Performance Of Localization Algorithms Research Articles

Simulation Tool for the Analysis of Cooperative Localization Algorithms for Wireless Sensor Networks.

Within the context of the Internet of Things (IoT) and the Location of Things (LoT) service, this paper presents an interactive tool to quantitatively analyze the performance of cooperative localization techniques for wireless sensor networks (WSNs). In these types of algorithms, nodes help each other determine their location based on some signal metrics such as time of arrival (TOA), received signal strength (RSS), or a fusion of them. The developed tool is intended to provide researchers and designers a fast way to measure the performance of localization algorithms considering specific network topologies. Using TOA or RSS models, the Crámer-Rao lower bound (CRLB) has been implemented within the tool. This lower bound can be used as a benchmark for testing a particular algorithm for specific channel characteristics and WSN topology, which allows determination if the necessary accuracy for a specific application is possible. Furthermore, the tool allows us to consider independent characteristics for each node in the WSN. This feature allows the avoidance of the typical “disk graph model,” which is usually applied to test cooperative localization algorithms. The tool allows us to run Monte-Carlo simulations and generate statistical reports. A set of basic illustrative examples are described comparing the performance of different localization algorithms and showing the capabilities of the presented tool.

Root Mean Square Minimum Distance as a Quality Metric for Stochastic Optical Localization Nanoscopy Images

A localization algorithm in stochastic optical localization nanoscopy plays an important role in obtaining a high-quality image. A universal and objective metric is crucial and necessary to evaluate qualities of nanoscopy images and performances of localization algorithms. In this paper, we propose root mean square minimum distance (RMSMD) as a quality metric for localization nanoscopy images. RMSMD measures an average, local, and mutual fitness between two sets of points. Its properties common to a distance metric as well as unique to itself are presented. The ambiguity, discontinuity, and inappropriateness of the metrics of accuracy, precision, recall, and Jaccard index, which are currently used in the literature, are analyzed. A numerical example demonstrates the advantages of RMSMD over the four existing metrics that fail to distinguish qualities of different nanoscopy images in certain conditions. The unbiased Gaussian estimator that achieves the Fisher information and Cramer-Rao lower bound (CRLB) of a single data frame is proposed to benchmark the quality of localization nanoscopy images and the performance of localization algorithms. The information-achieving estimator is simulated in an example and the result demonstrates the superior sensitivity of RMSMD over the other four metrics. As a universal and objective metric, RMSMD can be broadly employed in various applications to measure the mutual fitness of two sets of points.

Proposing a measurement criterion to evaluate the border problem in localization algorithms in WSNs

Localization algorithms are one of the most important protocols that may be used in many different fields of wireless sensor networks. The border problem is a considerable challenge for localization algorithms. In this regard, the sensor nodes that are placed on the boundary of a deployment area have a larger localization error as compared to the other sensor nodes. In this article, a new criterion is proposed for measuring the impact of the border problem on the performance of localization algorithms in isotropic networks. The performance of some range-free localization algorithms is studied by simulation. The results show that, contrary to LSVM and NN algorithms, the impacts of the border problem are reduced by a reduction in the dimensions of the deployment environment in the DV-hop algorithm. Besides, the effect of the border problem on the performance of the localization algorithms can be reduced by an increase in the number of anchor nodes. On the other hand, the number of sensor nodes does not have a significant impact on reducing the effect of the border problem in localization algorithms. Finally, a solution is proposed to reduce the negative impact of this issue on the performance of localization methods.

Assessment of Indoor Localization Algorithm Performance under Realistic Conditions

지역위치시스시템(LPS) 같은 non-cooperative 위치추정이라 불리는 기존 기술은 anchor node, 즉 위치가 알려진 센서노드들만을 활용하여 찾고자 하는 목표물, 즉 찾고자 하는 센서노드의 위치추정을 수행하였다. 하지만 위치가 알려진 센서들만을 활용하여 위치추정을 수행하는 것은 정확도에 제한을 줄 수밖에 없다. 이러한 문제를 해결하기 위해 cooperative 위치추정기법은 고안 및 연구되어져왔다. cooperative 위치추정은 위치가 알려진 센서 뿐만 아니라 위치가 알려지지 않은 센서들도 활용함으로써 정확도와 안정성을 향상시키는 기술이다. 이 논문에서는 실험결과가 현실적이라는 증거를 보여주기 위하여 실제 실내 환경에서 실제로 측정된 데이터를 활용하여 cooperative 위치추정이 non-cooperative 위치추정보다 더 성능이 우수하다는 결과를 보여줄 것이다. 또한, 실내실험환경에서 실제로 측정된 데이터와 시뮬레이션 데이터를 사용한 위치추정결과를 비교하여 시뮬레이션을 이용한 위치추정이 얼마나 현실적인 데이터인지 보여준다.

An improved centroid localization algorithm based on iterative computation for wireless sensor network

Wireless sensor network (WSN) is a basic component of internet and it plays an important role in many application areas, such as military surveillance, environmental monitoring and medical treatment. Node localization is one of the interesting issues in the field of WSN. Now, most of the existing node localization algorithms can be divided into two categories. One is range-based measurement and the other is range-free measurement. The localization algorithm of range-based measurement can achieve better location accuracy than the localization algorithm of range-free measurement. However, they are generally very energy consuming. Therefore, the range-free measurements are most widely used in practical applications. According to the application of localization algorithm in WSN by range-free measurements, an improved centroid localization algorithm based on iterative computation for wireless sensor network is proposed. In this algorithm, the position relationship of the closed area surrounded by the anchor nodes inside the unknown node's communication range and the unknown node is obtained by approximate point-in-triangulation test at first. Different position relationships determine different stopping criteria for iteration. Then, the centroid coordinates of the closed area surrounded by the anchor nodes inside the unknown node's communication range and the received signal strength (RSSI) between the centroid node and the unknown node are calculated. The anchor node with the weakest RSSI would be replaced by the centroid node. By this method, the closed area surrounded by the anchor nodes inside the unknown node's communication range is reduced. The location accuracy is increased by the cyclic iterative method. With the change of the anchor node ratio, the communication radius of the unknown node and the effect of RSSI error, the algorithm performance is investigated by using simulated data. The simulation results validate that although the improved centroid localization algorithm performance will be lost when the number of the anchor nodes inside the unknown node communication range decreases, the new approach can achieve good performance under the condition of few anchor nodes inside the unknown node communication range and this method is of strong robusticity against RSSI error disturbance.

5 G WiFi Signal-Based Indoor Localization System Using Cluster k-Nearest Neighbor Algorithm

Indoor localization based on existent WiFi signal strength is becoming more and more prevalent and ubiquitous. Unfortunately, the WiFi received signal strength (RSS) is susceptible by multipath, signal attenuation, and environmental changes, which is the major challenge for accurate indoor localization. To overcome these limitations, we propose the cluster k-nearest neighbor (KNN) algorithm with 5 G WiFi signal to reduce the environmental interference and improve the localization performance without additional equipment. In this paper, we propose three approaches to improve the performance of localization algorithm. For one thing, we reduce the computation effort based on the coarse localization algorithm. For another, according to the detailed analysis of the 2.4 G and 5 G signal fluctuation, we expand the real-time measurement RSS before matching the fingerprint map. More importantly, we select the optimal nearest neighbor points based on the proposed cluster KNN algorithm. We have implemented the proposed algorithm and evaluated the performance with existent popular algorithms. Experimental results demonstrate that the proposed algorithm can effectively improve localization accuracy and exhibit superior performance in terms of localization stabilization and computation effort.

Localization algorithms for multilateration (MLAT) systems in airport surface surveillance

We present a general scheme for analyzing the performance of a generic localization algorithm for multilateration (MLAT) systems (or for other distributed sensor, passive localization technology). MLAT systems are used for airport surface surveillance and are based on time difference of arrival measurements of Mode S signals (replies and 1,090 MHz extended squitter, or 1090ES). In the paper, we propose to consider a localization algorithm as composed of two components: a data model and a numerical method, both being properly defined and described. In this way, the performance of the localization algorithm can be related to the proper combination of statistical and numerical performances. We present and review a set of data models and numerical methods that can describe most localization algorithms. We also select a set of existing localization algorithms that can be considered as the most relevant, and we describe them under the proposed classification. We show that the performance of any localization algorithm has two components, i.e., a statistical one and a numerical one. The statistical performance is related to providing unbiased and minimum variance solutions, while the numerical one is related to ensuring the convergence of the solution. Furthermore, we show that a robust localization (i.e., statistically and numerically efficient) strategy, for airport surface surveillance, has to be composed of two specific kind of algorithms. Finally, an accuracy analysis, by using real data, is performed for the analyzed algorithms; some general guidelines are drawn and conclusions are provided.

A hop-count based positioning algorithm for wireless ad-hoc networks

We propose a range-free localization algorithm for a wireless ad-hoc network utilizing the hop-count metric's ability to indicate proximity to anchors (i.e., nodes with known positions). In traditional sense, hop-count generally means the number of intermediate routers a datagram has to go through between its source and the destination node. We analytically show that hop-count could be used to indicate proximity relative to an anchor node. Our proposed algorithm is computationally feasible for resource constrained wireless ad-hoc nodes, and gives reasonable accuracy. We perform both real experiments and simulations to evaluate the algorithm's performance. Experimental results show that our algorithm outperforms similar proximity based algorithms utilizing received signal strength and expected transmission count. We also analyze the impact of various parameters like the number of anchor nodes, placements of anchor nodes and varying transmission powers of the nodes on the hop-count based localization algorithm's performance through simulation.

Anomaly-based intrusion detection of jamming attacks, local versus collaborative detection

We present intrusion detection algorithms to detect physical layer jamming attacks in wireless networks. We compare the performance of local algorithms on the basis of the signal-to-interference-plus-noise ratio SINR executing independently at several monitors, with a collaborative detection algorithm that fuses the outputs provided by these algorithms. The local algorithms fall into two categories: simple threshold that raise an alarm if the output of the SINR-based metrics we consider deviates from a predefined detection threshold and cumulative sum cusum algorithms that raise an alarm if the aggregated output exceeds the predefined threshold. For collaborative detection, we use the Dempster-Shafer theory of evidence algorithm. We collect SINR traces from a real IEEE 802.11 network, and with the use of a new evaluation method, we evaluate both the local and the Dempster-Shafer algorithms in terms of the detection probability, false alarm rate, and their robustness to different detection threshold values, under different attack intensities. The evaluation shows that the cusums achieve higher performance than the simple threshold algorithms under all attack intensities. The Dempster-Shafer algorithm when combined with the simple algorithms, it can increase their performance by more than 80%, but for the cusum algorithms it does not substantially improve their already high performance.Copyright © 2013 John Wiley & Sons, Ltd.

Performance of localization algorithms for teleseismic mid-ocean ridge earthquakes: the 1999 Gakkel Ridge earthquake swarm and its geological interpretation

In 1999, an unusual earthquake swarm at the 85°E/85°N volcanic centre on the ultraslow spreading Gakkel Ridge, Arctic Ocean, was detected teleseismically. The swarm lasted over 9 months and counted 252 events with mb ≥ 3.1. It represents the strongest and largest ever recorded mid-ocean ridge earthquake swarm, and it occurred at a site where spreading rates are only about 10 mm yr–1. We relocated the earthquake swarm comparing the performance of three different localization algorithms: (1) the absolute least-squares routine HYPOSAT, (2) the absolute probabilistic routine NonLinLoc and (3) the relative least-squares routine Mlocate. The epicentres as calculated by each algorithm mostly did not agree within their error ellipses. Thus, the choice of location algorithm proved more critical than, for example, the choice of a local velocity model.We compiled a set of well-localized events which closely agree in at least two routines, mostly Mlocate and NonLinLoc. We conclude that the earthquake swarm of 1999 was related to a spreading episode and shows a complex interplay of tectonic and magmatic events. Our geological interpretation revealed three phases in swarm activity: In the first phase from January 17 up to February 1 fracturing of the crust took place, either as a result of or enabling magmatic intrusion. Seismicity in the second phase from February 2 to April 6 expanded along- and across axis. It showed signs of magmatic interaction, but a clear dyke migration pattern is absent. At the beginning of the third phase, a distinct break in the event rate suggested a change in the physical process, either an adjustment of the stress field to the new regime or a transition to an effusive stage.

Robust Localization of Nodes and Time-Recursive Tracking in Sensor Networks Using Noisy Range Measurements

Simultaneous localization and tracking (SLAT) in sensor networks aims to determine the positions of sensor nodes and a moving target in a network, given incomplete and inaccurate range measurements between the target and each of the sensors. One of the established methods for achieving this is to iteratively maximize a likelihood function (ML) of positions given the observed ranges, which requires initialization with an approximate solution to avoid convergence towards local extrema. This paper develops methods for handling both Gaussian and Laplacian noise, the latter modeling the presence of outliers in some practical ranging systems that adversely affect the performance of localization algorithms designed for Gaussian noise. A modified Euclidean distance matrix (EDM) completion problem is solved for a block of target range measurements to approximately set up initial sensor/target positions, and the likelihood function is then iteratively refined through majorization-minimization (MM). To avoid the computational burden of repeatedly solving increasingly large EDM problems in time-recursive operation, an incremental scheme is exploited whereby a new target/node position is estimated from previously available node/target locations to set up the iterative ML initial point for the full spatial configuration. The above methods are first derived under Gaussian noise assumptions, and modifications for Laplacian noise are then considered. Analytically, the main challenges to overcome in the Laplacian case stem from the non-differentiability of <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\ell_1$</tex> </formula> norms that arise in the various cost functions. Simulation results show that the proposed algorithms significantly outperform existing localization methods in the presence of outliers, while providing comparable performance for Gaussian noise.

A Security Localization method based on Threshold and Vote for wireless sensor networks

In this paper, we address wireless sensor network localization problems that have high reliability in an environment where physical node destruction is possible. We propose a new security localization algorithm TVSL (Threshold and Vote Security Localization) for wireless sensor network (WSN). This algorithm improves the safety performance of positioning process of nodes in WSN by using the threshold idea and vote with sensor nodes. The advantage of this algorithm is that no additional hardware is needed, easy integration with other localization algorithms, and the safety performance of localization algorithm is improved obviously. From the simulation of this algorithm, we get the expected results.

Empirical Evaluation of Wireless Localization when Using Multiple Antennas

We show that signal strength variability can be reduced by employing multiple low-cost antennas at fixed locations. We further explore the impact of this reduction on wireless localization by analyzing a representative set of algorithms ranging from fingerprint matching, to statistical maximum likelihood estimation, to threshold bounding of signal fingerprints, and to multilateration. Using an indoor wireless testbed, we provide experimental evaluation of the localization performance under multiple antennas. We found that in nearly all cases the performance of localization algorithms improved when using multiple antennas. Specifically, the median and the 90th percentile error can be reduced up to 70 percent. Additionally, we found that multiple antennas improve the localization stability significantly, up to 100 percent improvement, when there are small-scale three-dimensional movements of a mobile device around a given location.

Simulated performance of remote sensing ocean colour algorithms during the 1996 PRIME cruise

Coincident pigment and underwater radiometric data were collected during a cruise along the 20°W meridian from 60°N to 37°N in the north-eastern Atlantic Ocean as part of the Natural Environment Research Council (NERC) thematic programme: plankton reactivity in the marine environment (PRIME). These data were used to simulate the retrieval of two bio-optical variables from remotelysensed measurements of ocean colour (for example by the NASA Sea-viewing wide field-of-view sensor, SeaWiFS), using two-band semi-empirical algorithms. The variables considered were the diffuse attenuation coefficient at 490 nm, ( K d(490), units: m −1) and the phytoplankton pigment concentration expressed as optically-weighted chlorophyll- a concentration [ C a , units: mg m −3]. There was good agreement between the measured and the retrieved bio-optical values. Algorithms based on the PRIME data were generated to compare the performance of local algorithms (algorithms which apply to a restricted area and/or season) with global algorithms (algorithms developed on data from a wide variety of water masses). The use of local algorithms improved the average accuracy, but not the precision, of the retrievals: errors were still ±36% ( K d) and ±117% ( C a ) using local algorithms.