RSS-Based Outdoor Localization with Wireless Sensor Networks in Practice

The problem of distributed estimation of an unknown parameter corrupted by noise is studied in this paper. In consideration of the stringent bandwidth constraint in practical wireless sensor network (WSN) applications, a one-bit quantization scheme is adopted to compress local sensor observations. Imperfect data transmission between local sensors and a fusion center is considered and modeled as a Rayleigh fading channel. The conventional maximum likelihood estimation (MLE) usually involves high computational complexity. In this paper, we propose a simple mean estimator which requires only the mean of the channel gain. It is further modified by utilizing the properties of the intermediate parameter under estimation. Theoretical analysis and simulation results show that the proposed estimators are not only more computationally efficient than MLE, but also achieve near MLE performance over a wide range of the channel SNR and the number of sensors, which makes them suitable for practical resource-constraint WSN applications.

With the proliferation of the Internet of Things (IoT), employing Received Signal Strength (RSS) as a metric to determine the location of a target (e.g., person or mobile device) is of great interest in terms of cost and ease of implementation. Indeed, RSS measurements can be easily obtained for most off-the-shelf devices, such as WiFi- or ZigBee compatible devices or sensors. This paper deals with the indoor localization problem in wireless sensor networks (WSNs) and proposes a new approach for radio signal propagation modelling and localization estimation, that accounts for the imperfection of RSS measurements and the reliability of RSS sources by using the Dempster-Shafer Theory (DST). In the signal propagation modelling, key information regarding the geometry of indoor environment that is divided into zones separated by walls (zoning), are considered. Based on the number of walls, the RSS irregularities are estimated using different distance intervals, which are weighted by a probability density determined experimentally. To estimate the location of a target node, the PCR6 rule is used to combine the belief masses of the positions obtained from the probability density. In order to evaluate the performance of the proposed approach, an experimental WSN has been deployed in a living apartment. The obtained results demonstrate that the proposed approach improves the localization accuracy compared to the case without zoning. Moreover, the obtained localization mean error proves the feasibility of a precise localization of humans in indoor environments in the case of Ambient Assisted Living and Social Robotics applications.

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.

A wireless sensor network can be represented by a graph. While the network graph is extremely useful, it often exhibits undesired irregularity. Therefore, special treatment of the graph is required by a variety of network algorithms and protocols. In particular, many geometry-oriented algorithms depend on a type of subgraph called Delaunay triangulation. However, when location information is unavailable, it is nontrivial to achieve Delaunay triangulation by using connectivity information only. The only connectivity-based algorithm available for Delaunay triangulation is built upon the property that the dual graph for a Voronoi diagram is a Delaunay triangulation. This approach, however, often fails in practical wireless sensor networks because the boundaries of Voronoi cells can be arbitrarily short in discrete sensor network settings. In a sensor network with connectivity information only, it is fundamentally unattainable to correctly judge neighboring cells when a Voronoi cell boundary is less than one hop. Consequently, the Voronoi diagram-based Delaunay triangulation fails. The proposed algorithm employs a distributed approach to perform centroidal Voronoi tessellation, and constructs its dual graph to yield Delaunay triangulation. It exhibits several distinctive properties. First, it eliminates the problem due to short cell boundaries and thus effectively avoids crossing edges. Second, the proposed algorithm is proven to converge and succeed in constructing a Delaunay triangulation, if the CVT cell size is greater than a constant threshold. Third, the established Delaunay triangulation consists of close-to-equilateral triangles, benefiting a range of applications such as geometric routing, localization, coverage, segmentation, and data storage and processing. Extensive simulations are carried out under various 2D network models to evaluate the effectiveness and efficiency of the proposed CVT-based triangulation algorithm.

A single hop architecture exploiting cooperative beamforming for wireless sensor networks

In this work, we develop a framework for optimal joint Source-Channel Maximum Likelihood (SCML) decoding in Wireless Sensor Networks (WSNs). The proposed scheme employs a novel Generalized Likelihood Ratio Test based Prediction Likelihood Tree (PLT) approach to exploit the spatio-temporal narrowband properties of the sensor data for sequence detection in wireless sensor networks. Further, analytical bounds are derived to characterize the performance of the low complexity decision feedback and optimal Viterbi based Maximum Likelihood Sequence Detection (MLSD) for joint decoding over fading wireless channels, where only ad hoc schemes exist in current literature. The PLT based SCML scheme, which has a low complexity, is ideally suited for implementation in practical wireless sensor networks with limited computational power and achieves a performance close to the optimal MLSD bound. Simulation results are presented to validate the performance of the SCML algorithm and the proposed analytical bounds for sensor data reception in WSNs.

Received Signal Strength (RSS) localization is widely used due to its simplicity and availability in most mobile devices. The RSS channel model is defined by the propagation losses and the shadow fading. These parameters might vary over time because of changes in the environment. In this paper, the problem of tracking a mobile node by RSS measurements is addressed, while simultaneously estimating a two-slope RSS model. The methodology considers a Kalman filter with Interacting Multiple Model architecture, coupled to an on-line estimation of the observation's variance. The performance of the method is shown through numerical simulations in realistic scenarios.

Determining the physical positions of sensors has been a fundamental and crucial problem in wireless sensor networks (WSNs). Due to the inherent characteristics of WSNs, extremely limited resources available at each low-cost and tiny sensor node, connectivity-based range-free solutions could be a better choice to feature a low overall system cost. However, localization by means of mere connectivity may underutilize the proximity information available from neighborhood sensing. Although received signal strength (RSS) values are irregular and highly dynamic, it might provide heuristic information about which neighboring nodes are closer and which are further. We propose a distributed Mobile-beacon-assisted localization scheme based on RSS and Connectivity observations (MRC) with a specific trajectory in static WSNs. To ensure that MRC performs as true to reality as possible, we propose two improved approaches based on MRC to consider irregular radio scenario in the noisy environment. Comparing the performance with three typical range-free localization methods in static WSNs, our lightweight MRC algorithm with limited computation and storage overhead is more suitable for very low-computing power sensor nodes that can efficiently outperform better with only basic arithmetic operations in the simulation and physical environments.

Node localisation is a fundamental problem in wireless sensor networks. Many applications require the location information of sensor nodes. Received signal strength (RSS) is a simple and inexpensive approach for localisation purpose. However, the accuracy of RSS measurement is unpredictable owing to the nature of the radio frequency (RF) channel. An RSS-based Monte Carlo localisation scheme is proposed to sequentially estimate the location of mobile nodes, using the log-normal statistical model of RSS measurement. The RSS measurement is treated as the observation model in Monte Carlo method and the mobility feature of nodes as the transition model. Our method is widely applicable because the RSS function is easy to implement on nodes, and the mathematical model for mobile nodes may have non-analytic forms. Simulation results about localisation accuracy and cost show that this scheme is better than other methods.

Intelligent fusion of information derived from received signal strength and inertial measurements for indoor wireless localization

In wireless sensor networks, sensor node localization is an important topic because sensor nodes are randomly scattered in the region of interest and they get connected into the network on their own. Localization information is needed in routing of data, reducing energy consumption and in location dependent data acquisition. We propose a localization scheme plus a medium access protocol that: (a) reduces power consumption; (b) does not require high power beacons; (c) provides better localization accuracy; and (d) helps in reducing collisions. The proposed localization scheme is based on received signal strength by the three masters. Moreover, it is a distributed algorithm.

With the popularity of smart mobile devices, “context-aware” applications have attracted intense interest, for which location is one of the most essential contexts. Compared with outdoor localization, indoor localization has received much more attention from both academia and industry these days. Given the widespread use of WiFi hotspots, the received signal strength (RSS) fingerprint-based indoor localization technique is considered as a promising and practical solution because of its relatively high accuracy and low infrastructure cost. Inspired by our observation that sparsity is inherent to the WiFi signal, we present a new RSS fingerprint-based indoor localization approach, called SparseLoc . Through sparse representation of the fingerprints, SparseLoc can estimate a smart mobile device’s location with a small error most of the time. Although the correlation between neighboring fingerprints affects the localization accuracy, SparseLoc uses the similarity between principal components of fingerprints to alleviate this effect. Based on the empirical experiments, we demonstrate that SparseLoc improves the localization accuracy by over 25% compared with the existing WiFi signal-based localization methods.

Abstract: A wide set of applications have been designed for Wireless Sensor and Actuator Networks (WSAN). Localization algorithms implemented over a wireless link such as WSAN, are recently reaching high interest due the low recourses and precision challenge involved. Received Signal Strength (RSS) is considered the simplest way to compute position location on WSAN, even though it is highly affected by multipath channels and other interferences, which are not included on simulator software. The main contribution of this proposal lays on a comparative analysis between simulated WSAN's RSS-Based algorithms and its implementation on real WSAN nodes. In this way we were able to determine how some factors affect the localization environment. This implementation took advantage of an experimentally measured RSS, using XBee Series 1 transceivers, on a fixed grid topology.

Sensor localization based on Received Signal Strength Indication (RSSI) in Wireless Sensor Networks (WSNs) takes a place in research interest because received signal strength can be measured without additional hardware. However, RSSI based localization requires static configuration for anchor nodes to achieve high localization accuracy in reasonable time. Furthermore, when sufficient number of mobile nodes are chosen as anchors, RSSI based localization accuracy is radically influenced by measurement errors of signal strength in physical environments. As a result, RSSI based localization error may increase due to anchor mobility such as a case of close anchors and a case of approximately linear anchors. In this paper, a new localization scheme is proposed to solve such problems. In addition, the proposed scheme is enhanced to work in low anchor density to minimize additional equipment cost for anchor nodes. Performance comparison and simulation results show that the proposed scheme achieves better localization accuracy in fading environments (i.e. urban, suburban or rural) compared to existing RSSI solutions.

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.