven to be more resilient against insider or node misbehavior attacks.
Trust in the field of wireless communication networks may be defined as the degree of belief on the future behavior of other nodes, which is based on past experience and observations of the nodes' action. So, we give the definition of trust in WSNs
as follows: node 
trust
in node 
describes
the belief or expectation or assurance of sincerity, competence, and integrity of node 
future
action/behavior. The basic idea of trust based scheme is to quantify trust to describe the trustworthiness, reliability, or competence of individual nodes. Trust management system can be implemented in various applications for security management
such as secure protocol, secure data aggregation, trusted routing, and intrusion detection system. In recent years, lots of state-of-the-art models have been proposed in this field. Undoubtedly, the present achievements have greatly promoted related
research in improving security of WSNs. Even so, trust evaluation in WSNs is still a challenging issue. Some limitations are exhibited which need more attention to be solved.
Considerable research has been done on modeling and managing trust and reputation in WSNs. Many current studies have been done for trust establishment just only based on the communication interaction records between nodes without considering the data consistency, so they cannot be against attacks on data. While other studies combine multifactors to calculate the trust value, the multitrust sums up in weighted manner to compute the integrated trust. But the weights are obtained by expert opinion method or average weight method. The results of the prediction are subjective, which affect the scientific and flexibility of the trust decision. In addition, trust evaluation is a dynamic phenomenon and changes with time and environment condition. In many current trust models, the trust value is updated by a sliding time window using forgetting or aging mechanism. But the number of sliding windows is defined by expert opinion method. Once the number of the sliding time windows is confirmed, it is difficult to change. It makes the trust models unable to adapt to the dynamic changes of the network environment, which affects the accuracy of the result. 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. Moreover, some existing trust models rarely consider the influence of the energy consumption. Due to these reasons, there is a growing demand for adequate provision of an efficient dynamic trust evaluation model for WSNs; it can achieve accurate trust evaluation dynamically according to the environment and requirements and can realize the identification and defense of various types of malicious attack.
In this paper, an efficient dynamic trust evaluation model (DTEM) for WSNs is proposed that aims to address the above problems. In the proposed trust model, the trust value is calculated considering multitrust factors; it can achieve accurate trust evaluation. Moreover, DTEM can dynamically adjust the weights of direct trust and indirect trust. It reflects the dynamic adaptability of the trust computing. It also can dynamically adjust the parameters of the update mechanism to update the trust value to meet the actual needs of the network environment. The DTEM can be against various types of malicious attack and can be configured and effectively applied to different environments with different requirements. The major contributions of this paper are listed as follows:
(1)To improve the accuracy of trust evaluation, against attacks on data, the trust value is calculated considering direct trust and indirect trust. The direct trust is calculated considering multitrust including communication trust, data trust, and energy trust with the punishment factor and regulating function to meet the following: character "trust is hard to acquire and easy to lose". The indirect trust is evaluated conditionally by the trusted recommendations from a third party.
(2)To ensure that the trust model makes a decision more scientifically, dynamically, and adaptively, we define a dynamic balance weight factor function which is changed dynamically with the number of communication interactions. The adaptive dynamic balance weight factor dynamically adjusts the weight of the direct trust and indirect trust.
(3)To make sure that the proposed trust model can be configured and effectively applied to different environments with different requirements and against on-off attack, we give an update mechanism 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 update the trust value to adapt with different environments and requirements. Meanwhile, on-off attacks can be handled efficiently.
The remainder of this paper is organized as follows. Section 2 gives an overview of related works. Network model and attack model are described in Section 3. Section 4 gives the overview and process of the DTEM. Section 5 discusses trust model and trust evaluation mechanism in DTEM. In Section 6, the experiment is made under simulative environments and the performance of the DTEM is evaluated. Section 7 concludes this paper.