Weighted Centroid Localization Algorithm 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.

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.

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.

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.

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.

DV-Hop Localization Algorithm Based on Minimum Mean Square Error in Internet of Things

In the practical application of node positioning in the Internet of Things (IOT), the distribution of beacon nodes is generally non-uniform, so there is a certain error in positioning accuracy. In order to make the positioning more accurate, The MMSDV-Hop localization algorithm which improved DV-Hop localization algorithm is proposed in this paper. At first, this algorithm needs to determine the valid beacon nodes in a local range by selecting threshold value, and after using minimization criterion of mean-square error and correcting to obtain the final average hop distance. The dynamic weight is added with the hop number and the number of valid nodes as weights to determine the distance between the unknown nodes and the beacon nodes. A Weighted centroid localization algorithm and a weighted least square method are used to obtain an estimated position separately and the final position is determined by the arithmetic mean value of the two estimated positions. The Matlab simulation experiment shows that compared with the DV-Hop localization algorithm, the improved MMSDV-Hop localization algorithm has increased significantly in the positioning accuracy.

A Distance Boundary with Virtual Nodes for the Weighted Centroid Localization Algorithm.

In wireless sensor networks, accurate location information is important for precise tracking of targets. In order to satisfy hardware installation cost and localization accuracy requirements, a weighted centroid localization (WCL) algorithm, which is considered a promising localization algorithm, was introduced. In our previous research, we proposed a test node-based WCL algorithm using a distance boundary to improve the localization accuracy in the corner and side areas. The proposed algorithm estimates the target location by averaging the test node locations that exactly match with the number of anchor nodes in the distribution map. However, since the received signal strength has large variability in real channel conditions, the number of anchor nodes is not exactly matched and the localization accuracy may deteriorate. Thus, we propose an intersection threshold to compensate for the localization accuracy in this paper. The simulation results show that the proposed test node-based WCL algorithm provides higher-precision location information than the conventional WCL algorithm in entire areas, with a reduced number of physical anchor nodes. Moreover, we show that the localization accuracy is improved by using the intersection threshold when considering small-scale fading channel conditions.

An Event Reporting and Early-Warning Safety System Based on the Internet of Things for Underground Coal Mines: A Case Study

Fatal accidents associated with underground coal mines require the implementation of high-level gas monitoring and miner’s localization approaches to promote underground safety and health. This study introduces a real-time monitoring, event-reporting and early-warning platform, based on cluster analysis for outlier detection, spatiotemporal statistical analysis, and an RSS range-based weighted centroid localization algorithm for improving safety management and preventing accidents in underground coal mines. The proposed platform seamlessly integrates monitoring, analyzing, and localization approaches using the Internet of Things (IoT), cloud computing, a real-time operational database, application gateways, and application program interfaces. The prototype has been validated and verified at the operating underground Hassan Kishore coal mine. Sensors for air quality parameters including temperature, humidity, CH4, CO2, and CO demonstrated an excellent performance, with regression constants always greater than 0.97 for each parameter when compared to their commercial equivalent. This framework enables real-time monitoring, identification of abnormal events (>90%), and verification of a miner’s localization (with <1.8 m of error) in the harsh environment of underground mines. The main contribution of this study is the development of an open source, customizable, and cost-effective platform for effectively promoting underground coal mine safety. This system is helpful for solving the problems of accessibility, serviceability, interoperability, and flexibility associated with safety in coal mines.

A weighted centroid localization algorithm for randomly deployed wireless sensor networks

Localization is one of the important requirements in wireless sensor networks for tracking and analyzing the sensed data/events. In most of the applications of wireless sensor networks, the event information without its location information has no significance. The well known traditional Distance-Vector Hop (DV Hop) algorithm and weighted centroid DV Hop based algorithms can be easily implemented in real wireless sensor networks with low cost and no additional hardware requirement, but it has poor localization accuracy and high power consumption. In order to avoid these limitations, a weighted centroid DV-Hop algorithm is proposed in this paper. The proposed algorithm uses weights that consider the influence of different factors such as number of anchors, communication radius, and nearest anchor to determine location of unknown node. Simulation results and theoretical analysis prove that proposed algorithm outperforms the traditional DV-Hop algorithm in terms of localization error and power consumption.

Research on Underground Personnel Positioning Method Based on PSO-GSA Optimization

In the case of coal mine accidents, in order to ensure timely rescue of the suffering people in a complex environment of underground localization, focusing on Received Signal Strength Indicator (RSSI) in underground personnel positioning accuracy is low and the problem of dynamic tracing parameters changes. Therefore, using an improved gravitational search algorithm (GSA) for the weighted centroid localization that based on RSSI. Utilizing the log distance path loss model gets the distance between the beacon nodes and unknown nodes, and then through the weighted centroid localization algorithm perform the unknown node positioning. Finally, the improved GSA-PSO optimizes the preliminary location results and parameters. Proposed solutions to establish simulation model is verified in MATLAB, and use the on-chip system CC2430 chips experiment platform is established. Experimental results show the proposed method can improve both the positioning accuracy effectively and the adaptive ability of changeful environment.

Cyclic Weighted Centroid Algorithm for Transmitter Localization in the Presence of Interference

This paper addresses the problem of localizing a non-cooperative transmitter in the presence of a spectrally overlapped interferer in a cognitive receiver (CR) network. It has been observed that the performance of non-cooperative weighted centroid localization (WCL) algorithm degrades in the presence of a spectrally overlapped interferer. We propose cyclic WCL algorithm that uses cyclic autocorrelation (CAC) of received signals at CRs in the network to estimate the location coordinates of the target transmitter. Performance of the proposed algorithm is further improved by eliminating CRs in the vicinity of the interferer from the localization process. In order to identify and eliminate CRs in the vicinity of the interferer, the ratio of the variance and the mean of the square of absolute value of the CAC, referred to as feature variation coefficient, is used. Theoretical analysis of the cyclic WCL algorithm is presented in order to compute the root mean square error in the location estimates. We further study the impacts of the interferer’s power and location, CR density, and fading environment on the performance of cyclic WCL. The comparison between cyclic WCL and traditional WCL is also presented.

Discrete Weighted Centroid Localization (dWCL): Performance Analysis and Optimization

In this paper, a discrete version of weighted centroid localization algorithm (dWCL) is proposed for a wireless-sensor-network-based indoor positioning system. Under this architecture, a wireless device can estimate its own position based on the location information of connected wireless sensor nodes in the network. Unlike other weighted centroid localization algorithms, dWCL classifies the sensor nodes into a number of groups and assigns them weights taken from a set of discrete values. With the weighting function and the range information decoupled, the dWCL framework is flexible to adjust its performance in accordance with the requirements. Its capability to generalize some existing weighted centroid localization algorithms is also demonstrated. In addition, the performance of dWCL is characterized by the error distribution in terms of accuracy and precision . Further enhancement is achieved by optimizing the system parameters. Finally, the simulation results are presented to validate the proposed scheme and analytical framework.

Improved Weighted Centroid Localization with Quadratic Means for Indoor Mobile Robot Tracking

Nowadays, mobile robot tracking is the one of application in wireless sensor networks (WSNs). A one of promising localization algorithm in range-free algorithm is the weighted centroid localization (WCL) algorithm. This work proposed the improved WCL algorithm with quadratic means based on received signal strength (RSS). The ZigBee operated at 2.4 GHz frequency is used to measure in the indoor environment. The final results will be discuss about the comparison of the typically WCL algorithm and the improved WCL algorithm. From the results, the 1-meter-precision of improved WCL is 91.67% and WCL is 83.33%. Moreover, the improved WCL obtained the 16.67% no error of estimated position.

A Localization Algorithm for Wireless Sensor Network Based on the Weighted Correction in Geometric Measurement

Firstly, the characteristics of regular Zigbee localization algorithms-the received signal strength indicator algorithm (RSSI) and the weighted centroid localization algorithm are introduced. Then, the factors of the errors existing in the aforementioned algorithms are analyzed. Based on these above, the improved RSSI algorithm-correction geometric measurement based on weighted is proposed. Finally, utilizing this algorithm to design and implement the localization nodes, which have the CC2431 wireless microcontroller on them. The simulation and experimental results show that the accuracy of this localization algorithm improved about 2%, comparing with the regular algorithms.

A Weighted Centroid Localization Algorithm Based on RSSI and Virtual Static Beacon Nodes

A large number of beacon nodes will be applied to the weighted centroid localization algorithm, but too much hardware facilities will affect the efficiency of the algorithm. Using the cosine theorem, On the basis of the original beacon nodes, add some virtual static beacon nodes to participate in orientation, It can reduce the hardware cost required for positioning. In addition, the math on and analysis of the localization algorithm, by derivative principle explaining the weight coefficient and RSSI ranging influence on localization algorithm. Simulation experiments show that the weighted centroid algorithm based on VSBN needed fewer beacon nodes, high positioning accuracy.

The Research on Magnetic Target Detection Technology Based on Wireless Sensor Network

In the conditions of magnetic dipole model, this paper proposed forward a centroid localization algorithm on magnetic anomaly target based on wireless sensor network node which distribution are random and the improved the weighted centroid localization algorithm based on magnetic induction intensity. According to the fluctuation of magnetic field intensity which detected by magnetic sensors, that can detect the existence of magnetic anomaly target and its location. Established an experimental system of the wireless sensor network for magnetic anomaly detection whose core designs including the HMC1043 three-axis magnetic resistance sensor and the CC2530 Zigbee RF chip. The experimental results show that the algorithm can accurately positioning the magnetic anomaly target within the network.

Improved Weighted Centroid Localization Algorithm Based On Rssi Differential Correction

abstract Node localization is one of the key technologies of wireless sensor networks (WSN). Due to the indoor environment requires a high positioning accuracy, an improved algorithm based on RSSI is put forward. In terms of RSSI ranging, the error correction coefficient received from self-correcting locator beacon nodes, and then had been applied to distance solving from unknown nodes to the beacon nodes. In the choice of weights, the algorithm uses inverse distance and replaces the method of determining the distance and the reciprocal of the weights, and also fixes the weight factor. On the complexity of the algorithm, select one of the most recent four from unknown node to beacon nodes, reducing the amount of data calculation. Simulation results show that the proposed algorithm positioning accuracy has been greatly improved.

An Improved Weighted Centroid Localization Algorithm Based on RSSI

In wireless sensor networks, the precise positioning of unknown nodes is the precondition and security for all kinds of applications. In this paper, by analyzing the present research status of personnel positioning algorithm and on the basis of the study of traditional centroid algorithm, an improved based on RSSI (Received Signal Strength Indicator) of the weighted centroid localization algorithm is proposed. The algorithm takes the signal intensity meanwhile the distance between the unknown nodes and beacon nodes as a reference to correct RSSI value. Through the simulation of the localization algorithm it proves that the improved localization algorithm has higher precision than the traditional algorithm. Once the accidents can be timely and efficient rescue for the underground staff, better meet the actual demand of safety management.

An Improved Weighted Centroid Localization Algorithm in WSN

This paper presents a weighted centroid localization algorithm which based on the nearest beacon node. It utilizes Beacon nodes From the unknown node nearest to correct ranging error caused by RSSI distance, Increased unknown node capability which adapt neighboring nodes distributed environment. Improved the positioning accuracy,. Simulation results indicate that the Positioning accuracy of Improved Algorithm which is Proposed in this article has been significantly improved, the highest Enhanced can up to 30%.

A Novel Weighted Centroid Localization Algorithm Based on RSSI for an Outdoor Environment

Location information is one of the most important services in the field of wireless sensor networks (WSNs). Compared to other proposed non-interactive localization algorithms, the weighted centroid localization scheme only uses the received signal strength indication (RSSI), which makes it simple to implement and robust to variations in the propagation environment. In this paper, we propose a weight-compensated weighted centroid localization algorithm based on RSSI for an outdoor environment. By theoretically analyzing, the proposed algorithm has the advantage of lower complexity, little prior information and lower power consumption. The simulation results show that the proposed algorithm is better than AMWCL-RSSI (Anchor_optimized Modified Weighted Centroid Localization based on RSSI) and WCL (Weighted Centroid Localization) in terms of the localization accuracy. Our real experimental results also show that WCWCL-RSSI (Weight-Compensated Weighted Centroid Localization Based on RSSI) is better than WCL in terms of the localization accuracy. 