A hyperbolic location algorithm for various distributions of a Wireless Sensor Network

Yıldız, Doğan; Karagöl, Serap; Özgönenel, Okan

doi:10.1109/sgcf.2017.7947605

Cited by 4 publications

(4 citation statements)

References 12 publications

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“… Static Anchor and Static Node : In the localization schemes that fall into this class, both ANs and UNNs are static. Examples of this class can be found in Yildiz et al 15 and Wan et al 16 Static Anchor and Mobile Node: ANs are static while UNNs move through the ROI. This type is often used in applications where a sensor node is placed to a tracked target, such as an athlete and animal, and static anchors provide up‐to‐date location information to each node in the ROI with every movement.…”

Section: Related Terminologiesmentioning

confidence: 99%

A path planning model based on nested regular hexagons using weighted centroid localization

Karagöl

Yıldız

2021

Int J Communication

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Summary In many wireless sensor network (WSN) applications, the location of a sensor node is crucial for determining where the event or situation of interest occurred. Therefore, localization is one of the critical challenges in WSNs. Mobile anchor node assisted localization (MANAL) is one of the promising solutions for the localization of statically deployed sensors. The main problem in MANAL localization is that the path planning of the mobile anchor (MA) node should be done so that the localization error in the network will be minimal and that all unknown nodes in the network are covered. This paper proposes a new path planning approach called nested hexagons curves (NHexCurves) for MANAL. NHexCurves guarantees that it will receive messages from at least three non‐collinear anchors to locate all unknown nodes in the network. The proposed model has compared six different path planning schemes in the literature using weighted centroid localization (WCL). In these comparisons, first of all, localization errors of the models are compared using some statistical concepts. Second, the variation of the localization error according to parameters such as resolution (R) and the standard deviation of noise (σ) is observed. Then, with similar approaches, the standard deviation of errors, localization ratio, scalability performances, and finally, path lengths of the models are examined. The simulation results show that the NHexCurves static path planning model proposed in this study stands out compared to other models with high localization error and localization ratio performance, especially at low resolutions, due to its path design. At the same time, the lowest error values according to σ are obtained with the proposed model among all models considered.

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Section: Related Terminologiesmentioning

confidence: 99%

A path planning model based on nested regular hexagons using weighted centroid localization

Karagöl

Yıldız

2021

Int J Communication

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“…However, range-based methods give more accurate localization results. The most common range-free methods are Approximate Point in Triangle (APIT), Centroid, Distance Vector-hop (DV-hop), and Amorphous [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Some Static Path Planning Models Localization Performance in Obstacle-Presence Environment

Yıldız¹,

Karagöl²

2021

European Journal of Science and Technology

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Static anchors are generally used for the localization of Unknown Nodes (UNNs) in Wireless Sensor Networks (WSNs). However, it would be a more efficient approach to design a Mobile Anchor (MA) trajectory to cover all UNNs instead and to have the MA travel to broadcast its position at specific points along that trajectory. With this logic, many studies have been published in the literature in recent years. SCAN, HILBERT, SPIRAL, LMAT, Z-curve, H-curve, and M-curves static path planning models are examined in this study. The localization performances of these path planning models are compared with different performance evaluation criteria using the Weighted Centroid Localization (WCL) technique in the obstacle-presence scenario. The simulation results show the advantages of the H-curve model over existing schemes. The SPIRAL model performs worse than other models in the obstacle-presence scenario.

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“…Recently, source localization plays an increasingly significant role in detecting and tracking unlicensed devices, utilizing a series of technologies, e.g., time of arrival (ToA) [1], time difference of arrival (TDoA) [2], frequency difference of arrival, direction of arrival [3] or hybrid ones [4,5]. Among these, the TDoA technology is widely applied in consideration of a good tradeoff between performance and consumed resources for localization.…”

Section: Introductionmentioning

confidence: 99%

A Novel Experiment-Free Site-Specific TDoA Localization Performance-Evaluation Approach

Rodríguez-Piñeiro

Liu

et al. 2020

Sensors

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Time difference of arrival (TDoA) technology is widely utilized for source localization, which stimulates many studies on performance-evaluation approaches for TDoA localization systems. Some approaches using simulations are designed merely for a simple Line-of-Sight (LoS) scenario while some other ones using experiments show high cost and inefficiency. This paper proposes an integrated approach to evaluate a TDoA localization system in an area with a complicated environment. Radio propagation graph is applied through a simulation to obtain channel impulse responses (CIRs) between a source to be located and the TDoA sensors for the area. Realistic signals received by the sensors in baseband are emulated combining the source transmitted signal and the CIRs. A hardware unit takes charge of sending the radio emulated received signals to the system under test, which is consistent with real experimental measurements. Statistical analysis of the system is allowed based on localization errors obtained comparing the system’s estimates with the ground truth of the source location. Verified results for LoS and non-LoS scenarios with variable transmitted signal bandwidths and signal-to-noise ratios, as well as for three variations of the sensor locations in an automobile circuit, show the usability of the proposed experiment-free performance-evaluation approach.

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A hyperbolic location algorithm for various distributions of a Wireless Sensor Network

Cited by 4 publications

References 12 publications

A path planning model based on nested regular hexagons using weighted centroid localization

A path planning model based on nested regular hexagons using weighted centroid localization

Comparison of Some Static Path Planning Models Localization Performance in Obstacle-Presence Environment

A Novel Experiment-Free Site-Specific TDoA Localization Performance-Evaluation Approach

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