Comparison of indoor/outdoor, RSSI-based positioning using 433, 868 or 2400 MHz ISM bands

This paper compares accuracy of indoor positioning systems using one of three selected ISM bands: 433, 868 or 2400 MHz. Positioning is based on Received Signal Strength Indication (RSSI), received by majority of ISM RF modules, including low-cost ones. Investigated environment is single, indoor space (e.g. office, hall) and personal use, thus 2-dimensional (2D) coordinate system is used. Obtained results, i.a. average positioning error, are compared with similar measurements taken at outdoor, open space environment. The system is local, i.e. its operational area is limited by range of used RF modules – typical a few tens of meters. The main focus is research of how much accuracy (and usefulness) can be expected from standard RF modules working at typical ISM frequencies

Statistical analysis of indoor RSSI read-outs for 433 MHz, 868 MHz, 2.4 GHz and 5 GHz ISM bands

This paper presents statistical analysis of RSSI read-outs recorded in indoor environment. Many papers concerning indoor location, based on RSSI measurement, assume its normal probability density function (PDF). This is partially excused by relation to PDF of radio-receiver's noise and/or together with influence of AWGN (average white Gaussian noise) radio-channel – generally modelled by normal PDF. Unfortunately, commercial (usually unknown) methods of RSSI calculations, typically as "side-effect" function of receiver's AGC (automatic gain control), results in PDF being far different from Gaussian PDF. This paper presents results of RSSI measurements in selected ISM bands: 433/868 MHz and 2.4/5 GHz. The measurements have been recorded using low-cost integrated RF modules (at 433/868 MHz and 2.4 GHz) and 802.11 WLAN access points (at 2.4/5 GHz). Then estimated PDF of collected data is shown and compared to normal (Gaussian) PDF

Comparison of 2.4 and 5 GHz WLAN network for purpose of indoor and outdoor location

This paper presents comparison of prototype location system built with standard components of 2.4 and 5 GHz WLAN network infrastructure. The system can be used for personal or other objects’ positioning, both for indoor and outdoor environments. The system is local, i.e. its operational area is limited to WLAN network operating range. The system is based on standard and widely available WLAN components (access points, network adapters). The goal is to avoid any hardware and software modifications. Also position calculation should not be power hungry operation. Method of location is based in Received Signal Strength Indication (RSSI) returned by most of RF ICs (including WLAN). The main focus is research of how much accuracy (and usefulness) can be expected from standard WLAN hardware. Both static and dynamic scenarios have been tested and compared

Fault tolerant indoor localization using Wi-Fi

Precise Indoor Localization is a major component of numerous location based applications and services which perform indoor guidance and object tracking. There are many existing solutions which address the localization issue, but most of them do not provide a fault tolerant solution. In this work, we have developed a fault tolerant statistical method which leverages the existing infrastructure by using the readily available Wi-Fi Access Points. Our proposed method can be applied to any environment which has a Wi-Fi coverage and we do not assume the knowledge of the placement of the Access Points or any physical layout. Initially we map the signal strengths and the corresponding positions to obtain the RF distribution of the region and this is the offline phase. We develop different fault tolerant models and use an Android application for monitoring various Access Points to provide the status of the Access Points in the environment. During the online phase, we measure the signal strength at distributed locations in the environment and then, depending on the status obtained from the application, we use the appropriate scheme to obtain the corresponding locations. In specific we use a Maximum Likelihood Estimator to obtain the position from the previously recorded RF map. Further, we provide 95% confidence intervals for the location obtained by using a Bootstrap method. Our method, compared to other deterministic methods is more accurate and fault tolerant. We also provide the experimental results which validate the accuracy of our method in obtaining the user location

Unobtrusive Location-Based Access Control Utilizing Existing IEEE 802.11 Infrastructure

Mobile devices can sense several types of signals over the air using different radio frequency technologies (e.g., Wi-Fi, Bluetooth, cellular signals, etc.). Furthermore, mobile devices receive broadcast messages from transmitting entities (e.g., network access points, cellular phone towers, etc.) and can measure the received signal strength from these entities. Broadcast messages carry the information needed in case a mobile device chooses to establish communication. We believe that these signals can be utilized in the context of access control, specifically because they could provide an indication of the location of a user\u27s device. Such a “location proof” could then be used to provide access to location-based services. In this research, we propose a location-based access control (LBAC) system that utilizes tokens broadcasted by IEEE 802.11 (Wi-Fi) access points as a location proof for clients requesting access to a resource. This work differs from existing research in that it allows the verification of a client’s location continuously and unobtrusively, utilizing existing IEEE 802.11 infrastructure (which makes it easily deployable), and resulting in a secure and convenient LBAC system. This work illustrates an important application of location-based services (LBS): security. LBAC systems manage access to resources by utilizing the location of clients. The proposed LBAC system attempts to take advantage of the current IEEE 802.11 infrastructure, making it directly applicable to an existing ubiquitous system infrastructure

A statistical indoor localization method for supporting location-based access control

Location awareness is critical for supporting location-based access control (LBAC). The challenge is how to determine locations accurately and efficiently in indoor environments. Existing solutions based on WLAN signal strength either cannot provide high accuracy, or are too complicated to accommodate to different indoor environments. In this paper, we propose a statistical indoor localization method for supporting location-based access control. First, in an offline training phase, we fit a locally weighted regression and smoothing scatterplots (LOESS) model on the signal strength received at different training locations, and build a radio map that contains the distribution of signal strength. Then, in an online estimation phase, we determine the locations of unknown points using maximum likelihood estimation (MLE) based on the measured signal strength and the stored distribution. In addition, we provide a 95% confidence interval to our estimation using a Bootstrapping module. Compared with other approaches, our method is simpler, more systematic and more accurate. Experimental results show that the estimation error of our method is less than 2m. Hence, it can better support LBAC applications than others.</p

A statistical indoor localization method for supporting location-based access control

Location awareness is critical for supporting location-based access control (LBAC). The challenge is how to determine locations accurately and efficiently in indoor environments. Existing solutions based on WLAN signal strength either cannot provide high accuracy, or are too complicated to accommodate to different indoor environments. In this paper, we propose a statistical indoor localization method for supporting location-based access control. First, in an offline training phase, we fit a locally weighted regression and smoothing scatterplots (LOESS) model on the signal strength received at different training locations, and build a radio map that contains the distribution of signal strength. Then, in an online estimation phase, we determine the locations of unknown points using maximum likelihood estimation (MLE) based on the measured signal strength and the stored distribution. In addition, we provide a 95% confidence interval to our estimation using a Bootstrapping module. Compared with other approaches, our method is simpler, more systematic and more accurate. Experimental results show that the estimation error of our method is less than 2m. Hence, it can better support LBAC applications than others.</p