Continuous TOA Measurement Based Single-Anchor Indoor Localization

Localization has drawn much attention in the Internet of Things (IoT) era. Under traditional multilateration techniques, existing solutions usually need multiple anchor nodes to perform localization, which introduces more system complexity and cost. In this paper, the single anchor localization (SAL) is first modeled, where a multi-antenna anchor node is able to estimate the location of the target node using both angle and distance information. Then, according to SAL, we propose an accurate and distributed localization (ADL) algorithm, which can not only estimate the location of the target node with fewer anchor nodes but also be more accurate than the traditional multilateration method. Furthermore, we prove that the location estimate under ADL can converge towards the real location of the target node with probability 1. The lower and upper bounds of ADL are also derived under a bounded noise model. Extensive simulations are conducted to demonstrate the performance of ADL and the correctness of the theoretical results.

In many applications, the exact location of the sensor nodes is unknown after deployment. Localization is a process used to locate sensor nodes' positional coordinates, which is vital information. The localization is generally assisted by anchor nodes that are also sensor nodes but with known locations. Anchor nodes generally are expensive and need to be optimally placed for effective localization. Passive localization is one of the localization techniques where the sensor nodes silently listen to the global events like thunder sounds, seismic waves, lighting, etc. According to previous studies, the ideal location to place anchor nodes was on the perimeter of the sensor network. This may not be the case in passive localization, since the function of anchor nodes here is different than the anchor nodes used in other localization systems. We do extensive studies on positioning anchor nodes for effective localization. Several simulations are run in dense and sparse networks for proper positioning of anchor nodes. We show that, for effective passive localization, the optimal placement of the anchor nodes is at the center of the network in such a way that no three anchor nodes share linearity. The more the non-linearity, the better the localization. The localization for our network design proves better when we place anchor nodes to form right angles.

Geometric dilution of precision (GDOP) is defined as the ratio of the root-mean-square position determination error to the root-mean-square measurement error. Based on the Cramer-Rao lower bound (CRLB), new mathematical expressions of GDOP are presented in Time-of-Arrival (TOA) and Angle-of-Arrival (AOA) positioning systems in this paper. The expressions obviously reveal that the geometrical shape of the anchor nodes make a significant influence on GDOP. Then the influence of adding a new anchor node at different locations is analyzed. Simulations show that in both positioning systems, two optimal placement angles exist to make the GDOP smallest, when the original anchor nodes are no uniformly scattered. However, when uniformly scattered, the GDOP keeps constant no matter what angle the new anchor node is placed at. In AOA positioning systems, the GDOP is also affected by the distance between the newly added anchor node and the target, and increases when the distance increases. Meanwhile, the GDOP is always reduced when more anchor nodes are used.

Wireless Body Area Networks (WBAN) are endowed with relatively raw but intrinsic motion capture capabilities through radiolocation, which may be of interest for home activity monitoring, large-scale postural rehabilitation or gaming applications. In this context, we propose a solution to localize wearable wireless nodes relatively to a body-strapped Local Coordinate System (LCS). More particularly, we consider adapting a Constrained Distributed Weighted Multi-Dimensional Scaling algorithm (CDWMDS) that asynchronously estimates nodes' locations under fixed-length constraints. This algorithm is fed by inter-node range measurements based on e.g. Impulse Radio - Ultra Wideband (IR-UWB) Time Of Arrival (TOA) estimation. Several new enhancements to the nominal CDWMDS, including nodes censoring and updates scheduling for nodes' locations, are herein put forward to mitigate errors propagation and harmful effects caused by fast moving nodes. Simulation results are provided to illustrate the gains observed on the average location error per node under moderate pedestrian mobility, relying on a realistic biomechanical model.

Global Positioning Systems (GPS) are frequently used as a potential solution for localization applications. However, GPS does not work indoors due to a lack of direct Line-of-Sight (LOS) satellite signals received from the End Device (ED) due to thick solid materials blocking the ultra-high frequency signals. Furthermore, fingerprint localization using Received Signal Strength Indicator (RSSI) values is typical for localization in indoor environments. Therefore, this paper develops a low-power intelligent localization system for indoor environments using Long-Range Wide-Area Networks (LoRaWAN) RSSI values with Random Neural Networks (RNN). The proposed localization system demonstrates 98.5% improvement in average localization error compared to related studies with a minimum average localization error of 0.12 m in the Line-of-Sight (LOS). The obtained results confirm LoRaWAN-RNN-based localization systems suitable for indoor environments in LOS applied in big sports halls, hospital wards, shopping malls, airports, and many more with the highest accuracy of 99.52%. Furthermore, a minimum average localization error of 13.94 m was obtained in the Non-Line-of-Sight (NLOS) scenario, and this result is appropriate for the management and control of vehicles in indoor car parks, industries, or any other fleet in a pre-defined area in the NLOS with the highest accuracy of 44.24%.

Accurate local positioning systems usually use a network of anchor nodes at known locations to track mobile nodes based on the measurement of the time of arrival (TOA) at anchor nodes of beacon signals transmitted by the mobile nodes. To localize the mobile node either TOA processing, where the unknown transmit time is estimated along with the node location, or time difference of arrival (TDOA) processing, where the transmit time is eliminated before estimating the node location, can be used. We show that the position error bound of both these formulations are the same by analyzing the Cramér-Rao lower bound. When processing data collected in field trials, however, we observed that the TOA processing yields better localization accuracy, and explain this behavior using differential geometry-based curvature measures that show that the TDOA cost function has greater degree of non-linearity.

Wireless sensor network (WSN) is usually deployed in harsh environments for collecting and delivering the data to the remotely located base station. Due to the recent achievements in WSN, tiny and expensive sensors are used with capability of sensing large information and to propagate over longer distances. In range-based localization algorithms, anchor node whose location is known plays an important role. In order to resolve the challenges of traditional localization algorithms, an anchor-based node localization algorithm is proposed for WSN. The distance between anchor or known node and unknown node is measured by optimizing anchors and creating database of optimized anchors. Using trilateration method unknown nodes are located and are used as new anchors which will reduce localization algorithm’s dependency on anchors. The objective of the proposed work is to maximize the life time of the network by minimizing the energy consumption. From the simulation results, we show that the proposed algorithm increases lifetime of wireless sensor network.

COVID-19 digital contact tracing applications for smartphones have become popular worldwide to reduce the effects of the pandemic. We considered that contact information between smartphones used in these applications can be used for the indoor localization of pedestrians. In this paper, we propose two indoor pedestrian localization methods based on contact information obtained from Bluetooth low energy (BLE) beacons installed in pedestrian’s smartphones. Proposed method 1 is multilateration, and proposed method 2 solves a nonlinear optimization problem to further improve the accuracy of method 1. These two proposed methods comprise three steps: (1) the smartphones and anchor nodes recognize the proximity relationship with neighbor nodes using BLE signals transmitted from other smartphones and anchor nodes. The recognized proximity relationship is sent to a server. (2) The server estimates the distance between each node (smartphone or anchor node) from the proximity relationship. (3) The positions of smartphones are estimated based on the distance between nodes estimated by the server. We verified the localization accuracy of the proposed methods through simulation experiments. In an indoor area of 15 m × 30 m, the average localization error of the proposed method 2 was 0.74 m when the pedestrian density was 0.5 /m².

Summary The paper demonstrates an algorithm for using physics-informed neural networks in the workflow of microseismic data processing and more specifically the problem of localization of microseismic events. The proposed algorithm involves the use of a physics-informed neural network solution to the eikonal equation to calculate the traveltimes of the first arrivals. As a result, the network solution is compared with the observed arrival times to solve the inverse kinematic problem to determine the coordinates of the event locations. Using a synthetic 3D example, it was shown that the average absolute error of the arrival time misfit was less than 0.25 ms, and the average localization error did not exceed 4.5 meters.

Localization is an unavoidable procedure in location aware sensor networks. In such networks, management of a large amount of location information along with its processing and updating is highly desirable at a central station of the network. In this paper, we have discussed the implementation of software layer to be run on various types of sensor nodes in the localization network, which has been dealt with extensively along with some of the addressed problems and their respective solutions. In addition, the article discusses implementation of an already mathematical formulation of least squares trilateration, which has not yet been attempted in the space of wireless sensor networks. To support our clam we performed experimental analysis on $$telosb$$ motes. Experimental results of proposed framework shows that average error with respect to the physical location estimation can be reduced upto 46.66 % using 4 anchor node as compare to three anchor nodes at outdoor scenario and upto 40 % in indoor scenario.

Recently, studies focusing on identifying user’s current location for use in a wide variety of differentiated location-based and localization services have steadily increased. In particular, location awareness using wireless communication is gaining attention in indoor environments composed of many obstacles, where GPS signals cannot reach. Previously, studies have focused mostly on location precision, which resulted in using many beacon nodes, not considering the initial installation and maintenance costs, communication robustness, or power consumption. This makes it difficult to apply existing methods to various fields, especially in mobile nodes (i.e., wearable devices, mobile tags, etc.) with limited battery capacity. In this paper, we propose a hybrid situation-aware indoor localization (SAIL) system for real-time indoor localization using a combination of low frequency (LF) and Bluetooth Low Energy (BLE) 4.0. This approach allows us to work with limited battery capacity mobile devices, and identify tagged mobile nodes and their current location in relevance to the anchor node. In our experiment, we attached one anchor node at the entrance to indoor areas such as office or factory settings. Using our hybrid SAIL system, we were able to detect the passing of a mobile node through the entrance and recognize whether the node is entering or exiting the room by calculating the direction of movement as well as the distance from the entrance. This allowed us to distinguish the precise position in an indoor environment with the margin of error being 0.5 m. The signal attenuation due to obstacles is overcome by using LF communication in the 125-kHz band. This approach enables us to reduce the number of initially installed anchor nodes as well as the power consumption of the mobile node. We propose an indoor position recognition system, namely, the hybrid SAIL system, that can be applied to mobile nodes with limited battery capacity by reducing the system complexity and power consumption.

In a ZigBee network, low-cost, low-power sensor nodes are deployed to carry out specific tasks like environment sensing, home automation, human and animal condition monitoring, and robot navigation monitoring etc. Normally the network comprises of numerous sensor nodes deployed in different locations and a coordinator node where the sensor nodes report back the sensed data for possible analysis and further action. For certain applications it is desirable to be able to locate the sensor node within the network. Currently localisation can be achieved using time dependent methods (TOA and TDOA) or received signal strength (RSS). Time dependent approaches require time synchronisation at all times which can be difficult to achieve. RSS based method is simple and cost effective but require at least three anchor nodes for a nodes location to be calculated via triangulation. Such a method can increase the complexity and cost of a network deployment. This paper presents a single anchor based location system which utilizes a differential two-element antenna array to perform node localization. An experimental system is introduced and results demonstrate the effectiveness of the proposed system. The anchor node, which can also be the network gateway, is connected to two dipole antennas separated by ⋋/2. The relative phase of each antenna can be varied using a phase shifter. The combined output of the two antennas can be used to create sum and difference radiation patterns, and to steer the antenna radiation pattern. Experimental validation was performed using seven XBee sensor nodes. Accurate azimuthal localization (relative to the anchor node) can be achieved and range estimation can be made with minimal errors. Adaptive path-loss model estimation can further improve range estimation. Overall, this cost effective localisation system performs well for ZigBee based wireless sensor networks.

Wireless sensor network (WSN) localization has drawn attention in a broad spectrum of applications for security, disaster response, monitoring, surveillance and tactical system. The deployment of a large number of low-cost, low-power and multi-functional sensors is preferred, which provides an exceptional localization performance via cooperative communication. In this paper, we consider a cooperative localization in WSNs consisting of multiple anchor nodes (ANs) equipped with a linear antenna array of M elements, a single-antenna target and a central unit (CU). We focus on a synchronous network and all the channels are supposed to be frequency-flat. Our objective is to evaluate the performance of localization technique for the target's position that jointly exploits both Time-of-Arrival (TOA) and Angle-of-Arrival (AOA) measurements. We derive the Cramer-Rao bound (CRB) for a joint TOA/AOA-based localization. Additionally, for the performance comparison, the CRBs of solely TOA- and AOA-based localizations are also derived. By numerical results, our analysis is verified and it is observed that the joint TOA/AOA-based localization has a superior performance than either TOA- or AOA-based localization.

Distributed sensing, computing and communication capabilities of wireless sensor networks require, in most situations, an efficient node localization procedure. In the case of random deployments in harsh or hostile environments, a general localization process within global coordinates is based on a set of anchor nodes able to determine their own position using GPS receivers. In this paper we propose another anchor node localization technique that can be used when GPS devices cannot accomplish their mission or are considered to be too expensive. This novel technique is based on the fusion of video and compass data acquired by the anchor nodes and is especially suitable for video- or multimedia-based wireless sensor networks. For these types of wireless networks the presence of video cameras is intrinsic, while the presence of digital compasses is also required for identifying the cameras' orientations.

Distance Vector-Hop (DV-Hop) localization is a distributed, hop by hop positioning algorithm. Anchor nodes generate the packets of nodes position information to calculate the number of hops between the beacon nodes and the anchor nodes, and these packets are flooded in the respective Wireless Sensor Networks (WSN). The DV-Hop algorithm obtains the distance information from the unknown nodes to the anchor nodes through the network topology calculation rather than the radio wave signals measurement which are used in the range-based algorithm. However, flooding information from all anchors to all beacon nodes will become too expensive for large networks (i.e., cost, energy), even with low anchor fraction. The region of nodes flooding through the network needs to be confined within logical constraints to produce better accuracy to reduce the localization error while reducing the cost. This research proposes and develops an enhanced algorithm that can improve the region area for sensor nodes placement within the specific localization area. This research aims to produce a longer network lifetime by reducing the energy used of the sensor nodes and at the same time minimize the localization error. This research describes the flooding control of the node's placement for the localization of the DV-Hop algorithm. The placement of nodes is limited inside the region of the anchor nodes. The simulation environment setup comes with parameters such as the radius, area, number of nodes, types of nodes, the anchor proportion, and the value of the regions. The result shows an improvement for the localization error, the power consumption, and the accuracy of the nodes positioning.