Received Signal Strength Indicator Signals Research Articles

A system for monitoring human postures, seizures, and falls from bed using radio and surface electromyography signals

In this work, a system for monitoring human postures, seizures, and falls from bed using received signal strength indicator (RSSI) and surface electromyography (sEMG) signals is studied through experiments. In this proposed system, a person who is located inside a wireless link is monitored by considering the change in measured RSSI signals as the 2.4 GHz IEEE 802.15.4 signals received at a receiver. Human motions in bed that affect RSSI levels can be captured. Thus, with this technique, it does not raise a privacy concern compared with vision-based technology. Additionally, sEMG signals associated with muscle movements from human postures are recorded from the human body’s abdominal muscles. Eight different activities, including normal and critical events, are tested and evaluated. Experimental results indicate that the proposed system could automatically monitor different human postures in real-time. RSSI and sEMG signals correlated to each posture have their own patterns. Furthermore, the relationship between human behaviors and RSSI and sEMG levels is summarized.

Solar Irradiance and Temperature Effects on Signal Transmission Performance of LoRa Network for a Monitoring System on Island

With the increasing interest in wireless technologies, Long Range (LoRa) communication is one of the most promising alternatives for providing long-range and low-power-consumption wireless networks. Low-cost, long-distance communication, and long battery life shelf, are the best description of LoRa technology which is very useful for data transmission from an island environment. This study presents the effect of solar irradiance, air temperature, and onboard temperature on the LoRa transmission signal from the sensor node (Bidong Island). Solar irradiance was obtained from online weather satellite data (Solcast) and a solar irradiance meter, while air and onboard temperature were determined using a temperature sensor and thermal imager, respectively. The findings show that higher solar irradiance significantly decreases signal transmission strength, Received Signal Strength Indicator (RSSI). Likewise, with the decrease in onboard temperature, the RSSI signals perform better.

BLE-Based Indoor Localization: Analysis of Some Solutions for Performance Improvement.

This paper addresses indoor localization using an anchor-based system based on Bluetooth Low Energy (BLE) 5.0 technology, adopting the Received Signal Strength Indicator (RSSI) for the distance estimation. Different solutions have been proposed in the scientific literature to improve the performance of this localization technology, but a detailed performance comparison of these solutions is still missing. The aim of this work is to make an experimental analysis combining different solutions for the performance improvement of BLE-based indoor localization, identifying the most effective one. The considered solutions involve different RSSI signals' conditioning, the use of anchor-tag distance estimation techniques, as well as approaches for estimating the unknown tag position. An experimental campaign was executed in a complex indoor environment, characterized by the continuous presence in the movement of working staff and numerous obstacles. The exploitation of multichannel transmission using RSSI signal aggregation techniques showed the greater performance improvement of the localization system, reducing the positioning error (from 1.5 m to about 1 m). The other examined solutions have shown a lesser impact in the performance improvement with a decrease or an increase in the positioning errors, depending on the considered combination of the adopted solutions.

Overtaking Assistance System based on Signal Strength to Prevent Accidents in V2V Communication Environment

The directional antennas with received signal strength indicator (RSSI) output from 0.52 V to 1.34 V are designed as a receiving component. These antennas have the ability to rotate up to 180° to find the maximum signal strength angle. RSSI signal strength is converted as customized measurement unit as Analog Reading Units (ARU) which varies from 0ARU to 400ARU and signal receiver has −90 dBm receiving sensitivity. The experimental results demonstrate that, the variance of RSSI with speed of a vehicle shows a clear relationship at the position of signal receiving point. Two regions are defined as effective region and non-effective region to show the capability of the proposed model. Effective region is of 60 m range from one vehicle to the other vehicle which plays a role in the overtaking process. When a vehicle is detected within the range, the RSSI value is greater than 150ARU. When the distance is less than 160 m, the RSSI value will be greater than 200ARU. Results prove that the overtaking status of a vehicle is clearly identified as zones and sectors ranging from 1 m to 160 m.

Contactless monitoring of human behaviors in bed using RSSI signals.

In this paper, contactless monitoring and classification of human activities and sleeping postures in bed using radio signals is presented. The major contribution of this work is the development of a contactless monitoring and classification system with a proposed framework that uses received signal strength indicator (RSSI) signals collected from only one wireless link, where different human activities and sleep postures, including (a) no one in the bed, (b) a man sitting on the bed, (c) sleeping on his back, (d) seizuresleeping, and (e) sleeping on his side, are tested. With our proposed system, there is no need to attach any sensors or medical devices to the human body or the bed. That is the limitation of the sensor-based technology. Additionally, our system does not raise a privacy concern, which is the major limitation of vision-based technology. Experiments using low-cost, low-power 2.4GHz IEEE802.15.4 wireless networks have been conducted in laboratories. Results demonstrate that the proposed system can automatically monitor and classify human sleeping postures in real time. The average classification accuracy of activities and sleep postures obtained from different subjects, test environments, and hardware platforms is 99.92%, 98.87%, 98.01%, 87.57%, and 95.87% for cases (a) to (e), respectively. Here, the proposed system provides an average accuracy of 96.05%. Furthermore, the system can also monitor and separate the difference between the cases of the man falling from his bed and the man getting out of his bed. This autonomous system and sleep posture information can thus be used to support care people, physicians, and medical staffs in the evaluation and planning of treatment for the benefit of patients and related people. The proposed system for non-invasive monitoring and classification of human activities and sleeping postures in bed using RSSI signals.

Filtering Effect on RSSI-Based Indoor Localization Methods

Indoor positioning systems have become very popular in recent years because many applications need to know the physical location of objects. Received Signal Strength Indicator (RSSI) based localization services are rapid, flexible and reliable way of identifying, controlling and locating different objects electronically. However, RSSI is prone to noise and interference which can greatly affect the accuracy performance of the system. In this paper Internet of Things (IoT) technologies like low energy Bluetooth (BLE), WiFi, LoRaWAN and ZigBee are used to obtain indoor positioning. Adopting the existing trilateration and positioning algorithms, the Kalman, Fast Fourier Transform (FFT) and Particle filtering methods are employed to denoise the received RSSI signals to improve positioning accuracy. Experimental results show that choice of filtering method is of significance in improving the positioning accuracy. While FFT and Particle methods had no significant effect on the positioning accuracy, Kalman filter has proved to be the method of choice in for BLE, WiFi, LoRaWAN and ZigBee. Compared with unfiltered RSSI, results showed that accuracy was improved by 2% in BLE, 3% in WiFi, 22% in LoRaWAN and 17% in ZigBee technology for Kalman filtering method.

Wireless Communication Platform Based on an Embroidered Antenna-Sensor for Real-Time Breathing Detection.

Wearable technology has been getting more attention for monitoring vital signs in various medical fields, particularly in breathing monitoring. To monitor respiratory patterns, there is a current set of challenges related to the lack of user comfort, reliability, and rigidity of the systems, as well as challenges related to processing data. Therefore, the need to develop user-friendly and reliable wireless approaches to address these problems is required. In this paper, a novel, full, compact textile breathing sensor is investigated. Specifically, an embroidered meander dipole antenna sensor integrated into an e-textile T-shirt with a Bluetooth transmitter for real-time breathing monitoring was developed and tested. The proposed antenna-based sensor is designed to transmit data over wireless communication networks at 2.4 GHz and is made of a silver-coated nylon thread. The sensing mechanism of the proposed system is based on the detection of a received signal strength indicator (RSSI) transmitted wirelessly by the antenna-based sensor, which is found to be sensitive to stretch. The respiratory system is placed on the middle of the human chest; the area of the proposed system is 4.5 × 0.48 cm2, with 2.36 × 3.17 cm2 covered by the transmitter module. The respiratory signal is extracted from the variation of the RSSI signal emitted at 2.4 GHz from the detuned embroidered antenna-based sensor embedded into a commercial T-shirt and detected using a laptop. The experimental results demonstrated that breathing signals can be acquired wirelessly by the RSSI via Bluetooth. The RSSI range change was from -80 dBm to -72 dBm, -88 dBm to -79 dBm and -85 dBm to -80 dBm during inspiration and expiration for normal breathing, speaking and movement, respectively. We tested the feasibility assessment for breathing monitoring and we demonstrated experimentally that the standard wireless networks, which measure the RSSI signal via standard Bluetooth protocol, can be used to detect human respiratory status and patterns in real time.

Boat Detection and Tracking Using RSSI Signal in A Device-Free Environment

Abstract: This project presents a device-free human detection method for using Received Signal Strength Indicator (RSSI) measurement of Wireless Sensor Network (WSN) with packet dropout based on ZigBee. Packet loss is observed to be a familiar phenomenon with transmissions of WSNs. The packet reception rate (PRR) based on a large number of data packets cannot reflect the real-time link quality accurately. So, it raises a real-time RSSI link quality evaluation method based on the exponential smoothing method. Then, a device-free human detection method is proposed. Compared to conventional solutions which utilize a complex set of sensors for detection, the proposed approach achieves the same only by RSSI volatility. The experimental measurements are conducted in laboratory. A high-quality network based on ZigBee is obtained, and then, RSSI can be calculated from the receiver sensor modules. Experimental results show the uncertainty of RSSI change at the moment of human through the network area and confirm the validity of the detection method. Keywords: RSSI Signal, Detection and tracking

Research on Indoor Positioning Algorithm Based on SAGA-BP Neural Network

The Indoor positioning based on the ZigBee received signal strength index has attracted more and more researchers’ attention and, due to its low cost, low hardware power consumption and easy implementation. However, because of multipath effects and shadow effects, traditional indoor positioning algorithms cannot obtain good positioning effects. In order to improve the accuracy of ZigBee indoor positioning, this paper proposes an indoor positioning algorithm of annealing algorithm (SA) and genetic algorithm (GA) optimized neural network (SAGA-BP), and the superiority of this algorithm is proved through simulation and experiment. First, establish the position relationship between the received signal strength indicator(RSSI) and the target position, and arrange the node network structure model to collect signals to establish a fingerprint database. Then use the mechanism of the annealing algorithm combined with the genetic algorithm to optimize the initial weight and initial threshold of the neural network algorithm, so that it can quickly jump out of the local optimal solution and achieve high-precision positioning. Experiments have proved the effectiveness of the positioning algorithm. Compared with BP and GA-BP algorithms, SAGA-BP positioning algorithm has an average error of 0.75m for RSSI signals after acquisition and processing, and an average error of BP positioning algorithm is 1.24m. The average error of GA-BP algorithm is 0.98m. Thus, the SAGA-BP algorithm has higher positioning accuracy.

Real-time tracking of a moving target in an indoor corridor of the hospital building using RSSI signals received from two reference nodes.

In this paper, implementation and validation of a target tracking system based on the received signal strength indicator (RSSI) for an indoor corridor environment of the hospital is presented. Six tracking methods of a moving target (i.e., equipment, robot, or human) using RSSI signals measured from two stationary reference nodes located at the different sides of the corridor are proposed. A filter with its optimal weight value is also applied to smoothen and increase the accuracy of estimated position results (i.e., the x-position in the corridor). Additionally, a determination approach for finding the optimal parameters assigned for the proposed tracking methods and the filter are also introduced. The proposed methods are implemented in MATLAB/Simulink, and experiments using a 2.4 GHz, IEEE 802.15.4/ZigBee wireless network have been carried out in the indoor corridor of the hospital building. Experimental results obtained from the corridor size of 22 m demonstrate that our proposed methods can automatically and efficiently track the moving target in real time. The average distance errors, in the case of varying and manual tuning the optimal parameters of the proposed methods and the filter, reduce from 5.14 to 1.01 m and 4.55 to 0.86 m (i.e., two test cases; slow moving speed and double moving speed). Here, the errors decrease by 80.35% and 81.10%, respectively. For the case using the optimal parameters determined by the optimization approach, the average errors can reduce to 0.97 m for the first test case and 0.78 m for the second test case, respectively.Graphical abstractAn RSSI-based real-time tracking system for a moving target in an indoor corridor of the hospital building

Outdoor Localization Using BLE RSSI and Accessible Pedestrian Signals for the Visually Impaired at Intersections.

One of the major challenges for blind and visually impaired (BVI) people is traveling safely to cross intersections on foot. Many countries are now generating audible signals at crossings for visually impaired people to help with this problem. However, these accessible pedestrian signals can result in confusion for visually impaired people as they do not know which signal must be interpreted for traveling multiple crosses in complex road architecture. To solve this problem, we propose an assistive system called CAS (Crossing Assistance System) which extends the principle of the BLE (Bluetooth Low Energy) RSSI (Received Signal Strength Indicator) signal for outdoor and indoor location tracking and overcomes the intrinsic limitation of outdoor noise to enable us to locate the user effectively. We installed the system on a real-world intersection and collected a set of data for demonstrating the feasibility of outdoor RSSI tracking in a series of two studies. In the first study, our goal was to show the feasibility of using outdoor RSSI on the localization of four zones. We used a k-nearest neighbors (kNN) method and showed it led to 99.8% accuracy. In the second study, we extended our work to a more complex setup with nine zones, evaluated both the kNN and an additional method, a Support Vector Machine (SVM) with various RSSI features for classification. We found that the SVM performed best using the RSSI average, standard deviation, median, interquartile range (IQR) of the RSSI over a 5 s window. The best method can localize people with 97.7% accuracy. We conclude this paper by discussing how our system can impact navigation for BVI users in outdoor and indoor setups and what are the implications of these findings on the design of both wearable and traffic assistive technology for blind pedestrian navigation.

Automated book location and classification method using RFID tags for smart libraries

PurposeThere are hundreds of thousands of books in libraries, causing problems in searching and counting books. To overcome these problems, this research presents a received signal strength indicator (RSSI) dataset and a new automated book location classification model using the RSSI dataset. Materials and methodIn this study, a RSSI signals dataset was collected using a mobile vehicle from 3279 books. These books are randomly placed on six benches, 24 cabinets, and 144 racks. The primary objective of this work is to detect book position automatically using a simple learning model. Thus, a new automated book position detection model is presented and this model is tested on the RSSI dataset collected. (i) Multiscale principal component analysis (MSPCA) is considered a pre-processing method. (ii) Iterative minimum redundancy maximum relevance (ImRMR) algorithm was used to select the most informative features automatically. (iii) The most informative features selected are classified using the Decision Trees (DT) and Ensemble Bagged Trees (BT) algorithms. Three cases are defined according to bench, cabinet, and rack. ResultsThe recommended model yielded 97.34% 98.26% and 93.1% overall accuracies using the bench, cabinet, and rack cases consecutively deploying the BT classifier with 10-fold cross-validation. ConclusionsThe presented book position classification model reached above 90% for all cases and these results calculated and finding clearly demonstrates that this model is ready to use in a big library for book position detection.

Optimized CNNs to Indoor Localization through BLE Sensors Using Improved PSO.

Indoor navigation has attracted commercial developers and researchers in the last few decades. The development of localization tools, methods and frameworks enables current communication services and applications to be optimized by incorporating location data. For clinical applications such as workflow analysis, Bluetooth Low Energy (BLE) beacons have been employed to map the positions of individuals in indoor environments. To map locations, certain existing methods use the received signal strength indicator (RSSI). Devices need to be configured to allow for dynamic interference patterns when using the RSSI sensors to monitor indoor positions. In this paper, our objective is to explore an alternative method for monitoring a moving user’s indoor position using BLE sensors in complex indoor building environments. We developed a Convolutional Neural Network (CNN) based positioning model based on the 2D image composed of the received number of signals indicator from both x and y-axes. In this way, like a pixel, we interact with each 10 × 10 matrix holding the spatial information of coordinates and suggest the possible shift of a sensor, adding a sensor and removing a sensor. To develop CNN we adopted a neuro-evolution approach to optimize and create several layers in the network dynamically, through enhanced Particle Swarm Optimization (PSO). For the optimization of CNN, the global best solution obtained by PSO is directly given to the weights of each layer of CNN. In addition, we employed dynamic inertia weights in the PSO, instead of a constant inertia weight, to maintain the CNN layers’ length corresponding to the RSSI signals from BLE sensors. Experiments were conducted in a building environment where thirteen beacon devices had been installed in different locations to record coordinates. For evaluation comparison, we further adopted machine learning and deep learning algorithms for predicting a user’s location in an indoor environment. The experimental results indicate that the proposed optimized CNN-based method shows high accuracy (97.92% with 2.8% error) for tracking a moving user’s locations in a complex building without complex calibration as compared to other recent methods.

RSSI-Based Indoor Localization Using Multi-Lateration With Zone Selection and Virtual Position-Based Compensation Methods

In the well-known and widely used multi-lateration method, an unknown target position is estimated using reference node positions and received signal strength indicator (RSSI) levels which indicate the distances between the target and the reference nodes. Since the RSSI signal in physical environments is time-varying and fluctuates overtime due to multi-path effects, RSSI signal variation can significantly cause localization errors. Inaccurate results can lead to poor decisions in the overall system. In this paper, an extended multi-lateration method is proposed to increase the localization accuracy. The novelty of the proposed method is that the boundary consideration, the zone selection, and the estimated position compensation method based on virtual positions are developed and integrated with the traditional multi-lateration method. To verify the proposed method, experiments using a ZigBee, 2.4 GHz, IEEE 802.15.4 wireless sensor network deployed in a laboratory room (the area size of 4 m ×4 m) and a corridor of the building (22 m ×9.3 m) have been tested. Experimental results demonstrate that all estimated positions of targets provided by the proposed method are within the test area (or zone), while the traditional multi-lateration method very often provides estimated positions outside the test area. The results also indicate that, by the proposed method, the estimation errors of most targets are lower than the case the multi-lateration method, and only a few targets, the errors by both methods are the same. Finally, in average, the proposed method significantly provides estimation errors lower than the traditional method: 0.682 m and 1.603 m, respectively (for the laboratory room), and 1.776 m and 4.353 m, respectively (for the corridor of the building). Here, the proposed method outperforms the traditional method by 57.430% and 59.194%, respectively.

Enhanced Indoor Localization Based BLE Using Gaussian Process Regression and Improved Weighted kNN

Indoor positioning has attracted commercial developers and researchers in the last few decades. Global positioning system (GPS) cannot well localize in indoor environment. For clinical applications such as workflow analysis, Bluetooth Low Energy (BLE) beacons have been employed to map the positions of individuals in indoor environments. The received signal strength indicator (RSSI) fingerprinting plays a key role in the access point performance services. This paper deals with the issue of indoor localization based RSSI fingerprint using only RSSI vectors without any prior knowledge of the pose. We proposed to use machine learning (ML) combined with modified kNN algorithm to enhance the real-time performance and propose method to detect uncertainty of estimated pose. The mentioned ML algorithm is Gaussian Process Regression (GPR). In the online phase of our system, GPR gives a prediction for the location of the pose using RSSI vector. This prediction helps kNN algorithm therefore limits the searching region leading to reduce the computational cost. An analysis on the distribution of k nearest points is also presented which aims to evaluate the confidence of the estimated pose for extracting a list of trustable points. Furthermore, we also present an extrapolation process using this trust-list to have an optimized trajectory. The accuracy and timing analysis of our proposal was realized on a challenging BBIL dataset which contains very noisy RSSI signals due to a fast moving of the object. The experimental results show that our system exhibits a much better performance than traditional kNN or WkNN algorithms. The RMSE of our optimal trajectory is 1.78 m in the room with a dimension of 10 m $\times$ 25 m, which is competitive in comparison to other methods where initial pose is known.

Hybrid Indoor Positioning System for Pedestrians With Swinging Arms Based on Smartphone IMU and RSSI of BLE

Over the last decades, researchers have invented many positioning algorithms for pedestrians in the indoor environment, such as trilateration, fingerprint, and pedestrian dead reckoning (PDR) system. However, indoor positioning is still difficult to solve with current technology. The fingerprint and trilateration based on received signal strength indicator (RSSI) usually require filters to stabilize the RSSI signal, which will cause a delay in positioning, which is fatal for real-time positioning. The PDR system with the inertial measurement unit (IMU) of smartphones usually requires the direction of the Y-axis of smartphones parallel with movement. Nevertheless, pedestrians will swing their arms while walking normally, which is not considered in the traditional PDR. In this case, a huge bias in heading estimation is inevitable, which causes the traditional PDR system to be unavailable during walking with swinging arms. In this article, a hybrid indoor positioning algorithm based on IMU and RSSI is proposed for pedestrians with swinging arms. The PDR system is improved by analyzing the characteristics of walking postures. In order to eliminate the cumulative error existing in the traditional PDR system, we propose the multipoint positioning algorithm based on RSSI for calibration. Combined with the Kalman filter, positioning delay and error have been effectively reduced for indoor location-based services (ILBSs).

Hybrid Deep Learning Model Based Indoor Positioning Using Wi-Fi RSSI Heat Maps for Autonomous Applications

Positioning using Wi-Fi received signal strength indication (RSSI) signals is an effective method for identifying the user positions in an indoor scenario. Wi-Fi RSSI signals in an autonomous system can be easily used for vehicle tracking in underground parking. In Wi-Fi RSSI signal based positioning, the positioning system estimates the signal strength of the access points (APs) to the receiver and identifies the user’s indoor positions. The existing Wi-Fi RSSI based positioning systems use raw RSSI signals obtained from APs and estimate the user positions. These raw RSSI signals can easily fluctuate and be interfered with by the indoor channel conditions. This signal interference in the indoor channel condition reduces localization performance of these existing Wi-Fi RSSI signal based positioning systems. To enhance their performance and reduce the positioning error, we propose a hybrid deep learning model (HDLM) based indoor positioning system. The proposed HDLM based positioning system uses RSSI heat maps instead of raw RSSI signals from APs. This results in better localization performance for Wi-Fi RSSI signal based positioning systems. When compared to the existing Wi-Fi RSSI based positioning technologies such as fingerprint, trilateration, and Wi-Fi fusion approaches, the proposed approach achieves reasonably better positioning results for indoor localization. The experiment results show that a combination of convolutional neural network and long short-term memory network (CNN-LSTM) used in the proposed HDLM outperforms other deep learning models and gives a smaller localization error than conventional Wi-Fi RSSI signal based localization approaches. From the experiment result analysis, the proposed system can be easily implemented for autonomous applications.

Intelligent learning approach for UHF partial discharge localisation in air‐insulated substations

To achieve comprehensive insulation deterioration motoring of power equipment and early fault warning in air-insulated substations, a data-driven partial discharge (PD) source localisation method employing noisy ultra-high frequency (UHF) received signal strength indicator (RSSI) and particle filter is proposed in this study. Compared with the existing UHF time-difference-based techniques, UHF wireless sensor arrays and RSSI-based methods provide an economical and high-adaptability solution. However, owing to the multi-pathing and shadowing effects, UHF signal attenuation cannot be modelled. Therefore, a Kalman filter was employed to smoothen the RSSI signal. Furthermore, a semi-parametric regression model is proposed to achieve a more accurate relationship between the RSSI and the transmission distance. Finally, in contrast to traditional localisation algorithms directly based on the RSSI ranging model, a particle filter was used to achieve higher accuracy. It predicted the best distribution of the position of PD by learning and considering all the system states of the previous moment. The laboratory test was performed within an area of 6 m × 6 m, and the results demonstrate that the mean PD source localisation error was 1.16 m, which gives a potential application for the identification of power equipment with insulation deterioration in a substation, while the accuracy is still needed to be verified further by field tests.

A Design and Development of a Wireless Sensor Network for Potential Monitoring and Localization

This paper presents an analysis of the received signal strength indicator (RSSI) from the radio frequency signals for human identification in an indoor wireless sensor network (WSN). Instead of using closed-circuit television as the existing security platform, this indoor safety system was improved with a convenient, cheap, and low-power solution. The system was developed using 20 of ESP8266-12F Wi-Fi modules transmitters and another 2 of ESP8266-12F as the access points located in 3 m × 3 m area of interest. With a suitable coordinate of sensor nodes, a WSN telemetry could be established to minimize the blind spot area and limit the movement of the intruder with a minimum area of 0.2 m2. The RSSI measurement was repeatedly conducted for three different conditions, in an empty room, with the presence of a single intruder and the presence of multiple intruders. Based on the RSSI values, we found that there are distinctive features of data that can be utilized as flags for classifying the three above conditions. Besides that, to justify the efficiency of system performance, we also examined the sensitivity of RSSI values towards the variation of temperature. Our results show that the RSSI average values for both morning and night were practically the same. However, during the afternoon, the RSSI signal strength fluctuated by − 1.0 dBm. These results motivate the development of an alarm system that only uses the RSSI statistics to detect human presence.

Urban Wi-Fi fingerprinting along a public transport route

Abstract The outreach of Wi-Fi localization is extended in this study for urban wide applications as they provide the high potential to employ them for numerous applications for localization and guidance in urban environments. The selected application presented in this paper is the localization and routing of public transport smartphone users. For the conducted investigations, Received Signal Strength Indicator (RSSI) values are collected for users who are travelling from home in a residential neighbourhood to work in the city centre and return along the same route. Special tramway trains are selected which provide two on-board Wi-Fi Access Points (APs). Firstly, the availability, visibility and RSSI stability of the Wi-Fi signal behavior of these APs and the APs in the surrounding environment along the routes is analyzed. Then the trajectories are estimated based on location fingerprinting. A first analyses reveals that significant differences exists between the six employed smartphones as well as times of the day, e. g. in the morning at peak hours or at off-peak hours. From the long-time observations it is seen that the two on-board APs show a high stability of the RSSI signals at the same times of the day and along the whole route. It is therefore currently investigated how they can confirm and validate user localization along the route and if they can contribute to constrain the overall positioning solution in combination with the inertial smartphone sensors. Moreover, the railway track can serve as a further constraint. As an outlook on future work, the development of a Simultaneous Localization and Mapping (SLAM) solution with a fusion with the smartphone inertial sensors is proposed.