Accuracy analysis of BLE beacon-based localization in smart buildings

2021 ◽  
pp. 1-20
Author(s):  
Rosen Ivanov
Keyword(s):  
Use Case ◽  
Accuracy Analysis ◽  
Localization Algorithm ◽  
Computing Power ◽  
Smart Buildings ◽  
Localization Accuracy ◽  
Positioning Systems ◽  
Deployment Strategy ◽  
Accurate Localization ◽  
Localization Algorithms

The majority of services that deliver personalized content in smart buildings require accurate localization of their clients. This article presents an analysis of the localization accuracy using Bluetooth Low Energy (BLE) beacons. The aim is to present an approach to create accurate Indoor Positioning Systems (IPS) using algorithms that can be implemented in real time on platforms with low computing power. Parameters on which the localization accuracy mostly depends are analyzed: localization algorithm, beacons’ density, deployment strategy, and noise in the BLE channels. An adaptive algorithm for pre-processing the signals from the beacons is proposed, which aims to reduce noise in beacon’s data and to capture visitor’s dynamics. The accuracy of five range-based localization algorithms in different use case scenarios is analyzed. Three of these algorithms are specially designed to be less sensitive to noise in radio channels and require little computing power. Experiments conducted in a simulated and real environment show that using proposed algorithms the localization accuracy less than 1 m can be obtained.

scholarly journals Localization of isotropic and anisotropic wireless sensor networks in 2D and 3D fields

2021 ◽  
Author(s):  
Soumya J. Bhat ◽  
K. V. Santhosh
Keyword(s):  
Least Squares Method ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Localization Accuracy ◽  
Smart Systems ◽  
Reference Node ◽  
Accurate Localization ◽  
Localization Algorithms ◽  
Node Ratio

AbstractInternet of Things (IoT) has changed the way people live by transforming everything into smart systems. Wireless Sensor Network (WSN) forms an important part of IoT. This is a network of sensor nodes that is used in a vast range of applications. WSN is formed by the random deployment of sensor nodes in various fields of interest. The practical fields of deployment can be 2D or 3D, isotropic or anisotropic depending on the application. The localization algorithms must provide accurate localization irrespective of the type of field. In this paper, we have reported a localization algorithm called Range Reduction Based Localization (RRBL). This algorithm utilizes the properties of hop-based and centroid methods to improve the localization accuracy in various types of fields. In this algorithm, the location unknown nodes identify the close-by neighboring nodes within a predefined threshold and localize themselves by identifying and reducing the probable range of existence from these neighboring nodes. The nodes which do not have enough neighbors are localized using the least squares method. The algorithm is tested in various irregular and heterogeneous conditions. The results are compared with a few state-of-the-art hop-based and centroid-based localization techniques. RRBL has shown an improvement in localization accuracy of 28% at 10% reference node ratio and 26% at 20% reference node ratio when compared with other localization algorithms.

scholarly journals AGV Localization System Based on Ultra-Wideband and Vision Guidance

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 448 ◽  
Author(s):  
Xiaohao Hu ◽  
Zai Luo ◽  
Wensong Jiang
Keyword(s):  
Monocular Vision ◽  
Ultra Wideband ◽  
Localization Algorithm ◽  
Localization Accuracy ◽  
Localization Method ◽  
Localization System ◽  
Indoor Location ◽  
Accurate Localization ◽  
Vision Localization ◽  
Automated Storage

Aiming at the problems of low localization accuracy and complicated localization methods of the automatic guided vehicle (AGV) in the current automatic storage and transportation process, a combined localization method based on the ultra-wideband (UWB) and the visual guidance is proposed. Both the UWB localization method and the monocular vision localization method are applied to the indoor location of the AGV. According to the corner points of an ArUco code fixed on the AGV body, the monocular vision localization method can solve the pose information of the AGV by the PnP algorithm in real-time. As an auxiliary localization method, the UWB localization method is called to locate the AGV coordinates. The distance from the tag on the AGV body to the surrounding anchors is measured by the time of flight (TOF) ranging algorithm, and the actual coordinates of the AGV are calculated by the trilateral centroid localization algorithm. Then, the localization data of the UWB is corrected by the mean compensation method to obtain a consistent and accurate localization trajectory. The experiment result shows that this localization system has an error of 15mm, which meets the needs of AGV location in the process of automated storage and transportation.

Incremental Multi-Hop Localization Algorithm Based on Regularized Weighted Least Squares

2019 ◽  
Vol 33 (09) ◽  
pp. 1959032 ◽  
Author(s):  
Ru-Lin Dou ◽  
Bo Hu ◽  
Wei-Juan Shi
Keyword(s):  
Least Squares ◽  
High Efficiency ◽  
Least Squares Method ◽  
Weighted Least Squares ◽  
Localization Algorithm ◽  
Localization Accuracy ◽  
Weighted Least Squares Method ◽  
Localization Algorithms ◽  
Anchor Nodes ◽  
Regularized Method

Incremental multi-hop localization algorithm applies to networks with broad range and low density of anchor nodes. However, during the localization process, it tends to be affected by accumulative errors and collinear problem between anchor nodes. We have proposed an incremental multi-hop localization algorithm based on regularized weighted least squares method, and the algorithm uses weighted least squares method to reduce the influence of accumulative errors and uses regularized method to weaken the collinear problem between anchor nodes. The results of both real experiment and simulative experiment show that compared to previous incremental multi-hop localization algorithms, the algorithm proposed in this paper can not only well solve the accumulated errors problem and obtain high localization accuracy, but it has also considered the influence of collinear problem on localization computation during the localization process. We evaluate our method based on various network scenes, and analyze its performance. We also compare our method with several existing methods, and demonstrate the high efficiency of our proposed method.

scholarly journals An Enhanced Hybrid 3D Localization Algorithm Based on APIT and DV-Hop

2017 ◽  
Vol 13 (09) ◽  
pp. 69 ◽  
Author(s):  
Lianjun Yi ◽  
Miaochao Chen
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Hybrid Algorithm ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Localization Accuracy ◽  
3D Localization ◽  
Localization Algorithms ◽  
Wide Range

<p>Wireless sensor networks (WSN), as a new method of information collection and processing, has a wide range of applications. Since the acquired data must be bound with the location information of sensor nodes, the sensor localization is one of the supporting technologies of wireless sensor networks. However, the common localization algorithms, such as APIT algorithm and DV-Hop algorithm, have the following problems: 1) the localization accuracy of beacon nodes is not high; 2) low coverage rate in sparse environment. In this paper, an enhanced hybrid 3D localization algorithm is designed with combining the advantages of APIT algorithm and DV-Hop algorithm. The proposed hybrid algorithm can improve the localization accuracy of the beacon nodes in dense environments by reducing the triangles in the triangle interior point test (PIT) and selecting good triangles. In addition, the algorithm can combine the advantages of APIT algorithm and DV-Hop algorithm localization algorithm to calculate the unknown node coordinates, and also improve the location coverage of the beacon nodes in sparse environment. Simulation results show that the proposed hybrid algorithm can effectively improve the localization accuracy of beacon nodes in the dense environment and the location coverage of beacon nodes in sparse environment.</p>

scholarly journals Experimental Evaluation of an RSSI-Based Localization Algorithm on IoT End-Devices

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3931 ◽  
Author(s):  
Rosa Pita ◽  
Ramiro Utrilla ◽  
Roberto Rodriguez-Zurrunero ◽  
Alvaro Araujo
Keyword(s):  
Experimental Evaluation ◽  
Resource Constraints ◽  
Localization Algorithm ◽  
Resource Constrained ◽  
Received Signal Strength Indicator ◽  
Localization Accuracy ◽  
Location Data ◽  
Localization Algorithms ◽  
Quantitative Accuracy ◽  
The Difference

In recent years, wireless sensor networks (WSNs) have experienced a significant growth as a fundamental part of the Internet of Things (IoT). WSNs nodes constitute part of the end-devices present in the IoT, and in many cases location data of these devices is expected by IoT applications. For this reason, many localization algorithms for WSNs have been developed in the last years, although in most cases the results provided are obtained from simulations that do not consider the resource constraints of the end-devices. Therefore, in this work we present an experimental evaluation of a received signal strength indicator (RSSI)-based localization algorithm implemented on IoT end-devices, comparing its results with those obtained from simulations. We have implemented the fuzzy ring-overlapping range-free (FRORF) algorithm with some modifications to make its operation feasible on resource-constrained devices. Multiple tests have been carried out to obtain the localization accuracy data in three different scenarios, showing the difference between simulation and real results. While the overall behaviour is similar in simulations and in real tests, important differences can be observed attending to quantitative accuracy results. In addition, the execution time of the algorithm running in the nodes has been evaluated. It ranges from less than 10 ms to more than 300 ms depending on the fuzzification level, which demonstrates the importance of evaluating localization algorithms in real nodes to prevent the introduction of large overheads that may not be affordable by resource-constrained nodes.

scholarly journals Improved Recursive DV-Hop Localization Algorithm with RSSI Measurement for Wireless Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4152
Author(s):  
Sana Messous ◽  
Hend Liouane ◽  
Omar Cheikhrouhou ◽  
Habib Hamam
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Position Estimation ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Localization Error ◽  
Localization Algorithm ◽  
Localization Accuracy ◽  
Localization Algorithms ◽  
Recursive Computation

As localization represents the main backbone of several wireless sensor networks applications, several localization algorithms have been proposed in the literature. There is a growing interest in the multi-hop localization algorithms as they permit the localization of sensor nodes even if they are several hops away from anchor nodes. One of the most famous localization algorithms is the Distance Vector Hop (DV-Hop). Aiming to minimize the large localization error in the original DV-Hop algorithm, we propose an improved DV-Hop algorithm in this paper. The distance between unknown nodes and anchors is estimated using the received signal strength indication (RSSI) and the polynomial approximation. Moreover, the proposed algorithm uses a recursive computation of the localization process to improve the accuracy of position estimation. Experimental results show that the proposed localization technique minimizes the localization error and improves the localization accuracy.

A Sensor Node Localization Algorithm Based on Fuzzy RSSI Distance

2014 ◽  
Vol 543-547 ◽  
pp. 989-992
Author(s):  
Xiao Qin Li ◽  
Guang Rong Chen
Keyword(s):  
Sensor Node ◽  
Large Deviation ◽  
Sensor Nodes ◽  
Localization Algorithm ◽  
Node Localization ◽  
Localization Accuracy ◽  
Localization Algorithms ◽  
Traditional Algorithm ◽  
Intensity Changes ◽  
Better Than

The node self-localization is the basis of target localization for wireless sensor network (WSN), the WSN nodes localization algorithms have two types based on distance and non distance. The node localization based on RSSI is simple and widely used in application. According to the traditional WSN nodes localization algorithm, the RSSI signal intensity changes greatly and with nonlinearity. And it is converted into distance feature with a large deviation, which leads to inaccurate positioning and localization. In order to solve this problem, a sensor node localization algorithm is proposed based on fuzzy RSSI distance. The nodes information is collected based on RSSI ranging method. And the location information is processed with fuzzy operation. The disturbance from the environmental factors for the positioning is solved. The accuracy of the node localization is improved. Simulation result shows that this algorithm can locate the sensor nodes accurately. The localization accuracy is high, and the performance of nodes localization is better than the traditional algorithm. It has good application value in the WSN nodes distribution and localization design.

scholarly journals An Adaptive Location-Based Tracking Algorithm Using Wireless Sensor Network for Smart Factory Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Po-Chih Chiu ◽  
Kuo-Wei Su ◽  
Tsung-Yin Ou ◽  
Chih-Lung Yu ◽  
Chen-Yang Cheng ◽  
...  
Keyword(s):  
Fuzzy Inference ◽  
Signal Transmission ◽  
Signal Strength ◽  
Localization Algorithm ◽  
Inference System ◽  
Accurate Localization ◽  
Localization Algorithms ◽  
New Type ◽  
The Cost ◽  
Smart Factories

In recent years, how to improve the performance of smart factories and reduce the cost of operation has been the focus of industry attention. This study proposes a new type of location-based service (LBS) to improve the accuracy of location information delivered by self-propelled robots. Traditional localization algorithms based on signal strength cannot produce accurate localization results because of the multipath effect. This study proposes a localization algorithm that combines the Kalman filter (KF) and the adaptive-network-based fuzzy inference system (ANFIS). Specifically, the KF was adopted to eliminate noise during the signal transmission process. Through the learning of the ANFIS, the environment parameter suitable for the target was generated, to overcome the deficiency of traditional localization algorithms that cannot obtain real signal strength. In this study, an experiment was conducted in a real environment to compare the proposed localization algorithm with other commonly used algorithms. The experimental results show that the proposed localization algorithm produces minimal errors and stable localization results.

scholarly journals Localization of Sensor Nodes using Flooding in Wireless Sensor Networks

2013 ◽  
Vol 9 (3) ◽  
pp. 1153-1161
Author(s):  
Basavaraj K Madagouda ◽  
Varsha M Patil ◽  
Pradnya Godse
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Real Number ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Localization Accuracy ◽  
Localization Algorithms ◽  
Anchor Nodes

The accuracy of localization is a significant criterion to evaluate the practical utility of localization algorithm in wireless sensor networks (WSN). In mostly localization algorithms, one of the main methods to improve localization accuracy is to increase the number of anchor nodes. But the number of anchor nodes is always limited because of the hardware restrict, such as cost, energy consumption and so on. In this paper, we propose a novel which uses forwarding a query message in flooding technique for localization using anchor nodes and once a node localized it acts as virtual anchor node and it helps to localize remaining sensor nodes. It is scheme to increase and upgrade the virtual anchor nodes, while the real number of physical anchors is the same as before.

scholarly journals Localizing Persons Using Body Area Sensor Network

2012 ◽  
pp. 273-289
Author(s):  
Cheng Guo ◽  
R. Venkatesha Prasad ◽  
Jing Wang ◽  
Vijay Sathyanarayana Rao ◽  
Ignas Niemegeers
Keyword(s):  
Sensor Network ◽  
Signal Strength ◽  
Spatial Diversity ◽  
Location Estimation ◽  
Indoor Environments ◽  
Localization Algorithm ◽  
Body Area ◽  
Localization Accuracy ◽  
Network Location ◽  
Localization Algorithms

Context awareness is an important aspect in many ICT applications. For example, in an intelligent home network, location of the user enables session transfer, lighting, and temperature control, et cetera. In fact, in a body area sensor network (BASN), location estimation of a user helps in realizing realtime monitoring of the person (especially those who require help) for better health supervision. In this chapter the authors first introduce many localization methods and algorithms from the literature in BASNs. They also present classification of these methods. Amongst them, location estimation using signal strength is one of the foremost. In indoor environments, the authors found that the signal strength based localization methods are usually not accurate, since signal strength fluctuates. The fluctuation in signal strength is due to deficient antenna coverage and multi-path interference. Thus, localization algorithms usually fail to achieve good accuracy. The authors propose to solve this problem by combining multiple receivers in a body area sensor network to estimate the location with a higher accuracy. This method mitigates the errors caused by antenna orientations and beam forming properties. The chapter evaluates the performance of the solution with experiments. It is tested with both range-based and range-free localization algorithm that we developed. The chapter shows that with spatial diversity, the localization accuracy is improved compared to using single receiver alone. Moreover, the authors observe that range-based algorithm has a better performance.

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