Feasibility of RSSI based access network detection for multi-band WLAN using 2.4/5GHz and 60GHz

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.

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.

Localization is the crucial problem for mobile robot navigation. For indoor mobile robot, since a global positioning system (GPS) is incapable, another promising technique to detect the position is the received signal strength indicator (RSSI) from wireless communication. To improve the precision and robustness of mobile unit localization, an inertial measurement unit (IMU) is normally used. In this report, we propose the algorithm for mobile robot localization based on sensor fusion between RSSI from wireless local area network (WLAN) and an IMU. The proposed fusion scheme is based on the extended Kalman filter (EKF). The experiment is conducted by using mobile unit equipped with low-cost IMU and a wireless communication module together with access points to evaluate the performance of our algorithm, and the result is promising.

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.

Global positioning technologies such as the Global Positioning System (GPS) are ubiquitously available for different positioning applications. Within indoor environments, coverage of the explicit sensors based on GPS is limited. Developing an indoor location tracking system based on the Received Signal Strength Indicator (RSSI) of the Wireless Local Area Network (WLAN) is considered cost effective method. The widely used technique for estimating the position out of the RSSI measurements is the Extended Kalman Filter (EKF). However, EKF has high computational complexity due to the calculation of Jacobian matrices and suffers from filer instability. In this paper, we propose the Scaled Unscented Kalman Filter (SUKF), which is one of the Sigma Point Kalman Filters (SPKF) family, to overcome the limitations of the EKF. SUKF shall work over the WLAN IEEE 802.11n networks to exploit the RSSI range measurements for localizing and tracking of a mobile node. For performance evaluation, SUKF is compared with the EKF. Results are illustrated using Monte Carlo simulation in MATLAB.

In a location estimation with the received signal strength indication (RSSI) of a wireless signal in an area, the maximum likelihood (ML) function should match the real statistical property of the RSSI in the area. For a wireless local area network (WLAN)-based RSSI location estimation with a wideband signal in an office environment, the wideband signal experiences frequency selectively Rayleigh fading, so a complicated ML function containing several channel parameters needs to be derived in the environment. This paper shows that a simplified ML function containing only two channel parameters, which is optimum for a narrowband signal, is also applicable to an IEEE 802.11 g WLAN-based location estimation with a wideband signal. Computer simulation and experimental results show that the use of the simplified ML function introduces almost no degradation in the location estimation performance in typical office environments, as compared with the use of an exact ML function.

At present, the theory and algorithm of wireless indoor positioning systems have been widely researched. Some applications of these systems have been successfully used such as inventory management. Nevertheless, positioning using radio signals indoor is subject to multipath propagation and other causes. Therefore, how to determine the location accurately has been recognized as a difficult task. This paper focuses on one of the positioning methods called received signal strength indication(RSSI) in a Wireless Local Area Network(WLAN). To improve the positioning accuracy, we proposed a scheme which to estimate the distance between access point(AP) and mobile station(MS) using the average of pilot signal strength in frequency domain. We then propose another scheme to estimate the position of the MS based on the estimated distances. At last, the simulations results show that the proposed schemes has a higher degree accuracy compared with the conventional methods.

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'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.

A low power adaptive controlled current-reusing received signal strength indicator (RSSI) is proposed for the human body communication (HBC). The proposed RSSI has three low power features. First, the power on controller (PoC) scheme is proposed to achieve the duty control of the RSSI. It significantly reduces the average power consumption of RSSI over 90%. Second, the current stacking scheme is adopted to share both eight rectifiers and eight amplifiers, composing the RSSI. By the current reusing technique, the power consumption of the RSSI is reduced to 45%. In addition, the reconfigurable LNA is used in the front-end of HBC TRX. The RSSI adaptively controls the gain and noise figure of the LNA to optimize the power consumption. The proposed RSSI occupies 0.85mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> in 0.18-μm CMOS technology.

Localization for visually impaired people in dynamically changing environments with unexpected hazards and obstacles is a current need. Many techniques have been discussed in the literature with respect to location-based services and techniques used for the positioning of devices. Time difference of arrival (TDOA), time of arrival (TOA) and received signal strength (RSS) have been widely used for the positioning but narrow band signals such as Bluetooth cannot efficiently utilize TDOA or TOA. Received signal strength indicator (RSSI) to measure RSS, has been found to be more reliable. RSSI measurement estimations depend heavily on the environmental interference. RSSI measurement estimations of Bluetooth systems can be improved either by improving the existing methodologies used to implement them or by using fusion techniques that employ Kalman filters to combine more than one RSSI method to improve the results significantly. This paper focuses on improving the existing methodology of measuring RSSI by proposing a new method using trilateration for localization of Bluetooth devices for visually impaired people. To validate the new method, class 2 Bluetooth devices (Blue Giga WT-12) were used with an evaluation board. The software required was developed in National Instruments LabView. The PCB was designed and manufactured as well. Experiments were then conducted, and surface plots of Bluetooth modules were obtained to show the signal interference and other environmental effects. Lastly, the results were discussed, and relevant conclusions were drawn.

This paper proposes a system that estimates crowdedness, such as the numbers of people and wireless communication devices, from the received signal strength indication (RSSI), which is measured using sensor nodes already-deployed for a different purpose. For example, the system estimates crowdedness using sensor nodes of structural health monitoring. Our system consists of two parts: an RSSI synchronous measurement and a crowdedness estimation algorithm. The RSSI synchronous measurement enables us to measure and collect two kinds of RSSI by synchronized RSSI sampling: the inter-node RSSI and surrounding RSSI. The crowdedness estimation algorithm estimates the number of people from the collected inter-node RSSI and the number of wireless communication devices from the collected surrounding RSSI in a location. We evaluated the proposed system in the laboratory at Osaka University. The evaluation results demonstrate that the system estimates the presence or absence of people in the laboratory with approximately 92% accuracy and the number of people with approximately 79% accuracy, with errors of up to two people.

Localisation methods based on the received signal strength indicator (RSSI) are widely used in wireless sensor networks (WSNs). They offer an energy reduced and cost-effective solutions for wireless localisation. Due to radio irregularity, RSSI localisation is vulnerable to multipath, shadowing, interference and environmental effects, which reduces the accuracy of localisation results. In this paper, a new distance measurement correction algorithm is presented, which is based on feedback filter and hardware characteristic. It implements the feedback filter with consideration of historical values of RSSI. This enables to predict possible values of RSSI and thus nodes positions. Hardware characteristics, such as multiplication and summation capabilities are explored to improve the localisation accuracy. Real experiments were carried out on panStamp sensor nodes at two different temperatures. The new proposed RSSI technique shows a reduced localisation error compared to the simple RSSI method.

Future-generation radio access networks (RAN) are projected to fulfill the diverse requirements of user equipment (UE) by adopting a heterogeneous network (HetNet) environment. Necessary integration of different radio access technologies (RAT), such as 2G, 3G, 4G, wireless local area network (WLAN), and visible light communication (VLC) is inevitable. Moreover, UEs equipped with diverse requirements will be capable of accessing some or all the RATs. The complex HetNet environment with diverse requirements of UEs will present many challenges. The HetNet is likely to suffer severely from load imbalance among the base stations (BSs) from inheriting the traditional user association scheme such as max-SINR (signal-to-interference-plus-noise ratio)/max-RSSI (received signal strength indicator), unless some sophisticated schemes are invented. In this paper, a novel scheme is devised for a joint-user association for load balancing, where BSs are densely deployed and UEs typically have a certain degree of mobility. Unlike most of the present works, a dynamic network is considered where the position and channel condition of the UEs are not fixed. We develop two complex and distributed association schemes based on probability and d-choices, while carefully considering both loads of the BSs and SINR experienced by the UEs. Numerical results validate the efficiency of the proposed schemes by showing a received data-rate fairness among UEs and an improvement in the UE’s minimum received data rate.

Network Offloading schemes play a vital role when there is a massive growth in the capacity of Next Generation Networks. The aggregation of LTE (Long Term Evolution) and WLAN (Wireless Local Area Network) is meant to provide service with seamless connectivity for the User Equipment (UE). There are two major issues that have been identified in existing RSSI (Received Signal Strength Indicator) based offloading scheme such as: i) it fails to provide better performance ii) it requires frequent reconnection for establishing dual connectivity. UE (User Equipment) based offloading approach requires a lot of signaling between UE and Radio access network and thus lacks in resource utilization and energy efficiency. Network based offloading approach needs a network specific entity. In the proposed LWA-SA (LTE WLAN Aggregation-SDN Assisted) which is an SDN (Software Defined Networking) based offloading scheme. In this, a controller keeps track of ongoing sessions after offloading. Here, an algorithm is used based on Access Network Discovery and Selection Function (ANDSF) offloading scheme for the purpose of selecting an efficient Wi-Fi access point which increases the throughput and reduces the delay and also provides seamless connectivity to the UE.

Gesture detection is an important aspect of intelligent human-computer interactive systems. Recently, device-free gesture detection in wireless local area networks (WLANs) has been proposed to reduce the detection cost and complexity, whether using (1) the Received Signal Strength Indicator (RSSI), or (2) the more reliable Channel Side Information (CSI) . In this paper, we use CSI information extracted from packet transmissions to detect certain gestures and further distinguish between detected gestures by their strength. Experimental results establish the efficacy of the proposed techniques.