A parallel WOA with two communication strategies applied in DV-Hop localization method

Abstract

Wireless sensor network (WSN) can effectively help us monitor the surrounding environment and prevent the occurrence of some natural disasters earlier, but we can only get the information of the surrounding environment correctly if we know the locations of nodes. How to know the exact positions of nodes is a strict challenge in WSN. Intelligent computing algorithms have been developed in recent years. They easily solve complex optimization problems, especially for those that cannot be modeled mathematically. This paper proposes a novel algorithm, named parallel whale optimization algorithm (PWOA). It contains two information exchange strategies between groups, and it significantly enhances global search ability and population diversity of the original whale optimization algorithm (WOA). Also, the algorithm is adopted to optimize the localization of WSN. Twenty-three mathematical optimization functions are accustomed to verifying the efficiency and effectiveness of the novel approach. Compared with some existing intelligent computing algorithms, the proposed PWOA may reach better results.

An Adaptive Parallel Arithmetic Optimization Algorithm for Robot Path Planning

“…Due to the use of multiple CPUs, the search speed is accelerated. erefore, the algorithm with the parallel strategy performs better than the original algorithm [31][32][33][34][35]. With the increment of data, a single communication strategy is usually not adequate.…”

“…Parallel strategy is an effective algorithm optimization method, which can communicate and exchange information among groups. Some parallel algorithms are parallel SCA [31], parallel GA [32], parallel MVO [18], parallel WOA [33], parallel SCO [34], and parallel GWO [35]. However, there is still a lack of improvements on parallel strategies for AOA.…”

Rotated Black Hole: A New Heuristic Optimization for Reducing Localization Error of WSN in 3D Terrain

“…In this section, the novel algorithm is applied to reduce locate error of unknown nodes in 3D terrain. [59] has proved that the intelligence computing algorithm has a distinct effect on WSN in 2D plane. In this paper, the 2D plane is extended to a 3D terrain as shown in Figure 3.…”

Localization of isotropic and anisotropic wireless sensor networks in 2D and 3D fields

“…Most of these applications require the location of sensor nodes to be known. This is necessary to understand the location of observed events and to act on them [9]. Consider the case of the military surveillance application.…”