New Personnel Positioning Algorithm in Mine Based on PSO-GSA

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.

Localization is one of the supporting technologies in wireless sensor networks, and accuracy is a significant criterion to evaluate the practical utility of localization algorithm. The effect of environment parameters like path loss exponent to measurement accuracy in received signal strength indicator (RSSI) is considered in this paper. According to the relationship between distance and received signal power among beacon nodes, least squares method (LSM) is introduced to acquire current environment parameters. Then the estimated distance is converted to an appropriate weight. Finally, the location information of nodes can be calculated out by weighted Centroid localization algorithm (WCLA). Simulation results in MATLAB show that our algorithm (LS-WCLA) has good self-adaptability and robustness.

Increasing the location accuracy of the Indoor Positioning System (IPS) is an important research area in localization.Utilizing mobile beacons in IPS environment has made localization more accurate and cost-effective.This paper proposes an Filtered RSSI and Beacon Weight Approach (FRBW) based on improved Received Signal Strength Indicator (RSSI) values using a Kalman filter.This algorithm takes both the distance and improved RSSI measurements between beacon nodes into consideration.Kalman filter applied on the RSSI measurements that eliminate noise of the signal and then applied on FRBW positioning algorithm.The proposed algorithm was applied using eight beacons.The results show that this FRBW approach has better positioning accuracy and minimum location error, and can be applied in IoT applications in smart city.

To reduce the influence of received signal strength indication (RSSI) on ranging error, as well as the influence of least squares support vector regression (LSSVR) on localization algorithm, a node three-dimensional localization algorithm based on RSSI and LSSVR parameter optimization is proposed. First, the RSSI average values at 1–25m in four different directions are collected by experiments and the weighted recursive mean optimization method is used to optimize the values of RF factor and propagation factor. Then, the parameters of RBF kernel function and grid width of LSSVR are optimized. Finally, the RSSI range values are used as the input of LSSVR localization model, and the LSSVR regression model is used to solve, in this way, the location estimation of unknown WSN nodes is realized. The simulation results show that the average localization error of the algorithm without parameter optimization is 21.82%, and the localization error of the algorithm after parameter optimization is 11.70%, which has higher localization accuracy. At the same time, a node three dimensional localization experiment platform was built to verify the proposed algorithm in the actual environment, and the test results verified the effectiveness and superiority of the proposed algorithm.

Wireless sensor network localization is an important area that attracted significant research interest. Recently, received signal strength indicator (RSSI)-based range measurement technology is widely used in sensor network for its low-cost. But the radio signal transmission is influenced by the factors of channel attenuation and shielding obstacles, etc. Therefore, the accuracy of RSSI-based localization algorithm is not high. In this paper, a new localization algorithm which is based on the probability distribution model is proposed. Firstly, the anchor node which is nearest to the unknown node is made as the reference node in order to correct the RSSI-based ranging distance. Secondly, the common communication area where the unknown nodes may exist is divided into a number of spatial grids. And then, the confidence degree of each grid is calculated out according to the probability model of radio signal space transmission. Finally, based on the centroids of spatial grids, the coordinates of the unknown nodes are calculated out. Through this algorithm, the localization accuracy is improved greatly compared with conventional RSSI-based localization algorithms, which has been demonstrated by the simulating results.

Received Signal Strength Indicator (RSSI) is used in indoor positioning for measuring object distance to the base station. However, acquiring accurate RSSI values is challenging because wireless interference factors, such as multipath decline interference, make RSSI values of the same object fluctuate over time. Therefore, instead of a single RSSI, RSSI acquisition will collect a set of RSSI values from which the most moderate RSSI is derived. For this purpose, we propose an Enhanced Gaussian Mixture Model (EGMM) to derive a more precise RSSI for improving indoor positioning accuracy. EGMM enhances Gaussian Mixture Model (GMM) by applying Akaike information criterion (AIC) to determine the best K value for GMM to divide RSSI values into K sets representing signals from different paths. Then, EGMM identifies the most appropriate set of RSSI values to derive a more precise RSSI and thus improves the accuracy of indoor positioning. Our EGMM solution performs well in an open indoor space. The experiment is conducted with iBeacon devices, and the average error distance of EGMM is about 64% of those generated by existing Gaussian filtering. The average positioning error of EGMM is about 0.48 meter, which is adequate to indoor positioning accuracy.

As the geographical location of sensors is of great importance in the applications of a wireless sensor network, localization attracts significant research interest. Among the localization methods, received signal strength indicator (RSSI) localization algorithm is widely studied due to its low-cost. However, the radio signal transmission is affected by the factors of channel attenuation and noise, etc. Therefore, the actual location is impossible to estimate. To alleviate the effect of the RSSI measure errors, we proposed a novel error modified method for RSSI location algorithm based on neural network, short for RSSI-NN. First, we use the fitting parameters of the monitoring area to determine the parameters of RSSI model. Second, the distance between anchor node i and the unknown node Liis calculated. Then, the error modified algorithm based on neural network is proposed. Finally, we can obtain the modified value of the output layer data. Simulations demonstrate that RSSI-NN achieves lower localization error compared with conventional RSSI-based localization algorithm.

In underwater sensor localization scenes, beacon nodes (BNs) with known coordinates are deployed to localize unknown nodes (UNs). However, the BNs are likely to drift due to undercurrents, geological changes, and marine biological activities, which will lead to localization failure. This paper proposes underwater localization evaluation scheme (ULES) to improve localization accuracy under drift scenes. Reliability of BN is evaluated based on underwater drift, underwater environment, and underwater acoustic channel using an analytic hierarchy process and a grey correlation method. For each UN, four BNs in its communication range with high reliability are selected for underwater localization. This process can iterate for several times, and newly localized nodes act as temporary BNs in the next iteration. Simulations are performed for different localization schemes under different BN deployment schemes and scenarios. Results show that about a quarter more UNs obtain accurate localization comparing with the conventional schemes under beacon node drift scenes, which fully shows that ULES reduces average localization error under BN drift scenes.

As WiFi technology becomes more widespread and integrated, precise location tracking within complex indoor environments is gaining significance in contemporary public spaces.Nevertheless, challenges persist in indoor scenarios, including issues such as noise and multi-path effects stemming from the degradation of the Received Signal Strength Indicator (RSSI). Additionally, obstacles causing signal shading contribute to a decrease in the accuracy of indoor location tracking. Within this paper, we introduce an innovative indoor localization algorithm termed Vision Transformer Indoor Localization (VTIL). This algorithmleverages RSSI and Vision-Transformer (ViT) technologies to enhance indoor positioning accuracy. Initially, the RSSI fingerprints undergo normalization by scaling them to the maximum and minimum values. To mitigate the impact of noise and irrelevant features, the Principal Component Analysis (PCA) algorithmis then employed for effective feature extraction.Secondly, the RSSI fingerprint library is converted into an RSSI gray image library. Then the RSSI gray image is divided into several small blocks, and the position-coding input is performed in the form of a sequence. The ViT model divides the weight ratio of each block in the RSSI image to alleviate the impact of multi-path effects. According to the experimental results using public datasets, our approach achieves a noteworthy 37.26% reduction in the average distance estimation error when compared to existing indoor fingerprint localization algorithms.

Aiming at the problem in the DV-hop localization algorithm that there is a large error in the process of unknown nodes to obtain the average hop distance from the beacon node. In this paper, we analyze the influence factors of the node communication radius on the average hop distance, proposed a DV-hop algorithm Based on Hierarchical Processing of average hop distance. By studying the relationship between the average hop distance and the communication radius, the unknown nodes that are one hop away from the beacon node are divided by layers so that the unknown nodes of different levels can obtain the average hop distance with relatively small error. In the stage of estimating the position of the unknown node, the algorithm calculates the unknown node coordinates by using the maximum likelihood estimation method based on the lognormal distribution through the statistical analysis of node localization error. Simulation results show that the proposed algorithm can significantly improve the positioning accuracy in the case of large communication radius.

In this paper, an improved weighted centroid location algorithm for quadrilateral ranging based on received signal strength indication (RSSI) is used to reduce the measurement error of RSSI effectively. Furthermore, this paper puts forward the localization algorithm of indoor optimization RSSI based on Bayesian probability model and combines the least squares curve fitting to estimate the location of unknown nodes, which further improves the accuracy of the algorithm. In addition, the simulation results show that the improved centroid localization algorithm not only improves the accuracy greatly, and the robustness is also relatively high.

In the wireless sensor network (WSN) localization methods based on Received Signal Strength Indicator (RSSI), it is usually required to determine the parameters of the radio signal propagation model before estimating the distance between the anchor node and an unknown node with reference to their communication RSSI value. And finally we use a localization algorithm to estimate the location of the unknown node. However, this localization method, though high in localization accuracy, has weaknesses such as complex working procedure and poor system versatility. Concerning these defects, a self-adaptive WSN localization method based on least square is proposed, which uses the least square criterion to estimate the parameters of radio signal propagation model, which positively reduces the computation amount in the estimation process. The experimental results show that the proposed self-adaptive localization method outputs a high processing efficiency while satisfying the high localization accuracy requirement. Conclusively, the proposed method is of definite practical value.

Received Signal Strength Indication (RSSI) is a notoriously noisy metric, yet attractive for localization purposes. We study the effect of reducing the RSSI value to a single bit, essentially turning the RSSI measurement into a proximity indicator. We consider systems where a device, that needs to be localized, transmits and the transmission is received by a number of receivers placed at fixed and known locations. We consider two modes of 1-bit quantization: one (global) where the quantization is defined uniformly across all signal receivers, and one (local) where the RSSI is quantized separately for each receiver. We compare the effects of 1-bit quantization across three profiling k-NN-based localization algorithms, comparing them with each other in addition to comparing against results when no quantization is performed. We furthermore consider the case of global 2-bit quantization. Our study is based on profiling data collected before and after modifications were performed to the profiled environment, allowing us to also study the impact of those changes. Our results show comparable performance between no quantization and local 1-bit quantization, and similar results for global 2-bit quantization.

Aiming at the large positioning errors of traditional coal mine underground locomotive, an improved received signal strength indication (RSSI) positioning algorithm for coal mine underground locomotive was proposed. The RSSI value fluctuates heavily due to the poor environment of coal mine underground. The nodes with larger RSSI value corrected by Gaussian-weighted model were selected as beacon nodes. In order to reduce the positioning error further, the estimated positions of the locomotives were corrected by the weighted distance correction method. The difference between actual position and estimated position of beacon node was regarded as the positioning error and was given a corresponding weight. The results of simulation show that the positioning accuracy of Gaussian-weighted model is better than statistical average model and Gaussian model and it has a high positioning accuracy after correcting positioning error correction. In the 10 m of communication range, positioning error can be maintained at 0.5 m.

The coordinates of nodes are very important in the application of wireless sensor networks (WSN). The range-free localization algorithm is the best method to obtain the coordinates of sensor nodes at present. Range-free localization algorithm can be divided into two stages: distance estimation and coordinate calculation. For reduce the error in the distance estimation stage, a node localization algorithm for WSN based on virtual partition and distance correction (VP-DC) is proposed in this paper. In the distance estimation stage, firstly, the distance of each hop on the shortest communication path between the unknown node and the beacon node is calculated with the employment of virtual partition algorithm; then, the length of the shortest communication path is obtained by summing the distance of each hop; finally, the unknown distance between nodes is obtained according to the optimal path search algorithm and the distance correction formula. This paper innovative proposes the virtual partition algorithm and the optimal path search algorithm, which effectively avoids the distance estimation error caused by hop number and hop distance, and improves the localization accuracy of unknown nodes.