Range-Based Positioning with Self-Adapting Fireworks Algorithm for Wireless Sensor Networks

Abstract

Positioning is the basic function of wireless sensor networks (WSN). At present, range-based positioning is a common method to obtain the position of a node, but its accuracy has encountered a bottleneck. The fundamental reason is that it ignores the ranging information between blind nodes. Therefore, based on the design of the positioning optimization factor to adjust the weight of the ranging information between the blind nodes, the positioning model of minimizing the error is established. On the basis of designing the blind node positioning matrix, the multiblind node localization problem is transformed into a single-objective optimization task, and the model also supports two-dimensional and three-dimensional positioning. In order to solve the model efficiently, we added a self-adaptive function that matches the positioning requirements for the explosion search mechanism of the fireworks algorithm (FWA) and then proposed a self-adaptive FWA (SA-FWA). The experimental results on the real ranging dataset show that the model has higher accuracy than other methods and achieves the current optimal positioning error, which is 1.88 m for received signal strength data and 1.02 m for time of arrival information, respectively.

Improved DV-Hop based on parallel and compact whale optimization algorithm for localization in wireless sensor networks

“…To the present author's knowledge, most of the literatures on applications of WOA in WSN localization use range-based techniques [28][29][30][31][32]. The experimental results show that the proposed schemes achieve better localization accuracy, delivery radio, and delay.…”

“…In response to these shortcomings, researchers have improved WOA, such as chaotic WOA [18,19], improved WOA [20,21], binary WOA [22,23], hybrid WOA [24,25], and multi-objective WOA [26,27]. In recent years, WOA has been employed in range-based node localization algorithms [28][29][30][31][32], but few applications in DV-Hop. For instance, Chai et al [33] only utilized parallel techniques to improve WOA and applied it to enhance the performance of DV-Hop, but they do not take into account the memory consumption and storage capacity of the node.…”