Indoor position tracking using received signal strength‐based fingerprint context aware partitioning

More and more sensors and receivers are found nowadays in smartphones which can enable and improve positioning for Location-based Services and other navigation applications. Apart from inertial sensors, such as accelerometers, gyroscope and magnetometer, receivers for Wireless Fidelity (Wi-Fi) and GNSS signals can be employed for positioning of a mobile user. In this study, three trilateration methods for Wi-Fi positioning are investigated whereby the influence of the derivation of the relationship between the received signal strength (RSS) and the range to an Access Points (AP) are analyzed. The first approach is a straightforward resection for point determination and the second is based on the calculation of the center of gravity in a triangle of APs while weighting the received RSS. In the third method a differential approach is employed where as in Differential GNSS (DGNSS) corrections are derived and applied to the raw RSS measurements. In this Differential Wi-Fi (DWi-Fi) method, reference stations realized by low-cost Raspberry Pi units are used to model temporal RSS variations. In the experiments in this study two different indoor environments are used, one in a laboratory and the second in the entrance of an office building. The results of the second and third approach show position deviations from the ground truth of around 2 m in dependence of the geometrical point location. Furthermore, the transition between GNSS positioning outdoors and Wi-Fi localization indoors in the entrance area of the building is studied.

In this paper, we propose a surface correlation based fingerprinting method in sparse Wi-Fi environment. The proposed method can estimate location even with very few Wi-Fi APs by using accumulated spatial RSS pattern. We verify performance of the proposed method through several tests in a building with complex paths. Many studies have developed localization technologies that use measurements such as RSS (Received Signal Strength) or RTT (Round Trip Time) for Wi-Fi signal. All RF have signal noise and distortion problems as well as multipath problem in indoor environments. As the distance from the RF signal source increases, the discrimination of the RSS decreases due to RF signal noise and distortion. Therefore, ambiguity of RSS increases as the distance increases, the performance deteriorates. The conventional RF based localization method have tried to maintain dense RF environment to guarantee stable and accurate performance. However, in order to maintain the dense RF environment, it is becoming a barrier to commercialization of localization technology due to high cost of constructing RF infrastructure and maintenance difficulty. In this paper, we propose a Surface Correlation (SC) based fingerprinting technique to maintain high positioning performance even with few Wi-Fi APs. The surface is the accumulated RSS pattern data during the movement of a pedestrian, which means the spatial RSS pattern. Conventional fingerprinting methods using kNN (k-Nearest Neighbors) and WkNN (Weighted kNN) are very vulnerable to signal noise and distortion because they utilize instantaneous RSS data. The performance of Particle Filter (PF) based localization technology is also degraded in areas where Wi-Fi AP is sparsely distributed. However, the proposed method is very robust against signal noise and distortion because it compares the spatial RSS pattern with the reference RSS pattern on the fingerprinting database. This increases localization accuracy and availability for Wi-Fi coverage. For localization, the proposed method conduct correlation process for the spatial RSS pattern of a pedestrian with reference RSS pattern of database. So, the reference RSS pattern should be extracted from a reference trajectory that matches a pedestrian`s walking path. The proposed method utilize a database of ‘link-node’ structure to dynamically generate a reference trajectory in indoor environment with complicated path. It generate candidate reference trajectories using only MAC address and turn event of Pedestrian Dead-Reckoning (PDR). The proposed method fix a reference trajectory between these candidate trajectories through correlation process of measured RSS pattern with each reference RSS pattern. A reference point on the fixed reference having most similar RSS pattern to a pedestrian`s is considered as a current location. In order to improve performance at a fork where RSS ambiguity increases, the proposed method estimates preferentially for a reference trajectory matching with PDR turn event. The turn event can be wrong due to various motion of a pedestrian. The proposed method selects a correct reference trajectory by continuously performing correlation process on candidate reference trajectories. In order to verify the performance of the proposed surface correlation based fingerprinting method, we conducted several tests in a three-story building. Before the tests, we conducted a survey of Wi-Fi environment to construct fingerprinting database in the building. As a result of the survey, 170 APs are distributed in the building. We reduced the number of APs by 20% from 170 to 17, and analyzed performance of the proposed and conventional localization methods. The test results show that the performance of the conventional localization methods degrade as the number of applied APs decreases. The proposed fingerprinting method shows a 3 meter localization error even with very few Wi-Fi APs, and unlike the conventional fingerprinting method, the localization error is bounded to be within 5m due to the use of spatial RSS pattern. In this paper, we presented the fingerprinting method using the surface correlation in sparse Wi-Fi environment. The proposed fingerprinting method accumulates Wi-Fi RSS during movement of a pedestrian. The proposed method compares the spatial RSS pattern, which is accumulated RSS data, with reference RSS patterns of fingerprinting database. By comparing the RSS patterns, the proposed method greatly improves accuracy as well as stability. In addition, it can be effectively applied in a sparse Wi-Fi environment by improving the availability of ambiguous areas of Wi-Fi coverage. Through these results, we expect to be able to provide accurate and reliable location information even in indoor weak RF environment.

Location-based applications require knowing the user position constantly in order to find out and provide information about user’s context. They use GPS signals to locate users, but unfortunately GPS location systems do not work in indoor environments. Therefore, there is a need of new methods that calculate the location of users in indoor environments using smartphone sensors. There are studies that propose indoor positioning systems but, as far as we know, they neither run on Android devices, nor can work in real environments. The goal of this chapter is to address that problem by presenting two methods that estimate the user position through a smartphone. The first method is based on euclidean distance and use Received Signal Strength (RSS) from WLAN Acces Points present in buildings. The second method uses sensor fusion to combine raw data of accelerometer and magnetometer inertial sensors. An Android prototype that implements both methods has been created and used to test both methods. The conclusions of the test are that RSS technique works efficiently in smartphones and achieves to estimate the position of users well enough to be used in real applications. On the contrary, the test results show that sensor fusion technique can be discarded due to bias errors and low frequency readings from accelerometers sensor.KeywordsGlobal Position SystemReceive Signal StrengthWireless Local Area NetworkInertial SensorSensor FusionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Traditional received signal strength (RSS)-based localizations are often erroneous for the low-cost WSN devices. The reason is that the wireless channel is vulnerable to so many factors that deriving the appropriate propagation loss model for the WSN device is difficult. We propose a flexible location estimation algorithm using generalized regression neural network (GRNN) and weighted centroid localization. In the first phase of the proposed scheme, two GRNNs are trained separately for x and y coordinates, using RSS data gathered at the access points from the reference nodes. The networks are then used to estimate the approximate location of the target node and its close neighbors. In the second phase, the target node position is determined by calculating the weighted centroid of the Nc-closer neighbors. Performance of the proposed algorithm is compared with some existing RSS based techniques. Simulation and experimental results indicate that the location accuracy is satisfactory. The system performance is remarkably good in comparison with its simplicity and requiring no additional hardware.

The computational capabilities of off-the-shelf wireless sensors networks presents a limitation when more complex forms of localization algorithms are employed for location estimation purposes, particularly in an indoor environment. Range-free algorithms rely on Received Signal Strength (RSS) from sensors that are location aware (anchor nodes) as the major means of distance estimation. This paper presents a non-site specific algorithm for better estimating RSS relationship with distance. By employing a unique form of rationalization of raw RSS with respect to distance using the proposed algorithm, it is possible to enhance the reliability of RSS when employed in indoor Localization Algorithms. Consequently, this paper presents an innovative RSS-Distance rationalization algorithm for localization of objects in an indoor environment. The paper compared the proposed algorithm with Simple Moving Average (SMA) algorithm due to the wide applicability and ease of manipulation of SMA. The analysis of the proposed algorithm and SMA shows that the proposed algorithm better modifies RSS for more accurate position estimation in an indoor environment.

Aiming at the problem of low accuracy of node location in the local scope, a location algorithm for wireless sensor network with a mobile anchor node was proposed based on the improved k-nearest neighbor classification algorithm. The algorithm used K most similar reference nodes to calculate the coordinates of the unknown nodes. The location of the reference node is important. The Received Signal Strength Indication relative value between the unknown node and the reference node was acquired by using chi-square distance optimization. The Fisher criterion is used to select the reference nodes with the strong ability within the communication scope of unknown nodes and evaluate the error. The different weights are assigned to the reference nodes distribution, and the reference nodes with low signal intensity are deleted, and the reference nodes with shortest distance is the best. The proposed algorithm optimizes the selection of reference nodes. Experimental results show that the positioning accuracy is optimized by 14.54% with a smaller error distribution range compared with the K-Nearest Neighbor positioning algorithm.

In indoor location environment, shadowing, multipath and reflection phenomena make the position estimation inaccurate. Large number of Access Points (APs) equipped with single antenna could obtain enough Receive Signal Strength (RSS) information to describe signal characteristics of the target space. However, it results in a large investment costs. This paper provides a novel indoor positioning solution using the multi-antennas architecture to increase the accuracy of location estimation. The system obtains enough RSS to compute the user location and provides a reasonable cost of deployments in indoor location environment. Experimental and numerical results show that the proposed indoor location estimation demonstrates a high accuracy of positioning and outstanding performance when RSS is collected by smart antenna technique. More specifically, the accuracy is increased from 1.34% to 92.4% and the error variance is reduced by 96% as compared to the traditional location fingerprint method.

Unnecessary handoff is one of the key issues in mobile wireless network, since it consumes the network's resources that degrades the performance of the network. Handoff appears when the Receive Signal Strength (RSS) of a mobile node becomes below threshold value (i.e., an acceptable level of RSS for ensuring communication). Subsequently, the mobile node starts scanning to discover a new RSS from neighbors Access Point (AP) to keep continuing the communication. However, traditional handoff algorithm is not accurate due to only depend on the RSS. It is affected by many factors (e.g., shadowing, scattering, reflection, etc.) that initiate unnecessary handoff decision. In this paper, a new technique is proposed to mitigate the unnecessary handoff. The simulation result confirms the effectiveness of the proposal.

Using received signal strength (RSS) fingerprints from nearby Wi-Fi access points is a common approach to indoor localization in modern applications, in part due to its relatively low hardware cost and computational complexity. We present a sequential detection approach to RSS-based positioning that employs a Bayesian metric to identify the most likely path, given a time series of RSS measurements, traveled by the indoor user. Offline RSS training data is collected, and the k-nearest neighbors (KNN) algorithm is applied to approximate position likelihoods conditioned on RSS observations. The performance of the proposed approach is evaluated using data collected in an academic building at varying times of day and across several days. Results show that the sequential detection approach achieves strong performance even when only a small series of RSS measurements are available.

Common received signal strength (RSS) indoor local positioning systems (ILPS) are based on a distributed collection of sensor data using reference infrastructure components (reference node, beacon node) and a centralized position estimation. The maximum number of mobile blind nodes (BNs) which can be located simultaneously and the coverage of the system are limited due to the channel capacity of the backbone (wired or wireless) for the sensor data collection. To overcome the bus load issue for wired systems, a hierarchical bus structure with multiple gateway segments is proposed. A data compression using a distributed weighted centroid localization (dWCL) strategy with precomputed gateway segment estimations is implemented to extend the coverage of the localization system. Accuracy and coverage of centralized and distributed WCL algorithms are compared for a one-dimensional tracking simulation and 196 reference nodes, arranged in up to 28 gateway segments. Using distributed computations, the localization system's coverage is increased by factor 10 while the location estimation error increases only slightly.

In this paper, we introduce two indoor Wireless Local Area Network (WLAN) positioning methods using augmented sparse recovery algorithms. These schemes render a sparse user's position vector, and in parallel, minimize the distance between the online measurement and radio map. The overall localization scheme for both methods consists of three steps: 1) coarse localization, obtained from comparing the online measurements with clustered radio map. A novel graph-based method is proposed to cluster the offline fingerprints. In the online phase, a Region Of Interest (ROI) is selected within which we search for the user's location; 2) Access Point (AP) selection; and 3) fine localization through the novel sparse recovery algorithms. Since the online measurements are subject to inordinate measurement readings, called outliers, the sparse recovery methods are modified in order to jointly estimate the outliers and user's position vector. The outlier detection procedure identifies the APs whose readings are either not available or erroneous. The proposed localization methods have been tested with Received Signal Strength (RSS) measurements in a typical office environment and the results show that they can localize the user with significantly high accuracy and resolution which is superior to the results from competing WLAN fingerprinting localization methods.

With growing technology, Location Based Services (LBSs) are becoming a vital part of life. The rapid development of mobile internet has offered an opportunity for Wi-Fi indoor positioning to come under the spotlight due to its low cost. Here, indoor positioning is performed using Wi-Fi. The earlier solutions like GPS does not provide efficient positioning in indoor environment due to weak signal reception which makes Wi-Fi more reliable. However, nowadays the accuracy of the traditional indoor positioning algorithms like trilateration cannot meet the practical requirement. This paper proposes the performance analysis of indoor positioning algorithms using Wi-Fi technology based on Received Signal Strength (RSS) from the access points in the network. The positioning algorithms are based on location fingerprinting consisting of two stages: The offline database creation and the online positioning. The RSS values obtained by the mobile device at reference points are sent to the server where all the location fingerprints of all reference points are stored in a database. Coordinates of user position are calculated using positioning algorithms and the position is displayed in terms of 2Dcoordinates of the room and indoor map. Basic Fingerprint algorithm, RSS Fingerprint with minimum Euclidean Distance algorithm and RSS Fingerprint with Euclidean Distance and k-nearest neighbors' algorithm were implemented. Moreover comparison of their performance was carried out in terms of accuracy of position. From the result, it is found that an average accuracy of 0.4962 meters was achieved by RSS Fingerprint with minimum Euclidean distance algorithm.

In this paper, we propose a novel hybrid RSS/TOA positioning algorithm with a mobile anchor, which follows a spiral trajectory to cover the whole region of the wireless sensor networks (WSN). The conventional time of arrival (TOA) positioning algorithms have two considerable accuracy problems. Firstly, as the distance between the reference node and the sensor increases, its accuracy is degraded gradually. Secondly, when an obstacle exists between the reference node and the sensor, its accuracy is degraded greatly. In our proposed algorithm, the mobile anchor, which plays the same role as the reference node, moves on a spiral trajectory and transmits its signal periodically. The received signal strength (RSS) is utilized by the sensor not only to compute whether it is closed enough to the mobile anchor or not but also to distinguish the obstacle. As a result, the measurement range of the TOA ranging process is restricted within the interval of two adjacent spiral curves. Under the circumstance that no obstacle exists between the mobile anchor and the sensor, the TOA ranging process and the proposed two-step positioning calculation are operated to estimate the position of the sensor accurately. Simulations are performed to compare the performance of the proposed hybrid RSS/TOA positioning algorithm with the performance of the conventional TOA least square estimating positioning algorithm (TOA_LSE). Simulation results show that the average positioning error is reduced by 48.2% and the standard deviation of the positioning errors is reduced by 71.3%.

A new approach has been developed to provide an overview about signal behavior in indoor environments using Cost-231 Multiwall Model (Cost-231 MWM) and Adaptive Data Rate (ADR) method. This approach used as a reference for access point (AP) placement for campus building. The Cost-231 MWM plays a role in estimating the measured power received by user (usually called as Received Signal Strength Indicator/RSSI) by considering the existence of obstacles around the transmitter (AP). We used Institut Asia Malang environments as the case study and gave some recommendations for AP placement: ten optimal placements for the first, third and fourth floor, also seven optimal placements for the second floor. These recommendations were based on the RSSI for good and excellent level signal (-50 dBm until -10dBm). This research also uses the Adaptive Data Rate (ADR) mechanism approach to reduce the amount of packet loss (kbps) resulting from obstacles that cause attenuation (-dB). With the Adaptive Data Rate mechanism, it means increasing the number of access points, the signal attenuation (-dB) occurs from the obstacles (Walls) that are penetrated by the Radio Frequency device and causes attenuation (-dB), the more Access points on the Multi-Wall, will allow communication and data transmitting stability.

Localization algorithm in wireless sensor networks has attracted significant interest in recent years. The RSSI approach is considered as the cheapest solution allowing coarse-grain, yet effective for many applications in outdoor environments. However, when environments change, there exists a big localization error using RSSI without a reconstructing data set. In order to solve the problem of reestablishing the data set when the environments change, a novel one-dimensional localization algorithm based on received signal strength ratio (1-DIM-RSSR) is proposed for the one-dimensional wireless sensor network applications, such as road surveillance and pipeline transmission. While synthetically considering accuracy, power consumption, localization time, and device costs, we can only use coordinate x to locate the nodes by defining y as 0. Our algorithm is based on the theory as follows: the values of Signal Strength vary from one environment to another. When environments change, even the distance between the transmitter and the receiver remains unchanged, but there is still at least 10dBm's difference. On the contrary, the ratio of the received signal strength from two base stations varies little in the condition of the unchanged distance, and thus it avoids reconstructing the dataset when environments change. 1-DIM-RSSR comprises of two phases: the data set construction phase and the localization phase. In the first phase, N reference nodes are uniformly distributed between the two base stations BS1 and BS2. We collect the signal strength RRi1 and RRi1 from BS1 and BS2 respectively and get signal strength ratio Ri = RRi1/RRi2 at each point, where i ϵ [1, N] corresponding to the N references nodes. In the localization phase, we record the signal strength ratio Rʹ of mobile nodes and pick the one that best matches the observed signal strength ratio. Our idea is to compute the distance | Ri - Rʹ| at a fixed set of locations, and then pick the location that minimizes the distance. Simulation and experiment results show that localization precision of the proposed algorithm compared with the RSSI algorithm is improved when the environments change. The proposed method is easy to operate, and need not reconstruct the dataset when the environments change. So this method holds the promise for many applications such as monitoring product lines, road surveillance, and pipeline transmission. Meanwhile, since our algorithm is based on 1-D localization, how to extend this conception to 2-D or even 3-D localization will be our ongoing work.