The accuracy improvement using WLAN for indoor positioning system

The Global Positioning System (GPS) has eventually become a common positioning technology. While GPS enabled many applications, satellite signals have yet to overcome many obstacles to enable indoor positioning. Meanwhile, due to the wide deployment of Wireless Local Area Networks (WLAN) in recent years, WLAN positioning algorithms have become popular for mobile device positioning in indoor environments. The most accurate WLAN positioning algorithms exploit the so-called fingerprinting concept which consists of two stages. In the offline stage (training), the Received Signal Strength Indicator (RSSI) from a set of available Access Points (AP) is measured for a number of reference locations and stored in a database. Due to the availability of many APs and the complex structure of indoor environments, this information is distinctive for each reference location and thus is called a position fingerprint. In the online stage (testing), a mobile device receives RSSIs from the APs and their fingerprint is compared to the fingerprints that are stored in the database for a best possible match. WLAN fingerprinting can provide high accuracy in indoor environments when there are many APs which ensure fingerprint uniqueness. This paper provides a novel approach to use WLAN multipath signals as possible fingerprints for positioning algorithms for applications with lower number of access points. The paper provides an initial study.

In an infrastructure wireless LAN, the access point (AP) is responsible for connecting mobile stations (STA) and wired stations. Each access point is assigned on one channel. Traditionally, one station selects AP to connect is based on the received signal strength indicator (RSSI). This approach may cause all active mobile stations to connect to few APs and lots of contentions/collisions will occur by the Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA) protocol. Consequently, the total network throughput will be degraded. Contrarily, if all STAs can be equally distributed to all APs and the signal strength of any pair of STA and connected AP is still kept in an acceptable range, the spare bandwidth in wireless LAN (WLAN) will be utilized in a more efficient way. In this paper, a novel dynamic load balance algorithm is proposed for WLAN. Simulation results show the proposed algorithm has the ability to fairly distribute all STAs among APs. Moreover, it also maximizes the average RSSI between AP and connected STAs.

Chaotic deployment of Wireless Local Area Networks (WLANs) in dense urban areas is one of the common issues of many Internet Service Providers (ISPs) and Wi-Fi users. It results in a substantial reduction of the throughput and impedes the balanced distribution of bandwidth among the users. Most of these networks are managed independently and there is no cooperation among them. Moreover, the conventional association mechanism that selects the Access Points (APs) with the strongest Received Signal Strength Indicator (RSSI) aggravates this situation. In this paper, we present a versatile near-optimal solution for the fair bandwidth distribution over virtualized WLANs through dynamic association control. The proposed scheme is called ACO-PF, which is developed on top of Ant Colony Optimization (ACO) as a meta-heuristic technique to provide Proportional Fairness (PF) among the greedy clients. In fact, it presents a generic and centralized solution for ISPs that are using a common, virtualized or overlapped WLAN infrastructure for serving their customers. We have evaluated the efficacy of ACO-PF through numerical analysis versus popular existing schemes for both downlink and uplink scenarios. Our proposed technique has less complexity in terms of the implementation and running time for largescale WLANs and it can be easily developed and customized for different objective functions. In addition, it is implemented in a testbed environment to investigate the key challenges of real deployment scenarios.

The idea of smart and sustainable cities is incomplete without using information and communication technologies. Wireless Local Area Network (WLAN) plays a vital role in connecting different services, transferring data and providing indoor localization. WLAN fingerprinting technique has become the most common approach for indoor localization in smart cities. This technique utilizes received signal strength (RSS) and channel status from the installed WLAN Access Points (AP). Currently, WLAN planning relies on the site survey, which is laborious and uneconomical for large areas. The WLAN has a limited number of channels assigned in the frequency reuse strategy. An inefficient channel assignment strategy will lead towards increased interference, thus degrading the overall service. This paper proposes a WLAN channel assignment strategy for large and crowded indoor areas. The proposed scheme assigns the channels to installed APs for indoor localization in fingerprinting framework. The proposed technique ensures that the proposed AP channel assignment algorithm in our network-planning scheme mitigates interference.

In typical installation of an 802.11 based wireless local area network (WLAN), mobile hosts would access the network through access points. Since the communication link between two mobile stations in the same WLAN effectively becomes a two-hop path, even if they can hear each other, the throughput degrades significantly. In fact, when the number of the active mobile stations n increases, the throughput that every mobile station can achieve will decrease in inverse proportion to n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . In this paper, we propose the ExDCF protocol as an extension to standard DCF protocol in WLAN, which can improve the throughput between two mobile stations more than twice. Simulation results show that our ExDCF works better in WLAN than the standard DCF while remaining compatible to the standard DCF

The location method based on Received Signal Strength Indication (RSSI) ranging has the advantages of low development cost and simple implementation mechanism, and is the mainstream indoor location method nowadays. However, at present, there is a lack of systematic research on the characteristics of Access Point (AP) received signal strength, which cannot meet the signal strength characteristics required by specific indoor positioning environment. Detailedly, in this study, the indoor distribution characteristics of 2.4 GHz RSSI were analyzed experimentally from three influencing factors including antenna orientation of the receiver, type of wireless network card of the receiver and height difference of the transmitting and receiving antenna. The experimental results show that: (1) The RSSI value measured is the strongest When the antenna of the receiver is vertically oriented to the antenna of the transmitter, while the antenna is vertically backward to the transmitter, it is the opposite. The difference between the strongest signal and the weakest signal is 20%–25% at the same test point. (2) The network card with a large measurement range should be selected for data collection on the premise that the quantization step size is 1 and the smoothness is small. Avoiding using the network card for positioning with the maximum limit of measurement range is proposed also. (3) The path loss index of the ranging model is affected by the height difference of the antenna, resulting in a deviation of 0.1–0.2.

Positioning services are increasingly used for applications such as navigation, advertising and social media. While outdoor navigation based on GPS and/or cellular systems works well, indoor navigation is a much tougher challenge. This paper proposes an indoor localization using Wi-Fi received signal strength indicator (RSSI) measurements from existing access points. Using RSSI data from multiple access points, additional access points will be added in the system thru linear regression statistical model to create virtual access points (VAPs). This paper fuses the low-cost and flexible system of fingerprint localization with VAP creation and the improvement in accuracy by adapting Kalman Filter (KF) and Particle Filter (Pf). The effectiveness of using KF with VAP has demonstrated positive results in previous research. This paper aims to integrate the use of PF in the system. To improve the performance of indoor localization, the proposed model will be an improved virtual access point localization, proposed filtering (VAP+KF+PF).

This paper describes feasibility of RSSI (Received Signal Strength Indicator) based access network detection for multi-band WLAN (Wireless LAN) using 2.4/5GHz and 60GHz aiming at power-saving of multi-band WLAN device and higher spectrum efficiency. The proposed scheme detects 60GHz coverage by using 2.4/5GHz WLAN signals without using 60GHz WLAN signals for on/off control of 60GHz part of multi-band WLAN device. This makes it possible to turn off 60GHz part whenever a multi-band WLAN device is out of 60GHz coverage. Ray-tracing simulation results indicate that the proposed RSSI based access network detection is feasible.

In this paper, we present a wireless local area network (WLAN) indoor location system based on area zoning. The system estimates the zone where a mobile device resides by utilizing received signal strength indication (RSSI) from each access points (APs) and then calculates the mobile device&apos;s position. Experimental results show that the 25th and 50th percentile values of the location distance error are 1.57 m and 2.29 m respectively and indicate that the system has the higher degree of accuracy on estimating the position of wireless capable devices.

Received Signal Strength Indicator (RSSI) is used in indoor positioning for measuring object distance to the base station. However, acquiring accurate RSSI values is challenging because wireless interference factors, such as multipath decline interference, make RSSI values of the same object fluctuate over time. Therefore, instead of a single RSSI, RSSI acquisition will collect a set of RSSI values from which the most moderate RSSI is derived. For this purpose, we propose an Enhanced Gaussian Mixture Model (EGMM) to derive a more precise RSSI for improving indoor positioning accuracy. EGMM enhances Gaussian Mixture Model (GMM) by applying Akaike information criterion (AIC) to determine the best K value for GMM to divide RSSI values into K sets representing signals from different paths. Then, EGMM identifies the most appropriate set of RSSI values to derive a more precise RSSI and thus improves the accuracy of indoor positioning. Our EGMM solution performs well in an open indoor space. The experiment is conducted with iBeacon devices, and the average error distance of EGMM is about 64% of those generated by existing Gaussian filtering. The average positioning error of EGMM is about 0.48 meter, which is adequate to indoor positioning accuracy.

An indoor localization system in wireless sensor networks has become a hot development area. Received signal strength indicator (RSSI)-based localization is a promising technique since it requires a relatively low configuration, battery power and easy control. However, the received signal strength is influenced by channel interference and propagation environments. This characteristic affects channel stability and location accuracy in RSSI-based localization. As a result, we propose a novel indoor localization system consisting of a preprocessing method and a post-processing method. To improve channel stability, the pre-processing method selects an optimal channel in terms of the smallest distance error. The optimal channel is less affected to IEEE 802.11. To develop location accuracy, the post-processing method performs maximum likelihood estimation-based location tracking scheme considering the reliability of the RSSI value measurement. We apply three methods to the existing MLE to improve the reliability of the RSSI value. By using this indoor localization system with preprocessing and post-processing, the location error can be reduced. We also proved the performance of the indoor localization system by conducting experiments in a real indoor environment.

Nowadays many systems with diverse technologies such as (GPS, Wi-Fi, Bluetooth, ZigBee, Ultra Wide Band, Ultrasounds, Infrared, etc) can be used for indoor location. One of the biggest challenges in indoor location systems is to determine the actual indoor position of the user using a smart device (Phone, PC, etc), due to their instability, cost, high-power consumption, low accuracy and low precision. To overcome those problems, we have designed, implemented and emulated an indoor location system based on RSSI (Receive Signal Strength indicator) of the Bluetooth low energy 4.0 (BLE). BLE 4.0 technology is more advantageous in terms of long life expectancy, our approach is based on the deployment of equidistant nodes on the ceiling. The nodes are programmed to broadcast a periodic beacon at a time pace of 400ms and then to enter sleep mode. When a smart device enters within the broadcasting range, it locates the three access points (nodes) with the highest signal strength. This information can be calculated using one of the several localization algorithms (Dilatation). The proposed Dilatation algorithm can be easily implemented in the hardware due to its low complexity, the method can be successful even when using a few numbers of nodes. In average the system has about 0.5 ~ 1 meters of error.

The development of mobile computing and Wireless Local Area Networks (WLANs) has grown rapidly, nowadays. There are several approaches and practically work has been done on tracking mobile nodes and transmitting multimedia data among the network using wireless technology. Several applications and methods are being used to maintain the accuracy of tracking distance for the mobile nodes and broadcasting multimedia data at the same time. This paper focuses on the performance of indoor location tracking system on IPv6 Network Island with multiple real time applications that has location assisted transfer for mobile users and the variations of the results of developed system on different circumstances in indoor environment. RSSI (Received Signal Strength Indicator) mechanism has been used to locate the moving nodes. The developed location tracking server is having dynamic and centralized MySQL database management system. SIP (Session Initial Protocols) and customized Kphone user agent has been used to deploy the intercommunicating of multimedia data. The experiment has been conducted on different scenarios of an indoor environment with the effect of inter rooms.

Indoor positioning methods using location-sensitive features of available wireless signals can achieve high accuracy. In particular, many state-of-the-art methods exploit quite unique sets of location-dependent received signal strength (RSS) measurements from multiple wireless local area network (WLAN) access points (APs), also called as wireless fingerprints. However, reception of signals from WLAN APs is not often stable, and RSS measurements tend to be unavailable due to WLAN card or AP transient effects, limited sensitivity of WLAN cards, and fluctuating attenuation and reflection of signals due to a multipath environment, structural changes and moving objects. In certain hostile scenarios, bogus APs may be installed to disorient WLAN localisation algorithms. In this study, two approaches are proposed to mitigate the impact of faulty signal strength measurements. Performance figures are provided for both simulated and empirical environments in order to support conclusions.

With the fast-growing demand for mobile services, where to place the access points (APs) for providing uniformly and appropriately distributed signals in a wireless local area network (WLAN) becomes an important issue in the wireless network planning. Basically, AP placement will affect the coverage and strength of signals in a WLAN. The number and locations of APs in a WLAN are often decided on the basis of the trial-and-error method. Based on this method, the network planner first selects suitable locations to place APs through observation, and then keeps changing the locations to improve the signal strength based on the received signal. Such process is complicated, laborious and time-consuming. To overcome this problem, we investigate the back-propagation neural network (BPNN) algorithm to improve over the traditional trial-and-error method. Without increasing the number of APs, our approach only needs to adjust AP locations to overcome weak signal problems and thus increase the signal coverage for the Internet connection anywhere within the area. In our experiment, we established a WLAN on a C campus. Our experiments also indicate that placing APs according to the BPNN provided better signal coverage and met the students’ demands for connecting to the Internet from anywhere in the classroom.