• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.1
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• pp.33-42
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• 2013

This paper presents a multihop range-free localization algorithm to estimate the physical location of a normal node with local connectivity information in anisotropic sensor networks. In the proposed algorithm, a normal node captures the detour degree of the shortest path connecting an anchor pair and itself by comparing the measured hop count and the expected hop count, and the node estimates the distances to the anchors based on the detour degree. The normal node repeats this procedure with all anchor combinations and pinpoints its location using the obtained distance estimates. The proposed algorithm requires fewer anchors and less communication overhead compared to existing range-free algorithms. We showed the superiority of the proposed algorithm over existing range-free algorithms through MATLA simulations.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.829-832
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• 2010

Wireless Sensor Networks have been proposed for several location-dependent applications. For such systems, the cost and limitations of the hardware on sensing nodes prevent the use of range-based localization schemes that depend on absolute point to point distance estimates. Because coarse accuracy is sufficient for most sensor network applications, solutions in range-free localization are being pursued as a cost-effective alternative to more expensive range-based approaches. In this paper, we proposed a modified DV-Hop (range-free localization) algorithm which reduces node's location error and cumulated distance error by minimizing localization error. Simulation results have verified the high estimation accuracy with our approach which outperforms the classical DV-Hop.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.2
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• pp.574-592
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• 2016

Range-free localization algorithm computes a normal node's position by estimating the distance to anchors which know their actual position. In recent years, reliable anchor selection research has been gained a lot of attention because this approach improves localization accuracy by selecting the only subset of anchors called reliable anchor. The distance estimation accuracy and the geometric shape formed by anchors are the two important factors which need to be considered when selecting the reliable anchors. In this paper, we study the relationship between a relative position of three anchors and localization error. From this study, under ideal condition, which is with zero localization error, we find two conditions for anchor selection, thereby proposing a novel anchor selection algorithm that selects three anchors matched most closely to the two conditions, and the validities of the conditions are proved using two theorems. By further employing the conditions, we finally propose a novel range-free localization algorithm. Simulation results show that the proposed algorithm shows considerably improved performance as compared to other existing works.

• Journal of Korea Multimedia Society
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• v.15 no.10
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• pp.1228-1235
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• 2012

In this Paper, we propose an energy efficient localization technique based on WUSB (Wireless USB) over WBAN (Wireless Body Area Networks) protocol required for Wearable Computer systems. For this purpose, the proposed localization algorithm minimizes power consumption and estimates location without range information. It is executed independently on the basis of WUSB over WBAN protocol at each sensor node comprising peripherals of a wearable computer system. And it minimizes power consumption by estimating locations of sensor nodes with range-free method fast.

• Journal of IKEEE
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• v.11 no.4
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• pp.297-306
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• 2007

A sensor network is composed of a large number of sensor nodes that are densely deployed in a field. Each sensor performs a sensing task for detection specific events. After detecting this event, location information of the sensor node is very important. Range-based scheme of the proposed approaches typically achieve high accuracy on either node-to-node distances or angles, but this scheme have a drawback because all sensor nodes have the special hardware. On the other hand, range-free scheme provides economic advantage because of no needed hardware even if that leads to coarse positioning accuracy. In this paper, we propose a range-free localization algorithm without range information in wireless sensor networks. This is a range-free approach and uses a small number of anchor nodes and known sensor nodes. This paper develops a localization mechanism using the geometry conjecture (perpendicular bisector of a chord). The conjecture states that a perpendicular bisector of a chord passes through the center of the circle.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.7B
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• pp.595-605
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• 2012

DV-Hop is one of the well known range-free localization algorithms. The algorithm works well in case of isotropic network since the sensor and anchor nodes are placed in the entire area. However, it results in large errors in case of anisotropic networks where the hop count between nodes is not linearly proportional to the Euclidean distance between them. Hence, we proposed a novel range-free algorithm for anisotropic networks to improve the localization accuracy. In the paper, the Euclidean distance between anchor node and unknown node is estimated by the average hop distance calculated at each hop count with hop count and distance information between anchor nodes. By estimating the unknown location of nodes with the estimated distance estimated by the average hop distance calculated at each hop, the localization accuracy is improved. Simulation results show that the proposed algorithm has more accuracy than DV-Hop.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.61-80
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• 2018

Location information of individual nodes is important in the implementation of necessary network functions. While extensive studies focus on localization techniques in 2D space, few approaches have been proposed for 3D positioning, which brings the location closer to the reality with more complex calculation consumptions for high accuracy. In this paper, an effective range-free localization scheme is proposed for 3D space localization, and the sensitivity of parameters is evaluated. Firstly, we present an improved algorithm (MDV-Hop), that the average distance per hop of the anchor nodes is calculated by root-mean-square error (RMSE), and is dynamically corrected in groups with the weighted RMSE based on group hops. For more improvement in accuracy, we expand particle swarm optimization (PSO) of intelligent optimization algorithms to MDV-Hop localization algorithm, called PMDV-hop, in which the parameters (inertia weight and trust coefficient) in PSO are calculated dynamically. Secondly, the effect of various localization parameters affecting the PMDV-hop performance is also present. The simulation results show that PMDV-hop performs better in positioning accuracy with limited energy.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.4
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• pp.223-228
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• 2011

In this paper, a novel range-free localization algorithm for anisotropic networks is proposed. In the proposed method, the characteristics of the given network are considered and each sensor node estimates the relation between the hop counts and the geographical distances. Unlike most of the previous localization algorithms, the proposed method performs well not only in the isotropic network but also in the anisotropic networks. The proposed method is applied to both isotropic and anisotropic network topologies and the simulation results demonstrate that the method exhibits excellent and robust performances.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2627-2647
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• 2022

The easy scalability and low cost of range-free localization algorithms have led to their wide attention and application in node localization of wireless sensor networks. However, the existing range-free localization algorithms still have problems, such as large cumulative errors and poor localization performance. To solve these problems, an incremental strategy-based residual regression network is proposed for node localization in wireless sensor networks. The algorithm predicts the coordinates of the nodes to be solved by building a deep learning model and fine-tunes the prediction results by regression based on the intersection of the communication range between the predicted and real coordinates and the loss function, which improves the localization performance of the algorithm. Moreover, a correction scheme is proposed to correct the augmented data in the incremental strategy, which reduces the cumulative error generated during the algorithm localization. The analysis through simulation experiments demonstrates that our proposed algorithm has strong robustness and has obvious advantages in localization performance compared with other algorithms.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.2
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• pp.106-116
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• 2011

USN(Ubiquitous Sensor Network) environment permits us to access whatever information we want, whenever we want. The technologies to provide a basement to these environments premise an accurate location establishment. Especially, ITS(Intelligent Transportation Systems) is easily constructed by applying USN technology. Localization can be categorized as either Range-based or Range-free. Range-based is known to be not suitable for the localization based on sensor network, because of the irregularity of radio propagation and the additional device requirement. The other side, Range-free is much appropriated for the resource constrained sensor network because it can actively locate by means of the communication radio. But, generally the location accuracy of Range-free is low. Especially, it is very low in a low-density environment. So, these two methods have both merits and demerits. Therefore, it requires a new method to be able to improve tracking accuracy by combining the two methods. This paper proposes the tracking scheme based on range-hybrid, which can markedly enhance tracking accuracy by effectively using the information of surrounding nodes and the RSSI(Received Signal Strength Indication) that does not require additional hardware. Additionally, we present a method, which can improve the accuracy of vehicle tracking by adopting the prediction mechanism. Simulation results show that our method outperforms other methods in the transportation simulation environment.