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.
Acquiring precise localization information of sensor nodes is very important in wireless sensor networks. The 3DDV-hop localization algorithm suffers from large localization errors and high energy consumption. In order to improve positioning accuracy and reduce energy consumption, a 3DDV-hop node localization algorithm (3D-HCSSA) based on hop size correction and improved sparrow search optimization is proposed. The algorithm redefines the amendment factor and reduces the cumulative error caused by the hop counts in the traditional algorithm. A maximum distance similar link method based on a similar path search is proposed to find the most similar known node path pair from the target node to the noncoplanar known node link and correct the hop size between multihop counts. The sparrow search algorithm is improved by using the
k
-means clustering and sine cosine search strategy, which solves the problem that the traditional sparrow algorithm is easy to fall into the local optimum, accelerates the convergence speed, corrects the position deviation of the target node, and improves the positioning accuracy. Experiments demonstrate that the 3D-HCSSA algorithm can improve positioning accuracy and reduce energy consumption. Compared with the 3DDV-hop algorithm, 3D-GAIDV-hop algorithm, and HCLSO-3D algorithm, the 3D-HCSSA positioning accuracy is significantly improved.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.