Received Signal Strength Measurements Research Articles

Localizing WiFi Access Points Using Signal Strength

When estimating the location of WiFi access points (AP) in an anonymous environment, the required parameters of the radio propagation model are not readily obtainable. This letter investigates the use of received signal strength (RSS) for range-based AP localization when no information on the radio propagation model is provided. To achieve this goal, we linearly approximate the exponential relationship between RSS and distance, and apply a multilateration technique. Our simulation results validate that estimation of an AP location is possible given four or more RSS measurements at different locations.

Adaptive Decision Making Strategy for Handoff triggering and Network Selection

Next generation mobile networks (4G) is expected to integrate a large number of wireless technologies. However, this integration yields many challenges such as those pertaining to handoff triggering and decision making. Various approaches have been proposed to solve these problems, yet handoff initiation and network destination selection remain critical issues which are widely based on RSS (Received Signal Strength) measurements. Moreover, the use of context-awareness is very limited in the previous works. This paper proposes a new handoff decision strategy which aims to efficiently deal with handoff triggering and network destination selection with respect to mobile terminal requirements and network capabilities. Furthermore, we introduce a new score function that estimates network preferences for both voluntary and forced handoffs. Additionally, to render easier the accessibility to context information, we develop a context aware mechanism which is based on third party architecture. Finally, simulation results show that compared to RSS-based approaches, the proposed handoff decision strategy has greater respect for users’ requirements and preferences.

Mobile Location Estimation Using Fuzzy-Based IMM and Data Fusion

The location of mobile station is an important issue for wireless communication systems. A location estimation scheme using fuzzy-based Interacting Multiple Model (IMM) smoother is proposed in this paper. It combines the time-of-arrival (TOA) and the received signal strength (RSS) measurements to achieve high location accuracy. The fuzzy technique is used to interpolate several linear equations to approximate the nonlinear RSS measurement. The IMM is employed as a switch between the line-of-sight (LOS) and non-line-of-sight (NLOS) states which are considered to be a Markov process with two interactive modes. By integrating the fuzzy filtering and the IMM method for range estimation between the corresponding base station (BS) and mobile station (MS), the proposed robust scheme, in association with data fusion, can efficiently mitigate the NLOS effects on the measurement range error. Simulation results are given to confirm the performance of the proposed method.

An Information-Theoretic Approach for Energy-Efficient Collaborative Tracking in Wireless Sensor Networks

The problem of collaborative tracking of mobile nodes in wireless sensor networks is addressed. By using a novel metric derived from the energy model in LEACH (W.B. Heinzelman, A.P. Chandrakasan and H. Balakrishnan, Energy-Efficient Communication Protocol for Wireless Microsensor Networks, in: Proceedings of the 33rd Hawaii International Conference on System Sciences (HICSS '00), 2000) and aiming at an efficient resource solution, the approach adopts a strategy of combining target tracking with node selection procedures in order to select informative sensors to minimize the energy consumption of the tracking task. We layout a cluster-based architecture to address the limitations in computational power, battery capacity and communication capacities of the sensor devices. The computation of the posterior Cramer-Rao bound (PCRB) based on received signal strength measurements has been considered. To track mobile nodes two particle filters are used: the bootstrap particle filter and the unscented particle filter, both in the centralized and in the distributed manner. Their performances are compared with the theoretical lower bound PCRB. To save energy, a node selection procedure based on greedy algorithms is proposed. The node selection problem is formulated as a cross-layer optimization problem and it is solved using greedy algorithms.

Blind Position Location via Geometric Loci Construction

A new position location technique is proposed, using received signal strength measurements, which can be implemented using inexpensive off-the-shelf equipment. The proposed technique is based on geometric loci construction, and overcomes the need for onsite calibration measurements or propagation prediction tools and topographical/architectural plans of the covered area. The proposed algorithm exhibits similar simulated performance compared to a recently proposed positioning technique, but without the need of model calibration. In addition, measurements results are also presented, demonstrating the performance of the algorithm.

Received signal strength based mobile terminal positioning error analysis and optimization

The current paper considers the problem of Received Signal Strength (RSS) based Mobile Terminal (MT) positioning in cellular mobile networks like GSM and provides a set of MT–Base Station (BS) distance statistical estimators which improve positioning accuracy. The impact of both MT–BS distance estimation error (based on typical empirical path-loss models, i.e., Hata, Cost231) and trilateration to the MT position estimation accuracy is analyzed. The proposed set of MT–BS distance estimators improves the positioning accuracy by a factor of up to ∼50% according to an ideal simulation model. However, even with this improvement, RSS based positioning still provides accuracy similar to plain Cell Global Identity (CGI) technique. The paper then considers techniques which rely on a time-series of RSS measurements (i.e., Statistical Terminal Assisted Mobile Positioning (STAMP) [1] and MT tracking [2]) indicating that the proposed estimators can improve their performance too. To obtain a realistic picture, GSM field measurements are also analyzed indicating that the proposed estimators indeed deliver MT positioning accuracy improvement however, at a lower degree vs. the one theoretically expected.

Received Signal Strength-Based Wireless Localization via Semidefinite Programming: Noncooperative and Cooperative Schemes

The received signal strength (RSS)-based approach to wireless localization offers the advantage of low cost and easy implementability. To circumvent the nonconvexity of the conventional maximum likelihood (ML) estimator, in this paper, we propose convex estimators specifically for the RSS-based localization problems. Both noncooperative and cooperative schemes are considered. We start with the noncooperative RSS-based localization problem and derive a nonconvex estimator that approximates the ML estimator but has no logarithm in the residual. Next, we apply the semidefinite relaxation technique to the derived nonconvex estimator and develop a convex estimator. To further improve the estimation performance, we append the ML estimator to the convex estimator with the result by the convex estimator as the initial point. We then extend these techniques to the cooperative localization problem. The corresponding Cramer-Rao lower bounds (CRLB) are derived as performance benchmarks. Our proposed convex estimators comply well with the RSS measurement model, and simulation results clearly demonstrate their superior performance for RSS-based wireless localization.

A fuzzy set-based approach to range-free localization in wireless sensor networks

Localization in Wireless Sensor Networks (WSNs) refers to the ability of determining the positions of sensor nodes, with an acceptable accuracy, based on known positions of several anchor nodes. Among the plethora of possible localization schemes, the Received Signal Strength (RSS) based range-free localization techniques have attracted significant research interest for their simplicity and low cost. However, these approaches suffer from significant estimation errors due to low accuracy of RSS measurements influenced by irregular radio propagation. In order to tackle the problem of RSS uncertainty, in this work we propose a fuzzy set-based localization method as an enhancement of the ring-overlapping scheme [1]. In the proposed method, first we use a fuzzy membership function based on RSS measurements to generate fuzzy sets of rings that constrain sensor node position with respect to each anchor. Then we generate fuzzy set of regions by intersecting rings from different ring sets. Finally, we employ weighted centroid method on the fuzzy set of regions to localize the node. The results obtained from simulations demonstrate that our solution improve localization accuracy in the presence of radio irregularity, but even for the case without radio irregularity.

Analysis and Performance of a Smart Antenna for 2.45-GHz Single-Anchor Indoor Positioning

This paper presents the theoretical analysis and the experimental evaluation of a new switched beam antenna designed to operate at 2.45 GHz. The antenna enables direction of arrival estimation using six directional planar elements arranged to form a platonic solid geometry. It also supports polarization diversity, and it is suitable for single-anchor indoor positioning applications. We adopt the Cramer-Rao bound to study the estimation accuracy of the proposed antenna in absolute 2-D target positioning using received signal strength measurements. First, we describe the design principles for the radiators, we provide an extensive characterization of the switched antenna prototype, and we discuss positioning applications. We then report experimental data that support the results of the theoretical analysis and show consistency between theoretical expectation and the measurements. Finally, we discuss results from proof-of-concept operative indoor positioning example, showing an average localization error as low as 1.7 m.

Off-body UWB channel characterisation within a hospital ward environment

Received signal strength measurements and delay statistics are presented for both a stationary and mobile user equipped with a wearable UWB radio transmitter (TX) within a hospital environment. The measurements were made for both waist and chest mounted antennas using RF-over-fibre technology to eliminate any spurious electromagnetic scattering effects associated with metallic coaxial cables. The results show that received signal strength values were dependent on whether transmit and receive antennas had line of sight (LOS) and were also affected by body-shadowing and antenna-body position. For mobile conditions, received signal strength tended to be log normally distributed with non-LOS (NLOS) links having significantly lower mean values. Excess time delay results for mobile user tests were best described by the Weibull distribution. Overall, the results favoured the chest mounted antenna position, with higher mean signal levels, reduced mean excess delay and less difference between LOS and NLOS channels.

Algorithms and bounds for estimating location, directionality, and environmental parameters of primary spectrum users

Most existing work on dynamic spectrum access deals with creating a spectral and temporal map of spectrum white space, and then filling it. The spectrum can be better utilized by increasing the spatial awareness of secondary users to include knowledge of the locations of all primary and secondary users, as well as the orientations and parameters of their directional or omni-directional antennas. This paper derives a maximum likelihood (ML) algorithm, an approximate ML algorithm, and associated performance bounds for jointly estimating a transmitter's position, orientation, beam width, and transmit power, as well as the environment's path loss exponent, using received signal strength measurements. The methods can be used for either a primary or secondary user. Simulations are used to determine what types of sensor geometries lead to good estimates of each parameter, to evaluate the performance of the estimators, and to determine spectrum availability as a function of spatial coordinates.

Robust Indoor Positioning Provided by Real-Time RSSI Values in Unmodified WLAN Networks

The positioning methods based on received signal strength (RSS) measurements, link the RSS values to the position of the mobile station(MS) to be located. Their accuracy depends on the suitability of the propagation models used for the actual propagation conditions. In indoor wireless networks, these propagation conditions are very difficult to predict due to the unwieldy and dynamic nature of the RSS. In this paper, we present a novel method which dynamically estimates the propagation models that best fit the propagation environments, by using only RSS measurements obtained in real time. This method is based on maximizing compatibility of the MS to access points (AP) distance estimates. Once the propagation models are estimated in real time, it is possible to accurately determine the distance between the MS and each AP. By means of these distance estimates, the location of the MS can be obtained by trilateration. The method proposed coupled with simulations and measurements in a real indoor environment, demonstrates its feasibility and suitability, since it outperforms conventional RSS-based indoor location methods without using any radio map information nor a calibration stage.

Nonparametric Model Comparison and Uncertainty Evaluation for Signal Strength Indoor Location

Indoor location (IL) using received signal strength (RSS) is receiving much attention, mainly due to its ease of use in deployed IEEE 802.11b (Wi-Fi) wireless networks. Fingerprinting is the most widely used technique. It consists of estimating position by comparison of a set of RSS measurements, made by the mobile device, with a database of RSS measurements whose locations are known. However, the most convenient data structure to be used and the actual performance of the proposed fingerprinting algorithms are still controversial. In addition, the statistical distribution of indoor RSS is not easy to characterize. Therefore, we propose here the use of nonparametric statistical procedures for diagnosis of the fingerprinting model, specifically: 1) A nonparametric statistical test, based on paired bootstrap resampling, for comparison of different fingerprinting models and 2) new accuracy measurements (the uncertainty area and its bias) which take into account the complex nature of the fingerprinting output. The bootstrap comparison test and the accuracy measurements are used for RSS-IL in our Wi-Fi network, showing relevant information relating to the different fingerprinting schemes that can be used.

Dual power multiple access with multipacket reception using local CSI

Contention-based multiple access is a crucial component of many wireless systems. Multiple-packet reception (MPR) schemes that use interference cancellation techniques to receive and decode multiple packets that arrive simultaneously are known to be very efficient. However, the MPR schemes proposed in the literature require complex receivers capable of performing advanced signal processing over significant amounts of soft undecodable information received over multiple contention steps. In this paper, we show that local channel knowledge and elementary received signal strength measurements, which are available to many receivers today, can actively facilitate multi-packet reception and even simplify the interference canceling receiver's design. We introduce two variants of a simple algorithm called dual power multiple access (DPMA) that use local channel knowledge to limit the receive power levels to two values that facilitate successive interference cancellation. The resulting receiver structure is markedly simpler, as it needs to process only the immediate received signal without having to store and process signals received previously. Remarkably, using a set of three feedback messages, the first variant, DPMA-Lite, achieves a stable throughput of 0.6865 packets per slot. Using four possible feedback messages, the second variant, turbo-DPMA, achieves a stable throughput of 0.793 packets per slot, which is better than all contention algorithms known to date.

A Quasi EM Method for Estimating Multiple Transmitter Locations

We consider estimating multiple transmitter locations based on received signal strength measurements by a sensor network of randomly located receivers. This problem is motivated by the search for available spectrum in cognitive radio applications. We create a quasi expectation maximization (EM) algorithm for localization under lognormal shadowing. Simulated performance is compared to random guessing and to global optimization using constriction particle swarm (CPSO). Results show that the proposed quasi EM algorithm outperforms both alternatives given a fixed number of guesses, and the performance gap grows as the number of transmitters increases.

Statistical validation of WLAN range calculated with propagation models for industrial environments by chipset-level received signal strength measurements

The wireless range of an IEEE 802.11b/g system as predicted by an empirical model for path loss and temporal fading is validated statistically by measurements of the received signal strength or RSS reported by WLAN monitoring software. This validation is made for indoor industrial environments. In this work, a comparison between a link budget based on industrial propagation models and site surveys executed with WLAN monitoring software is made for two types of industrial environments, namely production centres and warehouses, and for three practically important 802.11b/g data rates of 2, 11 and 18 Mbps. In general, wireless ranges calculated with the link budget and extracted from the site survey measurements are in good agreement. An important finding is that in production centres, the site survey measurements show severe attenuation of the 802.11b/g signal in the presence of large obstacles with approximately the same height as the access point. This phenomenon can cause a noticeable overestimation of the real wireless range by the link budget. Furthermore, it is shown that changing the receiving antenna's height between 0.5, 1.5 and 2 m has only very limited effect on the wireless range.

Statistical NLOS Identification Based on AOA, TOA, and Signal Strength

Nonline-of-sight (NLOS) propagation is one of the challenges in radio positioning. Significant attention has been drawn to the mitigation of the NLOS effect in recent years. This paper focuses on the identification of NLOS conditions by employing the statistical decision theory. A Neyman-Pearson (NP) test method is first derived for scenarios where either 1-D or 2-D angular measurements are provided. A time-of-arrival (TOA) based method is then developed under idealized conditions to provide a performance reference. In the presence of both TOA and received signal strength (RSS) measurements, a joint identification method is derived to efficiently exploit the TOA and RSS measurements. Analytical expressions of the probability of detection (POD) and the probability of false alarm (PFA) are derived for all the scenarios considered. Two theorems and one corollary regarding the line-of-sight (LOS) conditions based on the angle of arrival (AOA) are also presented, and the proofs are provided. Simulation results demonstrate that the proposed methods perform well, and the joint TOA- and RSS-based method considerably outperforms the TOA-based methods. The proposed methods are robust to the model errors, as demonstrated through simulations. It is also shown that the analytical results agree well with the simulated ones.

Cooperative Mobile Positioning Based on Received Signal Strength

Mobile positioning using Received Signal Strength (RSS) measurements is regarded as a low cost solution which is applicable in a wide range of wireless networks. In this paper, we propose a cooperative RSS-based positioning algorithm, that relies on the promising idea of mobile to mobile communications in the next generation of cellular networks. Simulations performed in this paper indicate that utilizing the additional RSS data of the short range communications between Mobile Stations (MS's), enhances the accuracy of the traditional RSS-based positioning algorithms.

Using Low-Power Radios for Mobile Robots Navigation

Abstract This paper addresses the general field of mobile robots navigation within fixed sensor networks. We focus on an innovative navigation for indoor environments that is based on Radio-Frequency (RF) communication and its Received Signal Strength (RSS). We propose two methods for guiding a robot towards an RF beacon using low-power radios, namely the oblivious and the iterative methods. While the former has negligeable resource requirements, the latter requires more memory, processing power and odometry but it presents a substantially improved performance. We noticed that noisy RSS measurements are the main obstacle in our problem, since the noise incurs great ambiguity in the RSS gradient climbing. Thus, we introduce antenna diversity in the beacon and omnidirectional antenna with shielding plane in the robot to reduce multi-path phenomena and provide a better relationship between robot-beacon distance and RSS. The efficiency of these techniques as well as the performance of the oblivious and iterative methods are evaluated via simulations and practical experiments with an autonomous mobile robot. The results validate the methods and confirm the viability of RSS-based navigation.

Indoor Location System Based on Discriminant-Adaptive Neural Network in IEEE 802.11 Environments

This brief paper presents a novel localization algorithm, named discriminant-adaptive neural network (DANN), which takes the received signal strength (RSS) from the access points (APs) as inputs to infer the client position in the wireless local area network (LAN) environment. We extract the useful information into discriminative components (DCs) for network learning. The nonlinear relationship between RSS and the position is then accurately constructed by incrementally inserting the DCs and recursively updating the weightings in the network until no further improvement is required. Our localization system is developed in a real-world wireless LAN WLAN environment, where the realistic RSS measurement is collected. We implement the traditional approaches on the same test bed, including weighted kappa-nearest neighbor (WKNN), maximum likelihood (ML), and multilayer perceptron (MLP), and compare the results. The experimental results indicate that the proposed algorithm is much higher in accuracy compared with other examined techniques. The improvement can be attributed to that only the useful information is efficiently extracted for positioning while the redundant information is regarded as noise and discarded. Finally, the analysis shows that our network intelligently accomplishes learning while the inserted DCs provide sufficient information.