Routing requests in industrial wireless sensor networks (IWSNs) are always restricted by QoS. Therefore, finding a high-quality routing path is a key problem. In this paper, a clone adaptive whale optimization algorithm (CAWOA) is designed for reducing the routing energy consumption of IWSNs with QoS constraints, and a novel clone operator is proposed. More importantly, CAWOA innovatively adopts a discrete binary-based routing coding method, which provides strong support for optimal routing schemes. In addition, a novel routing model of IWSNs combined with QoS constraints has been designed, which involves comprehensive consideration of bandwidth, delay, delay jitter, and packet loss rate. Subsequently, in a series of simulations, the proposed algorithm is compared with other heuristic-based routing algorithms, namely, whale optimization algorithm (WOA), simulated annealing (SA), particle swarm optimization (PSO), and genetic algorithm (GA). The simulation results suggest that the CAWOA-based routing algorithm outperforms other methods in terms of routing energy consumption, convergence speed, and optimization ability. Compared with GA, SA, PSO, and WOA under the conditions that the number of nodes is 120, the maximum delay is 120 ms, the maximum delay jitter is 25 ms, the maximum bandwidth is 9 Mbps, and the packet loss rate is 0.02; the energy consumption of CAWOA-based routing is reduced by 12%, 17%, 19%, and 7%, respectively.

With the improvement in productivity and the popularization of industrial automation, industrial wireless sensor networks (IWSNs) have become an important tool for monitoring the production environment [

To reduce the transmission distance of data and improve the quality of service (QoS) of IWSN applications, routing optimization is a commonly used method. Routing refers to the data transmission path from the source node to the destination node. Since there are usually many choices for the routing path, the optimal routing is a key issue in IWSNs. The so-called optimal routing represents the path with the least energy consumption of routing under the given evaluation criteria, generally QoS restrictions. Nowadays, there are many literatures on the reasonable planning of IWSN routing, and the effect of routing schemes obtained by different methods and different evaluation criteria is not the same [

Hizal and Zengin [

The selection of the optimal routing path under QoS constraints is an NP-hard problem [

The main goal of this paper is to find a routing path with the lowest energy consumption in IWSNs under multiple QoS constraints. In general, the main contributions of this paper can be listed as follows:

A novel clone adaptive whale optimization algorithm (CAWOA) is proposed, which combines the advantages of clonal expansion and adaptive operator

CAWOA has made significant innovations to make it applicable for solving the discrete binary-based routing energy optimization problem in IWSNs with QoS constraints

A new cloning operator is proposed, which can perform hierarchical cloning of the population, thus effectively avoiding the situation of local optimum

A novel routing model of IWSNs that comprehensively considers network bandwidth, delay, delay jitter, and packet loss rate is established, and a fitness function for evaluating routing energy consumption is designed

CAWOA is compared with the genetic algorithm, particle swarm optimization, simulated annealing, and whale optimization algorithm in routing energy consumption, convergence speed, and optimization ability

The structure of this paper is organized as follows. The related works are discussed in Section

In recent years, the problem of wireless sensor QoS routing has attracted more and more people’s attention. In different application scenarios, the QoS constraints that need to be considered are not exactly the same, so the routing energy consumption generated is also different.

Nayyar and Singh [

Many researchers use heuristic algorithms to solve the optimal routing problem. Varshney et al. [

With the purpose of minimizing the energy consumption of IWSN routing under QoS constraints, we propose a novel IWSN routing model. The model takes into account the impact of QoS constraints that are very important in IWSNs on routing energy consumption, including delay, bandwidth, delay jitter, and packet loss rate. In addition, a novel clone adaptive whale optimization algorithm (CAWOA) is proposed to solve the routing path with the lowest energy consumption. Compared with other algorithms for routing optimization, CAWOA has faster convergence speed, higher solution quality, and stronger ability to jump out of the local optimum. Furthermore, the important parameters in CAWOA can be dynamically adjusted along with the running process of the algorithm, thereby enhancing its ability to search for the solution space.

IWSNs include sensor nodes, sink nodes, gateway nodes, and base stations. Normally, the sensor nodes send data to the sink nodes, the sink nodes receive the data and transmit it to the gateway nodes after preliminary processing, and then, the gateway nodes transmit the data to the base station so that the technical staff can plan the next step according to the data content. Since the principle of routing from sensor nodes to sink nodes is the same as that of routing from sink nodes to a gateway node, the optimal routing of IWSNs can be defined as the path with the lowest routing energy consumption under QoS constraints. An industrial wireless sensor network with 6 sensor nodes, 8 sink nodes, and one gateway node is shown in Figure

Composition of an industrial wireless sensor network.

In IWSNs, the main energy consumption of sensor nodes comes from data transmission, which is the so-called routing energy consumption. Routing refers to the data transmission path from the specified source node to the destination node. As shown in Figure

Route map in IWSNs.

Subsequently, to solve the routing energy consumption optimization problem of IWSNs with QoS constraints, the mathematical model is given in Sections

In the IWSN sensing model, since the sensing capabilities of sensors are limited, nodes can only transmit data to other nodes within the sensing range. The specific perception method is shown in

The IWSN routing model with QoS constraints can be represented by an undirected weight graph

Delay represents the average time it takes for data packets to be transmitted in IWSNs. Delay jitter denotes the fluctuation of data packet transmission time. Bandwidth refers to the amount of data that the sensor can transmit per unit time. Packet loss rate is the loss or damage of data packets during transmission. The above factors will affect the routing transmission quality of IWSNs. What is more, the specific QoS function definition is shown in Table

QoS function definition.

Function name | Abbreviation | Definition |
---|---|---|

Delay | DL | Average duration of data transmission |

Delay jitter | DLJ | Fluctuations of the transmission time |

Bandwidth | BW | The amount of data per unit time |

Packet loss rate | PLR | Data loss during transmission |

Cost | The overhead of data transmission |

Given a source node

Specifically,

In (

Assuming that the distance between two nodes is

In (

With the purpose of evaluating the energy consumption of routing, a fitness function is designed as shown in

In (

The design reason for the fitness function (

To optimize the routing energy consumption of IWSNs with QoS constraints, a novel clone adaptive whale optimization algorithm (CAWOA) is proposed. The whale optimization algorithm (WOA) has low computational complexity. In the early stage of the algorithm, WOA performs a global search, while in the later stage of the algorithm, it performs a local search, which can effectively obtain the routing path that meets the QoS constraints. Compared with other heuristic algorithms, WOA’s local search ability is stronger. Its major disadvantage is that it is easy to fall into the local optimum. However, the addition of the clone operator can effectively avoid the emergence of local optimal conditions. Furthermore, WOA has a faster convergence speed; this advantage can make it have higher practicability. As a result, CAWOA is inspired by the traditional WOA but has made significant improvements in convergence speed and optimization capabilities. By using CAWOA, the optimal routing path with the least energy consumption can be found; therefore, the network lifetime of IWSNs can be effectively extended for saving factory costs. In addition, different from the traditional WOA, the significant improvement of the CAWOA is the addition of the clone operator and the adaptive operator.

The process of CAWOA includes population coding and initialization, calculating fitness and finding the leading whale, adaptive encircling predation, bubble-net attacking, random search for prey, cloning operation, and termination operation.

The first step of applying CAWOA to the routing energy consumption optimization problem of IWSNs is to determine the encoding method. It is difficult to achieve the expected goal using conventional decimal coding in the routing problem, because the data does not need to pass through all nodes during the routing process, and the optimal routing is the path that has minimal energy consumption under the QoS constraints. Therefore, binary encoding is a desirable coding method, which has the characteristics of simple and easy encoding and decoding. Under the problem of binary encoding, 1 means passing through the node, and 0 means not passing through the node. Assuming there are 5 sensor nodes in IWSNs, the binary code of the individual whale can be expressed as

In (

After determining the coding method of the individual whale, the encoding of the whale population can be expressed as

In (

In the routing energy consumption optimization problem with QoS constraints, the fitness value of the whale represents the energy consumption of a route. Before other operations of CAWOA, it is necessary to calculate the fitness of each individual to find the position of the leading whale. The fitness value of each individual is obtained according to formula (

In CAWOA, the predation behavior of whales symbolizes the process of finding the optimal solution in the QoS routing energy consumption optimization problem. In the problem, the individual whale finds the position of the prey first and then surrounds the prey. The prey can be regarded as the leading whale, which means that other whales update their positions toward the position of the leader whale for carrying out the predation operation. Therefore, the first step in encircling predation is to calculate the distance between the individual whale and the leading whale, which is derived from

Then, the position of the leading whale affects the update of the position of the individual whale, and its formula is shown in

The addition of adaptive operators allows CAWOA to dynamically adjust the parameters according to the fitness value when the whale is preying, which speeds up the convergence speed of the algorithm.

In the problem of IWSN routing energy consumption optimization under QoS constraints, the bubble-net attacking behavior of whales helps to find a better solution. There are two strategies for simulating the bubble-net attacking: one is the shrinking encircling mechanism, and the other is the spiral updating position.

In CAWOA, the shrinking encircling means that the position update of the whale is performed according to equation (

The spiral updating position means that whales swim to the surface with a spiral posture and spit out varying size bubbles for preying on shrimp and fish. In this stage, the distance between the whale and the leading whale is first calculated, and the calculation formula is shown in

Then, the individual whale updates its position, as shown in

With the purpose of avoiding falling into a local optimum in solving the IWSN routing energy optimization problem under QoS constraints, the position of the whale in CAWOA cannot be updated only by the position of the leading whale, and sometimes, it must be updated with the position of the partner. Specifically, CAWOA at this stage is to conduct a random search for prey and obtain the next position of the whale, and this operation is carried out under the influence of the coefficient vector

In CAWOA, the cloning operation is inspired by the concept of cloning in biology, and its purpose is to improve the local search capability and convergence speed of the algorithm. Normally, the optimal solution of the IWSN routing problem with QoS constraints is related to the optimal individual in the current iteration process; however, traditional WOA performs many unnecessary operations. Therefore, the cloning operation can effectively improve the performance of the algorithm by expanding the number of individuals with high fitness. The cloning operation in CAWOA is divided into two parts: one is clonal expansion, and the other is high-probability mutation. The purpose of high-probability mutation operation is to reduce the possibility of falling into a local optimum, and for the same purpose, clonal expansion generally uses the way of multilevel cloning. The pseudocode of the cloning operation is shown in Algorithm

1. First perform multi-level cloning operations

2.

3.

4. Assign the individual of pop1(1) to pop2(p)

5.

6. Assign the individual of pop1(2) to pop2(p)

7.

8. Assign the individual of pop1(3) to pop2(p)

9.

10. Assign the individual of pop1(4) to pop2(p)

11.

12.

13. Then perform high-probability mutation operations

14.

15. r is a random number and r∈ [0,1]

16.

17. Perform mutation operation on pop2(p)

18.

19.

20. Return pop_2

If CAWOA reaches the specified number of iterations, the algorithm stops looping and outputs the result; otherwise, it returns to Section

The specific steps can be expressed as follows.

Determine the number of sensors and the population size, randomly generate the location of sensor nodes in the monitoring area, and randomly generate whale locations. Initialize the parameters of the CAWOA and set the initial iteration

Calculate the fitness of individual whales according to equation (

Entering the main loop of the algorithm, if

Update coefficient vector

Calculate and sort the fitness value of the whale population according to equation (

Calculate the fitness value according to equation (

Output the optimal solution

The flow chart of CAWOA can be expressed in Figure

Steps of CAWOA.

We made the following assumptions in the simulation:

Delay, bandwidth, delay jitter, and packet loss exist in the link and affect routing

The node is stimulated by physical signals before sending data

There are sending energy and receiving energy. The sending node generates sending energy by sending data, and the receiving node generates receiving energy by receiving data

To prove the effectiveness of the proposed algorithm in reducing the energy consumption of IWSN routing with QoS constraints, a series of simulations is carried out, and CAWOA is compared with the whale optimization algorithm, particle swarm optimization, genetic algorithm, and simulated annealing. The simulation experiment includes the trend of the algorithm for reducing the routing energy consumption, the speed of algorithm convergence, the percentage of energy consumption obtained after optimization, and the trend graph of energy consumption in large-scale IWSNs. Different types of simulations are carried out under different numbers of sensors, which can better reflect the practicality of the algorithm. In addition, all simulations are performed on a computer equipped with R7 4800H 2.9 GHz CPU, and the fitness function used in the algorithms is according to formula (

For the IWSN routing energy optimization problem with QoS constraints, the unified definition of public parameters helps to compare algorithms in a relatively fair situation. Therefore, the population size of the algorithms is set to 40, the number of iterations is set to 100, the sensors in IWSNs are distributed in a square area with a side length of 400, and the sensor coordinates are generated randomly. Specifically, CAWOA uses multilevel cloning, with the mutation probability set to 0.2,

Simulation parameters in Figure

Sensors | Delay (max) | Delay jitter (max) | Bandwidth (max) | Packet loss rate (max) | |
---|---|---|---|---|---|

Figure | 30 | 50 ms | 10 ms | 6 Mbps | 0.01 |

Figure | 40 | 60 ms | 15 ms | 6 Mbps | 0.01 |

Figure | 50 | 70 ms | 20 ms | 7 Mbps | 0.01 |

Figure | 60 | 80 ms | 25 ms | 7 Mbps | 0.01 |

Energy consumption comparing five algorithms: (a) 30 sensors; (b) 40 sensors; (c) 50 sensors; (d) 60 sensors.

In Figures

Simulation parameters in Figure

Sensors | Delay (max) | Delay jitter (max) | Bandwidth (max) | Packet loss rate (max) | |
---|---|---|---|---|---|

Figure | 40 | 80 ms | 10 ms | 6 Mbps | 0.01 |

Figure | 60 | 90 ms | 20 ms | 7 Mbps | 0.01 |

Figure | 80 | 100 ms | 30 ms | 8 Mbps | 0.01 |

Figure | 100 | 110 ms | 40 ms | 9 Mbps | 0.01 |

Convergence iteration comparison.

Figure

Simulation parameters in Figure

Sensors | Delay (max) | Delay jitter (max) | Bandwidth (max) | Packet loss rate (max) | |
---|---|---|---|---|---|

Figure | 60 | 60 ms | 10 ms | 6 Mbps | 0.01 |

Figure | 80 | 80 ms | 15 ms | 7 Mbps | 0.01 |

Figure | 100 | 100 ms | 20 ms | 8 Mbps | 0.02 |

Figure | 120 | 120 ms | 25 ms | 9 Mbps | 0.02 |

Comparison of energy consumption optimized by algorithms: (a) 60 sensors; (b) 80 sensors; (c) 100 sensors; (d) 120 sensors.

In Figures

Simulation parameters in Figure

Sensors | Delay (max) | Delay jitter (max) | Bandwidth (max) | Packet loss rate (max) | |
---|---|---|---|---|---|

Figure | 50 | 70 ms | 10 ms | 6 Mbps | 0.02 |

Figure | 100 | 80 ms | 15 ms | 7 Mbps | 0.02 |

Figure | 150 | 90 ms | 20 ms | 8 Mbps | 0.02 |

Figure | 200 | 100 ms | 25 ms | 9 Mbps | 0.02 |

Line charts of energy consumption comparison of algorithms in large-scale IWSNs: (a) 50 sensors; (b) 100 sensors; (c) 150 sensors; (d) 200 sensors.

In the real industrial production environment, there is often a large number of sensor nodes in IWSNs. Therefore, Figures

Comparison of optimization results of algorithms.

Number of sensors | Routing energy consumption (J) | ||||
---|---|---|---|---|---|

GA | SA | PSO | WOA | CAWOA | |

100 | 27.48 | 33.95 | 46.51 | 23.37 | 13.94 |

200 | 62.91 | 93.12 | 74.51 | 58.14 | 22.18 |

300 | 101.46 | 115.67 | 131.80 | 100.94 | 35.94 |

400 | 144.34 | 161.05 | 185.89 | 134.33 | 58.78 |

500 | 213.56 | 236.23 | 270.84 | 191.21 | 92.89 |

Simulation parameters in Table

Sensors | Delay (max) | Delay jitter (max) | Bandwidth (max) | Packet loss rate (max) |
---|---|---|---|---|

100 | 60 ms | 10 ms | 6 Mbps | 0.01 |

200 | 70 ms | 15 ms | 7 Mbps | 0.01 |

300 | 80 ms | 20 ms | 8 Mbps | 0.01 |

400 | 90 ms | 25 ms | 9 Mbps | 0.02 |

500 | 100 ms | 30 ms | 10 Mbps | 0.02 |

According to the results in Table

The purpose of this paper is to reduce the routing energy consumption of IWSNs under the QoS constraints; therefore, a novel clone adaptive whale optimization algorithm (CAWOA) is designed. The significant innovation of CAWOA is the adoption of the latest adaptive technology and cloning operation. What is more, the routing method of IWSNs is optimized and the energy consumption of the network is reduced. In addition, under the premise of considering the QoS constraints in IWSNs, we designed a novel IWSN routing model that takes into account the influence of network bandwidth, delay, delay jitter, and packet loss rate on sensor routing energy consumption. Subsequently, CAWOA is compared with the genetic algorithm, whale optimization algorithm, particle swarm optimization, and simulated annealing for proving its optimization of IWSN routing energy consumption with QoS constraints. The simulation results show that the proposed routing algorithm based on CAWOA is better than other algorithms in terms of routing energy consumption, convergence speed, and optimization capability. In addition, CAWOA is especially suitable for large-scale IWSNs. As the number of sensor nodes increases, the effect of CAWOA-based routing algorithms in reducing energy consumption becomes more obvious. Therefore, it can be concluded that the application of CAWOA can effectively reduce the routing energy consumption in IWSNs.

Future research should consider more complex IWSNs, including but not limited to heterogeneous IWSNs and mobile IWSNs. In addition, in more complex situations, machine learning technology can be used as a powerful tool, such as reinforcement learning, to further improve the reliability of IWSNs and reduce routing energy consumption.

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.

The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

The authors declare no conflict of interest.

This paper was funded by the Corps innovative talents plan, grant number 2020CB001; project of Youth and Middleaged Scientific and Techno-logical Innovation Leading Talents Program of the Corps, grant number 2018CB006; China Postdoctoral Science Foundation, grant number 220531; Funding Project for High Level Talents Research in Shihezi University, grant number RCZK2018C38; and Project of Shihezi University, grant number ZZZC201915B.