Search

For the bias of node received signal strength indication (RSSI)based on ZigBee network in complex environment, a wireless network ranging algorithm based on hybrid filtering is proposed. In this method, on the basis of the Kalman filtering, the hybrid filter based on the median adaptive weighted Gaussian filter is incorporated, and the Kalman filtering algorithm is used to remove the RSSI value with large volatility, and then the median value immunity principle and adaptive function are used to reduce the fluctuation of RSSI data. The simulation results show that the wireless network ranging algorithm based on hybrid filtering can greatly reduce the fluctuation caused by outliers and effectively improve the sampling accuracy of RSSI. [ABSTRACT FROM AUTHOR]

Wireless sensing is a crucial technology for building smart cities, playing a vital role in applications such as human monitoring, route planning, and traffic management. Analyzing the data provided by wireless sensing enables the formulation of more scientific decisions. The wireless sensing of dynamic events is a significant branch of wireless sensing. Sensing the specific times and durations of dynamic events is a challenging problem due to the dynamic event information is concealed within static environments. To effectively sense the relevant information of event occurrence, we propose a wireless sensing method for dynamic events based on RSSI, named RSSI-WSDE. RSSI-WSDE utilizes variable-length sliding windows and statistical methods to process original RSSI time series, amplifying the differences between dynamic events and static environments. Subsequently, z-score normalization is employed to enhance the comparability of the sensing effects for different dynamic events. Furthermore, by setting the adaptive threshold, the occurrence of dynamic event is sensed and the relevant information is marked on the original RSSI time series. In this study, the sensing performance of RSSI-WSDE was tested in indoor corridors and outdoor urban road environments. The wireless sensing of dynamic events, including walking, running, cycling, and driving, was conducted. The experimental results demonstrate that RSSI-WSDE can accurately sense the occurrence of dynamic events, marking the specific time and duration with millisecond-level precision. Moreover, RSSI-WSDE exhibits robust performance in wireless sensing of dynamic events in both indoor and outdoor environments. [ABSTRACT FROM AUTHOR]

This research paper conducts a thorough comparison of four prominent transmission technologies suitable for mobile robots operating in challenging environments. Emphasizing key factors such as signal strength, noise resistance, and data transfer efficiency, the study aims to identify the optimal communication solution in hostile conditions. The exploration delves into the intricacies of received signal strength indication (RSSI) and signal-tonoise ratio (SNR), revealing distinctive traits and trade-offs among the technologies. Navigating through the complexities of frequency bands, modulation types, and communication topologies, the paper examines the impact of obstacles, energy consumption dynamics, and potential real-world applications. Beyond contributing to the fields of robotics and communication, the study offers practical insights for stakeholders seeking resilient and efficient transmission methods for mobile robotic applications. Advocating for long range (LoRa) as the preferred transmission technology in hostile environments, the paper highlights its unmatched immunity to noise, stability, and minimal energy consumption. These findings provide valuable guidance for technology choices in collaborative mobile robot operations under challenging conditions. This research sets the stage for future developments in robotic communication, underscoring the crucial role of selecting the right transmission means for mission-critical applications in hostile environments. [ABSTRACT FROM AUTHOR]

Indoor localization of a mobile target represents a prominent application within wireless sensor network (WSN), showcasing significant values and scientific interest. Interference, obstacles, and energy consumption are critical challenges for indoor applications and battery replacements. A proposed tracking system deals with several factors such as latency, energy consumption, and accuracy presenting an innovative solution for the mobile localization application. In this paper, a novel algorithm introduces a self-localization algorithm for mobile targets using the wake-up media access control (MAC) protocol. The developed tracking application is based on the trilateration technique with received signal strength indication (RSSI) measurements. Simulations are implemented in the objective modular network testbed in C++ (OMNeT++) discrete event simulator using the C++ programming language, and the RSSI values introduced are based on real indoor measurements. In addition, a determination approach for finding the optimal parameters of RSSI is assigned to implement for the simulation parameters. Simulation results show a significant reduction in power consumption and exceptional accuracy, with an average error of 1.91 m in 90% of cases. This method allows the optimization of overall energy consumption, which consumes only 2.69% during the localization of 100 different positions. [ABSTRACT FROM AUTHOR]

An indoor localization system based on the RSSI-APIT algorithm is designed in this study. Integrated RSSI (received signal strength indication) and non-ranging APIT (approximate perfect point-in-triangulation test) localization methods are fused with machine learning in order to improve the accuracy of the indoor localization system. The system focuses on the improvement of preprocessing and localization algorithms. The primary objective of the system is to enhance the preprocessing of the acquired RSSI data and optimize the localization algorithm in order to enhance the precision of the coordinates in the indoor localization system. In order to mitigate the issue of significant fluctuations in RSSI, a technique including the integration of Gaussian filtering and an artificial neural network (ANN) is employed. This approach aims to preprocess the acquired RSSI data, thus reducing the impact of multipath effects. In order to address the issue of low localization accuracy encountered by the conventional APIT localization algorithm during wide-area localization, the RSSI ranging function is incorporated into the APIT localization algorithm. This addition serves to further narrow down the localization area. Consequently, the resulting localization algorithm is referred to as the RSSI-APIT positioning algorithm. Experimental results have demonstrated the successful reduction of inherent localization errors within the system by employing the RSSI-APIT positioning algorithm. The present study aims to investigate the impact of the localization scene and the number of anchors on the RSSI-APIT localization algorithm, with the objective of enhancing the performance of the indoor localization system. The conducted experiments demonstrated that the enhanced system exhibits several advantages. Firstly, it successfully decreased the frequency of anchor calls, resulting in a reduction in the overall operating cost of the system. Additionally, it effectively enhanced the accuracy and stability of the system's localization capabilities. In a complex environment of 100 m2 in size, compared with the traditional trilateral localization method and the APIT localization algorithm, the RSSI-APIT localization algorithm reduced the localization error by about 2.9 m and 1.8 m, respectively, and the overall error was controlled within 1.55 m. [ABSTRACT FROM AUTHOR]

As indoor positioning has been widely utilized for many applications of the Internet of Things, the Received Signal Strength Indication (RSSI) fingerprint has become a common approach to distance estimation because of its simple and economical design. The combination of a Gaussian filter and a Kalman filter is a common way of establishing an RSSI fingerprint. However, the distributions of RSSI values can be arbitrary distributions instead of Gaussian distributions. Thus, we propose a Fouriertransform Fuzzyc-means Kalmanfilter (FFK) based RSSI filtering mechanism to establish a stable RSSI fingerprint value for distance estimation in indoor positioning. FFK is the first RSSI filtering mechanism adopting the Fourier transform to abstract stable RSSI values from the low-frequency domain. Fuzzy C-Means (FCM) can identify the major Line of Sight (LOS) cluster by its fuzzy membership design in the arbitrary RSSI distributions, and thus FCM becomes a better choice than the Gaussian filter for capturing LOS RSSI values. The Kalman filter summarizes the fluctuating LOS RSSI values as the stable latest RSSI value for the distance estimation. Experiment results from a realistic environment show that FFK achieves better distance estimation accuracy than the Gaussian filter, the Kalman filter, and their combination, which are used by the related works. [ABSTRACT FROM AUTHOR]

As the location-based service (LBS) plays an increasingly important role in real life, the topic of positioning attracts more and more attention. Under different environments and principles, researchers have proposed a series of positioning schemes and implemented many positioning systems. With widely deployed networks and massive devices, wireless fidelity (Wi-Fi) technology is promising in the field of indoor positioning. In this paper, we survey the authoritative or latest positioning schemes for Wi-Fi-assisted indoor positioning. To this end, we describe the problem and corresponding applications, as well as an overview of the alternative methods. Then, we classify and analyze Wi-Fi-assisted indoor positioning schemes in detail, as well as review related work. Furthermore, we point out open challenges and forecast promising directions for future work. [ABSTRACT FROM AUTHOR]

The localization accuracy is susceptible to the received signal strength indication (RSSI) fluctuations for RSSI-based wireless localization methods. Moreover, the maximum likelihood estimation (MLE) of the target location is nonconvex, and locating target presents a significant computational complexity. In this paper, an RSSI-based access point cluster localization (APCL) method is proposed for locating a moving target. Multiple location-constrained access points (APs) are used in the APCL method to form an AP cluster as an anchor node (AN) in the wireless sensor network (WSN), and the RSSI of the target is estimated with several RSSI samples obtained by the AN. With the estimated RSSI for each AN, the solution for the target location can be obtained quickly and accurately due to the fact that the MLE localization problem is transformed into an eigenvalue problem by constructing an eigenvalue equation. Simulation and experimental results show that the APCL method can meet the requirement of high-precision real-time localization of moving targets in WSN with higher localization accuracy and lower computational effort compared to the existing classical RSSI-based localization methods. [ABSTRACT FROM AUTHOR]

In the context of the rapid development of the Internet and the Internet of Things technology, services based on location information have received more and more attention, and people gradually have higher expectations for the quality and experience of positioning services. At present, outdoor positioning technology is becoming mature, but different from empty outdoor areas, there is a highly complex indoor environment with many interference factors, so it is difficult to receive effective satellite signals. To realize the smooth transition of whole-field positioning, it is necessary to study an economical and efficient indoor positioning technology. The existing indoor positioning technologies have some problems, so this paper comprehensively uses the resource-rich Wi-Fi signal, Frequency Modulation (FM) signal and Digital Terrestrial Multimedia Broadcast (DTMB) signal as the positioning data sources, and proposes a fingerprint fusion positioning method based on the wireless signal distribution characteristic. Experiments show that the proposed method improves localization accuracy by 30% compared to localization with Wi-Fi alone. [ABSTRACT FROM AUTHOR]

In the transportation field, a promising architecture is vehicular Ad hoc networks (VANETs) for vehicle-to-vehicle communications. In VANETs, various challenges create by the changes in frequent topology for data delivery due to the variation of vehicle velocity with time. It's essential that the efficient routing protocol is designed for achieving the communication with reliability and stability. In a mobile environment, a promising approach is the clustering for efficient routing. For Mobile Ad-hoc Networks (MANETs), most of the clustering algorithms have been designed primarily and not suitable to build a cluster structure in VANETs as it doesn't design for link quality and vehicle behaviour. Both link quality for dissemination of data and vehicle behaviour for clustering are considered in the proposed routing protocol of an efficient cluster based SMART forwarding scheme. The proposed work selects Cluster Heads (CHs) based on the important parameters like link consistency rate (LCR) and Signal to Interference plus Noise Ratio (SINR). The LCR can be described as the amount of time the link is being active. The data dissemination is carried out by Received Signal Strength Indication (RSSI) based probability routing protocol, which measure the link quality based on Link Duration (LD), RSSI of the link. By analysing simulation results, the proposed scheme of SFTD dissemination shows high performance in terms of high Packet delivery Ratio (PDR), and low latency with efficient energy consumption rate.

Methods to prevent collisions between people to avoid traffic accidents are receiving significant attention. To measure the position in the non-line-of-sight (NLOS) area, which cannot be directly visually recognized, position-measuring methods use wireless-communication-type GPS and propagation characteristics of radio signals, such as received signal strength indication (RSSI). However, conventional position estimation methods using RSSI require multiple receivers, which decreases the position estimation accuracy, owing to the presence of surrounding buildings. This study proposes a system to solve this challenge using a receiver and position estimation method based on RSSI MAP simulation and particle filter. Moreover, this study utilizes BLE peripheral/central functions capable of advertising as the transmitter/receiver. By using the advertising radio waves, our method provides a framework for estimating the position of unspecified transmitters. The effectiveness of the proposed system is evaluated in this study through simulations and experiments in actual environments. We obtained an error average of the distance to be 1.6 m from the simulations, which shows the precision of the proposed method. In the actual environment, the proposed method showed an error average of the distance to be 3.3 m. Furthermore, we evaluated the accuracy of the proposed method when both the transmitter and receiver are in motion, which can be considered as a moving person in the outdoor NLOS area. The result shows an error of 4.5 m. Consequently, we concluded that the accuracy was comparable when the transmitter is stationary and when it is moving. Compared with conventional path loss, the model can measure distances of 3 m to 10 m, whereas the proposed method can estimate the "position" with the same accuracy in an outdoor environment. In addition, it can be expected to be used as a collision avoidance system that confirms the presence of strangers in the NLOS area. [ABSTRACT FROM AUTHOR]

As a classic positioning algorithm with a simple principle and low computational complexity, the trilateration positioning algorithm utilizes the coordinates of three anchor nodes to determine the position of an unknown node, which is widely applied in various positioning scenes. However, due to the environmental noise, environmental interference, the distance estimation error, the uncertainty of anchor nodes' coordinates, and other negative factors, the positioning error increases significantly. For this problem, we propose a new trilateration algorithm based on the combination and K-Means clustering to effectively remove the positioning results with significant errors in this paper, which makes full use of the position and distance information of the anchor nodes in the area. In this method, after analyzing the factors affecting the optimization of the trilateration and selecting optimal parameters, we carry out experiments to verify the effectiveness and feasibility of the proposed algorithm. We also compare the positioning accuracy and positioning efficiency of the proposed algorithm with those of other algorithms in different environments. According to the comparison of the least-squares method, the maximum likelihood method, the classical trilateration and the proposed trilateration, the results of the experiments show that the proposed trilateration algorithm performs well in the positioning accuracy and efficiency in both light-of-sight (LOS) and non-light-of-sight (NLOS) environments. Then, we test our approach in three realistic environments, i.e., indoor, outdoor and hall. The experimental results show that when there are few available anchor nodes, the proposed localization method reduces the mean distance error compared with the classical trilateration, the least-squares method, and the maximum likelihood. [ABSTRACT FROM AUTHOR]

The positioning algorithm based on received signal strength indication (RSSI) and the logarithmic distance path loss model (LDPLM) is widely used in indoor positioning scenarios due to its convenient detection and low costs. However, the classic LDPLM with fixed coefficients and fixed error estimation usually reduces the ranging accuracy, but it is rarely studied in previous literature. This study proposes an adaptive calibration ranging algorithm based on LDPLM, which consists of two parts: coefficient adaptive algorithm and error correction algorithm. The coefficient adaptive algorithm is derived by utilizing the error theory and the least squares method. The error correction algorithm is defined as the linear regression equation, in which coefficients are determined by the least squares method. In addition, to reduce the influence of RSSI's fluctuation on ranging accuracy, we propose a simple but effective filtering algorithm based on Gaussian. The experimental results show that compared with the classic LDPLM and polynomial fitting model, the ranging accuracy of the proposed algorithm is improved by 58% and 51%, respectively, and the positioning cumulative prediction error of the proposed model is reduced by 69% and 80%, respectively. [ABSTRACT FROM AUTHOR]

Real-time monitoring of objects is being applied in bridges, tunnels, water, and others. Hence, the monitoring of the concrete properties using the maturity method and monitoring the electrical resistivity is crucial for its evaluation. This paper evaluates the influence of construction material density on the performance of data transmission from IoT (internet of things) devices in a laboratory. Tests were carried out regarding signal strengths of IoT devices and the reliability of the temperature measurement and its relative error. The results show that for the devices evaluated, the density of the material in which the IoT device is inserted directly influences the RSSI (Received Signal Strength Indication) and the IoT connection. However, there is no change in the quality of data collected in the field, resulting in high reliability in data gathering by IoT devices and thus leading to correct analysis and decision making by the stakeholders. • Construction material with density lower than 1945 kg/m3 do not inhibit data transmission. • As the density increases, the RSSI decreases. • The RSSI intensity do not influence the data quality and errors. • Employing an external antenna increases the RSSI. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: The main goal is to detect the proximity and orientation of autistic individuals alongside those they are interacting with, and help them be more engaging in a social setting.    http://profs.sci.univr.it/~cristanm/ssp/ Faculty Adviser: LouAnne Boyd, Department of InformaticsGillian Hayes, Department of Informatics    hayesg@uci.eduFaculty Adviser: Prof. Pai H. Chou, Department of Electrical Engineering & Computer Science             phchou@uci.edu

With the development of the mobile Internet, the demand for indoor location based services(ILBS) is increasing. Applications and devices in indoor environments can determine their position based on Received Signal Strength Indication(RSSI) of bluetooth Beacons. However, because of multipath effects, shadow fading and the blockage of obstacles, RSSI values are unstable for positioning. The propagation model are various in different environments, which have caused great difficulty in high-precision indoor positioning. In this paper, a novel positioning algorithm based on cellular network and bluetooth Beacon's RSSI is proposed for ILBS. The whole algorithm is divided into two parts: Offline Preparation and Online Positioning. During Offline Preparation, Beacon node is cellular networked in regular quadrilateral; a universal ranging model is derived from traditional ranging model for high-precision positioning. During Online Positioning, missing RSSI values are replaced firstly; next, Outliers Removed Median-Kalman Filter is used to process RSSI, which makes the signal smoother to range; then, the cell in which the mobile terminal is located is determined according to the strongest four RSSI values; after that, using filtered RSSI, distances without height influence could be calculated; finally, Weighted Multi-Point Positioning Algorithm With Cellular Network Assistance is used to calculate the real-time location of the mobile terminal. Experiment results show that our algorithm achieves average accuracy in 0.3m-0.5m and limits the maximum error within 0.8 m.

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.

Abstract To improve the management of science and technology museums, this paper conducts an in-depth study on Wi-Fi (wireless fidelity) indoor positioning based on mobile terminals and applies this technology to the indoor positioning of a science and technology museum. The location fingerprint algorithm is used to study the offline acquisition and online positioning stages. The positioning flow of the location fingerprint algorithm is discussed, and the improvement of the location fingerprint algorithm is emphasized. The raw data of the RSSI (received signal strength indication) is preprocessed, which makes the location fingerprint data more effective and reliable, thus improving the positioning accuracy. Three different improvement strategies are proposed for the nearest neighbor classification algorithm: a balanced joint metric based on distance weighting and a compromise between the two. Then, in the experimental simulation, the positioning results and errors of the traditional KNN (k-nearest neighbor) algorithm and three improvement strategy algorithms are analyzed separately, and the effectiveness of the three improved strategy algorithms is verified by experiments.

Various technologies have been utilized today for recognizing client or user in the indoor areas. These technologies incorporate RSSI, Bluetooth Low Energy Beacons, Ultrasound waves, Vision-based advances, for example, fixed camera recordings QR codes, remote gadgets, etc. RSSI fingerprinting technique requires more effort and it is also expensive to be used for indoor localization frameworks working in real-time. In this research, indoor localization based on images is investigated as an option in contrast to other indoor positioning techniques using these days. Image-based indoor positioning is more affordable than RSSI based technologies being utilized. A mobile phone camera is utilized to take the pictures of area inside the building to find the user inside the building. Sensor data from various sensors isn't required or no extra framework is required to find the client in the building utilizing indoor positioning based on an image. Microsoft Azure Custom Vision Services are utilized to locate the client; MS Azure classifies the pictures in one of the labels made. Strategy's attainability is demonstrated by various investigations and accomplished accuracy and review is recorded above 90%. The average precision of the trained model is recorded above 95%. [ABSTRACT FROM AUTHOR]

The development of the Internet of Things (IoT) benefits from 1) the connections between devices equipped with multiple sensors; 2) wireless networks and; 3) processing and analysis of the gathered data. The growing interest in the use of IoT technologies has led to the development of numerous diverse applications, many of which are based on the knowledge of the end user's location and profile. This paper investigates the characterization of Bluetooth signals behavior using 12 different supervised learning algorithms as a first step toward the development of fingerprint-based localization mechanisms. We then explore the use of metaheuristics to determine the best radio power transmission setting evaluated in terms of accuracy and mean error of the localization mechanism. We further tune-up the supervised algorithm hyperparameters. A comparative evaluation of the 12 supervised learning and two metaheuristics algorithms under two different system parameter settings provide valuable insights into the use and capabilities of the various algorithms on the development of indoor localization mechanisms.

With the development of deep learning, fingerprints recognition based on neural networks is a widely used method in indoor localization. In this paper, we build a long short-term memory (LSTM) recurrent neuron network to make regression between fingerprints and locations in order to track the moving target. Simulations are in a BLE5.0 based environment and we use received signal strength indication (RSSI) as the element of fingerprints. Since the preparation of fingerprints is an inevitable and time-consuming process in the testing phase of LSTM, we propose two methods to improve the real-time performance of the localization without changing the structure of LSTM. A decentralized sorting algorithm is proposed to divide the received RSSI signals into multiple parts based on the MAC address of BLE5.0 equipment and use GPUs to sort each part. A complete fingerprint is a combination of these parts. Then, an optimization model aimed at maximum localization accuracy and minimal time used in the testing process of LSTM is proposed by changing the length of fingerprints. Many experiments simulated in different trajectories show that LSTM is more accurate in localization than many other neural networks. Further results demonstrate that using decentralized fingerprints preparation and finding an optimal fingerprint length can keep balance between the localization accuracy and real-time performance.

In the traditional grain depot, the indoor location accuracy of the temperature measurement nodes depends on a large number of beacon nodes, which brings great inconvenience to practical engineering applications. To simplify the application model, a differential location model is established according to the distance attenuation curve characteristics of RSSI(Received Signal Strength Indication). Moreover, a positioning method—based on RSSI differential location model of the grain temperature measurement node was proposed. The model adopts dynamic beacon nodes, and determines the nearest unknown node from the beacon node according to the RSSI value, and finally locates all the nodes. Experiments show that the temperature measurement nodes based on the RSSI differential location model do not need additional placement of beacon nodes, which can reduce the random error of the environment and the positioning accuracy is higher than that of the traditional ranging location model.

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. [ABSTRACT FROM AUTHOR]

Aiming at the problem that signal deviation caused by the device variability affects positioning accuracy, an indoor positioning algorithm combining BP neural network and weighted centroid positioning algorithm was proposed. In this paper, the RSSI data of different mobile phones were cleaned by outlier detection algorithm, and the cleaned data was used as the data source of BP neural network to train the model, thus obtaining a stable nonlinear BP model. On this basis, combined with the improved indoor positioning algorithm for indoor positioning. Experiment results showed the mean error, minimum error and maximum error of the proposed algorithm were 0.58 m, 0.24 m and 1.06 m, respectively, and the positioning accuracy was significantly higher than that of the existing similar algorithms. [ABSTRACT FROM AUTHOR]

To improve the management of science and technology museums, this paper conducts an in-depth study on Wi-Fi (wireless fidelity) indoor positioning based on mobile terminals and applies this technology to the indoor positioning of a science and technology museum. The location fingerprint algorithm is used to study the offline acquisition and online positioning stages. The positioning flow of the location fingerprint algorithm is discussed, and the improvement of the location fingerprint algorithm is emphasized. The raw data of the RSSI (received signal strength indication) is preprocessed, which makes the location fingerprint data more effective and reliable, thus improving the positioning accuracy. Three different improvement strategies are proposed for the nearest neighbor classification algorithm: a balanced joint metric based on distance weighting and a compromise between the two. Then, in the experimental simulation, the positioning results and errors of the traditional KNN (k-nearest neighbor) algorithm and three improvement strategy algorithms are analyzed separately, and the effectiveness of the three improved strategy algorithms is verified by experiments. [ABSTRACT FROM AUTHOR]

Indoor positioning systems using visible light communication (VLC) have potential applications in smart buildings, for instance, in developing economical, easy-to-use, widely accessible positioning system based on light-emitting diodes. Thus using VLCs, we introduce a new fuzzy-based system for indoor localization in this paper. The system processes data from transmitters (i.e., anchor nodes) and delivers the calculated position of a receiver. A particle swarm optimization (PSO) technique is then employed to obtain the optimal configuration of the proposed fuzzy logic controllers (FLCs). Specifically, the proposed PSO technique optimizes the membership functions of the FLCs by adjusting their range to achieve the best results regarding the localization reliability. We demonstrate the utility of the proposed approach using experiments. [ABSTRACT FROM AUTHOR]

This paper presents location-aware speakers for the immersive virtual reality environments as well as conventional surround sound systems. The surround sound system generally requires multiple speakers fixed in specific positions and connected to dedicated audio jack holes. In this paper, we propose wireless speakers that can aware their locations and dedicated sound channels without troublesome installations. The proposed speakers use the Internet of things devices by combining a Raspberry Pi and a beacon to each speaker, which enable smart and connected applications. Each speaker estimates distances to other speakers from received signal strength indication of beacons with bluetooth low energy signals. By analyzing the relative distances between speakers, we detect the speaker locations in various speaker setups. We experimented our method with three sound system formats in various sizes and analyzed the accuracy of the location detection.

With the addition of the Fine Timing Measurement (FTM) protocol in IEEE 802.11-2016, a promising sensor for smartphone-based indoor positioning systems was introduced. FTM enables a Wi-Fi device to estimate the distance to a second device based on the propagation time of the signal. Recently, FTM has gotten more attention from the scientific community as more compatible devices become available. Due to the claimed robustness and accuracy, FTM is a promising addition to the often used Received Signal Strength Indication (RSSI). In this work, we evaluate FTM on the 2.4 GHz band with 20 MHz channel bandwidth in the context of realistic indoor positioning scenarios. For this purpose, we deploy a least-squares estimation method, a probabilistic positioning approach and a simplistic particle filter implementation. Each method is evaluated using FTM and RSSI separately to show the difference of the techniques. Our results show that, although FTM achieves smaller positioning errors compared to RSSI, its error behavior is similar to RSSI. Furthermore, we demonstrate that an empirically optimized correction value for FTM is required to account for the environment. This correction value can reduce the positioning error significantly.

The crowd phenomenon frequently occurs in dense urban living environments. Crowd counting or estimation helps to develop management strategies such as designing safe public places and evacuation plan for emergencies. These strategies are different depending upon the type of event such as political and public demonstrations, sports, and religious events. However, estimating the number of people in crowds at closed or open environments is quite challenging because of the dynamics involved in the process. In addition, crowd estimation itself poses challenges due to randomness in crowd behavior, motion, and an area’s geometric specifications. Crowd behavior as well as the area parameters is studied before suggesting any possible technological solution for managing a crowd. This article presents a theoretical understanding of the major crowd size estimation approaches that cannot be achieved through the study of existing survey papers in this area, because the existing survey papers focus on particular technologies/specific areas with no or brief description of the involved steps. Besides, this article also highlights the strength and weakness of crowd size estimation solutions and their possible applications. It is, therefore, believed that the provided information would assist in developing an intelligent system for crowd management. [ABSTRACT FROM AUTHOR]

Background: Knowledge of the location of sensor devices is crucial for many medical applications of wireless body area networks, as wearable sensors are designed to monitor vital signs of a patient while the wearer still has the freedom of movement. However, clinicians or patients can misplace the wearable sensors, thereby causing a mismatch between their physical locations and their correct target positions. An error of more than a few centimeters raises the risk of mistreating patients.Objective: The present study aims to develop a scheme to calculate and detect the position of wearable sensors without beacon nodes.Methods: A new scheme was proposed to verify the location of wearable sensors mounted on the patient's body by inferring differences in atmospheric air pressure and received signal strength indication measurements from wearable sensors. Extensive two-sample t tests were performed to validate the proposed scheme.Results: The proposed scheme could easily recognize a 30-cm horizontal body range and a 65-cm vertical body range to correctly perform sensor localization and limb identification.Conclusions: All experiments indicate that the scheme is suitable for identifying wearable sensor positions in an indoor environment. [ABSTRACT FROM AUTHOR]

The electric field environment under high-voltage direct current (HVDC) transmission lines is an important design consideration. In the wireless sensor networks for electric field measurement system under the HVDC transmission lines, it is necessary to obtain the electric field distribution with multiple sensors. The accurate localisation of sensing nodes is essential to the analysis of measurement results. However, most current techniques are limited to constant measurement environment with fixed and known path-loss exponent. Here, the authors report a localisation method based on received signal strength indication with unknown path-loss exponent for the localisation of one-dimensional linear topology wireless networks in the electric field measurement system. The optimisation method is utilised to obtain the optimal pass-loss parameter without involving the previous environment parameters. Afterwards, simulations are employed to demonstrate the feasibility and the effectiveness of the proposed method by comparing with other methods.

As an important means of multidimensional observation on the sea, ocean sensor networks (OSNs) could meet the needs of comprehensive information observations in large-scale and multifactor marine environments. In what concerns OSNs, accurate location information is the basis of the data sets. However, because of the multipath effect—signal shadowing by waves and unintentional or malicious attacks—outlier measurements occur frequently and inevitably, which directly degrades the localization accuracy. Therefore, increasing localization accuracy in the presence of outlier measurements is a critical issue that needs to be urgently tackled in OSNs. In this case, this paper proposed a robust, non-cooperative localization algorithm (RNLA) using received signal strength indication (RSSI) in the presence of outlier measurements in OSNs. We firstly formulated the localization problem using a log-normal shadowing model integrated with a first order Taylor series. Nevertheless, the problem was infeasible to solve, especially in the presence of outlier measurements. Hence, we then converted the localization problem into the optimization problem using squared range and weighted least square (WLS), albeit in a nonconvex form. For the sake of an accurate solution, the problem was then transformed into a generalized trust region subproblem (GTRS) combined with robust functions. Although GTRS was still a nonconvex framework, the solution could be acquired by a bisection approach. To ensure global convergence, a block prox-linear (BPL) method was incorporated with the bisection approach. In addition, we conducted the Cramer−Rao low bound (CRLB) to evaluate RNLA. Simulations were carried out over variable parameters. Numerical results showed that RNLA outperformed the other algorithms under outlier measurements, notwithstanding that the time for RNLA computation was a little bit more than others in some conditions.

This paper presents a localization model employing convolutional neural network (CNN) and Gaussian process regression (GPR) based on Wi-Fi received signal strength indication (RSSI) fingerprinting data. In the proposed scheme, the CNN model is trained by a training dataset. The trained model adapts to complex scenes with multipath effects or many access points (APs). More specifically, the pre-processing algorithm makes the RSSI vector which is formed by considerable RSSI values from different APs readable by the CNN algorithm. The trained CNN model improves the positioning performance by taking a series of RSSI vectors into account and extracting local features. In this design, however, the performance is to be further improved by applying the GPR algorithm to adjust the coordinates of target points and offset the over-fitting problem of CNN. After implementing the hybrid model, the model is experimented with a public database that was collected from a library of Jaume I University in Spain. The results show that the hybrid model has outperformed the model using k-nearest neighbor (KNN) by 61.8%. While the CNN model improves the performance by 45.8%, the GPR algorithm further enhances the localization accuracy. In addition, the paper has also experimented with the three kernel functions, all of which have been demonstrated to have positive effects on GPR.

Visible light communication (VLC) based on light-emitting diodes (LEDs) technology not only provides higher data rate for indoor wireless communications and offering room illumination but also has the potential for indoor localization. VLC-based indoor positioning using the received optical power levels from emitting LEDs is investigated. We consider both scenarios of line-of-sight (LOS) and LOS with non-LOS (LOSNLOS) positioning. The performance of the proposed system is evaluated under both noisy and noiseless channel as is the impact of different location codes on positioning error. The analytical model of the system with noise and the corresponding numerical evaluation for a range of signal-to-noise ratio (SNR) are presented. The results show that an accuracy of <10 cm on average is achievable at an SNR > 12 dB. [ABSTRACT FROM AUTHOR]

Visible light communication systems are rapidly growing research areas with wide applications ranging from illumination and data communication. To achieve high data rates in such systems, a number of techniques have been employed such as equalization of transmission signals, deployment of complex data modulation and the use of multiple input multiple output (MIMO) systems. However, security in wireless telecommunication systems is a common concern. This paper proposes a secure MIMO-VLC systemthat relies on the position of the user by incorporating a new modified version of the Rivest-Shamir-Adleman (RSA) technique for encrypting the transmitted data in the media access control MAC layer. The performance of the positioning method is evaluated showing a positioning accuracy of less than 5 cm for a signal-to-noise ratio of 15 dB. Furthermore, the ability of the proposed system to control the size of the encrypted cell, depending on the application environment, is demonstrated. [ABSTRACT FROM AUTHOR]

Indoor positioning methods based on the received signal strength indication (RSSI) ranging technology are sensitive to various environmental noises, which cause positioning errors. An improved Wi-Fi indoor positioning method using an improved unscented Kalman filter and the particle swarm optimization (PSO) is proposed to reduce ranging error and improve positioning accuracy. The received signals are preprocessed by the improved unscented Kalman filter algorithm and then the improved PSO algorithm is used to optimize position calculation results. To demonstrate the utility of the proposed algorithm, simulations and experiments were performed for estimating the position of objects. Simulating results indicate that the mean error of the proposed algorithm is reduced by 31.87% in comparison with that of the unlicensed Kalman filter method. Experimental results show that the mean error of the proposed algorithm is reduced by 26.72% in comparison with that of the unlicensed Kalman filter method. Therefore, the proposed algorithm can effectively reduce the positioning error and improve the positioning accuracy. In the actual indoor positioning, it could get better positioning results. [ABSTRACT FROM PUBLISHER]

In recent years, as the rapid-growth of the 3rd generation terminals, applications and services, the network load increase especially when small-flow online applications are much more popular. The RSSI problem became one of the critical bottlenecks to enhance the using experience. With the deep analysis of RSSI mechanism of cdma2000 1x Ev-Do network, the different effects on network reverse noise and user's experience of different combinations of parameters were discussed, a new parameter template was created based on the optimal control of AT transmit power. The template has applied in Jiangsu province and significantly improved the network capacity and user's experience without increasing the cost.