RSS Ranging Based Wi-Fi Localization for Unknown Path Loss Exponent

Wireless sensor network is a key enabling technology for Ambient Intelligence, where location information is crucial for many applications. RSS-based ranging localization takes advantage of its low cost and low complexity, but it has an infeasible assumption of an accurate path loss exponent of the physical environment. In this paper, we study the impact of path loss exponent accuracy on the localization accuracy. We formulate the relationship between the path loss exponent estimate and localization error, and found the localization error of exponential order which we call the error magnification effect. By our in-depth investigation, we propose a passive and an active measures to suppress the error magnification effect, where the passive measure stabilizes the localization error of the spring-relaxation algorithm (SR), and the active measure introduces variable elasticity into the SR algorithm to cancel off the exponential ranging error. The combination of both measures forms our localization solution called variable elasticity spring-relaxation (VE-SR) localization. We conduct extensive simulation experiments to show the effectiveness of VE-SR in suppressing the error magnification effect in various experiment setup. For a wide variety of physical environments, VE-SR offers location estimation with an average accuracy of no more than 10% of transmission range.

Localization of wireless devices via the received signal strength (RSS) has become very popular in recent years owing to its economical and uncomplicated implementation. The performance of RSS based location systems is highly dependent on the knowledge of the propagation channel. An unrealistic assumption in many current studies is the possession of the exact knowledge of the path-loss exponent (PLE). This letter deals with the estimation of location coordinates in unknown path-loss model. First, the non-linear path-loss equations are linearized and the solution is obtained using linear least square (LLS) solution. Second, in order to underline the effects of inaccurate PLE assumption in noisy channels on location coordinates, error analysis is done when incorrect PLE is assumed, hence a closed form expression is derived for mean square error (MSE) and bias. Next, we transform the LLS problem into a single variable minimization problem to estimate jointly the location and PLE. Simulation results show that incorrect PLE assumption has superlative impact on MSE and bias of location estimates. Further simulation shows that the proposed technique exhibits acceptable performance while estimating the PLE however the estimates are biased. In order to achieve higher accuracy, the obtained estimates are used as an initial estimate to our previously proposed iterative algorithm [1]. The resultant MSE is then compared with the Cramer-Rao bound and the improved convergence is demonstrated.

Received signal strength (RSS)-based localization techniques have gained much attention in location-based services (LBSs). However, the coexistence of unknown path loss exponent (PLE), uncertain sensor positions, and impulsive noise poses serious challenges to localization accuracy. To address the problem, we first model the impulsive noise as a Mixture of Gaussian (MoG) distribution with unknown parameters. Thus, the noise model and the channel model can be refined using the observed data under the variational Bayesian inference (VBI) framework, which is defined as the model refinement learning. We then propose a corresponding online target localization procedure with the refined noise distribution, PLE and sensor positions. The Bayesian Cramer-Rao bound (BCRB) is finally derived in terms of all unknown parameters. Simulation results together with real experiment demonstrate that the proposed VBI algorithm can effectively learn the true noise distribution, and the developed localization method exhibits robust localization performance in various scenarios.

The car navigation service has opened through the global positioning system (GPS). People's lives have changed through car navigation services, and this service is indispensable nowadays. GPS provides reliable location accuracy in open space, but its location accuracy is degraded where multipath and other error factors are present. In particular, since GPS signals must be received in order to calculate the position, the navigation system cannot calculate the position in an indoor space such as underground parking lot, subway or tunnel where GPS signals are cut off. When car navigation is performed in an indoor parking lot where GPS signals are not transmitted, the user cannot calculate the current location, so the user has to go out by relying on the traffic sign because he does not know the current location. Also, when the user leave the parking lot, the car navigation system has problems. In an urban canyon, it is difficult to calculate accurate current position due to multipath. Because the route is calculated through the current location and route, the car navigation system provides the wrong route to the user. In addition, when entering a tunnel while using car navigation, the car navigation system calculates the position using the initial tunnel entry velocity estimated by the GPS. As the speed of the user increases or decreases, the position error also increases. Then the position suddenly changes when passing through the tunnel. And it is difficult to provide a proper guidance at the branch road or exit in the tunnel because of the positioning error. People are demanding a complete car navigation service, but the current navigation service provides the wrong route and position information to the user where the GPS signal quality is poor. In order to solve the above problem, in this paper, we have studied to improve the user's position accuracy by using LTE signal and mobile phone internal sensor. In general, observed time difference of arrival (OTDOA) technique is applied for the LTE based positioning. Because this method gives tens of meters of error, it is difficult to be used for the car navigation service. In our research, we estimate the position using the LTE signal strength. The difference from conventional fingerprinting method is that instead of using only current signal strength, the signal strength pattern accumulated while moving is utilized to estimate the user position. The signal strength increases as the user approach the access point (AP), and decreases as the user move away from the AP. Therefore, when a user moves and stores signal strength with respect to a specific AP, a pattern that could occur only in the corresponding region is saved. There are many similar candidates in the database when using only the current signal strength. However, when using a pattern, similar candidates exist only around the true position. And, when using several LTE signals, the positioning accuracy could be increased. Since the user receives multiple LTE signals, the pattern becomes surface. The surface is compared with the database, and the position with the highest correlation value is estimated as the current position. We call this method surface correlation (SC). In order to verify the performance of the proposed method, experiments were conducted in the Gangnam beltway tunnel in Seoul. The testbed consisted of three tunnel and total distance is almost 11.8 Km. In the tunnel, repeater and several antennas are installed to widen the area of the LTE signal. When an LTE signal is received by the vehicle, the largest signal strength is received every time when the vehicle passes the nearest antenna. As a result, for a specific LTE signal, when the vehicle has passed through the LTE signal region, a unique pattern with multiple peaks is generated. In order to estimate the position, we first estimate the position with the SC technique. Then, the estimated position is corrected by comparing the peak of the currently received pattern with the peak of the database. Through this method, the position was estimated with an error of less than 50meter in the tunnel. Conclusion In this research, the LTE signal strength based positioning technology is presented. Especially, this technology, SC, provides the positioning performance that could be used for the car navigation in the tunnel where GPS signal is blocked. We expected that user would be use the accurate car navigation service through this technology in the GPS challenging environment.

With the rapid advancement of the information age, an increasing number of people have started using Wi-Fi tethering, which can turn their mobile phones into mobile access points (APs) to meet their networking needs anywhere. However, the existing energy-saving mechanisms in IEEE 802.11 mainly aim at stations (STAs) and rarely consider APs. To solve the problem of mobile APs’ high energy consumption, we propose an energy-saving protocol for mobile APs, called GreenAP. On the one hand, the protocol is compatible with the original IEEE 802.11 standard. On the other hand, the adaptive strategy in the protocol ensures that the energy consumption of the AP is reduced without affecting the user experience. The energy-saving AP protocol is implemented in NS-2. The experimental results show that GreenAP-3 can enable APs’ sleep duration up to 74.7% when the traffic intensity is low, and the energy consumption can be reduced by 64.6% with small packet delay. In the case of high traffic intensity, the protocol can ensure less packet delay by adaptively adjusting APs’ sleep time, which guarantees that the user experience is not affected under any circumstances.

In indoor positioning system, the user’s location is estimated based on user fingerprints database collected. The received signal from a user at a certain location is compared with all database entries and the best match is selected as the correct estimation. For a given number of access points (APs), optimizing the locations of APs can significantly improve the performance of user location estimation. In this work, the probability of correct estimation is analyzed based on APs location. Both analytical expressions and numerical results are presented and discussed. It has been shown that the probability of correct estimation is highly affected by the APs locations. Furthermore, a new technique is proposed to remarkably reduce the search-cost when comparing a received user signal to the data-base. The performance of the system is analyzed considering different APs locations, different user locations, and different noise variances. It is shown that user’s location estimation is significantly improved when compared to existing techniques.

The access point (AP) deployment is a fundamental task for constructing an accurate localization system. Existing literature mainly deals with the AP placement problem using optimal geometry analysis since the target-AP geometry will affect the localization performance. However, some non-ideal phenomena in practical scenario, e.g., the existence of obstacles, array orientation and path loss, will degrade the accuracy of angle-of-arrival (AoA) estimation as well as the localization accuracy. In this article, we reformulate the AP planning incorporating these factors. We decompose the problem into two subproblems, namely AP selection problem and error minimization problem. The AP selection problem selects the minimum number of APs to satisfy a desired localization accuracy, aided by a refined orientation updating procedure. We design a centralized and a distributed error minimization algorithm to further decrease the localization error. The centralized algorithm shows superiority in time efficiency. Nevertheless, the case with large number of APs may lead to excessive computational cost. Accordingly, we further devise the distributed algorithm which is adaptive to large-scale deployment. Numerical studies in indoor environments with barriers are conducted to verify our proposed approach.

Selective AP probing for indoor positioning in a large and AP-dense environment

This paper describes research towards a system for locating wireless nodes in a home environment requiring merely a single access point. The only sensor reading used for the location estimation is the received signal strength indication (RSSI) as given by an RF interface, e.g., Wi-Fi. Wireless signal strength maps for the positioning filter are obtained by a two-step parametric and measurement driven ray-tracing approach to account for absorption and reflection characteristics of various obstacles. Location estimates are then computed using Bayesian filtering on sample sets derived by Monte Carlo sampling. We outline the research leading to the system and provide location performance metrics using trace-driven simulations and real-life experiments. Our results and real-life walk-troughs indicate that RSSI readings from a single access point in an indoor environment are sufficient to derive good location estimates of users with sub-room precision.

Meshed wireless back-haul networks are seen as an affordable technology to bring Internet connectivity into rural and previously unconnected regions. To date, the main focus is to provide access to classical services such as theWWWor email. Access to such services requires the users to use a personal computer or a recent smart phone. Especially in developing regions, the prevailing end user device is a mobile phone. In order to connect mobile phones to an IP-based back-haul network, the network access points must provide a mobile phone air interface which is usually based on GSM or UMTS. In order to avoid dependence on a costly 3GPP infrastructure, we propose to deploy GSM or 3GPP nano cells in order to terminate the mobile phone protocols immediately at the mesh access points. Hence, the voice or data traffic can be carried over IP-based networks using open protocols such as SIP and RTP. In this paper we present a meshed wireless back-haul network with access points that have been equipped with GSM nano-cells. The voice traffic generated by the mobile phones is carried across the mesh in parallel to typical web or video traffic. In this paper we evaluate the QoS handling received by the voice calls across our multi-hop wireless testbed and show that our architecture can provide the resource isolation required to offer uninterrupted VoIP services in parallel to typical Internet traffic.

Pre-crowdsourcing: Predicting wireless propagation with phone-based channel quality measurements

Current wireless networks are becoming increasingly heterogeneous both in terms of the radio access technologies (RATs) and the base station capabilities. Downlink rate or throughput in such heterogeneous networks is a crucial metric and has been primarily studied using system level simulations. To derive the distribution of rate analytically we develop a fairly general tractable model that consists of M different RATs, each deploying up to K different classes of access points (APs), where each class differs in transmit power, path loss exponent, and deployment density. Each class of APs is modeled as an independent Poisson point process (PPP), with mobile user locations modeled as another independent PPP, all channels further consisting of i.i.d. Rayleigh fading. Using a weighted association strategy, the distribution of rate over the entire network is derived. Further, it is shown that in a two-RAT setting there exists an optimum fraction of traffic that should be associated with each RAT to maximize rate coverage, defined as the fraction of users achieving a given rate.

Abstract. The acquisition of locations of Wi-Fi access points (APs) in urban buildings plays an important role in smart city applications, such as indoor navigation and social media data mining. This paper proposes a crowdsourcing-based approach for self-localization of Wi-Fi APs with the assistance of indoor pedestrian network (AP Detector). The features extracted from local opportunity signals are adopted for floor identification, and the crowdsourced indoor trajectories are segmented and matched with extracted indoor pedestrian network for the further trajectory calibration. In addition, the iteration unscented Kalman filter is applied for the location and bias estimation of local Wi-Fi FTM stations using the constructed Wi-Fi ranging model. The experimental results indicate that the proposed AP Detector can realize accurate location estimation of Wi-Fi APs, which also provides an effective way for autonomous construction of indoor navigation database and hybrid localization.

Due to the popularity of location-based services in environment with weak GPS signals, indoor location estimation problem have attracted more and more attention in recent years. Among several distance-related measurements, channel impulse response (CIR) reflects multi-path situation between the transmitter and receiver pair and is suitable to describe the characteristic of different positions. Note that CIR, which can be obtained from the inverse Fourier transform of channel frequency response in broadband wireless networks, is supported in most of the commercial standards. In this paper, a novel compressive sensing based location estimation using CIR measurements (CS-CIR) is proposed as well as fingerprinting algorithm. CIR information is collected from each reference point (RP) to access point (AP) and stored in the database. During the on-line stage, the mobile user measures CIR from the AP and compares measured CIR with those CIR values in the database. Note that user position is close to one of the RPs and user position vector is represented as a sparse vector. By applying compressive sensing theory, user position can be recovered by solving l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -minimization problem. Simulation result validate that the CS-CIR outperforms the K-nearest neighbour method using CIR measurements and conventional received signal strength based methods.

The ability to estimate the location of wireless device users has led to many applications that use location information to provide users and service providers with commercially valuable real time information. In this paper, we propose a low-complexity method for location estimation in an indoor wireless local area network (WLAN) deployment. The proposed method relies on measurements of signal strength (SS) of multiple access-points (APs) in a WLAN and is based on a piecewise-linear model that represents distances from APs in terms of SS measurements. The proposed scheme has significantly lower computational and storage requirements as compared to conventional location estimation methods.