With the rapid development and application of medical sensor networks, the security has become a big challenge to be resolved. Trust mechanism as a method of “soft security” has been proposed to guarantee the network security. Trust models to compute the trustworthiness of single node and each path are constructed, respectively, in this paper. For the trust relationship between nodes, trust value in every interval is quantified based on Bayesian inference. A node estimates the parameters of prior distribution by using the collected recommendation information and obtains the posterior distribution combined with direct interactions. Further, the weights of trust values are allocated through using the ordered weighted vector twice and overall trust degree is represented. With the associated properties of Tsallis entropy, the definition of path Tsallis entropy is put forward, which can comprehensively measure the uncertainty of each path. Then a method to calculate the credibility of each path is derived. The simulation results show that the proposed models can correctly reflect the dynamic of node behavior, quickly identify the malicious attacks, and effectively avoid such path containing lowtrust nodes so as to enhance the robustness.
Nowadays, with the rapid development of wireless communication technology and wearable medical sensors, the wireless medical sensor network becomes a promising technology and is changing the way people seek medical treatment [
Owing to these unique characteristics of MSNs and vulnerability to a wide variety of abnormal node behaviours, the traditional cryptography techniques [
Although the existing researches have made great progress, they are still in need of improvements in the following aspects. For one thing, these studies mainly focus on the establishment of trust models with regard to a single node, which are simple and coarsegrained. As is known to all, medical data are diverse and are of different importance. Thus they should be distinguished and the more important data should be transmitted through the more trustworthy nodes and paths. For this reason, it is very necessary to construct a finegrained trust model according to the importance of medical data to investigate the credibility of a single node, which can effectively avoid the strategic attacks. In order to achieve this purpose, Bayesian inference is adopted to measure the credibility of a single node in each interval, in which the interactions from neighbour nodes are used to obtain the prior information and the direct interactions are used to get the posterior distribution to estimate the trust value.
For another aspect, these works did not specify the measurement of the trust about the paths with several intermediate nodes from the source master nodes to the base station. The aforementioned trust model of single node is the basis of the path trust model. Building trust relationship between network nodes can be used to develop highlevel security solutions as auxiliary, such as security routing. Therefore, based on trust degrees of network nodes, a trust evaluation model with Tsallis entropy to measure the trustworthiness of each path is proposed in this paper. In the last respect, in the process of integrating several trust values into overall trust degree, the corresponding weights are obtained by using the ordered weighted vector twice, in which time sequence and relative size order are viewed as the induced factor, respectively. The medical data and packets are interchangeable hereinafter.
The structure of this paper is as follows. Section
The twolayer architecture was proposed applicable to medical sensor networks [
The twolayer architecture of MSNs.
As mentioned above, dynamic trust management must be combined with traditional cryptography techniques to ensure the security and credibility of MSNs. The proposed trust model is conducted based on some simple symmetric encryption/decryption algorithms and public key cryptography. These are described in detail as follows.
Due to some characteristics such as small capacity and limited resources, each sensor node employs the lightweight cryptography techniques [
Different from the sensor nodes, each master node contains adequate resources and capacity, so that it is able to take advantage of general symmetric cryptography techniques to encrypt/decrypt the data. There are additional three kinds of keys in each master node. Firstly, one key is distributed and updated by the base station, which is similarly generated through some public key technique. The medical data collected from sensor nodes are encrypted with this key and further delivered to the base station. Secondly, a multicast key is applied between a master node and its neighbor master nodes. When it wants to send request information to some neighbor nodes, the master node encrypts the information with this key. Finally, a pairwise key between master nodes is necessary. When the neighbors return information corresponding to the request information, the key is used to encrypt the reply information. The multicast key cannot be substituted for this key in order to prevent other neighbor nodes to hijack or tamper the reply messages.
In the MSNs, a database and a trust evaluation system are built in each master node
As we know, trust has the characteristics of time decay; that is, the interaction results farther from the current moment have the weaker influence on the current trust value. As a consequence, those interactions only in a certain period close to the current time are necessary to be analyzed so as to obtain the current trust degree. Given
Based on the above, node
For any
Master node
For any
After receiving
When
Although there are generally 3 kinds of Bayesian estimation based on the posterior distribution, the mean square error is minimized if the posterior mean is viewed as the Bayesian estimation. And in the case of the binomial distribution, the posterior mean value is more appropriate than maximum posterior estimation; therefore, formulas (
Based on
In the view of node
The setting of weight coefficients is critical and two factors are mainly considered. On the one hand, due to the characteristics of time decay, the influence of
Assume
Currently calculating the weighted vector based on maximum discrete degree is one better method, with which the vector is achieved as
For example, set
The changes of
From Figure
Through the above analysis, the weights can be allocated twice using an ordered weighted vector. Set
Substituting (
After a master node
Assume that there are multiple paths from a source master node to the base station, in which the trust degrees of intermediate nodes can be obtained from Section
Suppose that
The most trustworthy path is chosen via the hopbyhop way. The source master node first delivers the data to the most trustworthy neighbor node. After it receives, the neighbor node similarly chooses its own neighbor with the highest trust degree to transmit, and so on for all the intermediate nodes until the data reach the base station. However, the optimality of each hop does not necessarily make the whole path optimal. It can be verified still with the example in Method
From the above discussion, the trust degree of each path should be measured by comprehensive analysis of all the intermediate nodes, which ensures that the selected path is optimal at the most extent.
Through some associated properties of Tsallis entropy, path Tsallis entropy is put forward to measure the uncertainty of the whole path, which can synthesize the credibility of all the intermediate nodes. On that basis, trust degree of each path is calculated.
Assume that
By simply computing, it is known that
The function
Based on this, the path Tsallis entropy is proposed which is mainly to measure the uncertainty of each path.
The path Tsallis entropy of
Given
From formula (
The trust degree of path
Due to the fact that
For example, suppose there are multiple paths:
To sum up, assume that there are
In this section, several experiments are carried out in order to verify the performance of the proposed trust models. Experiment 1 is conducted to test the accuracy and dynamic of the trust model of single node under the circumstance that the behavior of single node changes dynamically. The robustness of resisting the strategic malicious attack is analyzed in Experiment 2. Subsequently the performance of path trust model based on Tsallis entropy is compared with the other two routing ways mentioned in Section
Parameters in the proposed trust models.
Parameters  Value 


5 

0.3 

−1 
This experiment is carried out to verify the performance of trust model based on single master node
The trust degree of a dynamical node.
When the probability varies dynamically from 1 to 0.6, the trust degrees descend obviously from the left part of Figure
The strategic malicious attack is a type of threat that malicious nodes which are aware of the presence of trust models launch. A malicious node behaves very well in the first several time units to increase its trust degree, and then it launches some attacks in the subsequent time units, such as discarding the packets with a certain probability. In this experiment, assume that a malicious node is honest in every 6 time units and becomes bad in the following 2 time units; that is, it will discard packets with probability 0.3. The trust degree varies with the periodical change of node behavior in Figure
The trust degree of a malicious node.
In the first 6 time units, the node forwards the packets honestly; hence its trust degree is nearly equal to 1. But starting from the 7th unit, it behaves maliciously and cannot deliver packets with probability 0.3. It can be found that the trust degree has sunk to 0.76 at the end of the 7th time unit. In the case that this malicious node continues its bad behavior, trust degree further falls to 0.7 in the 8th unit. Therefore, this tendency indicates that the proposed trust model is very sensitive to respond to abnormal behaviors. However, when this node behaves from bad to well, the rising speeding of trust degree is relatively low from the 9th to 14th unit and trust degree achieves 1 until the 14th unit. A similar situation occurs among the subsequent 8 time units. From the foregoing, the proposed trust model is able to identify the malicious behavior quickly so as to avoid it and prevent the packet delivery failure.
In order to measure the accuracy of path trust model, 20 master nodes and a base station are deployed in the MSN. These master nodes send packets to the base station according to a certain rate, and the base station computes the average successful delivery rate
The
In the left half of Figure
In this experiment, the situation that there are 3 kinds of packets with importance 1, 2, and 3, respectively, is analyzed. Assume that there are two types of nodes which successfully forward packets of importance
Trust degrees associated with importance.
In Figure
Additionally, suppose that there are two paths from a source master node to the base station. A node of type 1 is in a path and a node of type 2 is in the other path. Assume that the other intermediate nodes can deliver the packets successfully. The source master node randomly sends 300 packets with different importance to the base station. One way is that the source node selects the path with the node of type 1 to transmit the data, and the other is choosing the corresponding path according to the importance of packets. The successful packet delivery rates of the two ways are analyzed in Figure
The
From Figure
In this paper, a security and trust model is proposed as applicable to medical sensor networks. First of all, considering the importance of packets, the trust value of single node in each interval is derived based on Bayesian inference in which the interactions of neighbor nodes are viewed as prior information and then the posterior distribution is obtained, combined with direct interactions. The corresponding weights are further distributed through the ordered weighted vector twice to obtain the overall trust degree. On that basis, with the relevant properties of Tsallis entropy, path Tsallis entropy is defined to measure the uncertainty of each path and the trust degree of each path is shown. Subsequently, each source master node selects the most trustworthy path to forward it to the base station according to the importance of packets. The simulation results show that the proposed trust model is able to accurately reflect the dynamic of node behavior, identify quickly malicious behaviors, and achieve higher successful packets delivery rate so as to effectively improve the dynamic adaptability and robustness.
The authors declare that there is no conflict of interests regarding the publication of this paper.
The research work was supported by National Basic Research Program of China (973 Program) under Grant no. 2012CB315905.