Localization In Wireless Sensor Networks Research Articles

Dealing with Outliers in Wireless Sensor Networks Localization: An Iterative and Selection-Minimization Strategy

AbstractIn recent years, there has been considerable interest in robust range-based Wireless Sensor Network (WSN) localization due to the increasing importance of accurately locating sensors in various WSN applications. However, achieving precise localization is often hampered by the presence of outliers or underestimations in range measurements, particularly when employing the RSS technique. To tackle these issues, we introduce a Two-Step Localization (namely, the SelMin approach). In the initial phase, the approach utilizes Second-Order Cone Programming (SOCP) to minimize distance discrepancies. It does this by comparing a reference Euclidean Distance Matrix (EDM) with a weighted one derived from imprecise distances between sensor nodes. In the subsequent phase, a heuristic method is employed to identify a specific number of imprecise distances, referred to as outliers, that will be disregarded in the first phase, and this two-phase process continues iteratively. The experimental results demonstrate that the SelMin strategy performs better than the DSCL method when evaluated using the Root Mean Square Error (RMSE) metric. This superior performance is maintained even in challenging conditions, such as when there are many outliers (i.e, around 30$$\%$$ % ) in the network. This indicates that SelMin is a reliable and robust choice for these environments.

Localization in Wireless Sensor Networks by Hybridization between Optimization Algorithms of Particle Swarms and Fruit Flies

Localization in Wireless Sensor Networks by Hybridization between Optimization Algorithms of Particle Swarms and Fruit Flies

Research on WSN reliable ranging and positioning algorithm for forest environment

Wireless sensor network (WSN) location is a significant research area. In complex environments like forests, inaccurate signal intensity ranging is a major challenge. To address this issue, this paper presents a reliable WSN distance measurement-positioning algorithm for forest environments. The algorithm divides the positioning area into several sub-regions based on the discrete coefficient of the collected signal strength. Then, using the fitting method based on the signal intensity value of each sub-region, the algorithm derives the reference points of the logarithmic distance path loss model and path loss index. Finally, the algorithm locates target nodes using anchor nodes in different regions. Additionally, to enhance the positioning accuracy, weight values are assigned to the positioning result based on the discrete coefficient of the signal intensity in each sub-region. Experimental results demonstrate that the proposed WSN algorithm has high precision in forest environments.

A Novel Polytope Algorithm based On Nelder-mead Method for Localization in Wireless Sensor Network

Background and Objective: Magnificent localization precision and low operating expenses are the main keys and essential issues to managing and operating outdoor wireless sensor networks. This work proposes a novel and rigorous efficiency localization algorithm utilizing a simplex optimization approach for node localization. This novel optimization method is a direct search approach, and is usually directed to solve nonlinear optimization problems that may not have wellknown derivatives, and it is called the Nelder-mead Method (NMM). Methods: It is suggested that the objective function that will be optimized using NMM is the mean squared error of the range of all neighboring anchor nodes installed in the studied WSNs. This paper emphasizes employing a ranging technique called Received Signal Strength Indicator (shortly RSSI) to calculate the length of distances among all the nodes of WSNs. Results: Simulation results perfectly showed that the suggested localization algorithm based on NMM can carry out a better performance than that of other localization algorithms utilizing other optimization approaches, including a particle swarm optimization, ant colony (ACO) and bat algorithm (BA). This obviously appeared in several metrics of performance evaluation, such as accuracy of localization, node localization rate, and implementation time. Conclusions: The proposed algorithm that utilized NMM is more functional to enhance the precision of localization because of particular characteristics that are the flexible implementation of NMM and the free cost of using the RSSI technique.

Processing‐in‐memory based multilateration localization in wireless sensor networks using memristor crossbar arrays

SummaryMost of the multilateration algorithms for object localization rely heavily on matrix multiplication for calculating the precise location of the target from all the reference points in a 3D domain. A significant disadvantage is that performing matrix multiplication can be computationally expensive, particularly for larger matrices, leading to slower performance and longer processing times. In this article, we propose a Processing‐in‐Memory (PIM) based localization approach for multilateration using a memristor crossbar array for performing faster matrix multiplication. The experiments were conducted on real‐time data collected through ultra‐wideband (UWB) chip. The simulation results demonstrate that the proposed model is around 50% faster than the fastest known localization algorithm considered for evaluation and exhibits an average of 55% improvement in localization accuracy.

Multi-Strategy Fusion for Enhancing Localization in Wireless Sensor Networks (WSNs)

 Localization in wireless sensor networks (WSNs) plays a crucial role in various applications thatrely on spatial information. This paper introduces the Multi-Strategy Fusion for Localization model, whichintegrates optimization techniques (ABO, DSA, EHO, and KNN) and neurocomputing techniques (BP, MTLSTM,BILSTM, and Autoencoder) to enhance localization accuracy in WSNs. The work is divided into three phases: datacollection, model building, and implementation. The first and the last are carried out in the field, while the secondis made in the laboratory. The three phases involve a few general steps. The (1) Data Collection Phase includesfour steps: (a) Deploy three anchors at known locations, forming an equilateral triangle. (b) Each anchor startsbroadcasting its location. (c) Using an ordinary sensor, the RSSI of each anchor is measured at every possiblelocation where the signal of the three anchors can reach it. (d) Data is logged to a CSV file containing themeasuring location and the RSSI of the three anchors and their locations. The Model Building Phase includes (a)preprocessing of the collected data, and (b) building a model based on optimization and optimization techniques.The Implementation phase includes five steps: (a) Convoy sensors to the target field. (b) Manually deploy anchorsaccording to the distribution plan. (c) Randomly deploy ordinary sensors. (d) Each ordinary sensor starts ininitialization mode. When receiving a signal from three anchors, a sensor computes its location and stores it forfuture use. When a sensor has its location, it turns into operational mode. (e) A sensor in the operational modeattaches the location with sensed data each time it sends it to the sink or neighbors according to routing protocols(routing is not considered in this study). ABO and DSA optimization techniques show similar performance, withlower Mean Squared Error (MSE) values compared to EHO and KNN. ABO and DSA also have similar MeanAbsolute Error (MAE) values, indicating lesser average absolute errors. BP emerges as the top performer amongthe neurocomputing techniques, demonstrating better accuracy with lower MSE and MAE values compared toMTLSTM, BILSTM, and Autoencoder. Finally; The Multi-Strategy Fusion for Localization model offers aneffective approach to enhance localization accuracy in wireless sensor networks. The paper focuses on addressingthe correlation between wireless device positions and signal intensities to improve the localization process. Theobtained results and provided justification emphasize the significance and value of the model in the field oflocalization in WSNs. The model represents a valuable contribution to the development of localization techniquesand improving their accuracy to meet the needs of various applications. The model opens up opportunities for itsutilization in diverse domains such as environmental monitoring, healthcare, smart cities, and disastermanagement, enhancing its practical applications and practical significance.

Seagull optimization algorithm for node localization in wireless sensor networks

Seagull optimization algorithm for node localization in wireless sensor networks

Wireless Sensor Networks Node Localization Algorithm Based on Range Optimization and Graph Optimization

<p>To address the problem of low localization accuracy in the node localization algorithms of wireless sensor networks (WSN) based on received signal strength indication (RSSI) ranging, a WSN node localization algorithm based on ranging optimization and graph optimization is proposed. In terms of RSSI ranging, the outliers are removed using the Grubbs method, and the data are processed using a moving average smoothing-Gaussian hybrid filter to establish a Bessel function ranging model to reduce the ranging error; in terms of node localization, the signal strength data are employed to construct a distance cost term, a cost function model is built based on these cost terms, and graph optimization is adopted to minimize this function. Then, the node position is estimated to minimize the overall observation error. Simulation results indicate that the proposed algorithm has higher ranging and localization accuracy than existing ranging and localization algorithms, and it can meet the requirements of node localization in large-scale WSN.</p> <p> </p>

Localization in 3D Wireless Sensor Networks with Obstacle Consideration

Wireless sensor network (WSN) refers to the network formed by sensor nodes for communicating sensed data over the wireless medium. These sensor nodes are mostly deployed randomly in the target area, and hence estimating the coordinates of these sensor nodes through the localization process is an important activity. Majority of the localization algorithms existing in the literature assume the target area to be an obstacle-free 2D terrain. However, such algorithms are not suitable for real-life scenarios where WSNs are actually deployed on 3D terrains that may also have obstacles that hinder the radio signals from the deployed sensor nodes. Hence, factors such as the height of the terrain, the presence of obstacles, etc., require significant considerations while designing algorithms for WSN. The proposed research presents a localization scheme for 3D WSN where the sensor nodes are randomly deployed in a 3D area having large obstacles. The proposed scheme adopts a distributed approach for calculating the virtual coordinates of the sensors using four beacon nodes. The deployment area is divided into regions, and each node computes its virtual coordinates with respect to the obstacle. Simulation results indicate an average localization error of 6.93 m. The proposed scheme requires very less computational effort and can be easily adapted in different scenarios.

Effectiveness of Data Augmentation for Localization in WSNs Using Deep Learning for the Internet of Things.

Wireless sensor networks (WSNs) have become widely popular and are extensively used for various sensor communication applications due to their flexibility and cost effectiveness, especially for applications where localization is a main challenge. Furthermore, the Dv-hop algorithm is a range-free localization algorithm commonly used in WSNs. Despite its simplicity and low hardware requirements, it does suffer from limitations in terms of localization accuracy. In this article, we develop an accurate Deep Learning (DL)-based range-free localization for WSN applications in the Internet of things (IoT). To improve the localization performance, we exploit a deep neural network (DNN) to correct the estimated distance between the unknown nodes (i.e., position-unaware) and the anchor nodes (i.e., position-aware) without burdening the IoT cost. DL needs large training data to yield accurate results, and the DNN is no stranger. The efficacy of machine learning, including DNNs, hinges on access to substantial training data for optimal performance. However, to address this challenge, we propose a solution through the implementation of a Data Augmentation Strategy (DAS). This strategy involves the strategic creation of multiple virtual anchors around the existing real anchors. Consequently, this process generates more training data and significantly increases data size. We prove that DAS can provide the DNNs with sufficient training data, and ultimately making it more feasible for WSNs and the IoT to fully benefit from low-cost DNN-aided localization. The simulation results indicate that the accuracy of the proposed (Dv-hop with DNN correction) surpasses that of Dv-hop.

Square grid Path Planning for Mobile Anchor-Based Localization in Wireless Sensor Networks

Localization is to provide all sensor nodes with their geographical positions. A mobile anchor-based localization in WSNs uses a mobile anchor equipped with GPS, which travels along a predetermined path. At each specified beacon point, it broadcasts its current known position to help other sensor nodes with unknown locations estimate their positions. In this paper, we analyze the determination of beacon points based on a square grid. We propose an improved path planning model named Union-curve. Our proposed model incorporates all beacon points of five previously developed paths, namely, SCAN, HILBERT, S-type, Z-curve, and $\Sigma$-Scan on the commonly used square grid decomposition of area. Unknown sensor nodes estimate their positions using two techniques, APT and WCWCL-RSSI. Simulation results show that the proposed model has higher accuracy, with a big difference in error rate compared to the other models. In addition, this model guarantees maximum coverage with less path resolution value.

TDoA Localization in Wireless Sensor Networks Using Constrained Total Least Squares and Newton’s Methods

TDoA Localization in Wireless Sensor Networks Using Constrained Total Least Squares and Newton’s Methods

Clustering Based RSSI and AOA combination for Localization in Wireless Sensor Networks

Clustering Based RSSI and AOA combination for Localization in Wireless Sensor Networks

Convex Combination for Wireless Localization Using Biased RSS Measurements

Received signal strength- (RSS-) based localization in wireless sensor networks (WSNs) has attracted significant attention due to its advantages of low cost and simple implementation. In practice, RSS measurements may suffer from sensor biases, which deteriorates the localization accuracy. However, most of the existing localization methods are designed for bias-free measurements. In this paper, we propose a convex combination method for RSS localization in the presence of sensor biases. The parameter vector composed of unknown location and sensor biases is estimated simultaneously by using a convex combination of some virtual points. These virtual points form a convex hull, into which the parameter vector falls with large probability. By this, the original nonconvex estimation problem is converted to be convex. Numerical examples demonstrate the superiority of the proposed method in terms of localization accuracy, compared to the existing semidefinite programming (SDP) methods.

Cooperative Multi-Rigid-Body Localization in Wireless Sensor Networks Using Range and Doppler Measurements

Cooperative Multi-Rigid-Body Localization in Wireless Sensor Networks Using Range and Doppler Measurements

Localization Process for WSNs with Various Grid-Based Topology Using Artificial Neural Network

Wireless Sensor Network (WSN) is a technology that can aid human life by providing ubiquitous communication, sensing, and computing capabilities. It allows people to be more able to interact with the environment. The environment contains many nodes to monitor and collect data. Localizing nodes distributed in different locations covering different regions is a challenge in WSN. Localization of accurate and low-cost sensors is an urgent need to deploy WSN in various applications. In this paper, we propose an artificial automatic neural network method for sensor node localization. The proposed method in WSN is implemented with network-based topology in different regions. To demonstrate the accuracy of the proposed method, we compared the estimated locations of the proposed feedforward neural network (FFNN) with the estimated locations of the deep feedforward neural network (DFF) and the weighted centroid localization (WCL) algorithm based on the strength of the received signal index. The proposed FFNN model outperformed alternative methods in terms of its lower average localization error which is 0.056m. Furthermore, it demonstrated its capability to predict sensor locations in wireless sensor networks (WSNs) across various grid-based topologies.

Two-mobile anchor node-based localization in wireless sensor networks

Two-mobile anchor node-based localization in wireless sensor networks

An enhanced node segmentation and distance estimation scheme with a reduced search space boundary and improved PSO for obstacle-aware wireless sensor network localization

This paper proposes an enhanced method for localizing sensor nodes in wireless sensor networks with obstacles. Such environment settings lead to lower localization accuracy because locations are estimated based on detour distances circumventing the obstacles; we, therefore, improve the segmentation technique to address the issue as they divide the whole area into multiple smaller ones, each containing fewer or no obstacles. Nevertheless, when radio transmissions between sensor nodes are obstructed (as simulated by the radio irregularity model), the signal-strength variation tends to be high, reducing localization accuracy; thus, we provide a method for accurately approximating the distances between pairs of an anchor node (whose location is known) and an unknown node by incorporating the related error into the approximation process. Additionally, when the nodes with unknown locations are outside the polygon formed by the anchor nodes, the search area for localization is relatively large, resulting in lower accuracy and a longer search time; we then propose a method for reducing the size of approximation areas by forming boundaries based on the two intersection points between the ranges of two anchor nodes used to localize an unknown node. However, these reduced search areas could still be large; we further increase the accuracy of the PSO location estimation method by adaptively adjusting the number of particles. In addition, with PSO, the accuracy of unknown node location estimation depends on having a properly selected fitness function; therefore, we incorporate appropriate variables to reflect the distance approximation accuracy between each anchor-unknown node pair. In experiments, we measure performance in sensor node deployment areas of three different shapes: C-shaped, with 1 hole, and with 2 rectangular holes. The results show that our method provides higher localization accuracy than others in small-, medium-, and large-scaled WSNs. Specifically, our proposed method is 27.46%, 49.28%, 50.33%, and 74.62% more accurate on average than IDE-NSL, PSO–C, min-max PSO, and niching PSO, respectively.

Privacy-Preserving Distributed Iterative Localization for Wireless Sensor Networks

Effectively determining the accurate locations of sensor nodes while preserving data privacy is an interesting and challenging problem in the research of wireless sensor networks. On the basis of the well-performing distributed iterative localization framework in wireless sensor networks, this paper proposes a dual privacy-preserving scheme based on node-intermittent updates and noises injection. A comprehensive analysis of the localization algorithm in terms of convergence and privacy-preserving performance is presented. It is theoretically demonstrated that the localization algorithm can achieve global accurate localization without leaking the privacy information of sensor nodes. At last, the performance of the privacy-preserving localization algorithm is verified by building an experimental platform containing six robots.

Systematic Review on WSN Localization using Metaheuristic and Learning Methods Based on AI Techniques

The localization process in Wireless Sensor Networks (WSNs) is carried out for finding the exact position of the sensor node. WSN are applied in various number of applications like monitoring, humidity, hospitality and so on. Hundreds to thousands of nodes in the sensor sense the data to the base station. Thousands of nodes in the WSN makes the installing of Global processing System (GPS). It is more costly and the GPS not provide the exact location in the indoor environment. In spite of dense network, manual configuration is not possible in the indoor environment. This cause a raise in problem to identify the exact position without GPS or any manual configuration methods. This paper presents the methods to overcome the above issues in WSN localization, by path planning, metaheuristic and deep learning are the techniques having high scopes in the industries, mining etc. Many times sensing of data are restricted by accuracy, energy, power and efficiency in the nodes. This paper discuss the WSN localization process classifications and the methodologies in localization. Localization issues are minimized by various techniques like ToA, TDoA and RSSI. The types of localization, its applications are also discussed.