scholarly journals A Node Location Algorithm Based on Improved Whale Optimization in Wireless Sensor Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Pingzhang Gou ◽  
Bo He ◽  
Zhaoyang Yu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Wireless Sensor ◽  
Positioning Accuracy ◽  
Whale Optimization ◽  
Location Algorithm ◽  
Positioning Algorithm ◽  
Better Than

With the popularity of swarm intelligence algorithms, the positioning of nodes to be located in wireless sensor networks (WSNs) has received more and more attention. To overcome the disadvantage of large ranging error and low positioning accuracy caused by the positioning algorithm of the received signal strength indication (RSSI) ranging model, we use the RSSI modified by Gaussian to reduce the distance measurement error and introduce an improved whale optimization algorithm to optimize the location of the nodes to be positioned to improve the positioning accuracy. The experimental results show that the improved whale algorithm performs better than the whale optimization algorithm and other swarm intelligence algorithms under 20 different types of benchmark function tests. The positioning accuracy of the proposed location algorithm is better than that of the original RSSI algorithm, the hybrid exponential and polynomial particle swarm optimization (HPSO) positioning algorithms, the whale optimization, and the quasiaffine transformation evolutionary (WOA-QT) positioning algorithm. It can be concluded that the cluster intelligence algorithm has better advantages than the original RSSI in WSN node positioning, and the improved algorithm in this paper has more advantages than several other cluster intelligence algorithms, which can effectively solve the positioning requirements in practical applications.

scholarly journals Improved whale optimization algorithm and its application in heterogeneous wireless sensor networks

2021 ◽  
Vol 17 (5) ◽  
pp. 155014772110181
Author(s):  
Yinggao Yue ◽  
Hairong You ◽  
Shuxin Wang ◽  
Li Cao
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Optimization Algorithm ◽  
Whale Optimization Algorithm ◽  
Wireless Sensor ◽  
Network Coverage ◽  
Coverage Optimization ◽  
Network Cost ◽  
Whale Optimization ◽  
Heterogeneous Wireless Sensor Networks

Aiming at the problems of node redundancy and network cost increase in heterogeneous wireless sensor networks, this article proposes an improved whale optimization algorithm coverage optimization method. First, establish a mathematical model that balances node utilization, coverage, and energy consumption. Second, use the sine–cosine algorithm to improve the whale optimization algorithm and change the convergence factor of the original algorithm. The linear decrease is changed to the nonlinear decrease of the cosine form, which balances the global search and local search capabilities, and adds the inertial weight of the synchronous cosine form to improve the optimization accuracy and speed up the search speed. The improved whale optimization algorithm solves the heterogeneous wireless sensor network coverage optimization model and obtains the optimal coverage scheme. Simulation experiments show that the proposed method can effectively improve the network coverage effect, as well as the utilization rate of nodes, and reduce network cost consumption.

Maximizing lifetime of large-scale wireless sensor networks using multi-objective whale optimization algorithm

2019 ◽  
Vol 72 (2) ◽  
pp. 243-259 ◽  
Author(s):  
Mohammed M. Ahmed ◽  
Essam H. Houssein ◽  
Aboul Ella Hassanien ◽  
Ayman Taha ◽  
Ehab Hassanien
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Optimization Algorithm ◽  
Large Scale ◽  
Whale Optimization Algorithm ◽  
Wireless Sensor ◽  
Multi Objective ◽  
Whale Optimization

scholarly journals Solving Point Coverage Problem in Wireless Sensor Networks Using Whale Optimization Algorithm

2021 ◽  
Author(s):  
Mahnaz Toloueiashtian ◽  
Mehdi Golsorkhtabaramiri ◽  
Seyed Yaser Bozorgi Rad
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Optimization Algorithm ◽  
Energy Use ◽  
Whale Optimization Algorithm ◽  
Wireless Sensor ◽  
Dynamic Power Management ◽  
Coverage Problem ◽  
Whale Optimization ◽  
Point Coverage

Todays, dynamic power management methods that decrease the energy use of sensor networks after their design and deployment are of paramount importance. In Wireless Sensor Networks (WSN), coverage and detection quality are one aspect of service quality and power consumption reduction aspect. The aim of the coverage problem is to monitor at least one node at each point in the targeted area and is divided into three categories: border, area, and point coverage. In point coverage, which is our interest, the problem is to cover specific points of the environment scattered on the surface of the environment; their position is decided on and called the goal. In this paper, a new metaheuristic algorithm based on Whale Optimization Algorithm (WOA) is proposed. The proposed algorithm tries to find the Best Solution (BS) based on three operations exploration, spiral attack, and siege attack. Several scenarios, including medium, hard and complex problems, are designed to evaluate the proposed technique, and it is compared to Genetic Algorithm (GA) and Ant Colony Optimization (ACO) based on time complexity criteria in providing a suitable coverage, network lifetime, energy consumption. The simulation results show that the proposed algorithm performs better than the compared ones in most scenarios.

scholarly journals A Node Location Method in Wireless Sensor Networks Based on a Hybrid Optimization Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Jeng-Shyang Pan ◽  
Fang Fan ◽  
Shu-Chuan Chu ◽  
Zhigang Du ◽  
Huiqi Zhao
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Optimization Algorithm ◽  
Search Space ◽  
Wireless Sensor ◽  
Practical Applications ◽  
Hybrid Optimization Algorithm ◽  
Whale Optimization ◽  
Node Location ◽  
Received Signal Strength Indication

Wireless sensor networks (WSN) have gradually integrated into the concept of the Internet of Things (IoT) and become one of the key technologies. This paper studies the optimization algorithm in the field of artificial intelligence (AI) and effectively solves the problem of node location in WSN. Specifically, we propose a hybrid algorithm WOA-QT based on the whale optimization (WOA) and the quasi-affine transformation evolutionary (QUATRE) algorithm. It skillfully combines the strengths of the two algorithms, not only retaining the WOA’s distinctive framework advantages but also having QUATRE’s excellent coevolution ability. In order to further save optimization time, an auxiliary strategy for dynamically shrinking the search space (DSS) is introduced in the algorithm. To ensure the fairness of the evaluation, this paper selects 30 different types of benchmark functions and conducts experiments from multiple angles. The experiment results demonstrate that the optimization quality and efficiency of WOA-QT are very prominent. We use the proposed algorithm to optimize the weighted centroid location (WCL) algorithm based on received signal strength indication (RSSI) and obtain satisfactory positioning accuracy. This reflects the high value of the algorithm in practical applications.

scholarly journals A Novel Location Source Optimization Algorithm for Low Anchor Node Density Wireless Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1890
Author(s):  
Zhongliang Deng ◽  
Shihao Tang ◽  
Xiwen Deng ◽  
Lu Yin ◽  
Jingrong Liu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Optimization Algorithm ◽  
Fuzzy Comprehensive Evaluation ◽  
Wireless Sensor ◽  
Cooperative Localization ◽  
Fuzzy Evaluation ◽  
Node Density ◽  
Positioning Accuracy ◽  
Anchor Node

Location information is one of the basic elements of the Internet of Things (IoT), which is also an important research direction in the application of wireless sensor networks (WSNs). Aiming at addressing the TOA positioning problem in the low anchor node density deployment environment, the traditional cooperative localization method will reduce the positioning accuracy due to excessive redundant information. In this regard, this paper proposes a location source optimization algorithm based on fuzzy comprehensive evaluation. First, each node calculates its own time-position distribute conditional posterior Cramer-Rao lower bound (DCPCRLB) and transfers it to neighbor nodes. Then collect the DCPCRLB, distance measurement, azimuth angle and other information from neighboring nodes to form a fuzzy evaluation factor set and determine the final preferred location source after fuzzy change. The simulation results show that the method proposed in this paper has better positioning accuracy about 33.9% with the compared method in low anchor node density scenarios when the computational complexity is comparable.

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.

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