A Trust Evaluation Model for Wireless Sensor Networks

Our experiments are performed using Matlab to analyze the performance of the proposed algorithm similar to the literature. The concrete simulation scene is set to be 100 m × 100 m, with 50 randomly deployed nodes. Some parameters vary with the scenes and the purposes of experiment and will be explained in detail. The other default simulation parameters that we have chosen are summarized in Table 2.

In this section, the simulations can be divided into two parts. First, we analyze the performance of the DTEM, which includes the effect of dynamic weight value on integrated trust value and the direct trust value update mechanism. Then, we compare DTEM with the existing trust models, typical RFSN, and BTMS. The results demonstrate that the DTEM has a powerful capability of the trust estimation.

6.1. The Performance of the DTEM

In this section, we analyze the dynamic performance of the DTEM.

6.1.1. The Effect of Dynamic Weights on Integrated Trust Value

The value of \(\varphi\) is the degree of recognition of the direct trust and indirect trust of \(i\) to \(j\). The relationship between the dynamic weight of \(\varphi\) and the number of interaction times is shown in Figure 4. As shown in Figure 4, in the early stage of trust measurement, when the number of interactions is less than \(COM_{th}\), the trust calculation is much more dependent on the indirect trust value. With the increasing of the number of interactions, when the number of interactions is greater than \(COM_{th}\), the node \(i\) is more willing to believe their direct interactive experience, and the weight \(\varphi\) of direct trust will become much larger. Hence, the weight factor \(\varphi\) is dynamically changed with the interaction times. And the value of \(COM_{th}\) can be adjusted according to the actual demand of the network to achieve a more reasonable distribution of the weights between direct trust and indirect trust. In this paper, we give \(N = 1200\), and \(COM_{th} = N/3 \). And giving \(\varphi\) = 0.1, 0.5, 0.9 and the dynamic value, the effect of \(\varphi\) on the integrated trust value is shown in Figure 5, respectively. As shown in Figure 5, at the beginning, the interactions between nodes are very few; the effect of dynamic weight \(\varphi\) on integrated trust value is relatively close to \(\varphi\) = 0.1. That notes when the number of interactions between nodes is less, the trust value is more dependent on the indirect trust, and with the increasing of interaction times, the effect of dynamic weight \(\varphi\) on integrated trust value slowly trends towards \(\varphi\) = 0.9. That means with the increasing of the number of interactions between nodes, the integrated trust value metric measure is more dependent on direct trust. The results obtained are in agreement with the previous theoretical analysis. The weight factor \(\varphi\) can be dynamically adjusted according to the number of the interactions between nodes, which can better ensure the accuracy of trust measurement.

Figure 4 The dynamic weights of the direct values \(\varphi\) and \(1- \varphi\).

Figure 5 The effect of dynamic weights on integrated trust value.

6.1.2. The Effect of Update Mechanism on Direct Trust Value

From the analysis in Table 1, we can see that the IOWA operator is determined by \(m\) and \(\alpha\). In this section, the effect of different \(m\) and \(\alpha\) on the direct trust value is realized. Figure 6 shows the effect of the different \(\alpha\) on the update trust value when \(m = 4\). As shown in Figure 6, with the increasing of \(\alpha\), the update trust values are much closer to the trust value without update, which shows that the greater \(\alpha\) is, the less dependent the update trust value is on historical experience. Figure 7 shows the effect of the different \(m\) on the update trust value when \(\alpha\) = 0.7. As shown in Figure 7, with the increasing of \(m\), the updated trust value is more accurate, which shows that the update trust value is more dependent on the historical experience. According to dynamic adjusting of \(m\) and \(\alpha\), we can effectively control the impact of historical interactions on trust value and enhance the accuracy of trust value.

Figure 6 The effect of the parameter \(\alpha\) under \(m = 4\).

Figure 7 The effect of the parameter \(m\) under \(\alpha\) = 0.7.

6.2. Comparison of DTEM, RFSN, and BTMS

In this section, we compare DTEM with the existing trust models RFSN and BTMS. The former is one of the earliest classical trust schemes for WSNs; the latter is one of the representative classical trust schemes.

6.2.1. The Trust Evaluation

In this section, we assess the integrated trust of normal node and malicious node, respectively. It is assumed that a normal node always chooses to cooperate, and a malicious node always chooses not to cooperate. The target of this kind of attack is the routing protocol. As depicted in Figure 8, the integrated trust increases with the increasing of successful interactions among normal nodes and decreases with the increasing of unsuccessful interactions among malicious nodes in RFSN, BTMS, and DTEM. On the one hand, we can see intuitively that, for the integrated trust between normal nodes, the trust value increases faster than the other two algorithms in RFSN. In BTMS, the trust value increases more slowly than the other two algorithms because of the effect of the punishing factor at the beginning. In our proposed model in DTEM, the trust value changes with the increasing number of the interaction rounds. At the beginning, the trust value increases faster than BTMS and more slowly than the RFSN. After a few rounds, the increasing of the trust value becomes the slowest. On the other hand, for the integrated trust between malicious nodes in DTEM, the integrated trust value decreases fastest in all the algorithms. From the above analysis, we can get that the DTEM reflects the following character: "trust is hard to acquire and easy to lose". Having compared RFSN, BTMS, and DTEM, DTEM evaluates the trust more accurately among normal nodes. It reflects nodes' commutation behavior changing acutely and has more sensitive changing of the malicious actions, which can effectively identify routing attacks.

Figure 8 Comparison of the trust value of the normal node and malicious node.

6.2.2. The Data Attack

We analyze the efficacy of DTEM against faulty data and malicious data manipulation. The target of this type of malicious attack is communications data or messages. We generate a few common types of faults and fake data attacks together against the normal data. Firstly, we generate random data at randomly selected sensor nodes, but it does not affect the data communication. This means that although the sensor node sends false data, it can be considered as successful communication. Running RFSN, BTMS, and DTEM in this scene, the result is shown in Figure 9. As shown in Figure 9, in BTMS and RFSN, the trust value does not make any change. It is shown that these two algorithms do not consider the consistency of the data; they just only consider communication behaviors between nodes to calculate the trust value. In DTEM, the trust value decreases sharply, and then with the increasing of the number of interaction rounds, the trust value increases, but the trust value is always lower than 0.5. The result indicates that the resilience of DTEM is very good, which can fast and accurately identify the faulty data manipulation of malicious node. It is more sensitive in order to identify data information attacks compared with RFSN and BTMS.

Figure 9 Comparison of the trust value under data attack.

6.2.3. The On-Off Attack

In this section, we analyze the efficacy of DTEM against the on-off attack. This type of malicious attack is a special kind of attack, whose target is the trust management. The on-off attack malicious node alternates its behavior from malicious to normal and from normal to malicious so it remains undetected while causing damage. In this paper, we suppose that an on-off attacker behaves well in the first 30 interaction rounds to build up good reputation but behaves badly in the next 30 rounds. After that, it behaves well continuously. The result is shown in Figure 10. It is not difficult to see that the trust value increases in the first 30 interaction rounds, and the malicious node does nothing or only performs well. But in the next 30 rounds the trust value drops when the malicious node launches attacks. Having compared RFSN, BTMS, and DTEM, the DTEM can acutely reflect nodes' changing and sensitively detect on-off malicious attack. And more importantly, an on-off malicious node recovers its trust value more slowly and much longer. We can come to a conclusion that DTEM outperforms RFSN and BTMS against on-off attack. Meanwhile, the simulation result again verifies the character "trust is hard to acquire and easy to lose" in DTEM. This is because the trusted recommendation node selection mechanism and dynamic update mechanism are added to the process of trust evaluation, which makes the evaluation of trust between nodes more objective and accurate. It can effectively identify trust model attacks such as on-off attack and bad-mouth attack.

Figure 10 Comparison of the direct trust value under on-off attack.

6.2.4. The Detection Rate

In this section, the simulated malicious attacks are selective forwarding attack, on-off attack, conflicting behavior attack, data forgery attack, and data tampering attack. We vary the percentage of malicious nodes from 10 to 50 percent with a 10 percent increment. As shown in Figure 11, which gives the detection rate in different trust model, we can see that the performance of the DTEM is better than RFSN and BTMS. RFSN and BTMS are vulnerable against data forgery attack and data tampering attack. So, with the increasing of the number of malicious nodes, the detection rate decreases rapidly, but the DTEM keeps high detection rate. Hence, the DTEM is an efficient trust evaluation model which can identify different kinds of malicious nodes and can be dynamically adjusted according to the specific requirements of the network.

Figure 11 Comparison of the detection rate.

In order to better illustrate the operation mechanism and performance of DTEM, Table 3 shows the comparison of state of the art in terms of trust estimation method, direct trust factors, indirect trust module, integrated trust module, trust update module, and considered attacks. Through the above proof and analysis of the experiment, we can know that DTEM is an efficient dynamic trust evaluation model for WSNs. It can effectively identify various malicious attacks.

Table 3

Comparison of state-of-the-art trust model.