OpenHPS: Single Floor Fingerprinting and Trajectory Dataset

Indonesian Journal of Electrical Engineering and Computer Science

Wireless sensor network (WSN) can be used as a solution to find out the position of an object that cannot be reached by global positioning system (GPS), for example to find out the position of objects in a room known as Indoor Positioning. One method in indoor positioning that can be used is fingerprinting. Inside there are two main work phases, namely training and positioning. The training phase is the process of collecting received signal strength indication (RSSI) data levels from each sensor Node reference that will be used as a reference value for the positioning phase. The more sensor Nodes used, the longer the processing time needed in the training phase. This research focussed on the duration of the training phase, the implementation of which are used 4 sensor Nodes, namely Zigbee (IEEE 802.15.4 protocol) arranged according to mesh network topology, one as Node X (positioning target) and 3 as reference Nodes. There are two methods used in the training phase, namely fixed tar...

Cornell University - arXiv, 2021

Many applications require accurate indoor localization. Fingerprint-based localization methods propose a solution to this problem, but rely on a radio map that is effortintensive to acquire. We automate the radio map acquisition phase using a software-defined radio (SDR) and a wheeled robot. Furthermore, we open-source a radio map acquired with our automated tool for a 3GPP Long-Term Evolution (LTE) wireless link. To the best of our knowledge, this is the first publicly available radio map containing channel state information (CSI). Finally, we describe first localization experiments on this radio map using a convolutional neural network to regress for location coordinates.

2013

This paper presents an analysis of the behaviour of an indoor position system based on fingerprints and IEEE 802.15.4 that has been adapted to be tested in a competition, called EvAAL (Evaluating AAL Systems through Competitive Benchmarking), hold both in Madrid (tests) and Eindhoven (results). The objectives of this analysis are to determine the best algorithm that should have been applied in order to obtain the best results in the competition to use them in other environments. Among the different combinations that can be applied, i.e., the way the signature database is filled in and the algorithm uses to determine the closest location point, the best results are obtained using a global signature database where each signature entry is calculated by the medium of samples signatures database and the closest location is determined by a centroid algorithm with the parameter c set to 1.3. In this way, the error made improves the one obtained in the EvAAL, which is reduced by 100 centime...

Journal of Ambient Intelligence and Smart Environments

This paper presents results from comparing different Wi-Fi fingerprinting algorithms on the same private dataset. The algorithms where realized by independent teams in the frame of the off-site track of the EvAAL-ETRI Indoor Localization Competition which was part of the Sixth International Conference on Indoor Positioning and Indoor Navigation (IPIN 2015). Competitors designed and validated their algorithms against the publicly available UJIIndoorLoc database which contains a huge reference-and validation data set. All competing systems were evaluated using the mean error in positioning, with penalties, using a private test dataset. The authors believe that this is the first work in which Wi-Fi fingerprinting algorithm results delivered by several independent and competing teams are fairly compared under the same evaluation conditions. The analysis also comprises a combined approach: Results indicate that the competing systems where complementary, since an ensemble that combines three competing methods reported the overall best results.

Record - IEEE PLANS, Position Location and Navigation Symposium, 2012

This paper presents five methods for generation of WLAN maps for indoor positioning using crowdsourced fingerprints. A fingerprint is assumed to contain identifiers of WLAN access points, received signal strength values and, if the fingerprint is collected outdoors, a GPS position. The proposed methods use the fingerprints' information to generate a WLAN map that contains estimated access point locations. Two of the proposed methods use RSS values in access point location estimation. In our evaluation with simulations and with real data, the Access Point Least Squares method, which does not use RSS information, is the fastest and its accuracy is as good as more complex methods that use RSS information.

2019 IEEE Wireless Communications and Networking Conference (WCNC)

Conventional Radio Frequency (RF) based fingerprinting still remains one of the most popular methods amongst other indoor positioning techniques due to its inherent accuracy and reliability. However, not much prominence has been shown in analyzing certain factors that may affect the outcome of the fingerprinting method while designing the localization system. In this paper, we conduct a study to infer if a reduced number of receivers equipped with higher gain antennas can provide improved Bluetooth Low Energy (BLE) fingerprinting performance in a complex indoor environment. The evaluation is performed in a standard domestic apartment with an activity centric approach using a single wearable beacon and multiple receivers. A rank based route selection algorithm is used to list the candidate positions or routes that indicate the most likely path on which the subject was travelling. Furthermore, we discuss the benefits of implementing the inverse fingerprinting method with a trajectory based prediction model and also examine the effect of surrounding electrical interference. Experimental results indicate that an increased antenna gain in addition to deploying an adequate number of receivers have a positive effect on the overall ranking accuracy.

2019 15th International Conference on Network and Service Management (CNSM), 2019

To detect the position and the orientation of a mobile device within a Wireless Local Area Network (WLAN) covered by multiple access points (APs), the intrinsic properties of multipleinput multiple-output (MIMO) channels are used linking the received signal strength indicators (RSSIs) to the distance and exploiting the received signal correlation structures. Location and orientation fingerprinting is a map based positioning solution that stores for a given orientation past measurements of RSSIs at known reference/grid points in a database that is later used to localize a mobile device at an unknown location and with unknown orientation to the closest reference point. This paper focuses on processing the RSSI data vectors from multiple receiving antennas on a downlink by applying the core tools of Machine Learning (ML) classification methods to evaluate the effects of MIMO RSSI meta-data when capturing 802.11n/ac packets using commodity hardware. Specifically, the paper provides insights into the design of the overall location fingerprinting system operating with new WiFi physical link layer protocols. To verify the operation of the proposed system, experimental results are presented to investigate the impact of different factors, like the number of receive antennas, affecting the estimation accuracy for the location and the orientation of mobile user.

Positioning in indoor wireless environments is growing rapidly in importance and gains commercial interests in context-awareness applications. The essential challenge in localization is the severe fluctuation of receive signal strength (RSS) for the mobile client even at a fixed location. This work explores the major noisy source resulted from the multipath in an indoor wireless environment and presents an advanced positioning architecture to reduce the disturbance. Our contribution is to propose a novel approach to extract the robust signal feature from measured RSS which is provided by IEEE 802.11 MAC software so that the multipath effect can be mitigated efficiently. The dynamic multipath behavior, which can be modeled by a convolution operation in the time domain, can be transformed into an additive random variable in the logarithmic spectrum domain. That is, the convolution process becomes a linear and separable operation in the logarithmic spectrum domain and then can be effectively removed. To our best knowledge, this work is the first to enhance the robustness to a multipath fading condition, which is common in the environments of an indoor wireless LAN (WLAN) location fingerprinting system. Our approach is conceptually simple and easy to be implemented for practical applications. Neither a new hardware nor an extra sensor network installation is required. Both analytical simulation and experiments in a real WLAN environment demonstrate the usefulness of our approach to significant performance improvements. The numerical results show that the mean and the standard deviation of estimated error are reduced by 42% and 29%, respectively, as compared to the traditional maximum likelihood based approach. Moreover, the experimental results also show that fewer training samples are required to build the positioning models. This result can be attributed to that the location related information is effectively extracted by our algorithm.

Indoor positioning systems (IPS) have witnessed continuous improvements over the years. However, large-scale commercial deployments remain elusive due to various factors such as high deployment cost and lack market drivers. Among the state of the art indoor positioning approaches, the Wi-Fi fingerprinting technique, in particular, is gaining much attention due to its ease of deployment. This is largely due to widespread deployment of WiFi infrastructure and its availability in all existing mobile devices. Although WiFi fingerprinting approach is relatively low cost and fast to deploy, the accuracy of the system tends to deteriorate over time due to WiFi access points (APs) being removed and shifted. In this paper, we carried out a study on such deterioration, which we refer to as fingerprint health analysis on a 2 million square feet shopping mall in South of Kuala Lumpur, Malaysia. We focus our study on APs removal using the actual data collected from the premise. The study reveals the following findings: 1) based on per location pin analysis, ~50% of APs belong to the mall operator which is a preferred group of APs for fingerprinting. For some location, however, the number of operator-managed APs are too few for fingerprinting positioning approach. 2) To maintain mean error distance of ~5 meters, up to 80% of the APs can be removed using the selected positioning algorithms at some locations. At some other locations, however, the accuracy will exceed 5m upon >20% of APs being removed. 3) On average, around 40%-60% of the APs can be removed randomly in order to maintain the accuracy of ~5m.

2012

WiFi fingerprinting is currently one of the most popular techniques for indoor localization as it provides reasonable positioning accuracy while at the same time being able to exploit existing wireless infrastructure. To facilitate calibration efforts and to overcome fluctuations in location measurements, many indoor WiFi positioning systems utilize a discrete partitioning, e.g., a grid or a topological map, of the space where the positioning is being deployed. A major limitation of this approach, however, is that instead of considering spatial similarities in the signal environment, the partitioning is typically based on an uniform division of the space or topological constraints (e.g., rooms and walls). This can significantly decrease positioning accuracy when the signal environment is not sufficiently stable across all partitions. Moreover, current solutions provide no support for identifying partitions that are not compatible with the current wireless deployment. To overcome these limitations, we propose AWESOM (Activations Weighted by the Euclidean-distance using Self-Organizing Maps), a novel measure for automatically creating a discrete partitioning of the space where the WiFi positioning is being deployed. In addition to enabling automatic construction of a discrete partitioning, AWESOM provides a measure for evaluating the goodness of a given partitioning for a particular access point deployment. AWE-SOM also enables identifying partitions where additional access points should be deployed. We demonstrate the usefulness of AWESOM using data collected from two large scale deployments of a proprietary wireless positioning system in a hypermarket environment.