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Seyedali Mirjalili et al. (2014) introduced a completely unique metaheuristic technique particularly grey wolf optimization (GWO). This algorithm mimics the social behavior of grey wolves whereas it follows the leadership hierarchy and attacking strategy. The rising issue in wireless sensor network (WSN) is localization problem. The objective of this problem is to search out the geographical position of unknown nodes with the help of anchor nodes in WSN. In this work, GWO algorithm is incorporated to spot the correct position of unknown nodes, so as to handle the node localization problem. The proposed work is implemented using MATLAB 8.2 whereas nodes are deployed in a random location within the desired network area. The parameters like computation time, percentage of localized node, and minimum localization error measures are utilized to analyse the potency of GWO rule with other variants of metaheuristics algorithms such as particle swarm optimization (PSO) and modified bat algorithm (MBA). The observed results convey that the GWO provides promising results compared to the PSO and MBA in terms of the quick convergence rate and success rate.

In real-time environment, wireless sensor networks (WSNs) are deployed in a sensor field to screen the physical environment behaviors. From the most recent decades, the greater part of the scientists was pulled in extraordinary enthusiasm for WSN because of its minimal effort and low preparing capacities. WSNs have incomprehensible applications, for example, checking natural angles and physical marvels like temperature, environment observing, activity control observing, submerged acoustic observing, and patient social insurance checking. WSNs have many research issues that influence outline and execution of general system, for example, sending, time synchronization, restriction, least cost directing, and nature of administration and system security. Most of the articles and research proposals are introduced to solve these issues but still the challenging problem in WSN is localization [

In the past literature, wide range of localization algorithms and techniques are implemented to minimize the communication cost and to improve energy efficiency; however, a large portion of the calculations are application particular and the majority of the arrangements are not proper for extensive variety of WSNs. By and large, confinement is characterized into two unique classes, namely, extend based and run free restriction. Range measurement techniques help to estimate the location of sensor nodes in range based localization [

Many metaheuristics algorithms are applied to solve the localization issue in WSN, which drastically reduces the localization error. These algorithms belong to the family of trial and error problem solvers, which iteratively process the feasible solution and identify the nearest optimal solution to the various problems. In localization issues, various optimization algorithms like genetic algorithm, particle swarm optimization, shuffled frog leaping, cuckoo search, bat algorithms and so forth are aids to improvise the network performance by effective and efficient identification of unknown node position.

This paper is structured as follows: Section

In general, various localization procedures [

In gradient search techniques, identification of unknown node location is processed based on the first-order iterative methods. The following description explains the work carried out over past decades in solving localization issues. Firstly, DV-Hop Localization algorithm [

Likewise, various techniques are presented to address the issues of localization problem. Some of them are as follows: Akyildiz et al. [

Semidefinite programming depends on using convex optimization to address the node localization problem. Based on Biswas et al. [

Even though gradient method solves the localization problem in WSN, it lags in solving large scale scenarios; in order to overcome this issue localization problem is considered as an optimization one. Nowadays optimization algorithm [

Simulated annealing by Kannan et al. [

Particle swarm optimization (PSO) is a very popular algorithm which mimics the behavior of birds flocking and fish schooling. PSO for node localization [

Hybrid optimization techniques determine that two or three metaheuristics algorithms are merged together to form a new optimization algorithm. These algorithms aid in efficient findings of optimal solution within the minimum computation time. Some of the researchers used the hybrid algorithm to solve the localization problem. Firstly, Niewiadomska-Szynkiewicz and Marks [

To the best of our knowledge, so far, grey wolf optimization (GWO) algorithm was never used for localization problem. So, in this paper the grey wolf optimization algorithm is proposed to optimize the multimodal localization problem and it performs quite well in terms of identifying unknown node position and localization accuracy.

Metaheuristic optimization algorithms are becoming more familiar in engineering applications because they (i) rely on rather easy concepts and being straightforward to implement; (ii) do not require gradient information; (iii) can bypass local optima; (iv) are often used in a wide range of issues covering different disciplines. Vast numbers of algorithms are introduced for different combinatorial optimization problems. Grey Wolf optimization is one of the new algorithms proposed by Mirjalili et al., [

GWO algorithm is one of the interesting algorithms due to the group hunting strategy. Based on Muro et al., grey wolf hunting is classified into three categories (i) tracking, chasing, and approaching the prey, (ii) pursuing, encircling, and harassing the prey until it stops moving, and (iii) attacking towards the prey. In GWO, symbolic representation of alpha, beta, and deltas is represented as

In encircling prey, grey wolves recognize the location of prey and encircle them. In this phase, the position vector of the prey is defined and other search agents adjust its position based on the best solution obtained. The equation of encircling prey is given below:

The vectors

In hunting phase, grey wolves are directed by alpha (

Attacking prey phase helps candidate solution to identify the local solutions. In order to perform local search coefficient vector

The parameters

Initialize the Grey Wolf population

Evaluate the fitness of each search agent

Initialize the first best solution as

Second best solution as

Third best solution as

Update the current search agent position by equation (

Evaluate the fitness

Update the coefficient vector

If any better solution then update the best agents

Stop the process and visualize the first best agent

Flowchart of grey wolf optimization (GWO) algorithm.

WSN node localization problem formulates using the single hop range based distribution technique to estimate the position of the unknown node coordinates

Randomly Initialize the

Three or more anchor nodes within the communication range of a node are considered as localized node.

Neighbouring anchor node help to measure the location of localized node. Distance measurements are distracted due to environmental consideration; to eradicate it Gaussian noise

The optimization problem is formulated to minimize the error of localization problem. Each localizable target hub runs GWO calculation freely to restrict itself by identifying its position coordinates

The localization error is characterized as the interval between the original and evaluated areas of an obscure node which is figured as the mean of square root of interval of evaluated node coordinates

Repeat the Steps

In every evolution, the number of anchor nodes increases gradually based on the localized target nodes and these localized nodes are named referenced node. At the

In this segment, the point by point assessment of the GWO calculation is exhibited. For correlation, two different algorithms are utilized. They are altered PSO (PSO) [

Parameter setting for WSN.

Sensor nodes | 300 |

Anchor nodes | Varies on |

Deployment area |
^{2} |

Transmission range (meters) | Varies on |

Maximum number of iterations | 100 |

In this paper, deployment area of wireless sensor network is considered as

The performance of the GWO algorithm with other well-known optimization algorithms such as PSO and MBA has been used to analyse the performance of the proposed work. Mean localization error (MLE), Computational time, and number of Localized nodes (NL) are considered to evaluate the performance of the GWO algorithm.

In general, 300 sensor nodes are placed randomly in a deployment area. Nodes are classified into three categories anchor (position known node), target (unknown position node), and localized (reference node or position identified so far) node. Figure

PSO for WSN localization.

MBA for WSN localization.

GWO for WSN localization.

The results are analysed using the parameters such as mean localization error (MLE), Computational time, and number of Localized nodes (NL). The anchor nodes are varied from 10 to 100 for better efficiency in identifying unknown node positions. Initially, sensor nodes are scattered with 10 anchor nodes in the deployment area. PSO algorithm is applied into that scenario to identify the unknown node position. The results of the PSO with 10 anchor nodes are measured in Table

Comparative analysis of mean localization error (MLE), computation time, and number of localized node (NL).

Anchor |
PSO | MBA | GWO | ||||||
---|---|---|---|---|---|---|---|---|---|

MLE (%) | Time (s) | NL | MLE (%) | Time (s) | NL | MLE (%) | Time (s) | NL | |

10 | 0.5559 | 4.17 | 132 | 0.6982 | 3.99 | 178 | 0.7771 | 1.98 | 185 |

20 | 0.5488 | 3.61 | 139 | 0.6422 | 2.48 | 186 | 0.7762 | 2.21 | 196 |

30 | 0.5357 | 3.03 | 142 | 0.6358 | 3.25 | 195 | 0.7758 | 2.60 | 213 |

40 | 0.4787 | 5.11 | 154 | 0.6169 | 2.47 | 205 | 0.7511 | 2.50 | 221 |

50 | 0.4594 | 4.59 | 161 | 0.5798 | 2.25 | 210 | 0.7043 | 2.37 | 234 |

60 | 0.4592 | 5.59 | 173 | 0.5747 | 3.33 | 240 | 0.7011 | 2.55 | 249 |

70 | 0.4445 | 5.01 | 182 | 0.5425 | 1.86 | 246 | 0.6983 | 1.72 | 262 |

80 | 0.4433 | 5.58 | 196 | 0.5369 | 4.13 | 251 | 0.6974 | 2.06 | 271 |

90 | 0.4247 | 5.22 | 208 | 0.5205 | 1.75 | 258 | 0.6779 | 1.51 | 279 |

100 | 0.3054 | 5.93 | 219 | 0.5021 | 2.55 | 260 | 0.6586 | 1.80 | 286 |

Comparison results of anchor node versus MLE.

At the same time, we have measured running time of each algorithm with respect to increase in number of anchor nodes. All the computations are performed in the same system. The running time is measured in terms of seconds over 100 iterations. Figure

Comparison results of anchor node versus computation Time.

Comparison results of anchor node versus localized Node.

Transmission range of sensor node is considered as another parameter in wireless sensor network localization. The number of localized node increases gradually when the transmission range of sensor nodes increases which in turn obtains the minimum localization error. The transmission range of sensor nodes starts from 10 meters and gradually increases with 5 meters to analyse the performance of the algorithm. When the transmission range of the sensor node increases, the proposed algorithm obtains better result in terms of location accuracy. Table

Result by varying number of transmission range.

Transmission range (meters) | Localized nodes | ||
---|---|---|---|

PSO | MBA | GWO | |

10 | 86 | 118 | 125 |

15 | 101 | 136 | 148 |

20 | 110 | 149 | 156 |

25 | 128 | 167 | 173 |

30 | 133 | 175 | 184 |

35 | 140 | 180 | 193 |

40 | 149 | 191 | 209 |

Comparison by varying Transmission range and obtained localized node.

The proposed GWO algorithm for node localization problem is successfully implemented and the observed results show that the grey wolf optimization algorithm estimates the unknown node location and provides minimum localization error compared to other metaheuristic algorithms PSO and MBA. GWO algorithm is better due to its hierarchical leadership strategy. This strategy improves the solution (unknown node position) with the aid of three known solutions. It outperforms the triangulation methods for large scale environment.

Finally, this paper concludes the results that the GWO algorithm provides better performance in terms of location accuracy and minimization of localization error. Faster convergence rate and estimating the location of the node within the minimum computation time are additional advantage of GWO.

This review exhibited another swarm-based advancement calculation motivated by the leadership hierarchy of chasing conduct of dark wolves. This GWO encased three leaders to recreate the scan for prey, enclosing prey, and initiative chase conduct of dark wolves. GWO was identified to be sufficient competitive with other state-of-the-art metaheuristic methods to analyse exploration, exploitation, nearby optima evasion, and convergence behavior. Grey wolf optimization algorithm was never used for localization problem. This paper utilized GWO algorithm for localization problem and provided better results in finding location of unknown nodes. The numerical computation results such as convergence rate (minimum computation time) and success rate (maximum number of localized nodes) of proposed GWO algorithms are noted and it is compared with other variants such as PSO and MBA algorithms. The results of GWO algorithm are compared with other metaheuristics approaches and thereby it achieves better performance with respect to maximum number of localized nodes. Further, this algorithm can be tested in movable node networks such as Mobile ad hoc networks (MANET). In future, GWO algorithm can integrate with other variants of metaheuristic algorithms to form a hybrid algorithm for efficient move in convergence and diversity over the identification of maximum number of unknown node positions.

The authors declare that there are no conflicts of interest regarding the publication of this paper.