Raw Received Signal Strength Indicator Research Articles

Smartphone-Based Indoor Localization via Network Learning With Fusion of FTM/RSSI Measurements

This letter proposes a deep neural network (DNN)-based indoor localization approach that leverages WiFi Fine Timing Measurement (FTM) and Received Signal Strength Indicator (RSSI) as environment features to provide accurate location estimation. Our method uses DNN with raw FTM and RSSI measurements for self-learning and produces enhanced ranging information in the presence of measurement noise. Experimental data was obtained from real-world settings using commercial off-the-shelf devices in two different indoor office environments. The proposed solution was evaluated regarding the localization Mean Squared Error, demonstrating remarkable accuracy and outperforming state-of-the-art methods.

Path Forming of Healthcare Practitioners in an Indoor Space Using Mobile Crowdsensing.

While there are numerous causes of waste in the healthcare system, some of this waste is associated with inefficiency. Among the proposed solutions to address inefficiency is clinic layout optimization. Such optimization depends on how operating resources and instruments are placed in the clinic, in what order they are accessed to attain a particular task, and the mobility of clinicians between different clinic rooms to accomplish different clinic tasks. Traditionally, such optimization research involves manual monitoring by human proctors, which is time consuming, erroneous, unproductive, and subjective. If mobility patterns in an indoor space can be determined automatically in real time, layout and operation-related optimization decisions based on these patterns can be implemented accurately and continuously in a timely fashion. This paper explores this application domain where precise localization is not required; however, the determination of mobility is essential on a real-time basis. Given that, this research explores how only mobile devices and their built-in Bluetooth received signal strength indicator (RSSI) can be used to determine such mobility. With a collection of stationary mobile devices, with their computational and networking capabilities and lack of energy requirements, the mobility of moving mobile devices was determined. The research methodology involves developing two new algorithms that use raw RSSI data to create visualizations of movements across different operational units identified by stationary nodes. Compared with similar approaches, this research showcases that the method presented in this paper is viable and can produce mobility patterns in indoor spaces that can be utilized further for data analysis and visualization.

Urban Vehicle Localization in Public LoRaWan Network

Location-based services (LBS) such as LoRa geolocation are important aspects of IoT applications. In this article, we propose a hierarchical clustering-based technique for urban vehicle localization using received signal strength indicator (RSSI) measurements in a public LoRaWan network. The solution relies on a two-layer hierarchy: the first layer consists of a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -Means clustering to partition a large urban area into several regions based on geographical coordinates of the datapoints. A coarse localizer utilizes kernel density estimation to model the received signal distribution of each gateway and determine in which the most probable regions of interests the vehicle is located, followed by a finer localization step at the second layer. For each region, reference points are grouped based on the similarity between the gateway coverage vectors. A spatial kernel-based fingerprint method that adopts spatial co-location patterns between neighbors is introduced to provide support for further fine granularity positioning within each region. The Kullback–Leibler divergence is used to measure similarities between observations and fingerprints, and the final position estimation is based on a weighted kernel regression model. The system is evaluated using a publicly available LoRaWan data set collected in large urban areas in the city of Antwerp, Belgium. We are able to achieve a median error of 158.41 m and a mean error of 346.03 m based on the raw LoRa RSSI data, and it is reported as the best accuracy based on the same data set in the literature.

A Low Cost Indoor Positioning System Using Bluetooth Low Energy

In this paper, we present a Bluetooth Low Energy (BLE) based indoor positioning system developed for monitoring the daily living pattern of old people (e.g. people living with dementia) or individuals with disabilities. The proposed sensing system is composed of multiple sensors that are installed in different locations in a home environment. The specific location of the user in the building has been pre-recorded into the proposed sensing system that captures the raw Received Signal Strength Indicator (RSSI) from the BLE beacon that is attached on the user. Two methods are proposed to determine the indoor location and the tracking of the users: a trilateration-based method and fingerprinting-based method. Experiments have been carried out in different home environments to verify the proposed system and methods. The results show that our system is able to accurately track the user location in home environments and can track the living patterns of the user which, in turn, may be used to infer the health status of the user. Our results also show that the positions of the BLE beacons on the user and different quality of BLE beacons do not affect the tracking accuracy.

Learning RSSI Feature via Ranking Model for Wi-Fi Fingerprinting Localization

Wi-Fi fingerprinting is widely used in indoor localization due to the ubiquitous availability of Wi-Fi infrastructure in indoor environments. The basic assumption of fingerprinting localization is that the received signal strength indicator (RSSI) distance is consistent with the location distance. However, due to the fluctuation of Wi-Fi signals in indoor environments, the nearest neighbors selected using the RSSI distance may not be those whose corresponding locations are nearest to the target, which could lead to a large localization error. In this paper, we propose a novel fingerprinting method for indoor localization by transforming raw RSSI into features with a learned non-linear mapping function. To learn such mapping function, we design a triple loss function that measures the difference between the rank of RSSI distance and that of location distance. By minimizing the loss function iteratively, we can learn the non-linear mapping function with the gradient boosting regression forest (GBRF) method. Experiments have been conducted in a complex environment and experimental results show that our method outperforms the state-of-the-art methods.

RFID-Based Crack Detection of Ultra High-Performance Concrete Retrofitted Beams.

Ultra-high-performance concrete (UHPC) is a novel material with multiple known uses and many still yet to be discovered. Recently, the use of encasing welded shear studs in UHPC on the web of corroded steel beams was developed. This creates a bearing force transfer mechanism to bypass the corroded web plate. This new material and its uses come with many uncertainties in the short and long term. Structural health monitoring (SHM) can be a tool to observe the development. Specifically, radio frequency technology (RFID) can be used. RFID has existed commercially since the 1960s and has been used as a crack sensor before, but never with UHPC. RFID-based crack sensing is being used to monitor the UHPC retrofit. A crack is simulated on the UHPC specimen and then a commercial, low cost tag is secured. Using backscatter power, the tag reads the crack existence and its increasing volume with every new damage stage. Using a damage index, comparing the uncracked and each cracked stage, this method is not restricted to the raw received signal strength indicator (RSSI), which could be different at each tag. With this sensor, the small cracks that occur in UHPC during its creation can be monitored to ensure the capacity of the retrofitting is maintained. The tested RFID-based crack sensor can be used on various other forms of UHPC.

Implementation and test of an RSSI-based indoor target localization system: Human movement effects on the accuracy

The movement of humans in wireless networks is one of major effects leading to significant received signal strength indicator (RSSI) variation. Using fluctuated RSSI on estimating the target position in the RSSI-based indoor localization system can give large error and poor decision of the system. In this paper, how the human movement affects the accuracy of an implemented indoor target localization system is explored by experiments, and a proposed simple RSSI filtering solution as the guideline solution to directly handle such a research problem is also presented. For our purpose, firstly, the RSSI-based indoor target localization system, which consists of design communication operations for measuring the RSSI in the wireless network and selected well-known localization methods (i.e. the min-max and the trilateration methods) for estimating the target position, is implemented and tested. Secondly, selected well-known filtering methods (i.e. the moving average and the exponentially weighted moving average filters) and the span thresholding filter (i.e. the proposed solution) are applied for reducing the RSSI variation and the estimated position error caused by the human movement. Our experiments have been carried out in an indoor environment. An LPC2103F microcontroller interfacing with a 2.4 GHz CC2500 RF module is developed and employed as the wireless node. Experimental results reveal that the estimated position error determined by the min-max and the trilateration methods significantly increases during the human movement, and converts according to human movement patterns and numbers of movement people. Also, the results demonstrate that by applying the moving average filter with a high window size and the exponentially weighted moving average filter with an optimal weighting factor to raw RSSI data, the estimated position error is not much improved. In contrast, the span thresholding filter gives better results and can directly cope with the human movement problem. In average, the localization error and the standard deviation decrease 11.921% and 42.086% in the case of the min-max method, and they decrease 44.535% and 87.154% in the case of the trilateration method.

An Improved Indoor Location Technique using Kalman Filter

Indoor positioning technique is used to trace location of entities within a non-space environment riding from the incapability of GPS to do so. Most of indoor localization techniques proposed by researchers aimed at discovering an optimized solution for indoor location tracking with high precision and accuracy. This paper proposes an improved indoor location technique by implementing Trilateration and Kalman Filter technique that can manipulate noise signal deduced from raw Received Signal Strength Indicator (RSSI). Upon implementing the technique, observation and comparison are made to measure the effectiveness and reliability of the enhanced Kalman Filter in tracking indoor positioning. Our analysis and finding shows that the enhanced indoor positioning technique improves the accuracy significantly.

Spatial Region Estimation for Autonomous CoT Clustering Using Hidden Markov Model

This paper proposes a hierarchical dual filtering (HDF) algorithm to estimate the spatial region between a Cloud of Things (CoT) gateway and an Internet of Things (IoT) device. The accuracy of the spatial region estimation is important for autonomous CoT clustering. We conduct spatial region estimation using a hidden Markov model (HMM) with a raw Bluetooth received signal strength indicator (RSSI). However, the accuracy of the region estimation using the validation data is only 53.8%. To increase the accuracy of the spatial region estimation, the HDF algorithm removes the high-frequency signals hierarchically, and alters the parameters according to whether the IoT device moves. The accuracy of spatial region estimation using a raw RSSI, Kalman filter, and HDF are compared to evaluate the effectiveness of the HDF algorithm. The success rate and root mean square error (RMSE) of all regions are 0.538, 0.622, and 0.75, and 0.997, 0.812, and 0.5 when raw RSSI, a Kalman filter, and HDF are used, respectively. The HDF algorithm attains the best results in terms of the success rate and RMSE of spatial region estimation using HMM.

Design and Realization of Precise Indoor Localization Mechanism for Wi-Fi Devices

Despite the abundant literature in the field, there is still the need to find a time-efficient, highly accurate, easy to deploy and robust localization algorithm for real use. The algorithm only involves minimal human intervention. We propose an enhanced Received Signal Strength Indicator (RSSI) based positioning algorithm for Wi-Fi capable devices, called the Dynamic Weighted Evolution for Location Tracking (DWELT). Due to the multiple phenomena affecting the propagation of radio signals, RSSI measurements show fluctuations that hinder the utilization of straightforward positioning mechanisms from widely known propagation loss models. Instead, DWELT uses data processing of raw RSSI values and applies a weighted posterior-probabilistic evolution for quick convergence of localization and tracking. In this paper, we present the first implementation of DWELT, intended for 1D location (applicable to tunnels or corridors), and the first step towards a more generic implementation. Simulations and experiments show an accuracy of 1m in more than 81% of the cases, and less than 2m in the 95%.

Measurement Error Observer-Based IMM Filtering for Mobile Node Localization Using WLAN RSSI Measurement

This paper proposes a mobile node (MN) localization technique using received signal strength indicator (RSSI) data of wireless local area network. Range-domain measurement converted from the raw RSSI data can be used for localizing the MN. In indoor environments, however, the measurements may contain large errors caused by wall penetrating line-of sight (LOS) signals and non-LOS signals. The measurement errors cause large localization error. In this paper, a measurement error observer (MEO) is newly developed to mitigate the localization error. In addition, the MEO is integrated with an interacting multiple model (IMM) filter to take the flexible dynamic model of the MN into consideration. The purpose of the developed MEO-based IMM filter is to decouple the measurement errors from the localization and to adapt the filter for the flexible dynamics of the MN. Through comparative simulation and experimental tests, the performance of the developed filter is evaluated.