Range Free Localization Techniques in Wireless Sensor Networks: A Review

AbstractRecent developments in micro electro mechanical systems (MEMS) technology and wireless communication have propelled the growing applications of wireless sensor networks (WSNs). Wireless sensor network is comprised of large number of small and cheap devices known as sensors. One of the important functions of sensor network is collection and forwarding of data. In most of the applications, it is of much interest to find out the location of the data. This type of information can be obtained by use of localization techniques. So node localization is very crucial to find out the position of node with the help of localization algorithms. Hence, node localization becomes one of the fundamental challenges in WSNs. We make the rigorous reviews on different schemes of localization in sensor networks. On the basis of range measurements, the localization schemes can be broadly classified in two categories such as: range based and range free schemes. The cost and hardware limitation on sensing node preclude the use of range based localization schemes. In most of the sensor network application coarse accuracy is sufficient so range free localization schemes are considered as a substitute to range based schemes. In this paper, the detailed study has been carried out to understand and select the best range free localization algorithm for WSNs. At the end some issues are discussed for future research in the area of localization techniques for WSNs

Social Network Analysis Based Localization Technique with Clustered Closeness Centrality for 3D Wireless Sensor Networks

[EN] In this paper, we proposed a new wireless localization technique based on the ideology of social network analysis (SNA), to study the different properties of networks as a graph. Centrality is a main concept in SNA, so we propose using closeness centrality (CC) as a measurement to denote the importance of the node inside the network due to its geo-location to others. The node with highest degree of CC is chosen as a cluster heads, then each cluster head can form its trilateration process to collect data from its cluster. The selection of closest cluster based on CC values, and the unknown node's location can be estimated through the trilateration process. To form a perfect trilateration, the cluster head chooses three anchor nodes. The proposed algorithm provides high accuracy even in different network topologies like concave shape, O shape, and C shape as compared to existing received signal strength indicator (RSSI) techniques. Matlab simulation results based on practical radio propagation data sets showed a localization error of 0.32 m with standard deviation of 0.26 m.This work was fully supported by the Vice Chancellor Doctoral Scholarship at Auckland University of Technology, New Zealand.Ahmad, T.; Li, XJ.; Seet, B.; Cano, J. (2020). Social Network Analysis Based Localization Technique with Clustered Closeness Centrality for 3D Wireless Sensor Networks. Electronics. 9(5):1-19. https://doi.org/10.3390/electronics9050738S11995Zhou, B., Yao, X., Yang, L., Yang, S., Wu, S., Kim, Y., & Ai, L. (2019). Accurate Rigid Body Localization Using DoA Measurements from a Single Base Station. Electronics, 8(6), 622. doi:10.3390/electronics8060622Ahmad, T., Li, X., & Seet, B.-C. (2017). Parametric Loop Division for 3D Localization in Wireless Sensor Networks. Sensors, 17(7), 1697. doi:10.3390/s17071697Kaur, A., Kumar, P., & Gupta, G. P. (2019). 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Range-Free Localization with the Radical Line

Due to hardware and computational constraints, wireless sensor networks (WSNs) normally do not take measurements of time-of-arrival or time-difference-of-arrival for rangebased localization. Instead, WSNs in some applications use rangefree localization for simple but less accurate determination of sensor positions. A well-known algorithm for this purpose is the centroid algorithm. This paper presents a range-free localization technique based on the radical line of intersecting circles. This technique provides greater accuracy than the centroid algorithm, at the expense of a slight increase in computational load. Simulation results show that for the scenarios studied, the radical line method can give an approximately 2 to 30% increase in accuracy over the centroid algorithm, depending on whether or not the anchors have identical ranges, and on the value of DOI.Comment: Proc. IEEE ICC'10, Cape Town, South Africa, May, 201

Accurate range free localization in multi-hop wireless sensor networks

To localize wireless sensor networks (WSN)s nodes, only the hop-based data have been so far utilized by range free techniques, with poor-accuracy, though. In this thesis, we show that localization accuracy may importantly advantage from mutual utilization, at no cost, of the information already offered by the advancing nodes (i.e., relays) between all anchors (i.e., position-aware) and sensor nodes join up. In addition, energy-based informant localization approaches are generally established corresponding to the channel path-loss models in which the noise is mostly expected to shadow Gaussian distributions. In this thesis, we signify the applied additive noise by the Gaussian mixture model and improve a localization algorithm depend on the received signal intensity to attain the greatest likelihood location, estimator. By employing Jensen’s inequality and semidefinite relaxation, the originally offered nonlinear and nonconvex estimator is relaxed into a convex optimization difficulty, which is able to be professionally resolved to acquire the totally best solution. Moreover, the resultant Cramer–Rao lower bound is originated for occurrence comparison. Simulation and experimental results show a substantial performance gain achieved by our proposed localization algorithm in wireless sensor networks. The performance is evaluated in terms of RMSE in terms of three algorithms WLS, CRLR, and GMSDP based on using the Monte Carlo simulation with account the number of anchors that varying from anchor=4 to anchor =20. Finally, the GMSDP- algorithm achieves and provides a better value of RMSEs and the greatest localization estimation errors comparing with the CRLR algorithm and WLS algorithm

Development an accurate and stable range-free localization scheme for anisotropic wireless sensor networks

With the high-speed development of wireless radio technology, numerous sensor nodes are integrated into wireless sensor networks, which has promoted plentiful location-based applications that are successfully applied in various fields, such as monitoring natural disasters and post-disaster rescue. Location information is an integral part of wireless sensor networks, without location information, all received data will lose meaning. However, the current localization scheme is based on equipped GPS on every node, which is not cost-efficient and not suitable for large-scale wireless sensor networks and outdoor environments. To address this problem, research scholars have proposed a rangefree localization scheme which only depends on network connectivity. Nevertheless, as the representative range-free localization scheme, Distance Vector-Hop (DV-Hop) localization algorithm demonstrates extremely poor localization accuracy under anisotropic wireless sensor networks. The previous works assumed that the network environment is evenly and uniformly distributed, ignored anisotropic factors in a real setting. Besides, most research academics improved the localization accuracy to a certain degree, but at expense of high communication overhead and computational complexity, which cannot meet the requirements of high-precision applications for anisotropic wireless sensor networks. Hence, finding a fast, accurate, and strong solution to solve the range-free localization problem is still a big challenge. Accordingly, this study aspires to bridge the research gap by exploring a new DV-Hop algorithm to build a fast, costefficient, strong range-free localization scheme. This study developed an optimized variation of the DV-Hop localization algorithm for anisotropic wireless sensor networks. To address the poor localization accuracy problem in irregular C-shaped network topology, it adopts an efficient Grew Wolf Optimizer instead of the least-squares method. The dynamic communication range is introduced to refine hop between anchor nodes, and new parameters are recommended to optimize network protocol to balance energy cost in the initial step. Besides, the weighted coefficient and centroid algorithm is employed to reduce cumulative error by hop count and cut down computational complexity. The developed localization framework is separately validated and evaluated each optimized step under various evaluation criteria, in terms of accuracy, stability, and cost, etc. The results of EGWO-DV-Hop demonstrated superior localization accuracy under both topologies, the average localization error dropped up to 87.79% comparing with basic DV-Hop under C-shaped topology. The developed enhanced DWGWO-DVHop localization algorithm illustrated a favorable result with high accuracy and strong stability. The overall localization error is around 1.5m under C-shaped topology, while the traditional DV-Hop algorithm is large than 20m. Generally, the average localization error went down up to 93.35%, compared with DV-Hop. The localization accuracy and robustness of comparison indicated that the developed DWGWO-DV-Hop algorithm super outperforms the other classical range-free methods. It has the potential significance to be guided and applied in practical location-based applications for anisotropic wireless sensor networks

Improvement of range-free localization technology by a novel DV-hop protocol in wireless sensor networks

International audienceLocalization is a fundamental issue for many applications in wireless sensor networks. Without the need of additional ranging devices, the range-free localization technology is a cost-effective solution for low-cost indoor and outdoor wireless sensor networks. Among range-free algorithms, DV-hop (Distance Vector - hop) has the advantage to localize the mobile nodes which has less than three neighbour anchors. Based on the original DV-hop algorithm, this paper presents two improved algorithms (Checkout DV-hop and Selective 3-Anchor DV-hop). Checkout DV-hop algorithm estimates the mobile node position by using the nearest anchor, while Selective 3-Anchor DV-hop algorithm chooses the best 3 anchors to improve localization accuracy. Then, in order to implement these DV-hop based algorithms in network scenarios, a novel DV-hop localization protocol is proposed. This new protocol is presented in detail in this paper, including the format of data payloads, the improved collision reduction method E-CSMA/CA, as well as parameters used in deciding the end of each DV-hop step. Finally, using our localization protocol, we investigate the performance of typical DV-hop based algorithms in terms of localization accuracy, mobility, synchronization and overhead. Simulation results prove that Selective 3-Anchor DV-hop algorithm offers the best performance compared to Checkout DV-hop and the original DV-hop algorithm

무선 센서 네트워크 상에서의 효율적인 위치 추정 알고리즘 연구

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2015. 8. 김성철.In this dissertation, efficient localization algorithms for wireless sensor networks are represented. Localization algorithms are widely used in commercial systems and application. The localization techniques are anticipated to be developed for various environments and reduce the localization error for accurate location information because the user demands for more accurate positioning systems for medical care, home networks, and monitoring applications in personal range environments. A well-known localization system is GPS, with applications such as mobile navigation. The GPS shows good performance on road or roughly finding location system in outdoor environments but limited in indoor environments. Due to the development of handsets like smart phone, the users can easily receive the GPS signals and other RF signals including 3G/4G/5G signals, WLAN (Wireless Local Area Networks) signals, and the signals from other sensors. Thus, the various systems using localization schemes are developed, especially, the WSNs (Wireless Sensor Networks) localization system is actively studied in indoor environment without GPS. In this dissertation, the range-free localization algorithm and the range-based localization algorithm are reported for WSNs localization system. The range-free localization algorithms are proposed before to estimate location using signal database, called signal map, or the anchor nodes of antenna patterns, or ID configuration of the linked anchor nodes, etc. These algorithms generally need to additional hardware or have low accuracy due to low information for location estimation. The range-based algorithms, equal to distance-based algorithms, are based on received signal strength, RSSI, or time delay, TOA and TDOA, between the anchor nodes and a target node. Although the TOA and TDOA are very accurate distance estimation schemes, these scheme have the critical problem, the time synchronization. Although RSSI is very simple to setup the localization system with tiny sensors, the signal variation causes severe distance estimation error. The angle estimation, AOA, provides additional information to estimation the location. However, AOA needs additional hardware, the antenna arrays, which is not suitable for tiny sensors. In this dissertation, range-free and range-based localization algorithms are analyzed and summarized for WSNs with tiny sensors. The WSNs localization systems are generally used range-based algorithm. The range-based algorithms have major source of distance estimation error, and the distance estimation error causes severe localization error. In this dissertation, the localization error mitigation algorithms are proposed in two dimensional environments and three dimensional environments for WSNs. The mitigation algorithms in two dimensional environments consist of several steps, which are distance error mitigation algorithm, location error mitigation algorithm, and bad condition detection algorithm. The each algorithm is effective to reduce the localization error, but the accuracy of location estimation is the best when they are combined. The performance of proposed algorithms is examined with variation of received signal strength and it is confirmed that the combined proposed algorithm has the best performance rather than that of conventional scheme and each proposed algorithms. The three dimensional localization uses Herons formula of tetrahedron to calculate the target height, then transforms a two dimensional location computed by LLSE into a three dimensional estimated location. Simulation results validate the accuracy of the proposed scheme.Contents Chapter 1 Introduction...........................................................1 Chapter 2 Location estimation for wireless sensor networks.................................................................................................4 2.1 Introduction..................................................................................4 2.2 Range-free location estimation ...................................................7 2.2.1 Cell-ID location estimation .........................................................7 2.2.2 Fingerprint location estimation ...................................................8 2.2.3 Other range-free location estimation.........................................10 2.3 Range-based location estimation ..............................................12 2.3.1 Time delay based distance estimation.......................................12 2.3.2 Received signal strength based distance estimation .................16 2.3.3 Angle of arrival based location estimation................................18 2.4 Summary.......................................................................................20 Chapter 3 Two dimensional location estimation for wireless sensor networks......................................................................22 3.1 Introduction................................................................................22 3.2 Tri-lateration ..................................................................24 3.2.1 Linear least square estimation ..................................................24 3.2.2 The cases of tri-lateration .........................................................26 3.3 Geometric mitigation algorithm …............................................27 3.3.1 Motivation .................................................................................27 3.3.2 Algorithm explanation ..............................................................28 3.3.3 Simulation .................................................................................29 3.3.4 Conclusion ................................................................................34 3.4 Coordinate shift algorithm ..........................................................35 3.4.1 Motivation .................................................................................35 3.4.2 Algorithm explanation...............................................................36 3.4.3 Simulation .................................................................................41 3.4.4 Conclusion ................................................................................43 3.5 Bad condition detection algorithm ...............................................44 3.5.1 Motivation .................................................................................44 3.5.2 Algorithm explanation...............................................................45 3.5.3 Simulation .................................................................................51 3.5.4 Conclusion ................................................................................54 3.6 Conclusion..................................................................................55 Chapter 4 Three dimensional location estimation for wireless sensor networks .....................................................................56 4.1 Introduction................................................................................56 4.2 Motivation.....................................................................................57 4.2.1 Singular matrix problem…........................................................57 4.2.2 Short range location estimation.................................................59 4.3 Algorithm explanation....................................................................60 4.4 Simulation........................................................................................68 4.5 Conclusion..................................................................................72 Bibliography....................................................................................73 Abstract in Korean.....................................................................................78Docto

A Hybrid Modified Ant Colony Optimization - Particle Swarm Optimization Algorithm for Optimal Node Positioning and Routing in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have been widely deployed in hostile locations for environmental monitoring. Sensor placement and energy management are the two main factors that should be focused due to certain limitations in WSNs. The nodes in a sensor network might not stay charged when energy draining takes place; therefore, increasing the operational lifespan of the network is the primary purpose of energy management. Recently, major research interest in WSN has been focused with the essential aspect of localization. Several types of research have also taken place on the challenges of node localization of wireless sensor networks with the inclusion of range-free and range-based localization algorithms. In this work, the optimal positions of Sensor Nodes (SNs) are determined by proposing a novel Hybrid M-ACO – PSO (HMAP) algorithm. In the HMAP method, the improved PSO utilizes learning strategies for estimating the relay nodes\u27 optimal positions. The M-ACO assures the data conveyance. A route discovers when it relates to the ideal route irrespective of the possibility of a system that includes the nodes with various transmission ranges, and the network lifetime improves. The proposed strategy is executed based on the energy, throughput, delivery ratio, overhead, and delay of the information packets

Non-line-of-sight Node Localization based on Semi-Definite Programming in Wireless Sensor Networks

An unknown-position sensor can be localized if there are three or more anchors making time-of-arrival (TOA) measurements of a signal from it. However, the location errors can be very large due to the fact that some of the measurements are from non-line-of-sight (NLOS) paths. In this paper, we propose a semi-definite programming (SDP) based node localization algorithm in NLOS environment for ultra-wideband (UWB) wireless sensor networks. The positions of sensors can be estimated using the distance estimates from location-aware anchors as well as other sensors. However, in the absence of LOS paths, e.g., in indoor networks, the NLOS range estimates can be significantly biased. As a result, the NLOS error can remarkably decrease the location accuracy. And it is not easy to efficiently distinguish LOS from NLOS measurements. In this paper, an algorithm is proposed that achieves high location accuracy without the need of identifying NLOS and LOS measurement.Comment: submitted to IEEE ICC'1