Recurrent Neural Networks For Accurate RSSI Indoor Localization

This paper proposes recurrent neuron networks (RNNs) for a fingerprinting indoor localization using WiFi. Instead of locating user's position one at a time as in the cases of conventional algorithms, our RNN solution aims at trajectory positioning and takes into account the relation among the received signal strength indicator (RSSI) measurements in a trajectory. Furthermore, a weighted average filter is proposed for both input RSSI data and sequential output locations to enhance the accuracy among the temporal fluctuations of RSSI. The results using different types of RNN including vanilla RNN, long short-term memory (LSTM), gated recurrent unit (GRU) and bidirectional LSTM (BiLSTM) are presented. On-site experiments demonstrate that the proposed structure achieves an average localization error of $0.75$ m with $80\%$ of the errors under $1$ m, which outperforms the conventional KNN algorithms and probabilistic algorithms by approximately $30\%$ under the same test environment.Comment: Received signal strength indicator (RSSI), WiFi indoor localization, recurrent neuron network (RNN), long shortterm memory (LSTM), fingerprint-based localizatio

Continuous Space Estimation: Increasing WiFi-Based Indoor Localization Resolution without Increasing the Site-Survey Effort

Abstract Although much research has taken place in WiFi indoor localization systems, their accuracy can still be improved. When designing this kind of system, fingerprint-based methods are a common choice. The problem with fingerprint-based methods comes with the need of site surveying the environment, which is effort consuming. In this work, we propose an approach, based on support vector regression, to estimate the received signal strength at non-site-surveyed positions of the environment. Experiments, performed in a real environment, show that the proposed method could be used to improve the resolution of fingerprint-based indoor WiFi localization systems without increasing the site survey effortThis work has been funded by TIN2014-56633-C3-3-R (ABS4SOWproject) from the Ministerio de Economía y Competitividad and the University of Alcalá Postdoctoral Research program (30400M000.541A.640.17)S

An adaptive weighting algorithm for accurate radio tomographic image in the environment with multipath and WiFi interference

Radio frequency device-free localization based on wireless sensor network has proved its feasibility in buildings. With this technique, a target can be located relying on the changes of received signal strengths caused by the moving object. However, the accuracy of many such systems deteriorates seriously in the environment with WiFi and the multipath interference. State-of-the-art methods do not efficiently solve the WiFi and multipath interference problems at the same time. In this article, we propose and evaluate an adaptive weighting radio tomography image algorithm to improve the accuracy of radio frequency device-free localization in the environment with multipath and different intensity of WiFi interference. Field experiments prove that our approach outperforms the state-of-the-art radio frequency device-free localization systems in the environment with multipath and WiFi interference

SILS: a Smart Indoors Localization Scheme based on on-the-go cooperative Smartphones networks using onboard Bluetooth, WiFi and GNSS

Seamless outdoors-indoors localization based on Smartphones sensors is essential to realize the full potential of Location Based Services. This paper proposes a Smart Indoors Localization Scheme (SILS) whereby participating Smartphones (SPs) in the same outdoors and indoors vicinity, form a Bluetooth network to locate the indoors SPs. To achieve this, SILS will perform 3 functions: (1) synchronize & locate all reachable WiFi Access Points (WAPs) with live GNSS time available on the outdoors SPs; 2) exchange a database of all SPs location and time-offsets; 3) calculate approximate location of indoor-SPs based on hybridization of GNSS, Bluetooth and WiFi measurements. These measurements includes a) Bluetooth to Bluetooth relative pseudo ranges of all participating SPs based on hop-synchronization and Master-Slave role switching to minimize the pseudo-ranges error, b) GNSS measured location of outdoors-SPs with good geometric reference points, and c) WAPs-SPs Trilateration estimates for deep indoors localization. Results, obtained from OPNET simulation and live trials of SILS built for various SPs network size and indoors/outdoors combinations scenarios, show that we can locate under 1 meter in near-indoors while accuracy of around 2-meters can be achieved when locating SPs at deep indoors situations. Better accuracy can be achieved when large numbers of SPs (up to 7) are available in the network/vicinity at any one time and when at least 4 of them have a good sky view outdoors

Indoor positioning of shoppers using a network of bluetooth low energy beacons

In this paper we present our work on the indoor positioning of users (shoppers), using a network of Bluetooth Low Energy (BLE) beacons deployed in a large wholesale shopping store. Our objective is to accurately determine which product sections a user is adjacent to while traversing the store, using RSSI readings from multiple beacons, measured asynchronously on a standard commercial mobile device. We further wish to leverage the store layout (which imposes natural constraints on the movement of users) and the physical configuration of the beacon network, to produce a robust and efficient solution. We start by describing our application context and hardware configuration, and proceed to introduce our node-graph model of user location. We then describe our experimental work which begins with an investigation of signal characteristics along and across aisles. We propose three methods of localization, using a “nearest-beacon” approach as a base-line; exponentially averaged weighted range estimates; and a particle-filter method based on the RSSI attenuation model and Gaussian-noise. Our results demonstrate that the particle filter method significantly out-performs the others. Scalability also makes this method ideal for applications run on mobile devices with more limited computational capabilitie

In-home monitoring system based on WiFi fingerprints for ambient assisted living

This paper presents an in-home monitoring system based on WiFi fingerprints for Ambient Assisted Living. WiFi fingerprints are used to continuously locate a patient at the different rooms in her/his home. The experiments performed provide a correctly location rate of 96% in the best case of all studied scenarios. The behavior obtained by location monitoring allows to detect anomalous behavior such as long stays in rooms out of the common schedule. The main characteristics of the presented system are: a) it is robust enough to work without an own WiFi access point, which in turn means a very affordable solution; b) low obtrusiveness, as it is based on the use of a mobile phone; c) highly interoperable with other wireless connections (bluetooth, RFID) present in current mobile phones; d) alarms are triggered when any anomalous behavior is detected