Multi-floor Environment Research Articles

Development of an Indoor Delivery Mobile Robot for a Multi-Floor Environment

Development of an Indoor Delivery Mobile Robot for a Multi-Floor Environment

Wi-Fi Positioning Dataset with Multiusers and Multidevices Considering Spatio-Temporal Variations

Precise information on indoor positioning provides a foundation for position-related customer services. Despite the emergence of several indoor positioning technologies such as ultrawideband, infrared, radio frequency identification, Bluetooth beacons, pedestrian dead reckoning, and magnetic field, Wi-Fi is one of the most widely used technologies. Predominantly, Wi-Fi fingerprinting is the most popular method and has been researched over the past two decades. Wi-Fi positioning faces three core problems: device heterogeneity, robustness to signal changes caused by human mobility, and device attitude, i.e., varying orientations. The existing methods do not cover these aspects owing to the unavailability of publicly available datasets. This study introduces a dataset that includes the Wi-Fi received signal strength (RSS) gathered using four different devices, namely Samsung Galaxy S8, S9, A8, LG G6, and LG G7, operated by three surveyors, including a female and two males. In addition, three orientations of the smartphones are used for the data collection and include multiple buildings with a multifloor environment. Various levels of human mobility have been considered in dynamic environments. To analyze the time-related impact on Wi-Fi RSS, data over 3 years have been considered.

Artificial Neural Network-Based Uplink Power Prediction From Multi-Floor Indoor Measurement Campaigns in 4G Networks

Paving the path toward the fifth generation (5G) of wireless networks with a huge increase in the number of user equipment has strengthened public concerns on human exposure to radio-frequency electromagnetic fields (RF EMFs). This requires an assessment and monitoring of RF EMF exposure, in an almost continuous way. Particular interest goes to the uplink (UL) exposure, assessed through the transmission power of the mobile phone, due to its close proximity to the human body. However, the UL transmit (TX) power is not provided by the off-the-shelf modem and RF devices. In this context, we first conduct measurement campaigns in a multi-floor indoor environment using a drive test solution to record both downlink (DL) and UL connection parameters for Long Term Evolution (LTE) networks. Several usage services (including WhatsApp voice calls, WhatsApp video calls, and file uploading) are investigated in the measurement campaigns. Then, we propose an artificial neural network (ANN) model to estimate the UL TX power, by exploiting easily available parameters such as the DL connection indicators and the information related to an indoor environment. With those easy-accessed input features, the proposed ANN model is able to obtain an accurate estimation of UL TX power with a mean absolute error (MAE) of 1.487 dB.

Multi-Floor Indoor Trajectory Reconstruction Using Mobile Devices

An indoor trajectory is the path of an object moving through corridors and stairs inside a building. There are various types of technologies that can be used to reconstruct the path of a moving object and detect its position. GPS has been used for reconstruction in outdoor environments, but for indoor environments, mobile devices with embedded sensors are used. An accelerometer sensor and a magnetometer sensor are used to detect human movement and reconstruct the trajectory on a single floor. In an indoor environment, there are many activities that will create the trajectory similar to an outdoor environment, such as passing along the corridor, going from one room to another, and other activities. We need to analyse trajectories to obtain the movement patterns, understand the most frequently visited places or paths used as well as the least frequented ones. Furthermore, we can utilize movement patterns to obtain a better building design and layout. The latest studies focus on reconstructing the trajectory on a single floor. However, actual indoor environments are comprised of multi-floors and multi-buildings. The purpose of this paper is to reconstruct a trajectory in an indoor multi-floor environment. We have conducted extensive experiments to evaluate the performance of our proposed algorithms in a campus building. The result of our experiment shows that the height of the building can be detected using a barometer sensor that gives an atmospheric pressure reading which is then transformed by setting the range value according to the number of floors, enabling the sensors to detect activity in a multi-floor building. The readings obtained from the magnetometer sensor can be used to reconstruct the trajectory similar to the real path based on the direction and degree of direction. The system accuracy in recognizing steps in a multi-floor building is about 84%.

Indoor Multifloor Localization Method Based on WiFi Fingerprints and LDA

Indoor localization has elicited increasing attention because it has been widely used in indoor location-based services. At present, many complex scenarios for indoor localization require position estimation not only in single-floor environments but also in multifloor ones. However, existing works exhibit certain limitations in solving the problems that involve high computational complexity and floor localization accuracy. In this paper, a multifloor identification system based on WiFi fingerprint database is designed to address these issues. This floor identification system is divided into offline and online phases. In the offline phase, a localization fingerprint database is built based on WiFi nodes and a multifloor identification model is proposed based on linear discriminant analysis (LDA), called MA_LDA. In the online phase, the final floor number is determined, and the trained model is combined with the majority voting mechanism. After determining the floor number, an algorithm based on the k-nearest neighbor (KNN), called LL_KNN, is proposed to obtain the location information of a target on the floor. Real experiment results show that our system can identify the floor number by using only a little WiFi node fingerprint information rather than all the nodes to reduce the computational complexity. It works efficiently and achieves high fault-tolerance performance compared with existing approaches in locating targets in a multifloor environment.

Fast floor identification method based on confidence interval of Wi-Fi signals

The indoor positioning technology is based on the hotpots of location based services (LBS). However, most indoor positioning systems are two-dimensional and couldn’t meet the requirements of today’s LBS. The complex indoor structures and environment determine the floor positioning rather than the altitude positioning in the vertical direction, so the floor identification is the key to three-dimensional indoor positioning systems. There are many restrictions for the existing floor identification methods based on barometer or inertial sensor. They need to get the comparable data in advance, or detect the test data changes in a certain period of time for accurate identification. The current floor identification methods based on ordinary Wi-Fi fingerprints are less effective in the complex environment. Therefore, a new floor identification method based on confidence interval of Wi-Fi signals was developed in this paper, which was divided into the offline stage and the online stage. In the offline stage, the dynamic Wi-Fi signal sequences were collected fast. Then, the adaptive partitioning of Wi-Fi signal intervals was carried out according to RSSI distribution characteristics in the multi-floor environment. Finally, the confidence levels were calculated and the database of fingerprints was constructed. In the online stage, the matching between the test fingerprints and those in the database was applied to obtain the confidence of APs on each floor monitored by the test fingerprints. The sums of the confidence of APs on each floor were calculated, and the floor corresponding to the maximum value was judged as the target floor. To verify the performance of the proposed method, it was compared with the majority voting committees, K-means, Naive Bayes and KNN methods. The results indicate that it was better than other methods in large complex indoor scenes. Its identification accuracy rate was 92.2% and the error rate was 7.8% only one floor away. Moreover, it also could significantly reduce the size of the fingerprint database and further improve the efficiency of algorithm.

WIFI/PDR indoor integrated positioning system in a multi-floor environment

To improve the accuracy of indoor positioning for location-based services, we created an improved WiFi/PDR integrated positioning and navigation system where we are using Extended Kalman filter (EKF). The proposed algorithm first relies on MEMS in our mobile phone to estimate the velocity and headin

Hardware and software design of BMW system for multi-floor localization

Although the Micro Electro Mechanical System (MEMS) sensors are capable of providing short-term high positioning accuracy, every positioning result significantly depends on the historical ones, which inevitably leads to the long-term error accumulation. The Bluetooth Low Energy (BLE) is independent of the accumulative error, but the positioning accuracy is suffered by the irregular jump error resulted from the Received Signal Strength Indicator (RSSI) jitter. Considering the requirement of accurate, seamless, and consecutive positioning by the existing commercial systems, we propose a new integrated BLE and MEMS Wireless (BMW) system for multi-floor positioning. In concrete terms, first of all, the way of fingerprint database construction with the reduced workload is introduced. Second, the fingerprint database is denoised by the process of affinity propagation clustering, outlier detection, and RSSI filtering. Third, the robust M estimation-based extended Kalman filter is applied to estimate the two-dimensional coordinates of the target on each floor. Finally, the barometer data are used to calculate the height of the target. The extensive experimental results show that the proposed system can not only restrain the accumulative error caused by the MEMS sensors but also eliminate the irregular jump error from the BLE RSSI jitter. In an actual multi-floor environment, the proposed system is verified to be able to achieve the Root Mean Square (RMS) positioning error within 1 m.

An Integration Framework of Three-Dimensional Wireless Multimedia Sensor Network with Cloud Computing for Surveillance Applications

Objectives: Three-Dimensional Wireless Multimedia Sensor Networks (3-D WMSNs) are the type of WSN. These networks deal with multimedia content such as images in various formats, video, and audio etc. Moreover 3-D WMSNs are widely used for surveillance applications, underwater monitoring, aerial surveillance, urban warfare, monitoring infrastructure of national importance etc. Methods/Statistical Analysis: The system presents an integrated framework of three-dimensional wireless multimedia sensor network with cloud computing for surveillance applications. The integrated framework of 3D WMSNs with cloud computing is suitable for monitoring and surveillance in multi-floor buildings or marine environment. The client utilizes http protocol to interact with framework. At the server side, the sensor nodes collect the data and transfer the data to cloud where it is stored and processed in a distributed environment. On the client side, the user utilizes web services to retrieve data from the distributed SQL databases. Findings: User can fully access cloud service using devices that have internet capabilities. The work focuses distributed computing and resource allocation of processing three-dimensional wireless multimedia sensor data, and performance evaluation over various parameters settings in a cloud environment. Application/Improvements: We utilize CloudSim for simulating the cloud computing environment. The benefits of the system include basic computing hardware and reasonable storage capacities making it suitable for any 3D WMSN which provides real time monitoring and surveillance.

Multifloor Wi-Fi Localization System with Floor Identification

Indoor localization is of great importance in pervasive applications and RSS fingerprint is known as a quite effective indoor location method. Floor attenuation might not give enough margin discrepancy to classify two neighboring floors, such as windows nearby or ring structure. Fingerprint location using the nearest Euclidean distance to the reference point can be interfered by the neighboring floor. In this paper, a multifloor localization framework with floor identification is proposed. The discriminative floor model is trained to maximize between-class scatter and floor identification is triggered by stair walk and elevator events. In experiments, a real dataset is collected in the building of six floors to evaluate our method. The results show that our method can identify accurate location in multifloor environment.

Smartphone-Based Indoor Integrated WiFi/MEMS Positioning Algorithm in a Multi-Floor Environment

Indoor positioning in a multi-floor environment by using a smartphone is considered in this paper. The positioning accuracy and robustness of WiFi fingerprinting-based positioning are limited due to the unexpected variation of WiFi measurements between floors. On this basis, we propose a novel smartphone-based integrated WiFi/MEMS positioning algorithm based on the robust extended Kalman filter (EKF). The proposed algorithm first relies on the gait detection approach and quaternion algorithm to estimate the velocity and heading angles of the target. Second, the velocity and heading angles, together with the results of WiFi fingerprinting-based positioning, are considered as the input of the robust EKF for the sake of conducting two-dimensional (2D) positioning. Third, the proposed algorithm calculates the height of the target by using the real-time recorded barometer and geographic data. Finally, the experimental results show that the proposed algorithm achieves the positioning accuracy with root mean square errors (RMSEs) less than 1 m in an actual multi-floor environment.

Collaborative mapping of an earthquake‐damaged building via ground and aerial robots

AbstractWe report recent results from field experiments conducted with a team of ground and aerial robots engaged in the collaborative mapping of an earthquake‐damaged building. The goal of the experimental exercise is the generation of three‐dimensional maps that capture the layout of a multifloor environment. The experiments took place in the top three floors of a structurally compromised building at Tohoku University in Sendai, Japan that was damaged during the 2011 Tohoku earthquake. We provide details of the approach to the collaborative mapping and report results from the experiments in the form of maps generated by the individual robots and as a team. We conclude by discussing observations from the experiments and future research topics. © 2012 Wiley Periodicals, Inc.

Propagation characteristics and modeling in indoor multi-floor environment

In this paper, the wideband propagation characteristic for an indoor multi-floor scenario is investigated based on wideband multi-frequency channel measurements in two typical indoor environments. Based on the measurement results, the log-distance (LD) model is used to analyze path loss (PL) in different floors. We find that the total penetration loss does not increase linearly in dB with the number of floors traversed, and the propagation condition in same floor, that is line of sight (LOS) or non-LOS (NLOS), can affect the penetration loss significantly. Besides, additional loss caused by traversing floor upwards is 1–2 dB larger than that introduced by traversing floor downwards. Then, we adopt a small error-prediction attenuation factor (AF) model for PL prediction. The modeling process and rationality of the AF model are presented. Finally, delay spread of wideband indoor channel is analyzed. Statistics of time dispersion parameters are compared among floors and frequencies. It is found that the delay spread on the middle floors is the largest, the delay spread on higher floors is relatively small due to lack of power source, and its relation with frequency is not obvious.