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Localization of unmanned aircraft systems (UASs) in indoor scenarios and GNSS-denied environments is a difficult problem, particularly in dynamic scenarios where traditional on-board equipment (such as LiDAR, radar, sonar, camera) may fail. In the framework of autonomous UAS missions, precise feedback on real-time aircraft position is very important, and several technologies alternative to GNSS-based approaches for UAS positioning in indoor navigation have been recently explored. In this paper, we propose a low-cost IPS for UAVs, based on Bluetooth low energy (BLE) beacons, which exploits the RSSI (received signal strength indicator) for distance estimation and positioning. Distance information from measured RSSI values can be degraded by multipath, reflection, and fading that cause unpredictable variability of the RSSI and may lead to poor-quality measurements. To enhance the accuracy of the position estimation, this work applies a differential distance correction (DDC) technique, similar to differential GNSS (DGNSS) and real-time kinematic (RTK) positioning. The method uses differential information from a reference station positioned at known coordinates to correct the position of the rover station. A mathematical model was established to analyze the relation between the RSSI and the distance from Bluetooth devices (Eddystone BLE beacons) placed in the indoor operation field. The master reference station was a Raspberry Pi 4 model B, and the rover (unknown target) was an Arduino Nano 33 BLE microcontroller, which was mounted on-board a UAV. Position estimation was achieved by trilateration, and the extended Kalman filter (EKF) was applied, considering the nonlinear propriety of beacon signals to correct data from noise, drift, and bias errors. Experimental results and system performance analysis show the feasibility of this methodology, as well as the reduction of position uncertainty obtained by the DCC technique. [ABSTRACT FROM AUTHOR]

This research explores the concept of Worldwide one network (WON), a hypothetical ultra-large scale ad-hoc wireless network characterized by its non-hierarchical, open, scalable, homogeneous, and autopoiesis nature. The primary objectives are to address challenges in network formation, individual node unique addressing, and network management. This paper proposes a novel addressing mechanism named 'Cubid', which utilizes geo-coordinates as the primary identifier for network nodes with 1 m resolution and aims for at least 512 unique node addresses per cubic meter space on Earth. Unique three-dimensional address space, received signal strength based trilateration for network formation, address negotiation, and the use of Cubid as a MAC address to bypass traditional Layer 2–3 Internet Protocol activities are few of the differentiator aspects involved in this research work. Preliminary tests of this hypothetical network yield in practical viability of identifying network node's geographical coordinates with an accuracy of 3 m without GPS devices, and corresponding simulations results in an average frame delivery time of 27 ms over a 100-hop, varying hop length network path. These findings indicate that WON could serve as a viable alternative communication network, especially when substantial infrastructure-based networks, such as the Internet fails. [ABSTRACT FROM AUTHOR]

Automated radio telemetry systems have become a popular and invaluable tool in tracking the activity and movement of wild animals. However, many environmental conditions can hinder accuracy when tracking with this technology. For instance, study sites may contain multiple habitat types, each habitat uniquely affecting the signal strength received from tagged species. To investigate the influence of a structurally diverse study site on an automated radio telemetry system, we conducted this project at a restored and managed pine barren habitat that consisted of a mix of mature pitch pine, treated pitch pine, scrub oak, and hardwood forests. This site, Montague Plains Wildlife Management Area, Montague, Massachusetts, is also a known breeding ground for Eastern whip-poor-will (Antrostomus vociferus). To measure the relationship of radio signal strength with distance across each habitat, we used radio telemetry equipment manufactured by Cellular Tracking Technologies. We produced negative exponential decay functions measuring radio signal strength over distance and tested for differences among habitat types on radio signal strength (RSS). We found that decay function parameters significantly differed by habitat type, prompting us to investigate if accounting for these differences improved location estimate accuracy. To test this, we estimated known locations using trilateration methods with and without habitat calibration. Comparing these tests indicates that habitat-specific adjustments significantly improved location accuracy. Lastly, we visualized estimated RSS-based locations of 1 week of whip-poor-will data and compared them to GPS data generated from the same individual. Previous studies have accounted for types of environmental interference (like elevation) in the field but have avoided incorporating habitat-specific factors by working with node networks covering a relatively small area, but in this study, we examined the potential to scale up for larger areas and in more complex habitats. [ABSTRACT FROM AUTHOR]

لقد اعتنى اللغويون الأوائل بظواهر اللغة العربية ومن ذلك ظاهرة التثليث وهي موضوع بحثنا، وقد أفردت بالتصنيف وكان رائد التصنيف فيها هو محمد بن المستنير قطرب (تـ 206 هـ)، إلى أن جاء محمد بن علي الصبان (تـ 1206 هـ)، وألف هذا النظم جمع فيه (1116) كلمة مثلثة، جاءت في (1732) بيتًا، وبدأ النظم بمقدمة ذكر فيها سبب تأليف الكتاب، وأنه نظم المثلثات إجابة لسائل طلب منه ذلك، وذكر فيها أنه ربما يتبع فيه غيره، وأنه قد تفوته كلمات لا يوردها لضيق النظم، ثم قسم الكتاب إلى قسمين:القسم الأول: نظم فيه المثلث المختلف المعنى من الأسماء والأفعال في رجز مربع تام قفيت رابع أشطاره على روي اللام المكسورة.القسم الثاني: جعله للكلمات المثلثة المتحدة المعنى، نظمها في رجز تام مقفى، على طريقة المزدوجات أو المثنَّيَات: التي تقوم على أساس التصريع في أبيات النظم كلها، ثم ختم النظم بحمد الله والصلاة على النبي ﷺ وآله الأطهار - عليهم أزكى الصلاة والسلام.AbstractThe first linguists paid attention to the phenomena of the Arabic language, including the phenomenon of triliteral, which is the subject of our research. I singled out for classification, and the pioneer in this classification is Muhammad bin al-Mustanir Qutrob (d. 206 AH), until Muhammad bin Ali al-Sabban (d. 1206 AH) came and wrote this rhythm. He compiles it in (1116) trilateral words, which came in (1732) verses, and the compilation starts with an introduction in which he stated the reason for writing the book, and that he compiled the phonological variations in response to a questioner who asked him to do so, and in which he mentioned that he might follow others, and that some words may have been omitted due to the constraints of the versification. The book is divided into two sections:The first section: It consists of organizing phonological variations with different meanings of nouns and verbs in a complete quartet rajz, the fourth half of which rhymed on the narration of laam with kasra.The second section: It was dedicated to trilateral roots with unified meanings, organized in a complete rhyme quartet rajz following a structured pattern, based on the concept of doubling or pairing, which operates on the basis of leonine rhyme throughout the entire poetic composition. The compilation concluded with praise to Allah and blessings upon the Prophet Muhammad (peace be upon him) and his pure family.

Localization of unmanned aircraft systems (UASs) in indoor scenarios and GNSS-denied environments is a difficult problem, particularly in dynamic scenarios where traditional on-board equipment (such as LiDAR, radar, sonar, camera) may fail. In the framework of autonomous UAS missions, precise feedback on real-time aircraft position is very important, and several technologies alternative to GNSS-based approaches for UAS positioning in indoor navigation have been recently explored. In this paper, we propose a low-cost IPS for UAVs, based on Bluetooth low energy (BLE) beacons, which exploits the RSSI (received signal strength indicator) for distance estimation and positioning. Distance information from measured RSSI values can be degraded by multipath, reflection, and fading that cause unpredictable variability of the RSSI and may lead to poor-quality measurements. To enhance the accuracy of the position estimation, this work applies a differential distance correction (DDC) technique, similar to differential GNSS (DGNSS) and real-time kinematic (RTK) positioning. The method uses differential information from a reference station positioned at known coordinates to correct the position of the rover station. A mathematical model was established to analyze the relation between the RSSI and the distance from Bluetooth devices (Eddystone BLE beacons) placed in the indoor operation field. The master reference station was a Raspberry Pi 4 model B, and the rover (unknown target) was an Arduino Nano 33 BLE microcontroller, which was mounted on-board a UAV. Position estimation was achieved by trilateration, and the extended Kalman filter (EKF) was applied, considering the nonlinear propriety of beacon signals to correct data from noise, drift, and bias errors. Experimental results and system performance analysis show the feasibility of this methodology, as well as the reduction of position uncertainty obtained by the DCC technique.

In this paper, we propose a proximity-based adaptive positioning algorithm to address the challenge of positioning errors in indoor localization based on RSSI (received signal strength indicator). When positioning by trilateration, if a receiver is close to one AP, the signals of other APs become rapidly unstable, so positioning accuracy is reduced. Therefore, this paper proposes an algorithm to identify the proximity state with AP and adaptively determine the positioning technique based on this state. The proposed algorithm consists of three steps: RSSI error correction, adaptive location estimation, and post-processing. The RSSI error correction step corrects unstable RSSI. The adaptive location estimation step utilizes a modified proximity technique when identified as close to an AP, employing trilateration otherwise. Finally, in the post-processing step, an efficient filtering algorithm is applied. For the static state experiment, the accuracy of the proposed algorithm is found to be improved by about 28% compared to the method measured using only the trilateration technique applying the RSSI error correction step and post-processing step. The proposed algorithm improved the positioning accuracy of the entire area by improving accuracy in regions with weak signals without additional devices. [ABSTRACT FROM AUTHOR]

Accurate node localization in Wireless Sensor Networks (WSNs) is crucial for applications like environmental monitoring and military surveillance. Traditional localization methods, such as trilateration, often struggle with accuracy due to signal attenuation and environmental obstructions, leading to significant localization errors in practical scenarios. This paper aims to address the limitations of traditional localization methods by developing and evaluating a hybrid localization method that enhances accuracy and robustness in node localization within WSNs. The proposed method integrates the Bounding Box approach with the Harmony Search optimization algorithm, resulting in the Bounding Box Harmony Search (BBHS) method. The BBHS method utilizes the initial geometric constraints provided by the Bounding Box approach and refines these estimates using the global optimization capabilities of the Harmony Search algorithm. Simulation results, obtained using a custom-developed WSN localization simulator, demonstrate that the BBHS method significantly reduces localization errors compared to traditional trilateration and the standalone Bounding Box method. The BBHS method consistently provides enhanced accuracy and robustness across varying network conditions, highlighting its effectiveness in practical deployment scenarios. The advancements presented in this paper suggest that hybrid methods like BBHS represent a significant step forward in WSN localization technologies. By combining geometric constraints with optimization processes, the BBHS method overcomes the drawbacks of earlier techniques, paving the way for more reliable and resilient WSN operations.

This article examines the comparative effectiveness of three indoor node localization techniques—Multilateration, the Weighted Centroid algorithm, and Grid-based Received Signal Strength (RSS)—in wireless networking applications. The comparison is based on their performance against localization accuracy using RSS Indicator (RSSI) data in three experiments. The experiments utilized internally generated or real-world datasets with RSSI values for the unknown tag nodes. The datasets were obtained from various sources and evaluated in different scenarios to determine the efficiency of the three localization techniques. The results were evaluated and compared using mean error and standard deviation metrics. The findings indicate that trilateration achieves superior localization accuracy and precision in a Bluetooth Low Energy (BLE) environment compared to Wi-Fi and ZigBee. The Centroid technique showed the highest resistance to noise and outliers but is positioned biased (unlike Trilateration). Besides that, the Grid-based RSS technique is highly sensitive to noise, and theoretical RSS. These findings can greatly assist researchers and network operators in carefully selecting the most suitable localization technique for their wireless networking applications, taking into account the specific wireless technology utilized and their unique needs and limitations.

The Internet of Things (IoT) positioning technology has expanded the distribution space through information transmission between devices, thereby finding extensive application in diverse domains such as intelligent perception, intelligent recognition, intelligent management, etc. In recent years, scholars have been researching the optimal selection of reference points in IoT positioning due to the limited computing capacity of each device node and the interference caused by communication noise. The optimal selection of reference points enables the estimation of positioning information for IoT devices while minimizing positioning errors. In this paper, three properties of the distribution of positioning reference points based on trilateration are presented. By integrating the information space and physical space, the ultimate objective is to minimize the errors generated by unidentified nodes in the positioning process. In addition, a Joint Anchor Optimization Selection (JAOS) mechanism is proposed, the mechanism is based on the location of the relationship between multiple reference points, by choosing appropriate reference point to the unknown node position to improve the positioning accuracy. Finally, comprehensive simulations have been executed, demonstrating that the proposed theorem for distributing positioning reference points and the JAOS mechanism can effectively cater to the requirements of realtime positioning of mobile entities in the ubiquitous computing environment. Furthermore, it exhibits remarkable precision in positioning, thereby significantly enhancing the efficacy and energy efficiency of the system. [ABSTRACT FROM AUTHOR]

Target localization in a wireless sensor network (WSN) has received more and more attention in recent years, and has promoted many new applications due to the low cost, low bandwidth, low energy consumption, and collision avoidance mechanism. How to provide accurate location information has always been a hot research topic in 5G/B5G application scenarios. In this paper, the path loss information or received signal strength (RSS) of the received signal is considered in a WSN for the extended Kalman filter (EKF) to realize trajectory tracking of multiple targets, and the tracked targets are then localized through multiple sensors. Moreover, since there may be several objects or clutter interference in the communication environment, in order to reduce the impact of interference, we consider the probabilistic data association filter (PDAF) or probability hypothesis density filter (PHDF) to improve the tracking performance. Each sensor sends the received distance estimation information to the fusion center (FC), which calculates the optimal position for each target. Through simulation results, the proposed weighted least squares (WLS) trilateration method in this paper can effectively improve the average root mean squared error (RMSE) performance as sensors are evenly distributed around the tracking trajectories. [ABSTRACT FROM AUTHOR]

This article examines the comparative effectiveness of three indoor node localization techniques—Multilateration, the Weighted Centroid algorithm, and Grid-based Received Signal Strength (RSS)—in wireless networking applications. The comparison is based on their performance against localization accuracy using RSS Indicator (RSSI) data in three experiments. The experiments utilized internally generated or real-world datasets with RSSI values for the unknown tag nodes. The datasets were obtained from various sources and evaluated in different scenarios to determine the efficiency of the three localization techniques. The results were evaluated and compared using mean error and standard deviation metrics. The findings indicate that trilateration achieves superior localization accuracy and precision in a Bluetooth Low Energy (BLE) environment compared to Wi-Fi and ZigBee. The Centroid technique showed the highest resistance to noise and outliers but is positioned biased (unlike Trilateration). Besides that, the Grid-based RSS technique is highly sensitive to noise, and theoretical RSS. These findings can greatly assist researchers and network operators in carefully selecting the most suitable localization technique for their wireless networking applications, taking into account the specific wireless technology utilized and their unique needs and limitations. [ABSTRACT FROM AUTHOR]

Determining the localization and tracking of sensor nodes in indoor environments is the goal of this study. The difficulty of significant estimation errors in target localization brought on by unpredictable noise in received signal strength indicator (RSSI) readings, particularly in indoor environments, is a major area of research right now. This study proposed a hybrid technique called particle swarm optimization-generalized regression neural network (PSO-GRNN) to improve the sensor nodes' ability to estimate location and target tracking with more precision, as an alternative to conventional RSSI-based strategy. The GRNN algorithm can use the RSSI values as initiation data of the algorithm of GRNN to locate the location and tracing of the target node. An essential component of the GRNN architecture is the spread constant(s), The method of trial-and-error to select a value of spread constant(s), which is insecure and does not always yield the best result, is used to choose the parameter. The PSO method is used to determine the optimal GRNN spread constant value. The hybrid PSO-GRNN method was used to overcome these drawbacks and enhance localization and target tracking accuracy without the need for further apparatuses. The tracking algorithm PSO-GRNN the hybrid outperformed the conventional LNSM technique and produced impressive results. Comparing the proposed method to the conventional RSSI, a considerable gain of 87.58% is possible. [ABSTRACT FROM AUTHOR]

Indoor positioning has become an attractive research topic because of the drawbacks of the global navigation satellite system (GNSS), which cannot detect accurate locations within indoor areas. Radio-based positioning technologies are one major category of indoor positioning systems. Another major category consists of visible light communication-based solutions, as they have become a revolutionary technology for indoor positioning in recent years. The proposed study intends to make use of both technologies by creating a hybrid indoor positioning system that uses VLC and Bluetooth together. The system first collects the initial location information based on VLC proximity, then collects the strongest Bluetooth signals to determine the receiver's location using Bluetooth RSS (received signal strength) trilateration. This has been inspired by the fact that there have not been any studies that make use of both technologies with the same positioning algorithm, which can lead to pretty high accuracy of up to 0.03 m. [ABSTRACT FROM AUTHOR]

Wildfires pose significant threats globally, affecting human life, wildlife, and ecosystems. The detrimental effects of these fires are often exacerbated by inaccuracies in fire positioning systems and delays in alarm response, leading to rapid and uncontrolled fire spread, consequently affecting emergency response times. This paper presents a novel, real-time wildfire localization system that employs an optimized trilateration technique. The technique leverages anchor node technology for the localization process. The Nelder-Mead (NM) optimization algorithm is utilized to augment the trilateration technique, thereby enhancing the accuracy of the estimated coordinates for unidentified nodes. The proposed localization algorithm is deployed using cloud computing and the Internet of Things (IoT) MQTT communication protocol. Simulation results demonstrate that the proposed method maintains accurate localization performance, with an enhancement in fire localization accuracy of up to 83% with the optimized trilateration based on the NM algorithm. As a result, the proposed approach offers potential time-savings in the early detection of wildfires, thus contributing to more efficient emergency response measures. [ABSTRACT FROM AUTHOR]

This research aims at determining the epicentre, magnitude and energy dissipated during the earth-tremor which took place at Abaji-Abuja (Nigeria) on 10th January 2020 at 13:46:20 UTC. The event was recorded by Volksmeter II VMII-2RU broadband Seismographs located at the Earthquake and Space Weather laboratory, Obafemi Awolowo University, Ife, Osun state, Nigeria. The computed P-wave and S-wave arrival times and lag times were 35.8seconds, 36.1seconds and 33.3seconds recorded by three Seismographs Ife (1), Ife (2) and Ife (3) respectively. The WinQuake software was used for the analysis. Epicentre distances computed were 330.3 km, 333.5 km and 303.2 km from the three seismograms, using trilateration method. The results gave the epicentre location at 8049'N and 6047'E (Gurdi town, Yaba District, Abaji LGA, Abuja, Nigeria), focal depth of 5 km, and average epicentral distance of 322.3km from Ile-Ife. The tremor has a Local Magnitude of 2.25 MLand Coda Magnitude of 3.7Md, and the dissipated energy of 149.6 × 10-12 KJ. The implication of these results is that a local magnitude of 2.25 ML of 149.6 × 10-12 KJ of energy serves as an indicator to the future occurrence of another earthquake within the said region. Necessary precautionary measures should be taken when carrying out geological and construction works within the Abaji-Abuja environs. The massive rock blasting and quarrying must have reactivated the faults within the area. [ABSTRACT FROM AUTHOR]

Distance estimation in indoor localization is mainly solved by Received Signal Strength Indicator (RSSI). In our approach, the RSSI is determined empirically, and it's used to estimate the distance between the user and beacons in a mobile app solution. Trilateration and Kalman filter help obtain better accuracy. The paper proposes a classical solution for indoor localization, but with features like blueprint implementation, Google Maps integration, and manual beacon positioning. [ABSTRACT FROM AUTHOR]

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In this paper, we propose a proximity-based adaptive positioning algorithm to address the challenge of positioning errors in indoor localization based on RSSI (received signal strength indicator). When positioning by trilateration, if a receiver is close to one AP, the signals of other APs become rapidly unstable, so positioning accuracy is reduced. Therefore, this paper proposes an algorithm to identify the proximity state with AP and adaptively determine the positioning technique based on this state. The proposed algorithm consists of three steps: RSSI error correction, adaptive location estimation, and post-processing. The RSSI error correction step corrects unstable RSSI. The adaptive location estimation step utilizes a modified proximity technique when identified as close to an AP, employing trilateration otherwise. Finally, in the post-processing step, an efficient filtering algorithm is applied. For the static state experiment, the accuracy of the proposed algorithm is found to be improved by about 28% compared to the method measured using only the trilateration technique applying the RSSI error correction step and post-processing step. The proposed algorithm improved the positioning accuracy of the entire area by improving accuracy in regions with weak signals without additional devices.

Navigation is often regarded as one of the most-exciting use cases for Augmented Reality (AR). Current AR Head-Mounted Displays (HMDs) are rather bulky and cumbersome to use and, therefore, do not offer a satisfactory user experience for the mass market yet. However, the latest-generation smartphones offer AR capabilities out of the box, with sometimes even pre-installed apps. Apple's framework ARKit is available on iOS devices, free to use for developers. Android similarly features a counterpart, ARCore. Both systems work well for small spatially confined applications, but lack global positional awareness. This is a direct result of one limitation in current mobile technology. Global Navigation Satellite Systems (GNSSs) are relatively inaccurate and often cannot work indoors due to the restriction of the signal to penetrate through solid objects, such as walls. In this paper, we present the Pedestrian Augmented Reality Navigator (PAReNt) iOS app as a solution to this problem. The app implements a data fusion technique to increase accuracy in global positioning and showcases AR navigation as one use case for the improved data. ARKit provides data about the smartphone's motion, which is fused with GNSS data and a Bluetooth indoor positioning system via a Kalman Filter (KF). Four different KFs with different underlying models have been implemented and independently evaluated to find the best filter. The evaluation measures the app's accuracy against a ground truth under controlled circumstances. Two main testing methods were introduced and applied to determine which KF works best. Depending on the evaluation method, this novel approach improved the accuracy by 57% (when GPS and AR were used) or 32% (when Bluetooth and AR were used) over the raw sensor data. [ABSTRACT FROM AUTHOR]

The unpredictable noise in received signal strength indicator (RSSI) measurements in indoor environments practically causes very high estimation errors in target localization. Dealing with high noise in RSSI measurements and ensuring high target-localization accuracy with RSSI-based localization systems is a very popular research trend nowadays. This paper proposed two range-free target-localization schemes in wireless sensor networks (WSN) for an indoor setup: first with a plain support vector regression (SVR)-based model and second with the fusion of SVR and kalman filter (KF). The fusion-based model is named as the SVR+KF algorithm. The proposed localization solutions do not require computing distances using field measurements; rather, they need only three RSSI measurements to locate the mobile target. This paper also discussed the energy consumption associated with traditional Trilateration and the proposed SVR-based target-localization approaches. The impact of four kernel functions, namely, linear, sigmoid, RBF, and polynomial were evaluated with the proposed SVR-based schemes on the target-localization accuracy. The simulation results showed that the proposed schemes with linear and polynomial kernel functions were highly superior to trilateration-based schemes. [ABSTRACT FROM AUTHOR]

The LoRa is a novel radio communication technology providing low power and a high range of data transmission. The LoRa transmission may be used for a low-cost localization to estimate the network nodes’ location. Some recent research showed that the location could be found with reasonable accuracy, with median error as low as tens of meters. Still, such results are achieved in a controlled environment with low interferences. We first evaluate the LoRa localization using an extensive data set of a telemetric network of a few thousand devices. We show that although the direct positioning based on trilateration provides limited accuracy, the measurement of LoRa transmission may be successfully used to evaluate the credibility of location information. The information about which gateways received the data and the RSSI measurements allow us to verify if the potential coordinates of a location are accurate. We propose a metric for location verification and estimate its credibility on a sample of measurements from the LoRa telemetry network. [ABSTRACT FROM AUTHOR]

Observing the vehicles movement becomes an urgent necessity due to exponentially increasing numbers of vehicles in the world. However, to this regard, a good deal of research had been presented to estimate the exact physical position of the vehicle. The major challenges faced vehicle localization systems are large coverage areas required, positioning at diverse environments and positioning during a high-speed movement. However, in this paper, the challenges of employing the vehicle localization techniques, which rely on the propagation signal properties, are discussed. Moreover, a comparison between these techniques, in terms of accuracy, responsiveness, scalability, cost, and complexity, is conducted. The presented positioning technologies are classified into five categories: satellite based, radio frequency based, radio waves based, optical based, and sound based. The discussion shows that, both of satellite-based technology and cellular-based technology are emerge solutions to overcome the challenges of vehicle positioning. Satellite-based can provide a high accurate positioning in open outdoor environment, whereas the cellular-based can provide accurate and reliable vehicle localization in urban environment, it can support non-line of sight (NLOS) positioning and provide large coverage and high data transmission. The paper also shows that, the standalone localization technology still has limitations. Therefore, we discussed how the presented techniques are integrated to improve the positioning performance. [ABSTRACT FROM AUTHOR]

(1) Background: public transport demand dynamics represents important information for fleet managers and is also a key factor in making public transport attractive to reduce the environmental footprint of urban traffic. This research presents some experimental results on the assessment of low-energy communication technologies, such as Wi-Fi and Bluetooth, as support for people density and/or movement tracking sensing technologies. (2) Methods: the research is based on field measurements to determine the percentage of discoverable devices carried by people, in relation to the total number of physical persons in interest, different scenarios of mobile devices usage and evaluation of influences on radio signals' propagation, RSSI / RX read values, and efficiency of indoor localization, or in similar GPS-denied environments. Different situations are investigated, especially public transport-related ones, such as subway stations, indoors of commuting hubs, railway stations and trains. (3) Results: diagrams and experiments are presented, and models of signal behavior are also proposed. (4) Conclusions: recommendations on the efficiency of these non-conventional traveler and passenger flow tracking solutions and models are presented at the end of the paper. [ABSTRACT FROM AUTHOR]

Indoor positioning has become an attractive research topic because of the drawbacks of the global navigation satellite system (GNSS), which cannot detect accurate locations within indoor areas. Radio-based positioning technologies are one major category of indoor positioning systems. Another major category consists of visible light communication-based solutions, as they have become a revolutionary technology for indoor positioning in recent years. The proposed study intends to make use of both technologies by creating a hybrid indoor positioning system that uses VLC and Bluetooth together. The system first collects the initial location information based on VLC proximity, then collects the strongest Bluetooth signals to determine the receiver’s location using Bluetooth RSS (received signal strength) trilateration. This has been inspired by the fact that there have not been any studies that make use of both technologies with the same positioning algorithm, which can lead to pretty high accuracy of up to 0.03 m.

We are impacted by Internet connectivity and connect to the outside world using the global positioning system (GPS). We can use GPS to locate a person in a fully connected area which makes it possible to identify his location but in cases where the Global Positioning System (GPS) is not available there is no alternative to communicate to the other user specially when they are in critical situations. The work shows an effective local positioning technique using the trilaterationalgorithm which not only identifies the location of the user but also tries to analyze the locateddata through different evaluated mechanism such as packet delivery ratio, Inter arrival time and packet loss. The entire local positioning system uses the anchor nodes and Receiver Signal Strength Indicator (RSSI) values of its own Wi-Fi network to locate the user in unfavorable situations where he/she may not have chosen to be. Simulations in mat lab tool were taken for single cases of single channel node position and multiple node position. However, when we test for multiple node positions, the error or delay is greater in comparison. The hardware demonstration shows the positioning scenarios by taking anchor positioning of the nearby network in terms of coordinates i.e. 'x','y', 'z' and measures the signal strength of its locations and pageant the distance from the measured coordinates. [ABSTRACT FROM AUTHOR]

Robot localization is one of the most fundamental problems in motion planing, with many important applications in tracking vehicles and router systems. One way for localizing the robot is trilateration, which is used in the environments without GPS antenna by using a number of signals that can be measure their distance to robot at any time. It has already been shown that ⌊ 8 n 9 ⌋ landmarks are sufficient for trilaterating a simple n-side. Later, as a better result, it has been proved that ⌊ 2 n 3 ⌋ landmarks are sufficient for this purpose. In this paper, we discuss the problem for orthogonal polygons and show that n 2 landmarks suffice for trilaterating an orthogonal n-gon. This theoretical achievement is important for most evolutionary algorithms and neuro-computing techniques which need more speedup and high efficiency. [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]