Localization In Sensor Networks Research Articles

Trajectory tracking under compound noise environments based on weighted total least squares and forney-style factor graph

In compound noise environments, both additive noises and multiplicative noises influence the Time-of-arrival-based (TOA-based) distance measurements, which challenge the source node localization and trajectory tracking in practical sensor networks. In order to realize trajectory tracking under such noise conditions, in this paper, a compound noise parameter estimation algorithm based on Weighted Total Least Squares (WTLS) and a source node tracking algorithm based on Forney-style Factor Graph (FFG) are presented. Four parameters of the compound noise model are estimated based on ranging observations between position-known anchor nodes, and the trajectory tracking is achieved by applying a noise pre-standardized procedure in the FFG. Monte-Carlo simulations evaluate the performance of the proposed system, and the proposed noise parameter estimation algorithm performs better than the traditional Least Square (LS) method and the proposed trajectory tracking algorithm is realizable and robust.

Artificial intelligence techniques in wireless sensor networks for accurate localization of user in floor, building and indoor area

Artificial intelligence techniques in wireless sensor networks for accurate localization of user in floor, building and indoor area

Node Dynamic Localization and Prediction Algorithm for Internet of Underwater Things

This article investigates the underwater node dynamic localization and prediction problems in a dynamic sensor network. Node localization in the Internet of underwater things is the basis of target tracking and ocean monitoring. At present, most of the node location algorithms assume calm sea and fixed node location. However, the current velocity is uncertain in space and time. The nodes are drifted with the current motion. Therefore, most of the localization algorithms lose efficacy in the actual marine environment. In order to solve the above problems, a node dynamic prediction algorithm is proposed. First, the node mobility model is improved, which is more suitable for the actual marine environment. Second, a frequency-based anchor node prediction algorithm is designed to improve anchor node location accuracy. Third, when the ordinary node receives the signals sent by anchor nodes of different depths, a deep information-based weighted fusion method is designed for the ordinary node localization in order to mine more information in each direction. Finally, location and prediction simulation in sensor networks is carried out. The results show that the proposed node localization and prediction algorithm is more accurate than SMLP and high-precision localization with mobility prediction algorithms and prove the enhanced effect of our method in dynamic marine.

Prediction of shooter localization accuracy in an urban environment

The use of acoustic sensor networks for shooter localization can provide a vital contribution to situational awareness in urban environments. During mission planning, the performance prediction for acoustic shooter localization plays a central role as it allows an optimization of the sensor positions in a sensor network. Instead of a sound propagation-based approach, in our work we focus on an information theoretical analysis using the Cramér-Rao bound to predict the achievable shooter localization accuracy. Through this approach, we have shown that accounting for incomplete and heterogeneous acoustic measurement data sets leads to maximization of the fusion gain and consequently to improved achievable localization accuracy. We validated the match between predicted and actual experimental performance in free-field measurements with supersonic gunshots including varying sensor-to-shooter geometries, weapon types, and various measurement types. By measuring signatures of impulsive gas cannon shots in urban terrain, we analyzed the effect of buildings to the sensor performance and achieved a significant improvement in the localization performance prediction by adjusting the sensor model depending on whether line-of-sight or non-line-of-sight conditions to the target exists.

A novel Sugeno type 2 Fuzzy system for finding error positions applying localization in wireless sensor network

In various usages where Wireless Sensor Networks (WSNs) have been utilized, this is vital to identify wherefrom Sensor nodes (SNs) or which position valuable details is attained. The Global Positioning System (GPS) is traditionally utilized to find out position. Nevertheless, GPS schemes are not perfect for various usages because of their extreme power utilization and high charge. As a substitute to GPS, distance and position may be approximated during the practice of more than three SNs with identified positions. Received Signal Strength Indication (RSSI) is the easiest and largely economical method applied to decide distance and position, and is a criterion aspect on each sensor. Howbeit, RSSI might be influenced through sound and ecological difficulties. Due to this, this is not easy to place up a statistical model for RSSI. The range free technique has various approaches to evaluate the location of SNs in a particular area. In our research work, Centroid localization is employed for development and investigation. RSSI is attained by evaluating the distance between every anchor node (AN) and the SN. Evaluating the position of every SN would be carried out with the help of centroid technique. However, the weights remain the chief metrics in the centroid connection; those are the output of the Sugeno Type 2 fuzzy scheme. Actually, the sugeno type 2 fuzzy technique is obtained RSSIs being inputs to depict the outputs; those are weights of every AN to the SN.

Path planning mechanism for mobile anchor-assisted localization in wireless sensor networks

The detection of a deployed sensor node location plays a vital role in all domains of Wireless Sensor Networks since its location provides the source of the information. Many approaches have been proposed in recent years. One of these approaches uses a single location-aware node, known as the Anchor or Beacon, that exchanges information with other nodes to help in their position estimation. Although this approach provides good accuracy, it creates new challenges in the picture. The key issue is to design an optimal trajectory for the anchor node in order to achieve maximum localization accuracy in the shortest amount of time. In this paper, we propose a static trajectory for a mobile beacon node for localization in WSNs. This trajectory ensures that the localization success ratio can be improved for lower communication range of an anchor node with higher localization precision and minimum localization time as compared to other static models. The proposed trajectory also overcomes the collinearity problem and ensures that all the unknown sensor nodes receive good quality beacon positions for position estimation.

A localization and deployment model for wireless sensor networks using arithmetic optimization algorithm

With the advancement of sensor technologies, Wireless Sensor Networks (WSN) are envisioning a rich variety of promising services in many fields. WSN is formed by the deployment of sensor nodes in the regions of interest using a deterministic or random deployment strategy. The random deployment strategy is more suitable in large monitoring areas and harsh environments. But, in this type of deployment, coverage holes and disconnected networks can exist. Important events may get unnoticed reducing the reliability of the networks. Since the locations of nodes are unknown in a randomly deployed WSN, it is difficult to locate the holes. For this, we are proposing a localization and deployment model. The localization algorithm uses Arithmetic Optimization Algorithm (AOA) and the results of this algorithm are further used to develop a deployment model to achieve a completely connected network. This algorithm is tested in various fields. The algorithm is able to localize nodes accurately and identify the coverage holes with an error rate of less than 0.27% when the Average Localization Error (ALE) is within 5m.

An experimental evaluation of localization methods used in wireless sensor networks

<span class="fontstyle0">The problem of localization in wireless sensor networks has received considerable attention from researchers over the past decades. Several methods and algorithms have been proposed to solve this problem. The effectiveness of these algorithms depends on the accuracy of the estimated positions and the information required to calculate the coordinates. In this paper, we propose to evaluate four of the most commonly used localization methods in sensor networks. Our study considers a mathematical description of the studied methods in order to evaluate their complexity, and then a practical implementation on the simulation tool Cooja. We evaluate the performance of the studied methods as a function of the number of deployed sensor nodes and their degree of mobility in terms of several performance metrics. The objective is to reveal the most suitable localization method for a particular case of deployment. Improvement proposals are also provided to improve the most relevant localization method for the investigated study.</span> <br /><br />

A Low-Power Distributed Visual Sensor Network for Real-Time Barcode Localization and Identification.

A novel low-power distributed Visual Sensor Network (VSN) system is proposed, which performs real-time collaborative barcode localization, tracking, and robust identification. Due to a dynamic triggering mechanism and efficient transmission protocols, communication is organized amongst the nodes themselves rather than being orchestrated by a single sink node, achieving lower congestion and significantly reducing the vulnerability of the overall system. Specifically, early detection of the moving barcode is achieved through a dynamic triggering mechanism. A hierarchical transmission protocol is designed, within which different communication protocols are used, depending on the type of data exchanged among nodes. Real-Time Transport Protocol (RTP) is employed for video communication, while the Transmission Control Protocol (TCP) and Long Range (LoRa) protocol are used for passing messages amongst the nodes in the VSN. Through an extensive experimental evaluation, we demonstrate that the proposed distributed VSN brings substantial advantages in terms of accuracy, power savings, and time complexity compared to an equivalent system performing centralized processing.

Exploiting perturbed and coalescent anchor node geometry with semidefinite relaxation for sensor network localization

Localization in wireless sensor networks (WSNs) suffer from performance issues whenever the anchor nodes (which are aware of their location) are subjected to motion from their usual position. Moreover, accurate localization demands more anchor nodes which is a scarce resource and needs to be used judiciously. In the current work, we propose a novel framework that addresses these two prime concerns by harnessing the inter relationship of anchor node geometry. For an unknown source node surrounded by anchor nodes, the anchors lying on the inner boundary of the deployment geometry may be carrying closely related information about source node, leading to redundancy and inefficient utilization. By anticipating the level of correlation between these anchors, localization can be made more frugal. Rigorous mathematical analysis is carried out to derive lower bounds on estimated locations. Based on fisher information from two proposed models, a convex estimation objective function is formulated using semidefinite programming (SDP) approach to validate the theoretical proceedings. Based on the findings, the proposed method is able to successfully extract useful information about the unknown source node location with limited number of anchor nodes, hence achieving superior localization.

Optimal Trained Deep Neural Network for Localization in Wireless Sensor Network

Wireless Sensor Network (WSN) localization has been bloomed as an active area of research in this era. In fact, WSN differs from the traditional network in diverse aspects and therefore requires novel algorithms for addressing specific challenges like the identification of the unknown node location in hazardous environments. In this paper, a new localization model is introduced by the range-based localization approach via an optimization-assisted deep learning model. The proposed work undergoes two major phases: (a) training phase and (b) localization phase. The trained Deep Neural Network (DNN) with the measured distance-based features like the “Angle of Arrival (AoA) and Received Signal Strength Indicator (RSSI)” find out the place of the unknown node more precise. Further, to enhance the localization accuracy, the weight of DNN is tuned via a novel hybrid optimization algorithm named as Lion-Assisted Firefly Algorithm (LAFA) model. The proposed LAFA is the concept of both the Lion Algorithm (LA) and Firefly Algorithm (FF). Finally, the evaluation of the presented work is done in terms of error measures.

InPhase: Phase-based Ranging and Localization

Ranging and subsequent localization have become more and more critical in today’s factories and logistics. Tracking goods precisely enables just-in-time manufacturing processes. We present the InPhase system for ranging and localization applications. It employs narrowband 2.4 GHz IEEE 802.15.4 radio transceivers to acquire the radio channel’s phase response. In comparison, most other systems employ time-of-flight schemes with Ultra Wideband transceivers. Our software can be used with existing wireless sensor network hardware, providing ranging and localization for existing devices at no extra cost. The introduced Complex-valued Distance Estimation algorithm evaluates the phase response to compute the distance between two radio devices. We achieve high ranging accuracy and precision with a mean absolute error of 0.149 m and a standard deviation of 0.104 m. We show that our algorithm is resilient against noise and burst errors from the phase-data acquisition. Further, we present a localization algorithm based on a particle filter implementation. It achieves a mean absolute error of 0.95 m in a realistic 3D live tracking scenario.

An adaptive multi-group slime mould algorithm for node localisation in wireless sensor networks

Node localisation is a common and significant practical application question in wireless sensor network (WSN). The goal of this problem is to use anchor nodes in the network to estimate the geographical location of the unknown node. A novel algorithm, named adaptive multi-group slime mould algorithm (AMSMA), is proposed in this study. The improved slime mould algorithm uses the multi-group strategy and adaptive communication mechanism to alleviate the lack of population diversity, development and exploration imbalance of the slime mould algorithm. The proposed AMSMA was tested under CEC2013 test suite. Compared with SMA and corresponding optimisation algorithms, the AMSMA is more effective and efficient. In addition, a novel localisation algorithm based on AMSMA is proposed. The AMSMA-Distance Vector-Hop (AMSMA-DV-Hop) is applied to the localisation of WSN. Compared with some other existing localisation algorithms, the proposed AMSMA-DV-Hop is an effective algorithm for the localisation of WSN.

An adaptive multi-group slime mould algorithm for node localisation in wireless sensor networks

An adaptive multi-group slime mould algorithm for node localisation in wireless sensor networks

An Optimal Anchor Placement Method for Localization in Large-Scale Wireless Sensor Networks

Localization is an essential task in Wireless Sensor Networks (WSN) for various use cases such as target tracking and object monitoring. Anchor nodes play a critical role in this task since they can find their location via GPS signals or manual setup mechanisms and help other nodes in the network determine their locations. Therefore, the optimal placement of anchor nodes in a WSN is of particular interest for reducing the energy consumption while yielding better accuracy at finding locations of the nodes. In this paper, we propose a novel approach for finding the optimal number of anchor nodes and an optimal placement strategy of them in a large-scale WSN, based on the output of Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) methods. As an initial step in this approach, the virtual localization process is executed over a virtual coordinate system in order to optimize the efficiency of the localization process. GWO and PSO methods are compared with a coverage-based analytical method and machine learning approaches such as Support Vector Machine (SVM) regression and Multiple Regression. The simulations we run with different numbers of nodes in a WSN and different communication ranges of nodes demonstrate that the proposed approaches are superior for minimizing the localization errors while reducing the number of anchor nodes.

Maximum likelihood based underwater localization algorithm aided with depth measurements

Environmental monitoring applications are growing in popularity as the awareness of severe climate change risk rises. In recent times, the focus is moving toward using autonomous robotic systems for underwater monitoring missions. Underwater localization is a crucial part that often requires expensive equipment and labor. Reducing deployment cost and effort can be achieved through the utilization of autonomous surface vessels (ASV) for underwater localization of acoustic sensor networks. In this paper we propose a modification of TDoA-based AML localization algorithm which incorporates depth measurements in position determination procedure. Due to specific nature of the application the algorithm was analyzed in situations that commonly arise in underwater localization missions, which is localizing underwater agent using surface positioned acoustic anchors. Localization performance is analyzed for different locations and noise levels. Sensor depth influence on localization performance was investigated.

TDOA-Based Robust Sound Source Localization With Sparse Regularization in Wireless Acoustic Sensor Networks

Time difference of arrival (TDOA) measurements, which are contaminated by large values of error, known as outliers, would have a significant impact on the accuracy of sound source localization (SSL) in wireless acoustic sensor networks (WASNs). Few techniques are reported in the literature to tackle SSL in WASNs by taking TDOA outliers into consideration. To mitigate the effect of outliers on the accuracy of SSL, we propose outlier-resistant robust sound source localization (RSSL) algorithms based on sparse regularization using an unsynchronized network of microphone arrays. The TDOA errors are divided into two components: a) energy-bounded inliers and b) outliers. Assuming that outliers are sparse in the measurement set, we formulate the RSSL problem as that of minimizing the number of outliers, mathematically, a <inline-formula><tex-math notation="LaTeX">$\ell _{0}$</tex-math></inline-formula> (pseudo)-norm optimization problem with non-convex constraints. Five sub-optimal RSSL solvers are derived, among which the first two solvers are applicable to the scenario involving only outliers while the last three solvers concentrate on the scenario incorporating both outliers and inliers. In common, these solvers exploit a convex approximation technique called Concave Convex Procedure to dispose of the non-convex constraints. Differently, the first solver approximates the original <inline-formula><tex-math notation="LaTeX">$\ell _{0}$</tex-math></inline-formula> (pseudo)-norm cost function with the <inline-formula><tex-math notation="LaTeX">$\ell _{1}$</tex-math></inline-formula> norm while a concave surrogate function is adopted in the second solver to yield a tighter approximation to the <inline-formula><tex-math notation="LaTeX">$\ell _{0}$</tex-math></inline-formula> (pseudo)-norm. Apart from the application of these two approximation techniques, the third and fourth solvers relax the non-convex <inline-formula><tex-math notation="LaTeX">$\ell _{2}$</tex-math></inline-formula> norm constraint with the <inline-formula><tex-math notation="LaTeX">$\ell _{\infty }$</tex-math></inline-formula> norm. The fifth solver is dedicated to the <inline-formula><tex-math notation="LaTeX">$\ell _{1}$</tex-math></inline-formula> norm regularization problem with the Lasso formulation, which is equivalent to the M-estimator of Huber&#x2019;s function solved via the iteratively reweighted least squares paradigm. Experimental results validate the effectiveness and robustness of the proposed algorithms.

Distributed Frobenius-Norm Finite Memory Interacting Multiple Model Estimation for Mobile Robot Localization

This paper proposes a novel state estimation algorithm, called the distributed Frobenius-norm finite memory interacting multiple model (DFFM-IMM) estimation algorithm, for mobile robot localization in wireless sensor networks (WSNs). The proposed algorithm involves finite memory estimation based on recent finite measurements; such estimation facilitates robust localization in cases of missing measurements and robot kidnapping. Furthermore, the proposed algorithm employs IMM, which facilitates accurate localization if a mobile robot abruptly changes its speed and course. Notably, average-consensus-based distributed processing renders the proposed DFFM-IMM algorithm computationally efficient, and hence, real-time processing for very short sampling times of the WSN is possible. The proposed algorithm’s performance is demonstrated by comparing it with a centralized Frobenius-norm finite memory IMM (CFFM-IMM) estimation algorithm and a localization algorithm on the basis of simulations and experiments.

Improved Chan Algorithm Based Optimum UWB Sensor Node Localization Using Hybrid Particle Swarm Optimization

The localization of Wireless sensor networks (WSNs) has been recognized as one of the most challenging problems to overcome. Thus, much work has been given to solving this difficult problem. In emergency services, navigational systems, civil/military surveillance etc., locating the signal source in a WSN is essential. A novel approach for sensor node localization using range-based localization methodology has been proposed to overcome this issue. The problem is expressed in the form of a maximum probability distribution function. The use of an RSSI-based Time Difference of Arrival (TDOA) measurement model, along with the Chan algorithm, is used to find the coordinates of unknown nodes has been proposed. With the help of ultra-wideband, this research aims to develop new and precise localization algorithms for wireless sensor networks (WSNs). This work offers localization using two-hybrid localization algorithms, i.e., ELPSO (Ensemble learning particle swarm optimization) and PSO- BPNN (Back-propagation neural network optimized by particle swarm optimization). Further, the error optimization accuracy has been compared between those algorithms using simulations. The proposed techniques consistently offer a better localization accuracy than the conventional algorithms available in the literature. The new localization methods with optimal techniques reduce the error value to a minimal distance. The distance value of localization error is nearly2.7cms compared to other designs from the literature. It is noted as significantly less.

Distributed Finite Memory Estimation From Relative Measurements for Multiple-Robot Localization in Wireless Sensor Networks

Mobile robot localizations have been extensively studied, and various algorithms for multiple-robot localization have been developed. However, existing methods for multiple-robot localization often exhibit poor performance under harsh conditions, such as missing measurements and sudden appearance of obstacles. To overcome this problem, this paper proposes a novel method for multiple-robot localization in wireless sensor networks. The proposed method is theoretically based on the finite memory estimation and utilizes relative distance and angle measurements between robots. Thus, the proposed method is referred to as distributed finite memory estimation from relative measurements (DFMERM). Due to the finite memory structure, the DFMERM has inherent robustness against computational and modeling errors. Moreover, the novel distributed localization method using relative measurements shows the robustness against missing measurements. Robust DFMERM localization performance is experimentally demonstrated using multiple mobile robots under the harsh conditions.