Moth Flame Optimization Algorithm Range-Based for Node Localization Challenge in Decentralized Wireless Sensor Network

2019 ◽  
Vol 10 (1) ◽  
pp. 82-109 ◽  
Author(s):  
Mihoubi Miloud ◽  
Rahmoun Abdellatif ◽  
Pascal Lorenz
Keyword(s):  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Dimensional Space ◽  
Fitness Function ◽  
Low Cost ◽  
Bat Algorithm ◽  
Wireless Sensor ◽  
Flower Pollination Algorithm ◽  
Node Localization ◽  
Moth Flame Optimization Algorithm

Recently developments in wireless sensor networks (WSNs) have raised numerous challenges, node localization is one of these issues. The main goal in of node localization is to find accurate position of sensors with low cost. Moreover, very few works in the literature addressed this issue. Recent approaches for localization issues rely on swarm intelligence techniques for optimization in a multi-dimensional space. In this article, we propose an algorithm for node localization, namely Moth Flame Optimization Algorithm (MFOA). Nodes are located using Euclidean distance, thus set as a fitness function in the optimization algorithm. Deploying this algorithm on a large WSN with hundreds of sensors shows pretty good performance in terms of node localization. Computer simulations show that MFOA converge rapidly to an optimal node position. Moreover, compared to other swarm intelligence techniques such as Bat algorithm (BAT), particle swarm optimization (PSO), Differential Evolution (DE) and Flower Pollination Algorithm (FPA), MFOA is shown to perform much better in node localization task.

A Novel Bat Algorithm for Line-of-Sight Localization in Internet of Things and Wireless Sensor Network

2019 ◽  
pp. 213-239 ◽  
Author(s):  
Mihoubi Miloud ◽  
Rahmoun Abdellatif ◽  
Pascal Lorenz
Keyword(s):  
Wireless Sensor Network ◽  
Internet Of Things ◽  
Comparative Study ◽  
Swarm Intelligence ◽  
Dimensional Space ◽  
Bat Algorithm ◽  
Low Energy ◽  
Wireless Sensor ◽  
Large Area ◽  
Node Localization

WSNs have recently been extensively investigated due to their numerous applications where processes have to be spread over a large area. One of the important challenges in WSNs is secure node localization. Its main objective is to protect the circulated information in WSN for any attack with low energy. For this reason, recent approaches relying on swarm intelligence techniques are called and the node localization is seen as an optimization problem in a multi-dimensional space. In this chapter, the authors present an improvement to the original bat algorithm for information protecting during the localization task. Hence, the proposed approach computes iteratively the position of the nodes and studied the scalability of the algorithm on a large WSN with hundreds of sensors that shows pretty good performance. Moreover, the parameters are simulated in different scenarios of simulation. In addition, a comparative study is conducted to give more performance to the proposed algorithm.

scholarly journals Performance of Elephant Herding Optimization and Tree Growth Algorithm Adapted for Node Localization in Wireless Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2515 ◽  
Author(s):  
Ivana Strumberger ◽  
Miroslav Minovic ◽  
Milan Tuba ◽  
Nebojsa Bacanin
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Swarm Intelligence ◽  
Tree Growth ◽  
Optimization Algorithm ◽  
Environmental Control ◽  
Wireless Sensor ◽  
Problem Instance ◽  
Localization Problem ◽  
Node Localization

Wireless sensor networks, as an emerging paradigm of networking and computing, have applications in diverse fields such as medicine, military, environmental control, climate forecasting, surveillance, etc. For successfully tackling the node localization problem, as one of the most significant challenges in this domain, many algorithms and metaheuristics have been proposed. By analyzing available modern literature sources, it can be seen that the swarm intelligence metaheuristics have obtained significant results in this domain. Research that is presented in this paper is aimed towards achieving further improvements in solving the wireless sensor networks localization problem by employing swarm intelligence. To accomplish this goal, we have improved basic versions of the tree growth algorithm and the elephant herding optimization swarm intelligence metaheuristics and applied them to solve the wireless sensor networks localization problem. In order to determine whether the improvements are accomplished, we have conducted empirical experiments on different sizes of sensor networks ranging from 25 to 150 target nodes, for which distance measurements are corrupted by Gaussian noise. Comparative analysis with other state-of-the-art swarm intelligence algorithms that have been already tested on the same problem instance, the butterfly optimization algorithm, the particle swarm optimization algorithm, and the firefly algorithm, is conducted. Simulation results indicate that our proposed algorithms can obtain more consistent and accurate locations of the unknown target nodes in wireless sensor networks topology than other approaches that have been proposed in the literature.

scholarly journals E-Mail Spam Filtering Through Feature Selection Using Enriched Firefly Optimization Algorithm

2021 ◽  
Vol 12 (5) ◽  
pp. 1248-1255
Author(s):  
T. Poonkodi, Et. al.
Keyword(s):  
Optimization Algorithm ◽  
Dimensional Space ◽  
Fitness Function ◽  
Low Cost ◽  
Similarity Measures ◽  
Feature Space ◽  
Firefly Optimization ◽  
Textual Features ◽  
E Mail ◽  
Firefly Optimization Algorithm

E-mail is the most common method of communication because due to its ability to obtain, the rapid modification of messages and low cost of distribution. Spam causes traffic issues and bottlenecks that limit the amount of memory and bandwidth, power and computing speed. For data filtering, various approaches exist that automatically detect and suppress these indefensible messages. A methodology based on Sine- Cosine Algorithm (SCA) introduced which address the problem of space and time complexities are increased in E-Mail spam detection. In this method, WordNet optimized semantic ontology applies different methods based on semantics and similarity measures to reduce the large number of extracted textual features. This paper proposed the Enriched Firefly Optimization Algorithm (EFOA) method effectively selecting suitable features from an upper dimensional space using the fitness function. Once the best feature space is identified through EFOA, the spam classification is done using ANN. Intially, E-mail spam dataset is preprocessed, then the extracted textual features are Semantic-based reduction and Features weights updated using optimized semantic WordNet. The results obtained showed that the ANN classifier after selection of features using EFOA was able to classify e-mails as spam and non-spam. This EFOA demonstrates that the proposed method has led to a remarkable improvement compared to the SCA methods.

scholarly journals Genetic Algorithms for Localization in WSN

2020 ◽  
Vol 9 (5) ◽  
pp. 2397-2401
Keyword(s):  
Wireless Sensor Networks ◽  
Genetic Algorithms ◽  
Bat Algorithm ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Localization Error ◽  
Node Localization ◽  
Soft Computing Techniques ◽  
Anchor Nodes ◽  
The Mean

In wireless sensor networks, localization is a way to track the exact location of sensor nodes. Occasionally node localization may not be accurate due to the absence or limitation of anchor nodes. To reduce the mean localization error, soft computing techniques such as BAT and bacterial foraging driven bat algorithm (BDBA) are utilized in literature. For better localization with reduced error, in this paper, firefly driven bat algorithm (FDBA) is proposed, which combines the heuristic of firefly and BAT algorithms. Our proposed FDBA algorithm provides better localization in terms of error of 60% and 40 % less error as compared to BAT and BDBA algorithm, respectively.

Nelder Mead with Grasshopper Optimization Algorithm for Node Localization in Wireless Sensor Networks

2020 ◽  
Vol 17 (12) ◽  
pp. 5409-5421
Author(s):  
M. Santhosh ◽  
P. Sudhakar
Keyword(s):  
Optimization Algorithm ◽  
Wireless Sensor ◽  
Target Area ◽  
Node Localization ◽  
Localization Accuracy ◽  
Simplex Search ◽  
Grasshopper Optimization Algorithm ◽  
Anchor Nodes ◽  
Series Of Experiments ◽  
Grasshopper Optimization

Node localization in wireless sensor network (WSN) becomes essential to calculate the coordinate points of the unknown nodes with the use of known or anchor nodes. The efficiency of the WSN has significant impact on localization accuracy. Node localization can be considered as an optimization problem and bioinspired algorithms finds useful to solve it. This paper introduces a novel Nelder Mead with Grasshopper Optimization Algorithm (NMGOA) for node localization in WSN. The Nelder-Mead simplex search method is employed to improve the effectiveness of GOA because of its capability of faster convergence. At the beginning, the nodes in WSN are arbitrarily placed in the target area and then nodes are initialized. Afterwards, the node executes the NMGOA technique for estimating the location of the unknown nodes and become localized nodes. In the subsequent round, the localized nodes will be included to the collection of anchor nodes to perform the localization process. The effectiveness of the NMGOA model is validated using a series of experiments and results indicated that the NMGOA model has achieved superior results over the compared methods.

scholarly journals Research on Algorithm of Three-Dimensional Wireless Sensor Networks Node Localization

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Jiang Minlan ◽  
Luo Jingyuan ◽  
Zou Xiaokang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Optimization Method ◽  
Wireless Sensor ◽  
Minimum Problem ◽  
Localization Algorithm ◽  
Node Localization ◽  
Anchor Nodes

This paper proposes a three-dimensional wireless sensor networks node localization algorithm based on multidimensional scaling anchor nodes, which is used to realize the absolute positioning of unknown nodes by using the distance between the anchor nodes and the nodes. The core of the proposed localization algorithm is a kind of repeated optimization method based on anchor nodes which is derived from STRESS formula. The algorithm employs the Tunneling Method to solve the local minimum problem in repeated optimization, which improves the accuracy of the optimization results. The simulation results validate the effectiveness of the algorithm. Random distribution of three-dimensional wireless sensor network nodes can be accurately positioned. The results satisfy the high precision and stability requirements in three-dimensional space node location.

scholarly journals An effective Bat algorithm for node localization in distributed wireless sensor network

2017 ◽  
Vol 1 (1) ◽  
pp. e7 ◽  
Author(s):  
Miloud Mihoubi ◽  
Abdellatif Rahmoun ◽  
Pascal Lorenz ◽  
Noureddine Lasla
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Bat Algorithm ◽  
Wireless Sensor ◽  
Node Localization
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