Wireless sensor networks (WSN) can be used in many fields. In wireless sensor networks, sensor nodes transmit data in multi hop mode. The large number of hops required by data transmission will lead to unbalanced energy consumption and large data transmission delay of the whole network, which greatly affects the invulnerability of the network. Therefore, an optimal deployment of heterogeneous nodes (ODHN) algorithm is proposed to enhance the invulnerability of the wireless sensor networks. The algorithm combines the advantages of DEEC (design of distributed energy efficient clustering) clustering algorithm and BAS (beetle antenna search) optimization algorithm to find the globally optimal deployment locations of heterogeneous nodes. Then, establish a shortcut to communicate with sink nodes through heterogeneous nodes. Besides, considering the practical deployment operation, we set the threshold of the mobile location of heterogeneous nodes, which greatly simplifies the deployment difficulty. Simulation results show that compared with traditional routing protocols, the proposed algorithm can make the network load more evenly, and effectively improve energy-utilization and the fault tolerance of the whole network, which can greatly improve the invulnerability of the wireless sensor networks.
Wireless sensor network (WSN) has been regarded as an important part of the Internet of Things (IoT), informatization, intelligence, and the Internet of Everything are closely related to it [
Small world network has both short average path length and high clustering coefficient. In sensor networks, the small average path length means that nodes only need less hops and energy when transmitting data to sink nodes [
In wireless sensor networks, cluster heads consume more energy because they have to collect and forward the information perceived by nodes in the cluster. Cluster heads which close to sink nodes consume more energy because they undertake more forwarding tasks. In order to solve the problem of excessive and fast network energy consumption caused by unbalanced load, heterogeneous nodes are introduced which can communicate directly with sink nodes in the network, and the energy of the heterogeneous nodes can be supplemented. Heterogeneous sensor networks with small world characteristic are constructed by using the super link formed between heterogeneous nodes and sink as a shortcut. The addition of heterogeneous nodes and the construction of super links can balance the network load and enhance the invulnerability of the network.
In recent years, the small world characteristic has been widely concerned by researchers. The small world characteristic shows how everyone in the real world connects with others. Milgram conducted a series of mail delivery experiments in 1967 [
In 1998, Watts and Strogatz propose a network model, which shows that rewiring several links in a regular ring grid graph (the end point of the rewiring link is randomly selected from the graph) can greatly reduce the average path length between any two nodes in the graph, while still maintaining a high degree of clustering in adjacent nodes [
Heterogeneous nodes that can communicate directly with sink nodes are introduced in this paper. Super link which is between heterogeneous nodes and sink is as a shortcut to construct heterogeneous sensor networks with small world characteristic. In heterogeneous sensor networks, the location and number of heterogeneous nodes have a great impact on the performance of the network. In order to effectively reduce the energy consumption of the network and prolong the network life cycle, the primary task of the algorithm is to determine the number and location of heterogeneous nodes.
Ordinary nodes are powered by mobile power, while heterogeneous nodes are powered by solar panels, which means, ordinary nodes have limited energy, while heterogeneous nodes can ignore energy consumption. The data transmission mode of super link in heterogeneous network is shown in
In a wireless sensor network, sensor nodes are powered by mobile power, and the total energy of the whole network is limited. As shown in
DEEC algorithm is a clustering algorithm to balance the energy of multi-level heterogeneous networks. Its main principle is to select the cluster head according to the ratio of the residual energy of each node to the average energy of the network. Nodes with high initial energy and residual energy are more likely to be selected as cluster heads than nodes with low energy, which can balance the energy and prolong the life cycle of the network [
In DEEC algorithm, the calculation method of the probability that a node in the network is selected as the cluster head is shown in
Where
Due to the different initial energy and energy consumption of each node [
After the nodes are clustered by DEEC algorithm, the ordinary nodes transmit the data to the cluster head. The cluster head processes the received data centrally, which can not only reduce energy consumption, but also increase the amount of data transmission. The transmission of network nodes will change accordingly with different nodes selected as cluster heads, so as to make the energy consumption more balanced and enhance the invulnerability of the network.
Suppose that the initial position of heterogeneous nodes is
When the cluster head transmits data directly to the sink, the node
The data transmission mode of the cluster heads is determined according to the transmission distance
It is assumed that
When
Similarly, when
Finally, when the heterogeneous node
Because the initial location
After clustering by DEEC algorithm, BAS algorithm [
The optimization steps of the algorithm expressed by mathematical model are as follows:
When heterogeneous node is in any position, the direction of progress is random. In dimension The position of the left and right sides of the heterogeneous node ( According to the objective function
In The update rule for the front progress length
In
Because BAS algorithm has excellent convergence speed and low complexity. It is very suitable for the optimization of heterogeneous node positions.
The simplified deployment diagram is shown in
Finally, the minimum value
The cost should be considered when building heterogeneous sensor networks. The number of heterogeneous nodes can’t be too many, but must meet the network performance requirements. In order to solve this problem, BAS algorithm is introduced after each round of clustering to find the optimal deployment location of heterogeneous nodes, so as to realize the location updating of heterogeneous node. Different from traditional methods of building a small world by fixed multiple heterogeneous nodes in WSN, a heterogeneous network is proposed according to the ODHN algorithm. Therefore, only one heterogeneous node can achieve good results, which not only saves the cost, but also optimizes the network structure.
The movement of heterogeneous node will consume a lot of energy. In practical applications, real-time deployment can’t be realized according to the optimal location of heterogeneous node after each round of clustering. Through experiments, the distribution of the optimal locations is concentrated in several regions. Most of the optimal positions are distributed in the area far from the sink node. This is because the area farther away from the sink node has more load, and heterogeneous node can be deployed there to balance the load. As shown in
Therefore, mobility threshold of heterogeneous node is set (ODHN-SMT algorithm). If the distance of the optimal locations is less than the threshold, the heterogeneous node will not move. On the contrary, heterogeneous node move to the optimal location of current round. As shown in
If the threshold is too large, the frequency of heterogeneous node movement will be reduced, which can reduce energy consumption. But it will reduce the accuracy of the heterogeneous node location. Therefore, it will reduce the transmission efficiency and life cycle of the network.
On the contrary, if the threshold is too small, the frequency of heterogeneous node movement will increase and the system energy consumption will increase. But the accuracy of heterogeneous node location will also improve. So, the transmission efficiency and life cycle of the network will be improved.
Therefore, it is necessary to comprehensively consider the overall deployment cost and the invulnerability of the network. It is necessary to set an appropriate threshold. We conducted many experiments. According to the regional range of the optimal location obtained from the experiments, we get the movement threshold as 60 m which is shown in
The ultimate goal of the algorithm is to improve the energy efficiency and prolong the life cycle of the network. In the simulation analysis, we compare the network in five aspects: the number of dead nodes (life cycle), the amount of data transmission, energy consumption, the number of cluster heads and load distribution.
We deploy 100 sensor nodes in the monitored area to simulate ODHN algorithm, ODHN-SMT algorithm and DEEC algorithm. Other conditions are set as follows.
The energy of ordinary nodes is limited. Sink node is located in the monitoring area, and the energy can be supplemented; Each node has an independent ID number; Each node has the ability to sense and transmit data.
The network simulation parameter settings are shown in
Parameter name | Parameter value |
---|---|
Network area | 100 m × 100 m |
Number of nodes | 100 |
Sink node coordinates | (50,50) |
Number of routing execution rounds | 5000 |
Node initial energy | 0.5 J |
Energy consumption ERX of transmitting ETX and receiving circuit | 5 × 10^−8 J |
Energy consumption of power amplifier circuit Emp | 1.3 × 10^−13 J |
The number of dead nodes can intuitively display the length of the network life cycle. As shown in
The amount of data transmission determines the accuracy and application way of data analysis. A large amount of data transmission can make the realization of functions more effective.
The energy will gradually decrease over time until the nodes die. Reducing energy consumption can prolong the network life cycle. The comparison of energy consumption between ODHN algorithm and DEEC algorithm is shown in
The cluster heads can not only aggregate the data of the member nodes, but also reduce the amount of data transmission and save energy consumption. Moreover, with the different selection of cluster heads, the transmission paths of these nodes will change accordingly, so that the energy is consumed evenly to improve the survival time of nodes. Therefore, the generation of cluster heads is of great significance. The comparison of the number of cluster heads produced by the ODHN algorithm, ODHN-SMT algorithm and DEEC algorithm is shown in
The load balanced degree is determined by the ratio of the number of member nodes in the cluster, the number of cluster heads and the total number of the sensor nodes. The more the number of cluster head nodes, the smaller the value of the balanced degree, which indicates that the network is more balanced. When the network load is balanced, the network operation efficiency is high, and the value of network load balanced degree is small and stable. When the network load is unbalanced, the operation efficiency decreases, and the value of network load balanced degree is large. As shown in
Nodes that close to the sink consume more energy because of more transmitting tasks. The cluster head node consumes too much energy because it needs to process and transmit the data uploaded by the member nodes. Because heterogeneous node is added, the cluster heads that near to the heterogeneous node can transmit data to heterogeneous node, and then the heterogeneous node transmit data to sink through super link. Therefore, the excessively fast energy consumption caused by unbalanced network load is solved.
100 sensor nodes are deployed in the monitoring area, as shown in
As time passed, due to unbalanced energy consumption, some nodes consume energy more quickly, and their colors gradually change from blue to red. When the nodes die, they are represented in black.
The simulation results show that the network with heterogeneous node can balance the network load, and improve the connectivity between sensor nodes, which can greatly increase the life cycle of the network.
An optimal heterogeneous node deployment algorithm (ODHN algorithm) is proposed for clustering heterogeneous wireless sensor networks. By building a heterogeneous network model with small world characteristic, optimal deployment of heterogeneous nodes is studied, which can optimize the routing efficiency and communication efficiency of wireless sensor networks. In ODHN algorithm, cluster heads are selected in a probabilistic manner according to the ratio of the residual energy of each node to the average energy of the network. After each round of clustering, BAS algorithm is introduced to find the optimal deployment location of heterogeneous node, so as to realize the location update of heterogeneous nodes from the first round to the last round. However, in practical application, it is impossible to update the location of heterogeneous node at each round. Therefore, the heterogeneous node mobility threshold is defined, and an ODHN-SMT algorithm is proposed. The simulation results show that the algorithm can greatly prolong the life cycle and improve the invulnerability of the wireless sensor networks.