Nodes localization in a wireless sensor network (WSN) aims for calculating the coordinates of unknown nodes with the assist of known nodes. The performance of a WSN can be greatly affected by the localization accuracy. In this paper, a node localization scheme is proposed based on a recent bioinspired algorithm called Salp Swarm Algorithm (SSA). The proposed algorithm is compared to well-known optimization algorithms, namely, particle swarm optimization (PSO), Butterfly optimization algorithm (BOA), firefly algorithm (FA), and grey wolf optimizer (GWO) under different WSN deployments. The simulation results show that the proposed localization algorithm is better than the other algorithms in terms of mean localization error, computing time, and the number of localized nodes.
Wireless sensor networks (WSNs) are networks that consist of a few autonomous sensor nodes that are distributed in a specific area for cooperatively sensing their environment. In the last decade, WSNs have been employed in many real-life applications such as healthcare, home automation, traffic surveillance, and environmental monitoring [
Accurate node localization is a critical issue in WSNs. Localization problem in WSNs means calculating the positions of unknown sensor nodes. In many environments where the sensor nodes of a WSN are deployed, people may not be able to go and fix these nodes. In these environments, the sensor nodes are usually randomly scattered in random locations; therefore, the sensor nodes usually take random positions. On the other hand, in many applications, the information collected by the sensor nodes of a WSN may be useless if the positions of the sensor nodes that gathered the information are unknown. This emphasizes the need for an accurate node localization scheme [
In order to localize the sensor nodes of a WSN, a GPS can be attached to each sensor during the network deployment. Then, the GPS can be used to find the coordinates of the sensor nodes. However, using GPS for localizing sensor nodes is undesirable and impractical solution because of many reasons such as cost, inaccessibility, sensor nodes may be deployed indoors, and climatic conditions may disturb the GPS reception ability [
Recently, node localization in WSNs is handled as a multimodal and multidimensional optimization problem that can be solved using population-based stochastic approaches. In the literature, many metaheuristic algorithms have been employed to solve the localization problem in WSNs. These algorithms have succeeded in reducing the localization error dramatically. These algorithms attempt to solve an optimization problem by trial and error in which the feasible solutions are processed, and the nearest optimal solution is identified. Currently, various optimization algorithms such as genetic algorithm (GA), cuckoo search (CS), gravitational search algorithm (GSA), butterfly optimization algorithm (BOA), particle swarm optimization (PSO), artificial bee colony (ABC), etc. have been employed effectively in specifying the positions of the unknown nodes in WSNs [
The main contribution of this paper is using the Salp Swarm Algorithm (SSA) for the first time ever to localize the nodes of WSNs. The performance of the proposed SSA-based localization algorithm is analyzed and compared with particle swarm optimization (PSO), butterfly optimization algorithm (BOA), firefly algorithm (FA), and grey wolf optimizer (GWO) algorithms. The results have shown that the SSA-based localization algorithm is better than the previously mentioned localization algorithms in terms of computing time, number of localized nodes, and localization accuracy.
The remaining sections of the paper are organized as follows: Section
In the last years, many optimization algorithms have been employed for addressing the problem of node localization in WSNs [
Low et al. have introduced a low-cost localization system that depends on the measurements obtained from a pedometer and communication ranging among adjacent nodes. The proposed system does not require good network connectivity and presents good performance in sparse networks. A probability-based algorithm that needs a nonlinear optimization problem solving is employed to provide the localization information. Moreover, the particle swarm optimization (PSO) has been employed to determine the optimum location of the sensor nodes. Experimental results have proved that the proposed system has a good performance [
Manjarres et al. have presented a hybrid node localization algorithm based on Harmony Search (HS) algorithm with a local search procedure. The main objective of the proposed algorithm is to address the localization problem and to distribute its burdens over an iterative process. Additionally, the proposed algorithm employs certain connectivity-based geometrical constraints to decrease the area in which each node can be located. The simulation results have verified that the proposed algorithm is better than another simulated annealing- (SA-) based localization algorithm in terms of both localization error and computational cost [
Li et al. have suggested a self-adaptive artificial bee colony (SAABC) node localization algorithm that considers the whole effects resulting from employing dynamic topology. The proposed algorithm provides good performance in WSNs with both random distributing nodes and dynamic topology. Additionally, the obtained simulation results have shown that the proposed localization algorithm provides better node localization precision and precision stability compared to the DV-Hop algorithm without the need for extra devices or overhead in communication [
Tamizharasi et al. have proposed a novel hybrid node localization algorithm based on bacterial foraging algorithm (BFA) and particle swarm optimization (PSO). The main design objectives of the proposed algorithm are to enhance the efficiency and accuracy of BFA and to avoid getting stuck in a local extreme. In the proposed algorithm, PSO is merged into the chemotaxis of BFA to speed up the convergence rate. Moreover, the global search ability is improved by proposing the elimination probability in elimination-dispersion based on the energy of bacteria. The obtained simulation results have verified that the proposed hybrid algorithm outperforms the BFA [
Tang et al. have proposed a sensor nodes localization algorithm, which depends on a new intelligent optimization algorithm called plant growth simulation algorithm (PGSA) that simulates the growth of plants. In their work, they proposed inserting the plant root of adaptive backlight function into the original PGSA for improving convergence time and localization precision. The obtained simulation results have verified that the proposed algorithm is better than the simulated annealing algorithm (SAA) in terms of localization accuracy and computing time [
Jegede and Ferens have employed the genetic algorithm (GA) for learning the environmental issues within a WSN for effectively localizing its sensor nodes. For every coordinate in the grid network area, given random perturbations of received signal strength (RSS), GA would be able to learn the environment and to decrease the possible errors associated with the RSSI measurement taken for each coordinate. The conducted simulation shows that GA can reasonably localize sensor nodes using the coordinates of three anchors [
Goyal and Patterh have proposed a cuckoo search- (CS-) based node localization algorithm for estimating the coordinates of sensor nodes in WSNs. In the proposed algorithm, no weight coefficient is employed for controlling the global search ability. The conducted simulation has shown that the proposed localization algorithm is better than the particle swarm optimization (PSO) and various variants of biogeography-based optimization (BBO) in terms of localization accuracy [
Dan and Xian-bin have presented a distributed two-phase PSO algorithm for efficiently and precisely localizing the sensor nodes in addition to solving the flip ambiguity problem. In the first phase, the initial search space is defined using the bounding box method. In the second phase, a refinement process is performed for correcting the error resulting from the flip ambiguity. Additionally, the proposed algorithm attempted localizing sensor nodes that have only two references or three near-collinear references. The conducted simulation has proved the effectiveness of the proposed algorithm [
Krishnaprabha and Gopakumar have proposed a node localization algorithm based on gravitational search algorithm (GSA). In the proposed work, node localization in WSNs is formulated as a nonlinear optimization problem. Also, the proposed algorithm tried to handle the flip ambiguity problem and to localize the sensor nodes that collinear anchor nodes through the refinement phase. The obtained simulation results have shown that the proposed localization algorithm has good performance [
Peng and Li have focused on range-free localization as a cost-effective alternative compared to range-based approaches. However, they noticed that range-free localization suffers from higher localization error compared to the range-based algorithms. In order to deal with this problem, they presented an improved version for DV-Hop, which is a popular rang-free approach that depends on hop-distance estimation. The improvement in the DV-Hop algorithm is performed based on a genetic algorithm. Simulation results have shown that the proposed localization algorithm has better localization accuracy compared to other localization algorithms [
Sai et al. have presented a hybrid node localization algorithm in WSNs, which depends on the measurements of the received signal strength (RSS) and parallel firefly algorithm (PFA). Taking into consideration the distance factor, the proposed algorithm transforms the node localization problem in WSN into a nonlinear unconstrained optimization problem that is defined by an enhanced objective function. In the proposed algorithm, PFA estimates the coordinates of sensor nodes using the distances between a sensor and a few numbers of its 1-hop neighbors. Simulation results have shown that the proposed approach is better than PSO, GA, PFA, and RSS in terms of localization accuracy [
Sivakumar and Venkatesan have provided two-phase node localization algorithm in WSNs. In the first phase, the positions of sensor nodes are roughly estimated using a range-free localization method called mobile anchor positioning (MAP). In the second phase, the proposed algorithm attempts to reduce the localization error by employing a certain metaheuristic approach. In their work, to perform the second phase, they employed bat optimization algorithm (BOA), modified cuckoo search (MCS), and firefly optimization algorithm (FOA) resulting in three localization algorithms namely, BOA-MAP, MCS-MAP, and FOA-MAP. The experimental results have shown that FOA-MAP is better than both BOA-MAP and MCS-MAP in terms of root mean square error (RMSE) [
Sun et al. have proposed a multiobjective node localization algorithm based on multiobjective particle swarm optimization, called multiobjective particle swarm optimization localization algorithm (MOPSOLA). The multiobjective functions include the space distance and the geometric topology constraints. In the proposed algorithm, the size of the archive remains limited using a dynamic method. Simulation results have shown that the proposed algorithm has achieved significant enhancements in terms of localization accuracy and convergence rate [
Arsic et al. have proposed a node localization algorithm based on fireworks algorithm (FWA). The proposed algorithm provides optimal results in case of no ranging errors and provides good results in case of ranging errors. Moreover, they proposed an enhanced fireworks algorithm (EFWA), which achieved better results compared to FWA. Also, the proposed localization algorithm outperforms the existing PSO-based localization algorithm [
Shieh et al. have compared several well-known heuristics such as genetic algorithm (GA) and particle swarm optimization (PSO) to more recent methods such as grey wolf optimizer (GWO), firefly algorithm (FA), and brain storm optimization (BSO) algorithms in terms of sensor nodes localization accuracy. Also, they proposed an enhancement in the localization algorithms to increase the number of localized nodes. The improved algorithms have been compared to the original ones in terms of the number of localized nodes and execution time in different network deployments [
Cheng and Xia have proposed a cuckoo search (CS) algorithm-based node localization algorithm. In the proposed method, the step size has been modified to obtain a global optimal solution in a short time. Also, the candidate solutions’ fitness is used for constructing mutation probability to avoid local convergence. The performance of the proposed algorithm has been evaluated using different anchor density, node density, and communication range in terms of average localization error and localization success ratio. The simulation results have proved that the proposed algorithm is better than the standard CS and PSO in terms of average localization error and convergence time [
Daely and Shin have proposed a node localization algorithm based on dragonfly algorithm (DA) optimization algorithm. The proposed localization algorithm was designed to determine the positions of the nodes which are randomly distributed in a specific area. The simulation results proved that the proposed DA based algorithm is better than PSO in terms of localization accuracy [
Nguyen et al. have employed the multiobjective firefly algorithm to estimate the coordinates of sensor nodes in WSNs. The used objective functions depend on two criteria, namely, the nodes’ distance constraint and geometric topology constraint. The simulation results have shown the superiority of the proposed algorithm compared to well-known localization methods in terms of localization accuracy and convergence rate [
Arora and Singh have used the butterfly optimization algorithm to localize the sensor nodes in WSNs. The proposed localization algorithm has been validated using different numbers of nodes with distance measurements corrupted through the Gaussian noise. The simulation results have shown that the proposed localization algorithm is better than several well-known localization algorithms including particle swarm optimization (PSO) and firefly algorithm (FA) in terms of localization accuracy [
Ahmed et al. have presented a node localization algorithm based on whale optimization algorithm (WOA) whose main objective is to localize sensor nodes in WSNs accurately [
Kaur and Arora have compared the performance of several bioinspired algorithms including flower pollination algorithm (FPA), firefly algorithm (FA), grey wolf optimization (GWO) and particle swarm optimization (PSO) in localizing the sensor nodes of WSNs. The performance of the different algorithms has been evaluated in terms of several performance metrics including localization accuracy, computing time, and several localized nodes. The simulation results have shown the superiority of FPA compared to the other algorithms in terms of localization accuracy [
To the best of our knowledge, the Salp Swarm Algorithm (SSA) algorithm was never used for the localization problem in WSNs so far. Therefore, the main objective of this paper is to employ the SSA algorithm for handling the localization problem in WSNs and is to evaluate its performance against several well-known swarm intelligence algorithms. The basic ideas behind the SSA and other swarm algorithms are given in the next section.
Swarm intelligence (SI) is a relatively new interdisciplinary field of research, which has gained huge popularity in these days. Algorithms belonging to this domain draw inspiration from the collective intelligence emerging from the behavior of a group of social insects (like bees, termites, and wasps). It has successfully been applied to several real-world optimization problems [
Particle swarm optimization (PSO) is a swarm optimization algorithm proposed by Eberhart and Kennedy in 1995. It is inspired by the collective behavior of bird flocking and fish schooling. It employs several particles that simulate a swarm population moving around in the search space to find the best solution. Each particle provides a candidate solution for the problem and is usually represented as a point in D-dimensional space. The position of each particle is represented as a vector
Butterfly optimization algorithm (BOA) is a swarm intelligence algorithm that is recently proposed by Arora and Singh [
In BOA, butterflies are the search agents that perform optimization. Each butterfly emits fragrance with some intensity. This fragrance is propagated and sensed by other butterflies in the region. The fragrance emitted by butterfly is correlated with the butterfly’s fitness. This means that the fragrance of a butterfly changes according to its current location [
Firefly algorithm (FA) is a nature-inspired algorithm proposed by Yang and He [ Fireflies are unisex which means that a firefly can get attracted to any other firefly regardless their sex. The attractiveness of fireflies is directly proportional to their brightness. Thus, for any two flashing fireflies, the firefly with less brightness moves toward the one with higher brightness. If there is no a brighter firefly than a particular firefly, the latter moves randomly. The brightness of a firefly is calculated using an objective function.
A firefly’s attractiveness is proportional to the light intensity visualized by other fireflies in the region; therefore, the relationship between the attractiveness
Grey wolf optimizer (GWO) is a recent swarm intelligence algorithm inspired by the grey wolf community. It is developed by Mirjalili et al. in 2014. Grey wolf is a very dangerous creature which belongs to the Canidae family. Grey wolves usually live in packs that consist of 5 to 12 wolves. Each group has social dominance hierarchy: alpha, beta, and omega, in order. The alphas are a male and female which represent the leaders of the group. The betas are the second level of management hierarchy. The omegas are the final level in the hierarchy [
In order to mathematically model the social hierarchy of wolves in GWO, the fittest solution is referred to as the alpha (
With these equations, a search agent updates its position according to
Salps are part of Salpidae family with the limpid cylinder design body. They look like jellyfishes in texture and movement. The shape of a Salp is shown in Figure
(a) Individual Salp. (b) Salps chain [
Originally, the Salps population is divided into two groups to formulate the mathematical model for Salp chains: head and followers. The head position is at the beginning of the chain while the rest of the chain is referred to as the followers [
The Salps location is determined likewise swarm-based methods, by an n-dimensional search area via considering the number of variables inside the presented problem represented by
Equation (
The parameters
Because the time in optimization is an iteration, the discrepancy between iterations is equal to 1 and considering
Pseudocode of the SSA algorithm.
WSN node localization problem is formulated 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 a localized node.
Let (
The optimization problem is formulated to minimize the error of the localization problem. The objective function for the localization problem is formulated as
All target localized nodes (
The performance of SSA algorithm evaluated using
Repeat the steps 2–5 until all unknown/target nodes get localized or no more nodes can be localized.
In this section, the proposed WSN localization approach is evaluated under different scenarios, and its performance is compared to four other swarm-based algorithms (PSO, BOA, FA, and GWO) in terms of localization accuracy and computing time. The computations of the different algorithms are performed using MATLAB R2012b on a machine of Intel Core i7 CPU, 4 GB RAM, and Windows7 operating system. The parameters’ values of the deployment area are shown in Table
Parameters setting of simulation environment.
Parameters | Values |
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Sensor nodes | Varies on |
Anchor nodes | Varies on increment |
Node transmission range ( |
30 m |
Deployment area | 100 m × 100 m |
Maximum number of iterations | 100 |
For BOA, the sensory modality
In all conducted experiments, the coordinates of central nodes (
Node localization using different numbers of target nodes and anchor nodes. (a) Target nodes = 25; anchor nodes = 10. (b) Target nodes = 50; anchor nodes = 15. (c) Target nodes = 75; anchor nodes = 20. (d) Target nodes = 100; anchor nodes = 25. (e) Target nodes = 125; anchor nodes = 30. (f) Target nodes = 150; anchor nodes = 35.
In this section, SSA and the other swarm algorithms have been evaluated under different scenarios in terms of localization error, computing time, and number of localized nodes. The obtained results of the different algorithms are shown in Table
Performance metrics of different localization algorithms.
Target nodes | Anchor nodes | No. of iterations | PSO | BOA | FA | GWO | SSA | ||||||||||
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T(s) |
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T(s) |
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25 | 10 | 25 | 0.818 | 0.40 | 16 | 0.232 | 0.37 | 22 | 0.265 | 0.6 | 19 | 0.744 | 0.22 | 20 | 0.465 | 0.35 | 22 |
50 | 0.812 | 0.40 | 15 | 0.225 | 0.38 | 23 | 0.262 | 1.2 | 20 | 0.741 | 0.41 | 21 | 0.462 | 0.35 | 23 | ||
75 | 0.803 | 0.41 | 18 | 0.223 | 0.39 | 25 | 0.258 | 1.5 | 21 | 0.741 | 0.54 | 21 | 0.458 | 0.36 | 23 | ||
100 | 0.792 | 0.41 | 18 | 0.221 | 0.40 | 25 | 0.251 | 1.8 | 19 | 0.740 | 0.79 | 23 | 0.451 | 0.37 | 24 | ||
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50 | 15 | 25 | 0.419 | 0.71 | 41 | 0.338 | 0.84 | 47 | 0.477 | 1.6 | 46 | 0.690 | 0.42 | 44 | 0.477 | 0.67 | 43 |
50 | 0.426 | 0.73 | 47 | 0.332 | 0.85 | 48 | 0.473 | 1.9 | 49 | 0.688 | 0.63 | 45 | 0.472 | 0.69 | 47 | ||
75 | 0.429 | 0.76 | 46 | 0.326 | 0.86 | 49 | 0.465 | 2.5 | 49 | 0.686 | 0.81 | 46 | 0.468 | 0.69 | 48 | ||
100 | 0.434 | 0.76 | 48 | 0.323 | 0.86 | 49 | 0.465 | 3.5 | 48 | 0.682 | 0.98 | 48 | 0.464 | 0.70 | 50 | ||
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75 | 20 | 25 | 0.735 | 1.31 | 73 | 0.257 | 1.49 | 66 | 0.519 | 2.9 | 71 | 0.641 | 0.72 | 72 | 0.519 | 0.90 | 69 |
50 | 0.728 | 1.32 | 74 | 0.257 | 1.49 | 66 | 0.513 | 3.8 | 72 | 0.641 | 0.95 | 72 | 0.513 | 0.92 | 72 | ||
75 | 0.728 | 1.33 | 74 | 0.255 | 1.50 | 70 | 0.504 | 4.7 | 73 | 0.638 | 1.3 | 73 | 0.504 | 0.95 | 73 | ||
100 | 0.724 | 1.35 | 75 | 0.253 | 1.52 | 72 | 0.503 | 5.2 | 73 | 0.635 | 1.4 | 74 | 0.503 | 0.96 | 75 | ||
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100 | 25 | 25 | 0.661 | 2.10 | 97 | 0.355 | 2.40 | 97 | 0.711 | 3.8 | 98 | 0.611 | 1.1 | 95 | 0.511 | 1.31 | 98 |
50 | 0.658 | 2.16 | 97 | 0.355 | 2.44 | 99 | 0.709 | 4.2 | 98 | 0.606 | 1.5 | 97 | 0.509 | 1.33 | 98 | ||
75 | 0.642 | 2.17 | 99 | 0.333 | 2.47 | 100 | 0.702 | 5.6 | 99 | 0.602 | 1.8 | 98 | 0.502 | 1.36 | 99 | ||
100 | 0.641 | 2.20 | 100 | 0.331 | 2.50 | 100 | 0.704 | 6.3 | 98 | 0.602 | 2.1 | 98 | 0.504 | 1.37 | 100 | ||
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125 | 30 | 25 | 0.754 | 4.87 | 120 | 0.549 | 3.84 | 122 | 0.829 | 2.7 | 122 | 0.589 | 1.5 | 122 | 0.529 | 1.67 | 123 |
50 | 0.748 | 4.86 | 121 | 0.548 | 3.85 | 123 | 0.824 | 4.5 | 123 | 0.580 | 2.2 | 123 | 0.524 | 1.68 | 124 | ||
75 | 0.750 | 4.89 | 122 | 0.534 | 3.86 | 124 | 0.822 | 5.9 | 125 | 0.580 | 2.8 | 123 | 0.522 | 1.70 | 125 | ||
100 | 0.752 | 4.95 | 125 | 0.534 | 3.89 | 124 | 0.822 | 6.5 | 124 | 0.572 | 3.3 | 125 | 0.522 | 1.72 | 125 | ||
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150 | 35 | 25 | 0.625 | 5.41 | 145 | 0.766 | 5.88 | 147 | 0.911 | 2.5 | 149 | 0.559 | 2.8 | 148 | 0.511 | 2.12 | 149 |
50 | 0.622 | 5.42 | 146 | 0.765 | 5.61 | 148 | 0.909 | 4.2 | 150 | 0.547 | 3.6 | 149 | 0.509 | 2.14 | 149 | ||
75 | 0.619 | 5.44 | 148 | 0.763 | 5.64 | 149 | 0.904 | 6.4 | 150 | 0.523 | 4.3 | 150 | 0.504 | 2.16 | 150 | ||
100 | 0.616 | 5.45 | 150 | 0.763 | 5.69 | 149 | 0.904 | 7.2 | 149 | 0.523 | 4.8 | 150 | 0.504 | 2.18 | 150 |
Under the different scenarios (number of nodes/number of anchors), it is noticed that for all the localization algorithms, increasing the number of iterations increases both of the number of localized nodes and the computing time while reduces the localization error. This notice is rational because increasing the number of iterations means higher amounts of computations, which requires longer computation time. On the other side, increasing the number of iterations means that the chance to find a better solution get bigger; hence, the number of localized nodes get larger and the value of localization error get smaller. For better results analysis, the experimental results are summarized in Table
Summary of experimental results of the different localization algorithms.
Target nodes | Anchor nodes | PSO | BOA | FA | GWO | SSA | ||||||||||
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25 | 10 | 0.79 | 0.40 | 18 | 0.22 | 0.38 | 25 | 0.25 | 1.8 | 19 | 0.74 | 0.54 | 21 | 0.45 | 0.35 | 24 |
50 | 15 | 0.43 | 0.76 | 46 | 0.32 | 0.86 | 49 | 0.42 | 2.5 | 49 | 0.69 | 0.81 | 46 | 0.46 | 0.69 | 48 |
75 | 20 | 0.72 | 1.35 | 75 | 0.25 | 1.52 | 68 | 0.51 | 4.7 | 73 | 0.64 | 0.95 | 72 | 0.50 | 0.96 | 73 |
100 | 25 | 0.65 | 2.16 | 100 | 0.35 | 2.45 | 100 | 0.70 | 5.3 | 98 | 0.60 | 2.1 | 98 | 0.51 | 1.35 | 100 |
125 | 30 | 0.74 | 4.90 | 123 | 0.54 | 3.87 | 124 | 0.82 | 6.5 | 124 | 0.58 | 2.8 | 123 | 0.52 | 1.70 | 125 |
150 | 35 | 0.62 | 5.43 | 149 | 0.76 | 5.65 | 149 | 0.90 | 7.2 | 149 | 0.52 | 4.3 | 150 | 0.50 | 2.15 | 150 |
Based on Table
The graphical representations of the experimental results for the different performance metrics are shown in Figures
The localization error of the different localization algorithms in different WSN deployments.
The computing time of the different localization algorithms in different WSN deployments.
The number of localized nodes of the different localization algorithms in different WSN deployments.
Accurate node localization concerns many applications that adopt WSNs. In this paper, a node localization algorithm has been proposed based on a novel bioinspired algorithm called Salp Swarm Algorithm (SSA) which handled the node localization problem as an optimization problem. The proposed algorithm has been implemented and validated in different WSN deployments using different numbers of target nodes and anchor nodes. Moreover, the proposed algorithm has been evaluated and compared to four well-known optimization algorithms, namely PSO, BOA, FA, and GWO, in terms of localization accuracy, computing time, and several localized nodes. The obtained simulation results have proved the superiority of the proposed algorithm compared to the other localization algorithms regarding the different performance metrics. In the future work, the proposed approach can be hybridized with other algorithm to reduce the localization error.
No data were used to support this study. Because our article discusses the problem of localization in WSNs, our experimental results have been applied based on several networks’ size.
The authors declare that they have no conflicts of interest.