Weighted Centroid Localization Research Articles

Improved weighted centroid localization algorithm based on multiple magnetic beacons

To address challenges associated with the traditional drill positioning method, which demands manual walking tracking and imposes stringent environmental conditions, this paper introduces an improved weighted centroid localization (WCL) algorithm based on multiple magnetic beacons. This algorithm alleviates the environmental requirements. Initially, a magnetic beacon measurement model immune to sensor attitude is formulated, followed by the development of a positioning model based on multiple magnetic beacons. The WCL algorithm is then introduced and refined for positioning with multiple magnetic beacons. Finally, the effectiveness of the proposed approach is validated through simulation experiments, revealing an average error of 0.632 m in large-scale positioning. This demonstrates clear advantages over traditional methods, making it highly applicable.

Development of an indoor corridor localization system based on a WCL method with corridor area selection and distance compensation

SummaryIn this paper, a received signal strength indicator (RSSI)‐based weighted centroid localization (WCL) method for indoor corridor scenarios is developed and tested. The contribution of the proposed system is that, to scope the area of an unknown target position, the possible corridor area where the target node should be located is automatically selected, and only reference nodes deployed in such an area are applied for position estimation. Additionally, to improve the estimation precision, distance values converted from measured RSSIs and used for the WCL are also compensated to alleviate the RSSI variation problem caused by physical environments. Therefore, our methods can save a number of reference nodes to be used, while localization accuracy can also be achieved. Experiments in the corridor environment using a 2.4 GHz IEEE 802.15.4 wireless sensor network with four reference nodes deployed in the test field dimension of 22 m × 9.3 m have been performed. Experimental results demonstrate that the proposed method, which requires only two reference nodes, outperforms the original WCL method with four reference nodes by 53.053%, as indicated by average localization errors. The results also reveal that the proposed method provides error distances lower than the WCL method for all test target positions, while all estimated positions fall within the corridor areas by 100%.

Antenna Position Estimation Based on Weighted Centroid Localization for Short-Range MIMO Transmission

Antenna Position Estimation Based on Weighted Centroid Localization for Short-Range MIMO Transmission

Localization Process for WSNs with Various Grid-Based Topology Using Artificial Neural Network

Wireless Sensor Network (WSN) is a technology that can aid human life by providing ubiquitous communication, sensing, and computing capabilities. It allows people to be more able to interact with the environment. The environment contains many nodes to monitor and collect data. Localizing nodes distributed in different locations covering different regions is a challenge in WSN. Localization of accurate and low-cost sensors is an urgent need to deploy WSN in various applications. In this paper, we propose an artificial automatic neural network method for sensor node localization. The proposed method in WSN is implemented with network-based topology in different regions. To demonstrate the accuracy of the proposed method, we compared the estimated locations of the proposed feedforward neural network (FFNN) with the estimated locations of the deep feedforward neural network (DFF) and the weighted centroid localization (WCL) algorithm based on the strength of the received signal index. The proposed FFNN model outperformed alternative methods in terms of its lower average localization error which is 0.056m. Furthermore, it demonstrated its capability to predict sensor locations in wireless sensor networks (WSNs) across various grid-based topologies.

Accuracy of UWB Path Loss-Based Localization of Wireless Capsule Endoscopy.

Wireless capsule endoscopy (WCE) is used to diagnose lesions in the gastrointestinal (GI) tract. The physicians require to know the exact position of the lesions which can be performed by localizing the WCE in the GI tract. In this paper, we propose ultra-wideband (UWB) path loss-based WCE localization and compute the Cramer-Rao lower bound (CRLB) to evaluate the accuracy bounds of localization in the small intestine. First, we propose the estimation of smoothed path loss by minimizing the path loss deviations caused by shadow fading effects of body tissues. Then, the estimated path loss is used to estimate the degree of path loss and to compute the weight of the sensor's positions. Finally, we propose the smoothed path loss degree-based weighted centroid localization (SPLD-WCL) algorithm to estimate the location of the WCE. We simulate the proposed SPLD-WCL algorithm and verify the accuracy by comparing it to the computed CRLB. The proposed SPLD-WCL localization algorithm shows a significantly high accuracy of localization with a 6.83 mm root mean square error (RMSE) without any advanced knowledge of unknown parameters and bounds.

Weighted Centroid Localization Algorithm Based on MEA-BP Neural Network and DBSCAN Clustering

In order to overcome RSSI ranging error and improve the accuracy of positioning results, a weighted localization algorithm based on MEA-BP Neural Network and DBSCAN clustering is proposed in this paper. This algorithm uses MEA-BP Neural Network (MEA-BP NN) model to optimize ranging information firstly, then it uses trilateral measurement method to get multiple initial localization results about unknown node and form a set. After clustering the results by DBSCAN and eliminating noise points, the estimated coordinate of unknown node in each cluster is obtained by using the weighted centroid localization algorithm based on collinearity. Next the number of core points in each cluster is regarded as weight value, the weighted centroid localization algorithm is used again, thus the final coordinates of unknown node can be got. Simulation results show that the localization accuracy of wireless sensor network can be improved significantly by using this algorithm in two-dimensional scene.

Using a lightweight security mechanism to detect and localize jamming attack in wireless sensor networks

Recent years have seen a rise in the popularity of the Internet of Things (IoT) and its applications, due to its adaptability, usability, varied application potential, and ease of deployment. On the other hand, security threats get more attention. Where wireless sensor networks (WSN), which is the core of IoT, are vulnerable to the jammer's attack due to the fact that they are shared and have open access to a wireless channel. Moreover, the difficult conditions in which nodes are distributed, such as underwater, fires, volcanoes, animal habitats, or battlefields, node localization is a significant challenge for the majority of WSN applications. Additionally, the lack of accuracy in traditional localization algorithms has prompted researchers to propose optimization techniques capable of improving localization accuracies, such as genetic algorithms, gravitational search algorithms, neural networks, and deep learning; however, these techniques require additional hardware to perform their complex calculations. On the other side, uncomplicated algorithms like Virtual Force Iteration Localization (VFIL), Weighted Centroid Localization (WCL), and Centroid Localization (CL) are less accurate. To bridge that gap, this work proposes a lightweight algorithm for accurately locating the jammer's coordinates. Our algorithm consists of three phases firstly, a simple way to detect a jamming attack. Second novel designed technique to draw the jammed region using the mapping protocol. Moreover, a range-free technique is proposed to locate the jammer based on geometric calculations. Finally, A set of simulation experiments was undertaken to assess the suggested algorithm's performance. Experiments reveal that the suggested method is capable of precisely locating the jammer. Additionally, the presented method demonstrates superior efficiency in comparison to existing methods.

A Wearable Capsule Endoscope Electromagnetic Localization System Based on a Novel WCL Algorithm.

The wearable localization system for wireless capsule endoscopy (WCE) is a potential technology to realize rapid diagnosis and treatment of the gastrointestinal (GI). However, the electromagnetic localization accuracy of WCE still needs to be improved. In this paper, based on RSSI electromagnetic fading model, the accurate fitting parameter values are obtained by Kalman filter and the least square method. A novel weighted centroid localization (WCL) algorithm based on exponential weights is proposed, which can achieve high-accuracy localization by using only sparse reception matrix. The simulation results show that when the standard deviation of the localization data is 7.85, the localization root mean square error (RMSE) is 25.4 mm; when the standard deviation of the localization data is 5.475, the localization RMSE is 2.5 mm. These two localization RMSEs are 38% and 79% less than those of the conventional centroid localization algorithm, respectively. An experimental platform of wearable wireless communication and localization system using 24 array receiving antennas is developed in human phantom environment. The experimental results show that the wearable WCE electromagnetic localization system based on the proposed algorithm achieves a localization RMSE of 36.3 mm, which is 17% lower than that of the conventional centroid localization algorithm and meets the needs of clinical diagnosis.

Experimental Evaluation of Indoor Localization Methods for Industrial IoT Environment

Normal 0 false false false EN-US X-NONE HI With the evolution of new technology, GPS brings a lot of revolution in the Localization system but it is not an effective solution in Indoor Environment. This is because of the fact that the signals coming from the satellite are attenuated, absorbed, scattered by the walls, roofs, and other objects. Due to this, lots of errors may arise in the system. To overcome this problem sensor node based localization system is used for mobile IoT domain. Recently, in IoT based system, various sensor nodes have been used in different kinds of localization based applications such as Location-Based Services (LBS) and Proximity-Based Services (PBS). However, such sensor nodes may not be stable in every environment to provide an accurate positioning. Although there are different localization techniques are available to find out the location of any object, but there is a common challenge of finding best localization technique suitable for most of the environment. This work investigates different existing indoor localization methods for its practical suitability in Lab and actual Industrial environment for deployment on IoT nodes. A mobile application has also been developed to implement four state-of-the-art localization techniques for getting the position and calculating its accuracy in different environments. During the experiment, BLE Beacons are used as sensor nodes due to their ease of deployment, lower complexity, lower cost, and higher power consumption. The error in the accuracy of estimated position got calculated in terms of Average Error. According to the experimental result in real environments it has been revealed that the Weighted Centroid Localization technique provides better accuracy in industrial as well as laboratory environment. Normal 0 false false false EN-US X-NONE HI /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; text-align:justify; mso-pagination:widow-orphan; font-size:11.0pt; mso-bidi-font-size:10.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Mangal; mso-bidi-theme-font:minor-bidi;} Normal 0 false false false EN-US X-NONE HI <w:LsdException Locked="fals

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

SummaryIn 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.

Obstacle Handling Mechanism for Mobile Anchor Assisted Localization in Wireless Sensor Networks

Localization is necessary for applications such as warning systems, where the sensed data along with the location of occurrence is essential for decision making in Wireless Sensor Networks (WSNs). Equipping each sensor node with a global positioning system (GPS) unit is infeasible, as it increases both the power consumption and cost of the deployed network. A more practical approach is to employ a single or a few GPS-equipped mobile anchor nodes (MANs). These MANs localize the unknown sensor nodes by broadcasting their current locations periodically while moving in the area of interest. In a realistic environment, obstacles may be present in the deployment area that obstruct the radio connectivity between unknown sensors and the MANs. In this paper, we propose a mechanism to handle irregular obstacles in the path of various existing static trajectories. Also, we have considered an accurate and reliable channel model to provide a realistic evaluation of the proposed method. The results are compared on the basis of four different localization techniques, namely, accuracy priority trilateration (APT), time priority trilateration (TPT), weighted centroid localization (WCL) and weight-compensated weighted centroid localization (WCWCL). Experimental and simulation results show that the proposed method effectively handles regular and irregular obstacles in the deployed environment.

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

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.

Path Planning for Mobile-Anchor Based Wireless Sensor Networks Localization: Obstacle-Presence Schemes.

In many Wireless Sensor Network (WSN) applications, the location of the nodes in the network is required. A logical method to find Unknown Nodes (UNNs) in the network is to use one or several mobile anchors (MAs) equipped with GPS units moving between UNNs and periodically broadcast their current location. The main challenge at this stage is to design an optimum path to estimate the locations of UNNs as accurately as possible, reach all nodes in the network, and complete the localization process as quickly as possible. This article proposes a new path planning approach for MA-based localization called Nested Hexagon Curves (NHexCurves). The proposed model’s performance is compared with the performance of five existing static path planning models using Weighted Centroid Localization (WCL) and Accuracy Priority Trilateration (APT) localization techniques in the obstacle-presence scenario. With the obstacle-handling trajectories used for the models, the negative impact of the obstacle on the localization is reduced. The proposed model provides full coverage and high localization accuracy in the obstacle-presence scenario. The simulation results show the advantages of the proposed path planning model with the H-curve model over existing schemes.

Enhancing localization accuracy of collaborative cognitive radio users by internal noise mitigation

Location information of mobile primary users is one of the essential requirements for an underlay cognitive radio user to utilize the licensed spectrum efficiently. The performance of various location-based applications such as global navigation satellite system, device to device communication in dense urban 5G network also depends on the localization accuracy. In this paper, a collaborative localization scheme based on received signal strength has been proposed. The weighted centroid localization algorithm has been applied in the proposed network scenario to compute location coordinates of the mobile primary user. Since the channel noise effects are random and unavoidable, this paper has focused on the mitigation of the internal noise by designing a suitable reconfigurable FIR filter after the demodulator stage of a cognitive radio receiver circuit to improve precision of signal measurement during primary user localization. The localization error rate has come down to (1.3–1.62) % after internal noise mitigation. The enhancement in the localization accuracy improves the overall spectrum utilization efficiency and reduces the miss detection and false detection probabilities in the proposed underlay network.

A Comparative Study of RSSI-Based Localization Methods: RSSI Variation Caused by Human Presence and Movement

In a received signal strength indicator (RSSI) based localization system, the presence or movement of humans is one of the major effects causing RSSI variation. Using RSSI data during such a situation to estimate the target position can give large errors. Regarding this problem, in this paper, a comparison of several RSSI-based localization methods with and without human presence and movement were investigated experimentally. The major contribution of this work is that the well-known and widely used RSSI-based localization methods presented in the literature, including the min–max, the trilateration, the weighted centroid localization (WCL), and the relative span exponential weighted localization (REWL) methods, were tested. Thus, how human presence or absence influences the accuracy of these methods, and which methods show the best estimates while tolerating human movement effects can be investigated. The experiments were carried out in a laboratory and in a parking building. The results demonstrate that, without human movement effects, all methods perform very similarly. In contrast, human movements significantly increased estimation errors.Here, the maximum distance errors of the min–max, the trilateration, the WCL, and the REWL are 1.34 m, 4.09 m, 1.25 m, and 1.24 m, respectively. Obviously, the min–max, the WCL (with an optimal parameter), and the REWL (with the optimal parameter) can well tolerate the RSSI variations caused by human movements and provide significantly better accuracy than the trilateration method. Based on these findings, all the mentioned localization methods should be further improved to deal with the human movement problem.

Enhanced the Weighted Centroid Localization Algorithm Based on Received Strength Signal in Indoor Wireless Sensor Network

A challenging problem that arises in the Wireless Sensor Network (WSN) is localization. It is essential for applications that need information about target positions, are inside an indoor environment. The Localization scheme presented in this experiment consists of four anchor nodes that change their position coordinates and one target node that is used to control the distance. The Localization algorithm designed in this paper makes use of the combination of two algorithms; the Received Strength Signal Indication (RSSI) and Weight Centroid Localization Algorithm (WCLA), called the RSSI-WCLA algorithm. The laboratory results show that the fusion between the RSSI-WCLA algorithm is outstanding than RSSI and WCLA algorithms itself in terms of localization accuracy. However, our proposed algorithm shows that the maximum error distance is less than 0.096m.

Closed-Form Approximation of Weighted Centroid Localization Performance

Weighted centroid localization (WCL) based on received signal strength measurements is an attractive low-complexity solution that enables sensor networks to have a geolocation awareness of the radio environment. In this letter, we propose an analytical framework to calculate the performance of WCL, combining the Taylor series expansion of the logarithm of the estimation error and Jensen's inequality. In particular, we derive easy-to-handle expressions of the root mean square error and bias of both two- and one-dimensional localization errors without involving any integral. The proposed approach can tackle scenarios with independent and identically distributed shadowing as well as correlated shadowing. Numerical results confirm that the proposed framework is capable of predicting the performance of WCL remarkably well.

Optimization and Integration of RFID Navigation System by Using Different Location Algorithms

The detection of mobile objects locations is vital for many fields of technology such as banking systems, social media, and driving assistance technologies (i.e. Google Maps). This approach is firstly proposed for supporting security and arm forces. Then, location-based services have gained the attention of data scientist so, it has become essential for providing access for particular files of service in accordance with geographical area/countries restrictions. So-to-say, location-based services have become paramount in modern days technologies. These systems are more likely required large knowledge of signal processing, radio frequency and electromagnetic theories. Several estimation algorithms are deployed in this paper for evaluating the location of mobile object over small scale indoor environment. In order to tackle connectivity problems such as No Line of Sight (LoS), energy consumption and time delay; Radio-frequency Identification (RFID) system is used to satisfy the requirements of real-life systems. In this paper, three location optimization algorithms namely Triangulation, Weighted Centroid Localization (WCL) and Bayes Decision Rule (with Kalman filter integration) have been used to evaluate the location of that mobile object. Every algorithm is examined with efforts in order the accuracy of each one to detect the location. However, the performance of all the algorithms is compared; the outcomes of Kalman filter integration with Bayes Decision rule algorithm have been more accurate.

Accurate Analysis of Weighted Centroid Localization

Source localization of primary users (PUs) is a spectrum awareness feature that can be very useful in enhancing the functionality of cognitive radios (CRs). When the cooperating CRs have limited information about the PU, weighted centroid localization (WCL) based on received signal strength measurements represents an attractive low-complexity solution. This paper proposes a new analytical framework to accurately calculate the performance of WCL based on the statistical distribution of the ratio of two quadratic forms in normal variables. In particular, we derive an analytical expression for the root mean square error and an exact expression for the cumulative distribution function of the two-dimensional location estimate. The proposed framework accounts for the presence of independent and identically distributed shadowing as well as correlated shadowing with distance-dependent intensity. The methodology is general enough to include the analysis of the one-dimensional error, which leads also to the evaluation of the bias of the position estimate. Numerical results confirm that the analytical framework is able to predict the performance of WCL capturing all the essential aspects of propagation as well as CR network spatial topology.

Simulation Evaluation of Filtering Method for Improving Pedestrian Positioning Accuracy Using Signal Strengths

In recent years, we have been able to use various services using the position information of smartphones and tablets. In addition, research on intelligent transport systems (ITS) has been actively conducted. To consider reducing traffic accidents by exchanging position information between pedestrians and vehicles by vehicle-to-pedestrian communication, we require accurate position information for pedestrians and vehicles. The GPS (global positioning system) is the most widely used method for acquiring position information. However, in urban areas, the GPS signal is affected by the surrounding buildings, which increases the positioning error. In this study, a method to improve the positioning accuracy of pedestrians using the signal strengths from vehicles and beacons was proposed. First, a Kalman filter was applied to the signal strength. Then, the path loss index was dynamically calculated using vehicle-to-vehicle communication. Finally, the position of a pedestrian was obtained using weighted centroid localization (WCL) after filtering the nodes. The positioning accuracy was evaluated using a simulator and demonstrated the superiority of the proposed method.