Wireless sensor network (WSN) is a kind of distributed and self-organizing networks, in which the sensor nodes have limited communication bandwidth, memory, and limited energy. The topology construction of this network is usually vulnerable when attacked by malicious nodes. Besides, excessive energy consumption is a problem that can not be ignored. Therefore, this paper proposes a secure topology protocol of WSN which is trust-aware and of low energy consumption, called TLES. The TLES considers the trust value as an important factor affecting the behavior of node. In detail, the TLES would take trust value, residual energy of the nodes, and node density into consideration when selecting cluster head nodes. Then, TLES constructs these cluster head nodes by choosing the next hop node according to distance to base station (BS), nodes’ degrees, and residual energy, so as to establish a safe, reliable, and energy saving network. Experimental results show that the algorithm can effectively isolate the malicious node in the network and reduce the consumption of energy of the whole network.
With the development of wireless communications, electronics, and sensing technology, the wireless sensor networks (WSN) [
In general, the WSN nodes are equipped with independent battery and usually deployed of large numbers in the wild places where people almost could not reach. It is an impossible mission to recharge or replace the sensor battery. In order to reduce the energy consumption, the communication radius of node is strictly limited. The topology protocols of WSN commonly focus on how to separate the whole network into clusters and how to make multihops construction among these cluster heads for transferring sensor data to base station by self-organization. In the open, distributed, and dynamic environment, the construction of network topology is vulnerable, which may lead entire network to be unsafe. For WSN, how to ensure the security of the communication is an important issue in the process of constructing network topology [
In recent years, people have put forward different security routing protocols. Most of these are based on the traditional security mechanisms of the cryptosystems, which need much more memory and energy consumption. The wireless sensor network is composed of many small sensor nodes with limited bandwidth and stringent node constraints in terms of power and memory. What is more, the cryptosystems can only resist external attack; once internal nodes have mutations or attacks, they will not be able to be identified. So the traditional cryptosystems of traditional security mechanisms are not fully applicable to wireless sensor networks [
To solve the above problems, researchers have proposed the trust management mechanism. Trust is defined as the binary relation occurring in subject and object. The trust management mechanism depends on the history record of object behavior or interaction behavior. The record is used to calculate a trust value. The trust value provides a prediction of future behavior and determines the object’s next step. The evaluation of the trust value includes node trust, link trust, and service trust. This strategy makes the trust management mechanism effective in improving the security of the network in an open environment.
This paper tries to take node trust into consideration when building a network topology, so as to ensure the security of the network communication. The TLES algorithm is based on the analysis of node behavior. It develops a variety of trust factors and then performs comprehensive analysis with direct information and recommended information. This working principle can dynamically reflect changes in the trust value between the nodes. TLES combines trust value, residual energy, and density together. This algorithm uses the local optimum principle to choose the cluster head node. Cluster head selects the next hop based on the residual energy, distance to BS, and degree of other cluster heads. As a result, it can effectively eliminate the malicious nodes in network and achieve safe, rational node communication. Besides, the energy consumption of the network is reduced.
The rest of this paper organization is as follows: the second part is a brief review of the related research work; the third part presents the system model and the problem description; the fourth part shows the details of the topology algorithm; the fifth part is about simulation results and the analysis; the last part is conclusions for summary and future work.
There have been many researches on WSN trust models. Zhan et al. proposed a plane routing protocol based on trust, which is called TARF. The TARF uses the trust value and energy cost to decide the routing path. This protocol can prevent malicious nodes from tampering with routing information and misleading network traffic [
These above methods mainly focus on single network security threats without considering trust value across the board; thus it may ignore security and performance defects of the trust routing itself. For example, the computation of trust value is too complex, malicious nodes are difficult to identify, and key nodes are vulnerable. Therefore, this paper proposes a secure routing algorithm based on trust for wireless sensor network (TLES). The TLES synthesizes direct and recommended information for trust calculation. So it can dynamically reflect the change of trust value between nodes. Besides, it takes trust value, energy cost, and node density into consideration. The nodes compete and select a cluster head. The cluster head node chooses the next-hop node according to energy cost, distance, and degree. Using this strategy, the TLES can effectively eliminate the malicious nodes in the network. It can also ensure security and rationality of node communication effectively, as well as reducing network energy cost.
This paper proposes TLES, which lets node construct the topology structure of the whole network according to the neighbor node’s trust value, residual energy, and distance to base station. Models and problems of TLES topology construction are described as follows.
Supposing all sensor nodes have the same initial trust value, energy value, and status; there is only one BS node in the WSN, and the BS node’s energy is infinite; once a sensor node has been deployed, it cannot be moved; node is not equipped with GPS, but each node can know the location information of the current node; a sensor node has many energy levels, so the sensor nodes can dynamically adjust the model of the energy according to the transmission distance.
The first to fourth are the basic properties of wireless sensor networks, and the fifth property is defined energy levels for the communication within the cluster and the communication between clusters; the two communication modes have different energy consumption.
This paper uses the same wireless communication model in [
The energy consumption of sensor nodes receiving
In order to solve these weaknesses existing in the previous studies, TLES protocol needs to meet the conditions as follows: the network node communication radius is less than or equal to the it is difficult for nodes to obtain global information, with the increasing scale of WSN; the node should construct the whole network topology only by local neighbor nodes’ information; the node’s trust value is dynamic, the changes of which should be able to accurately reflect the node security; in TLES algorithm, all nodes try their best to deliver packets to their next node, integrating a variety of trust mechanisms to select a neighbor node with the highest trust value as the cluster head node; communication between cluster head nodes should try to satisfy the free space model; the communication radius is less than or equal to the
TLES algorithm consists of two parts. The first part is to calculate the trust value of nodes and select cluster head nodes according to the trust value, residual energy, and the density of nodes. If the ordinary node’s trust value is less than a certain threshold, it could not be allowed to join any cluster heads. The second part is to build a weighted tree. All the cluster head nodes select the next-hop nodes, according to the node information including the value of residual energy, the distance between cluster and BS, and the value of clusters’ degree, so as to construct the whole network topology and transmit the information to the BS node finally.
Trust depends on the subject’s (evaluating node) assessment to the object (evaluated node) and the recommendation of other nodes, and the value will change according to object’s behavior. Considering the characteristic of self-organizing and multiple hops in wireless sensor network, the trust evaluation mechanism should be set up with no core node. Nodes monitor each other’s behavior between neighbors, and use the direct and indirect trust value to get comprehensive trust values.
In formula (
The variation of sending rate factor.
It is clear that the range of
In formula (
In formula (
Suppose that nodes
In formula (
When selecting the next-hop node, each node is subjective to judge whether the next-hop node could be trusted by calculating the trust of the next-hop node. In order to reduce deviation, the indirect trust value also should be considered, and formula is as follows:
In formula (
Before the first choice of cluster, base station nodes globally broadcasted, each node receives the base station’s information and calculates the distance between itself and base stations. Then, each node broadcasts information of itself in local area within the range of distance
Figure all nodes broadcast their information within the scope of the all the nodes calculate their own compare its own
The competition of cluster head.
Algorithm BS node broadcast information of base station; all the nodes are not isolated and the energy is greater than zero; broadcast their information with range of the node with the maximum cluster head nodes wait for the join massages from other nodes.
( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( (
In this section, we want to set up a multihop topology among all the cluster head nodes by generating a weighted spanning tree construction. For convenience of discussion, all the nodes mentioned in this section represent cluster head nodes.
Cluster head selects the next hop from other cluster head nodes, considering the residual energy, the distance between BS and the next node, and degree of the next node. In this paper, the concept of the degree is not just the number of nodes connected directly. For example, the degree of node A is the number of all the nodes, which take the node A as a root node and need node A to forward their information. In TLES, each cluster node broadcasts its information, and the radius of broadcast is
How to select the next-hop method is as follows. Node selects the node whose distance with BS node is less than the distance between the current node and BS node to join the set V from the nodes that have sent Message1 to the current node. Select the next-hop node in the set V according to formula ( If node has received the Message1 from the BS node, its next hop is the BS node. Consider
In formula (
Figure
Select the next-hop node.
In this paper, the concept of the degree is not just the number of nodes connected directly. The degree of one node is the number of all the nodes, which take this node as a root node and need this node to forward information. After the selection of next cluster head node, the next-hop node’s degree should be updated. The formula is as shown in
Figure
Update of degree.
Algorithm BS node broadcasts its information; all the nodes radiobroadcast the information of themselves, and the radiodistance is nodes receive the radiomessages from the nodes whose distance with the nodes are less than all surviving nodes in the network according to the neighbor node information choose the next-hop node, if the distance of node node node
( ( ( ( ( ( ( ( ( ( ( ( ( (
The experimental environment is as follows: the area of network is
The initial value of the parameter.
Parameter | Value |
---|---|
Etx |
|
Erx |
|
Efs |
|
Emp |
|
EDA |
|
Control packet length | 100 bits |
Data packet length | 4000 bits |
SINK | (0, 0) |
The format of the Message1.
ID | Residual energy | Distance with BS | Location | Degree |
The experiment can be divided into three parts. First, we analyze the detection accuracy of malicious nodes by setting different isolation threshold values. Second, we use the better threshold, gotten by the first part, we analyze the change of average sending ratio, the change of average consistency ratio, and the change of average packet delivery ratio as the change of communication round, in order to verify whether the proposed algorithm could isolate the malicious nodes effectively and improve the average sending ratio, the average consistency ratio, and the average packet delivery ratio of the network. At last, we compare the consumption of energy of TLES with the consumption of energy of some hierarchical routing protocols including LEACH, LEACH, and LEACH_MF.
In Figure
Proportion of malicious nodes and detection accuracy.
The average value of malicious node’s trust.
All the nodes are fully trusted at the beginning of the experiment; that is to say, each node’s trust value is 1. The average consistency ratio, the average sending ratio, and the average packet delivery ratio of the whole network are 1. At the beginning of the communication, all the nodes are fully trusted and malicious nodes have not been isolated. Because malicious nodes exist in the network, a lot of abnormal behaviors that include loss of packet, wrong packet, or node not sending packets or sending too much packets will occur, all the three trust factors will decline in the former stage. With the increased rounds of communication, the malicious node will be detected and isolated slowly, and these bad behaviors will decrease relatively, so, in the later communication stage of the entire network, all the three trust factors will increase with the increased rounds of communication.
In order to verify the changes of three trust factors, we got Figures
The average sending ratio.
The average consistency ratio.
Average packet delivery ratio.
Compared with Figures
The last part experiments the energy consumption in comparison with LEACH, LEACH-MF, CMRA, and TLES. LEACH, LEACH_MF [
The comparison of energy consumption.
The number of rounds represents the lifetime of network in this simulation. The lifetime of network contains three kinds of definitions: the first node dies, half of nodes die, and the last node dies. In this experiment, we adopt the fist definition (the fist node dies) to count lifetime of network.
The comparison of energy consumption (LEACH, LEACH-MF, CMRA, and TLES) in the different scale of network is shown in Figure
The relationship between the monitoring area and lifetime of network (the first node dies).
Through Figures
This paper proposed a secure topology protocol of WSN, that is, TLES. The trust mechanism used in TLES is introduced. Trust factors were defined by the node’s historical behavior, and the trust value of each node was calculated according to the comprehensive value of direct trust and indirect trust, which are related to the trust factors. TLES uses the idea of clustering. First of all, the cluster heads were selected according to the trust value, residual energy, and density of nodes. Then, the cluster heads choose the next-hop node by the residual energy, the distance to BS, and degree of candidate node. After that, the construction of the whole network topology was built. Experimental results show that TLES can eliminate the malicious nodes in network effectively, so as to ensure the safety and rationality of node communication. At the same time, it can also reduce the energy consumption of the network.
The existing problems of this paper are focused on the following two aspects. First, this paper improves the average packet delivery ratio and increases the calculation leading to the increase of packet delay. Second, the mobile sensor network and heterogeneous network would become the new characteristics of network. It is important to figure out how to make improvement in the future.
The authors declare that there is no conflict of interests regarding the publication of this paper.
This work was sponsored by the Natural Science Foundation of Hunan Province, China (13JJ3091 and 14JJ3062), National Nature Science Foundation, China (61202462 and 61300036), and the Fundamental Research Funds for the Central Universities, China.