Sea Turtle Foraging and Hydrozoan Optimization Algorithm-based NLOS Node Positioning Scheme for Reliable Data Dissemination in Vehicular Ad hoc Networks

Subramanian, P.; Vijayakumaran, C.; Hussan, M. I. Thariq; Tamilarasan, N.; Sahayaraj, J. Martin

doi:10.1007/s11277-022-09768-x

Cited by 2 publications

(1 citation statement)

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“…The center vector of the Wifi fingerprint dataset is taken as the Wifi fingerprint without saving the whole dataset, and it is experimentally proven that this method can achieve a correct rate of more than 90% when the acquisition points are separated by a distance of 2 meters [13]. The advantage of using this method is that a large number of Wifi fingerprint datasets can be reduced and the datasets can be saved to mobile terminals, thus enabling offline localization; after the user Wifi list data is sent to the server, the server makes the Wifi fingerprints of the user's location and its vicinity into the client Wifi fingerprint dataset, and the ware requests the fingerprints near the next location to continue offline localization, and Wifi fingerprint localization can be applied to a large number of users positioning methods [14][15].…”

Section: Figure 1 Wifi Fingerprint Localization Processmentioning

confidence: 99%

Indoor Localization Algorithm Based on Artificial Neural Network

2022

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Indoor environment is complex and changeable, and the application scenario of indoor positioning(IP) requires higher accuracy, while the technology of outdoor positioning cannot meet people's requirements for IP, so IP technology is born. Since artificial neural network(ANN) are highly adaptive, fault-tolerant and suitable for complex indoor environment analysis, this paper introduces neural networks to solve the indoor localization problem. After the simulation study of ANN localization algorithm(LA) in this paper, the localization error accumulation probability and root mean square error of localization are compared between ANN localization model and other classical LAs, and it is found that compared with several LAs such as KNN, MLP and SVR, ANN LA has better performance and higher localization accuracy under low signal-to-noise ratio.

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Section: Figure 1 Wifi Fingerprint Localization Processmentioning

confidence: 99%