Adaptive range-based localization algorithm based on trilateration and reference node selection for outdoor wireless sensor networks

Luomala, Jari; Hakala, Ismo

doi:10.1016/j.comnet.2022.108865

Cited by 34 publications

(17 citation statements)

References 22 publications

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“…Reference [9], reference [10], reference [11], and reference [12] as the comparison method of the methods in the text, based on the Formula (31) to calculate the five methods in different target distances, for the target tracking error results, the test results are shown in Table V. After analyzing the test results in Table V, it is concluded that under different target distances, five methods are used for target tracking, and the maximum tracking errors of reference [9], reference [10], reference [11], and reference [12] are 0.277m, 0.224m, 0.219m, and 0.255m respectively; The maximum tracking error is 0.142m after target tracking by the method. Hence, this method has a better target tracking effect, because the method in this paper builds a target tracking model based on the sensor node position after accurate positioning, so it can improve the target tracking accuracy.…”

Section: After Analyzing the Test Results In Tablementioning

confidence: 99%

“…In study [9], an adaptive distance-based localization (ARBL) algorithm based on the trilateral measurement and reference node selection is studied. The algorithm locates nodes according to the evaluation results of node geometry, to improve the positioning accuracy of sensor nodes and provide a basis for target tracking; If the evaluation of node geometry is not accurate enough or has errors, it will lead to the inaccuracy of node positioning results obtained by this method, thus affecting the effect of target tracking.…”

Section: Introductionmentioning

confidence: 99%

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A Study on Wireless Sensor Node Localization and Target Tracking Based on Improved Locust Algorithm

SONGHE,

2024

ijacsa

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To improve the positioning accuracy of wireless sensor nodes and ensure the target tracking effect, a wireless sensor node positioning and target tracking method based on an improved locust algorithm is proposed. The DV Hop algorithm is used to calculate the minimum hops and average hops distance between the unknown node and each anchor node to obtain the location of the unknown node, realize the rough positioning of wireless sensor nodes, and analyze the positioning error to determine the positioning accuracy target function; The improved locust algorithm is used to solve the positioning accuracy objective function to obtain the sensor node positioning results with the minimum error; The target tracking model and the target is calculated. According to the target observation information obtained by all sensor nodes, the target state in the wireless sensor network model is tracked using the probability hypothesis density filtering algorithm. The test results show that the algorithm has better performance, the spatial evaluation index results are all lower than 0.020, and the individual distribution in the solution set is better; The location of each unknown node in different node distribution states can be obtained; The positioning error under the surface and plane is less than 0.012; The maximum error of target tracking is 0.142m; It can track single target and multiple targets.

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Section: After Analyzing the Test Results In Tablementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Study on Wireless Sensor Node Localization and Target Tracking Based on Improved Locust Algorithm

SONGHE,

2024

ijacsa

View full text Add to dashboard Cite

show abstract

“…It was shown in [ 23 , 24 , 19 , 15 , 11 ] that the adaptive algorithms strongly depend on the information stored at training phase (at the offline phase, where by the required pre-information are recorded). This adds memory and power requirements in order to attain accurate position estimation [ 4 , 17 , 14 ].…”

Section: Related Workmentioning

confidence: 99%

Indoor positioning using circle expansion-based adaptive trilateration algorithm

Ibwe

Pande

Abdalla

et al. 2023

Journal of Electrical Systems and Inf Technol

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The increasing availability of mobile devices with wireless communications capabilities has stimulated the growth of indoor positioning services. Indoor positioning is used to locate, in real time, devices’ positions for easy access. The indoor positioning, however, is challenging compared to outdoor positioning due to the large number of obstacles. Global positioning system is ideal for outdoor localization but fails in indoor environments with limited space. Recent development of the Internet of Things (IoT) has brought forth portable and cost-effective wireless technologies that can be used for indoor positioning. In this work, an adaptive trilateration algorithm based on received signal strength indicator (RSSI) was proposed. To assess the positioning accuracy of the proposed algorithm, Bluetooth Low Energy (BLE), Wi-Fi (IEEE 802.11n), ZigBee and LoRaWAN IoT technologies were used. Results show that the error performance is improved by 4% in BLE, 17% in ZigBee, 22% in Wi-Fi and 33% in LoRaWAN when compared to the existing related work.

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“…Single antennas are used to identify the presence of an individual in the area (e.g. Motus; Taylor et al, 2017), while arrays of receivers can be used to estimate location based on the relationship between recorded radio signal strength (RSS, measured in decibels) and distance from each receiver (Luomala & Hakala, 2022). The distances around receivers are radii whose overlapping circumferences may be calculated through triangulation (e.g.…”

Section: Introductionmentioning

confidence: 99%

Tracking small animals in complex landscapes: a comparison of localisation workflows for automated radio telemetry systems

Rueda-Uribe,

Sargent,

Echeverry-Galvis

et al. 2024

Preprint

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Automated radio telemetry systems (ARTS) have the potential to revolutionise our understanding of animal movement by providing a near-continuous record of individual locations in the wild. However, localisation error in data generated by ARTS can be very high, especially in natural landscapes with complex vegetation structure and topography. This curtails the ecological questions that may be addressed with this technology. Here, we set up an ARTS grid in a valley with heterogeneous vegetation cover in the Colombian high Andes and applied an analytical pipeline to test the effectiveness of localisation methods. We performed calibration trials to simulate animal movement in high- or low-flight, or walking on the ground, and compared workflows with varying decisions related to signal cleaning, selection, smoothing, and interpretation, along with four multilateration approaches. We also quantified the influence of spatial features on the system's accuracy. We tested the grid by deploying tags on two high-altitude hummingbirds, the Great Sapphirewing (Pterophanes cyanopterus) and Bronze-tailed Thornbill (Chalcostigma heteropogon). Results showed large variation in localisation error, ranging from only 0.4-43.4 m from known locations up to 474-1929 m, depending on the localisation method used. The lowest average median error across calibration tracks was 105 m. In particular, we found that the selection of higher radio signal strengths and data smoothing based on the temporal autocorrelation in movement data are useful tools to improve accuracy. Moreover, the variables that significantly influence localisation error include terrain ruggedness, height of movement, vegetation type, and the location of animals inside or outside the grid area. In the case of our study system, thousands of location points were successfully estimated for two hummingbird species that previously lacked movement ecology data. Our case study on hummingbirds suggests ARTS grids can be used to estimate small animals' home ranges, associations with vegetation types, and seasonality in occurrence. We present a comparative localisation pipeline, highlighting the variety of possible decisions while processing radio signal data. Overall, this study provides guidance to improve the resolution of location estimates, broadening the application of this tracking technology in the study of the spatial ecology of wild populations.

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Adaptive range-based localization algorithm based on trilateration and reference node selection for outdoor wireless sensor networks

Cited by 34 publications

References 22 publications

A Study on Wireless Sensor Node Localization and Target Tracking Based on Improved Locust Algorithm

A Study on Wireless Sensor Node Localization and Target Tracking Based on Improved Locust Algorithm

Indoor positioning using circle expansion-based adaptive trilateration algorithm

Tracking small animals in complex landscapes: a comparison of localisation workflows for automated radio telemetry systems

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