Efficient localization of target in large scale farmland using generalized regression neural network

Jondhale, Satish R.; Deshpande, Rajkumar S.

doi:10.1002/dac.4120

Cited by 18 publications

(6 citation statements)

References 38 publications

(108 reference statements)

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“…This means that the calculated distances of u 4−1 and b 3 coincide with the measured value ρ 3 . Refer to the cosine position result [31][32][33][34][35].…”

Section: Positioning Using Salam Algorithmmentioning

confidence: 99%

3D Localization for Mobile Node in Wireless Sensor Network

Javed

Tang

Saleem

et al. 2022

Wireless Communications and Mobile Computing

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Wireless sensor network (WSN) is an emerging technology that can detect, collect, and transmit information in a specific unknown area in an unknown environment. It is currently playing an increasingly important role in the fields of national defense, medical and health, and daily life. WSN node location information is extremely important in many WSN applications. The data information collected by WSN is developed based on known node location information. The node location is one of the important issues in WSNs. Location information is very important for wireless sensors. A WSN without sensor node location information is meaningless because almost all WSN applications need to know node location information, such as animal populations, tracking research, early warning of building fires, management of goods in warehouses, and traffic monitoring systems. Several research works are underway to expand the 2D positioning algorithm in WSN to 3D regardless of the deployment structure of sensor nodes. This paper proposes an improved Savarese algorithm to the problem of singularity in WSN node localization. The proposed algorithm is a modified version of the conventional Savarese algorithm, and it solves the singularity problem and improved the positioning accuracy. Simulation results show that the proposed algorithm effectively improved system performance, and the accuracy is improved over 2.83% and 2.96% than the existing algorithms. The proposed scheme is effective for indoor environments while it can be deployed outdoor for small-scale.

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“…This means that the calculated distances of u 4−1 and b 3 coincide with the measured value ρ 3 . Refer to the cosine position result [31][32][33][34][35].…”

Section: Positioning Using Salam Algorithmmentioning

confidence: 99%

3D Localization for Mobile Node in Wireless Sensor Network

Javed

Tang

Saleem

et al. 2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

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“…Localization algorithms are also widely used in some practical target tracking applications. Jondhale et al [13] have proposed a range-free generalized regression neural network localization algorithm as an alternative to the traditional range-based trilateration technique for a largescale wheat farmland. ey also present the modified Optimal Fitted Parametric Exponential Decay Model-(OFPEDM-) based signal path loss model to deal with the issue of environmental dynamicity.…”

Section: Related Workmentioning

confidence: 99%

Modified Least Squares Algorithm for Three-Dimensional Target Location Based on Wireless Communication Base Stations

Zhang

Liu

2020

Complexity

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The observed values of time of arrival (TOA) for the radio signals between the target and the wireless communication base stations are mainly affected by signal non-line-of-sight (NLOS) propagation in target location. TOA with NLOS makes a lot of signal noises and propagation delays, that is, location errors. For the first time, this paper focuses on the problem of modifying the Z-axis location coordinates in three-dimensional (3D) target location. A novel algorithm is proposed by establishing the modified least squares 3D location model for the accurate target location. Meanwhile, an optimal base station selection strategy is proposed by using the spectral clustering algorithm, which is based on the spatial distribution of the base stations. Compared with the existing algorithms, the proposed algorithm in this paper has better performance on the accurate target 3D location in real scenes, which has a high value of practical application. The simulations illustrate that the location error of the proposed algorithm is smaller than those of other existing algorithms based on the same simulation data and conditions.

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“…In the present research work, we will not use machine learning; however, the following references provided us important information regarding the localization algorithms of state of the art. Jondhale and Deshpande 8 propose a better performance on localization accuracy over traditional trilateration irrespective using a generalized regression neural network (GRNN) algorithm. Moreover, Yi et al 9 exploit Wi‐Fi trilateration‐based indoor localization system which involves minimum labor‐cost calibration to achieve a good accuracy that makes this method a prominent research solution to location‐based services.…”

Section: Related Workmentioning

confidence: 99%

Geolocation analysis for Search And Rescue systems using LoRaWAN

Bouras

Γκάμας

Kokkinos

et al. 2020

Int J Communication

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Summary Low‐power wide‐area network (LPWAN) technologies are aiming to provide power‐efficient solutions to the field of Internet of Things (IoT). Over the last years, we have seen a significant development within the area of IoT applications. For many applications, the problem of localization (i.e., determine the physical location of nodes) is critical. An area study of such use case is also the rescue monitor systems. In this study, we start by describing a solution designed for the long range wide area network (LoRaWAN) to localize position of IoT modules such as wearables used from vulnerable groups. Through performance study of the behavior of a LoRaWAN channel and using trilateration and RSSI information, the localization of an IoT wearable can be acquired within a small range. Routing people in need is one of the use cases the above mechanism could be integrated so as to be able to be tracked by familiar people. After that, we evaluate the usage of mathematical model of multilateration algorithms using time difference of arrival (TDoA) as a solution for positioning over LoRaWAN. The research is carried out using simulations in Python by configuring the constant positions of the Gateways inside an outdoor area. The proposed algorithms can be integrated in application for tracking people at any time and especially routing people from vulnerable groups. Through multilateration and algorithm's prediction, we can have an accuracy of 40–60 m in location positioning, ideal for search and rescue use cases. We finally summarize the above algorithms' estimation and general behavior in a SAR system.

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Efficient localization of target in large scale farmland using generalized regression neural network

Cited by 18 publications

References 38 publications

3D Localization for Mobile Node in Wireless Sensor Network

3D Localization for Mobile Node in Wireless Sensor Network

Modified Least Squares Algorithm for Three-Dimensional Target Location Based on Wireless Communication Base Stations

Geolocation analysis for Search And Rescue systems using LoRaWAN

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