1. Introduction
Wireless sensors are widely deployed on a large scale in commercial fields [
1,
2], but are limited by node costs, communication efficiency between nodes, and energy consumption [
3,
4,
5], e.g., in forest and grassland fire risk monitoring and early warning. The problem of wireless sensor deployment is considered as deploying a certain number of nodes to meet monitoring needs, that is, finding the number and location of deployed nodes. The goal of solving this problem is to find as few sensors as possible to meet the monitoring requirements and reduce the communication cost. It is transformed into an optimal sensor node solution set, which is an NP-hard problem. The sensor deployment problem has diminishing returns, e.g., submodularity [
6,
7,
8]. Initially, when a small number of sensors are deployed, each new sensor will significantly improve its deployment utility. As more sensors are placed, the improvement in utility from adding new sensors diminishes. Krause [
9] showed that for problems with submodularity, at least the
approximation of the optimal solution can be obtained using the greedy algorithm.
Many methods have been proposed for sensor deployment. In [
10], Huang et al. assumed that the node’s perception ability is a circular area. That is, targets within the circular area are fully perceived, and targets outside the circular area will not be perceived. In [
11], Guestrin et al. proposed the use of mutual information-based optimization criteria so that the set of deployed nodes contains information about unselected points, and the monitoring is quite accurate. In [
12], Cheng et al. proposed a Markov random field model to describe the data correlation between sensor nodes. In [
9], Krause deployed sensors with a greedy algorithm to maximize the amount of information, but neglecting the influence of the communication distance between nodes. In [
13], Krause et al. improved the greedy algorithm and proposed the Padded Sensor Placements at Informative and cost-Effective Locations (pSPIEL) algorithm to solve the problem of sensor deployment optimization under the constraint of communication distance; however, a large number of sensors needed to be deployed. In [
14], Mariohat et al. established a Gaussian model, improved the greedy algorithm under the constraint of fixed costs, and proposed the SUPSUB method to minimize the submodular set function, while neglecting the influence of the communication distance between nodes on the deployment. The sensor placement problem considering communication distance is a constrained optimization problem. The bi-projection neural network proposed by Xia et al. [
15] can effectively solve large-scale constrained optimization problems, and has good stability and faster convergence [
16]. Liu et al. [
17] proposed a ML-OAXSMT-PSO construction algorithm, which can significantly reduce the total cost.
During communication transmission, wireless sensors have limited energy, but effective clustering nodes can better save energy and extend the life cycle of the entire network. There are various energy-saving methods. Guo et al. [
18] proposed the FTAOA algorithm to minimize task completion time to save node energy. Cheng et al. [
12] proposed the NSA algorithm to reasonably deploy nodes and significantly improve network lifetime. Liu et al. [
19] proposed the KPNS algorithm to appropriately select more active nodes for monitoring, so that the energy is fully utilized. Effective node clustering can greatly save energy and extend the life cycle of the entire network. The LEACH protocol balances the energy of each sensor in the entire network by randomly selecting cluster heads [
20]. However, it has the problem of uneven number and distribution of cluster heads [
21], for not having considered the transmission distance. This causes either the nodes far away from the base station to be selected as the cluster head or the nodes far away from the cluster head to die prematurely [
22]. In [
23], Simon proposed the biogeography-based optimization algorithm with advantages of simple operation, few parameters, and high search accuracy [
24]. In [
25], Pal and others used the Biogeography-Based Optimization (BBO) algorithms to select cluster heads and cluster nodes, and obtained good energy efficiency. However, the authors only took the distance between cluster heads and the distance between nodes in the cluster into consideration, while neglecting the energy consumed by data transmission between nodes.
Deploying wireless sensors is limited by cost and power consumption [
26]. Therefore, the following two issues need to be considered during deployment: one is to achieve efficient data collection; the other is to use as few sensors as possible and minimize the communication distance between sensors to reduce the total energy consumption. Because of the existing problem of deploying fewer sensors in terms of the distance between nodes, and the deficiency of some popular algorithms in the field of sensor deployment, we shall proceed as follows in this paper. The mutual information is used to describe the correlation between the observed and the unobserved points. The communication distance is described by the connection of the graph and the Improved Heuristic Ant Colony Algorithm-Chaos Optimization of Padded Sensor Placements at Informative and cost-Effective Locations (IHACA-COpSPIEL) algorithm is used to choose the optimized submodular model. By considering the distance between clusters, the distance between nodes in the cluster, and the energy consumption of data transmission by the nodes, we obtain an optimized routing protocol in which the BBO algorithm is used to transmit data with an improved cost performance.
The structure of this article is as follows. In
Section 2, wireless sensor deployment optimization is introduced. The routing protocol of the wireless sensor network based on the BBO algorithm is presented in
Section 3. In
Section 4, we introduce the experimental verification, discuss the deployment effects using different algorithms, and analyze the performance of the protocol for the BBO algorithm. Finally, this article is summarized in
Section 5.
3. Routing Protocols for Wireless Sensor Networks
3.1. Communication Model
The energy consumption of data sent by sensor nodes is shown in Equation (
16).
where
k is the number of bits of transmitted data,
d is the transmission distance,
is the energy consumption of the transmitting circuit to send
k bit data, and
is the transmission power amplifier transmitting
k bit data when the transmission distance is
d.
is the unit energy consumption of the transmitting or receiving circuit, and
is the threshold.
is the energy consumption parameter of the transmission power amplifier under the free space channel model and
is the energy consumption parameter of the transmission power amplifier under the multipath fading channel model.
The calculation of the energy consumption of the receiving circuit to receive
k bit data is shown in Equation (
17).
3.2. Optimal Clustering
The number of cluster heads has a great impact on network performance. According to [
14], the optimal number of cluster heads is shown in Equation (
18).
In Equation (
19),
is the number of nodes in set
A,
M is the area side length, and
is the distance from the node to the base station.
The probability of a node being elected as a cluster head is shown in Equation (
19).
3.3. Fitness Function
The fitness values are based on parameters used to achieve the best solution. It considers intra-cluster compactness, inter-cluster separation and total energy consumption.
Tightness refers to the internal distance, that is, the distance between the nodes in the cluster and the cluster head ().
Separability refers to the distance between clusters, that is, the minimum distance between cluster heads.
The total energy consumption refers to both the cluster head communication energy consumption and ordinary node communication energy consumption , of which the energy consumption of the cluster head includes the energy consumption required to receive data sent by the nodes in the cluster, the energy consumption required to collect data for fusion, and the energy consumption required to send data to the base station. The energy consumption of ordinary nodes includes the energy consumption required to send data to the cluster head. Assume that the total number of nodes is , the number of cluster heads is m, and the ordinary nodes in each cluster are , ,..., .
In Equation (
16),
is the energy consumed by unit bit data fusion, and
is the distance between the cluster head and the base station.
In Equation (
17),
is the distance between the nodes in the cluster and the cluster head.
In Equation (
18), the closer the distance between the cluster nodes and the cluster head in a cluster, the better. The greater the separation between cluster heads, the better the total energy consumption. The fitness function is as follows.
In Equation (
19),
+
+
=1.
3.4. Routing Protocol Based on BBO Algorithm
BBO algorithm is an information intelligence heuristic algorithm first proposed by Dan Simon in 2008. The habitats of biological populations have their corresponding Habitat Suitability Index (HSI), which is used to describe the quality of the habitat environment, and the factors that affect the fitness index are called Suitable Index Variables (SIVs). The BBO algorithm has the advantages of simple operation, fast convergence, and fewer parameters [
28]. The standard BBO algorithm uses a simple linear migration model, but in the real biogeographic environment, species migration often occurs randomly and does not follow this rule. Complex and natural migration models are much better than simple migration models [
23,
29]. In this paper, a cosine migration model is used. When the number of species in the habitat is either large or small, the change in the immigration rate
and the emigration rate
is relatively stable. When the number of species in the habitat is neither large nor small, the immigration rate
and the emigration rate
changes quickly. The expression of the cosine migration model is shown in Equations (26) and (27).
In Equations (26) and (27), I is the maximum value of the immigration rate, E is the maximum value of the emigration rate, k is population number and n is the maximum population number.
The mutation operator provides a certain global search capability for the algorithm through the mutation of the habitat’s own information.
In Equation (
28),
is the maximum mutation rate,
is the probability that habitat
i has
s species, and
=
.
The steps of optimizing wireless sensor network routing protocol based on the BBO algorithm are as follows. Lines 3–7 select CH with complexity . Lines 9–31 reach the minimum fitness value with complexity where q is the number of iterations. Lines 10–20 calculate the migration rate with complexity . Lines 21–28 calculate the mutation rate with complexity and lines 32–35 calculate with complexity where is the number of nodes (). The computational complexity of Algorithms 2 is per round approximately.
Algorithm 2 Biogeography-Based Optimization (BBO)-based routing protocol process. |
Input: node coordinates, energy model |
Output: residual energy per round, number of dead nodes, number of surviving nodes |
1: Initialize parameters: number of habitats n, maximum emigration rate E, maximum immigration rate I, |
probability of species number for each habitat , maximum number of species , |
maximum number of rounds |
2: l = 1: |
3: j = 1: n |
4: Select CH according to Equation (19) |
5: Initialize population randomly |
6: Calculate the fitness value of habitat j according to Equation (25) |
7: |
8: Keep habitat with the smallest fitness values as elite habitat |
9: habitat does not reach minimum fitness value |
10: k = 1: n |
11: Calculate the migration rate according to Equation (26) |
12: is greater than a uniformly distributed pseudo random number in [0,1] |
13: t = 1: n |
14: Calculate the migration rate according to Equation (27) |
15: is greater than a uniformly distributed pseudo random number in [0,1] |
16: The roulette selection method is used to select the population to move out of the habitat t |
and move into the habitat k |
17: |
18: |
19: |
20: |
21: i = 1: n |
22: Habitat i is not an elite habitat |
23: Calculate the mutation rate according to Equation (28) |
24: is greater than a uniformly distributed pseudo random number in [0,1] |
25: Select population mutations in habitat i randomly |
26: |
27: |
28: |
29: Calculate fitness value |
30: Replace the worst habitats with elite habitats |
31: |
32: Calculate the shortest distance from ordinary nodes to CH |
33: Calculate the energy consumed by ordinary nodes to CH to transmit and receive data packets |
34: Calculate the energy consumed by CH to sink nodes to transmit and receive data packets |
35: Calculate the remaining energy, dead nodes, and surviving nodes of the sensor network |
36: All network nodes are dead |
37: |
38: |
39: |
40: |
5. Conclusions
In order to reduce costs and save energy, this paper proposes a large-scale sensor deployment method called the IHACA-COpSPIEL algorithm and a routing protocol based on the BBO algorithm. Mutual information is introduced to describe the correlation between observed points and unobserved points, a mathematical model with submodularity is established, and the edges of graph theory are used to represent communication costs. The pSPIEL algorithm with enhanced optimization ability by a chaos operator and the ant colony algorithm with improved heuristic function and pheromone update mechanism are used to find the optimal path. What has been studied can further solve the sensor deployment problem under the constraint of communication cost. Finally, the BBO algorithm-based routing protocol transmits data to the deployed sensors. The computational complexity of the IHACA-COpSPIEL is , and the computational complexity of the routing protocol based on the BBO algorithm is . The experiments show that the deployment algorithm proposed in this paper has better sensor deployment capabilities. This deployment algorithm reduces the communication cost by 38.42% compared with the greedy algorithm. It also reduces the number of sensors and has a longer life cycle. Compared with the LEACH protocol, the BBO algorithm-based routing protocol has lower energy consumption and longer network life.
In the future, we intend to use a discrete event simulator (DES) such as NS-3 to further combine practical application scenarios to improve the effectiveness of the algorithm. Our vision for future work is as follows.
We will complete the IHACA-COpSPIEL protocol design in the NS-3. We will refer to the RFC document of Multi-Protocol Label Switching protocol, and elaborate on the design and implementation of each basic component of IHACA-COpSPIEL, including the forwarding equivalence class (FEC), next hop label forwarding entry (NHLFE), FEC to NHLFE mapping (FTN), etc. By statically configuring the label forwarding table, the communication between private networks through the backbone network by IHACA-COpSPIEL forwarding will be realized.