Localization In Wireless Sensor Networks Research Articles

A Systematic Review of Localization in WSN: Machine Learning and Optimization‐Based approaches

SummaryIn recent years, wireless sensor networks (WSNs) have been widely used in various applications. The localization problem has been identified as one of the biggest problems faced by WSNs. The traditional localization techniques may not be able to handle the issues during the scenario to estimate the location of sensor nodes due to anchor mobility, mobile WSNs, latency, energy harvesting, unfavorable environmental states, and many more issues. However, these issues open the door for the amalgamation of machine learning (ML) and optimization techniques with localization techniques. Motivated by the earlier discussion, we explored various ML and optimization techniques to estimate the location coordinates in a sensor network in this paper. Finally, a comparison of existing ML algorithms concerning optimization techniques has been presented, highlighting their improved outcomes. This research offers a detailed survey by exploring the various parameters for location estimation through tabular forms by incorporating ML and optimized localization techniques. A survey of surveys is also presented to identify the key limitations of existing surveys and to introduce the novelty in the comprehensive study done in this paper. A year‐wise evaluation of ML Techniques with localization (2011–2022) is also discussed and presented over various performance parameters, including energy‐efficiency, accuracy, error, and complexity. This discussion concluded that Hybrid Techniques are least explored for using optimized localization machine learning. Further, a summarized discussion of the various comparison tables paves the path for future research in the area of localization in WSN.

Meta-heuristic-based guidance model for node localization in wireless sensor networks

An important requirement in many applications in Wireless sensor networks (WSNs) is the location of nodes within the network. Manual location provision options have become practically impossible, necessitating the use of self-localization approaches for the provision of location information. In one such approach, the mobile-assisted localization approach, a mobile anchor (MA) goes through the network and provides updated location information to the nodes. However, the implementation of solutions with highly MAs is limited in many real-time applications, with typical networks having irregular and unpredictable obstacles, vast distances between nodes, and large energy requirements. To make MAs more efficient in providing localization information, we proposed the Flow Direction Path Planning (FDPP) model, where the planning for movement is achieved using meta-heuristic physics-based algorithms following a real-time approach. The model shows better outcomes than traditional models, with improved localization accuracy, ratios, precision, and lower computational times.

RSSI-based geometric localization in wireless sensor networks

RSSI-based geometric localization in wireless sensor networks

Mobile Anchor and Kalman Filter Boosted Bounding Box for Localization in Wireless Sensor Networks

Event detection is usually the primary purpose of wireless sensor networks (WSNs). Therefore, it is crucial to determine where and when an event occurs in order to map the event to its spatio-temporal domain. In WSN localization, a few anchor nodes are those aware of their locations via the Global Positioning System (GPS), which is energy-consuming. Non-anchor nodes self-localize by gathering information from anchor nodes to estimate their positions using a localization technique. Traditional algorithms use at least three static anchors for the localization process. Recently, researchers opted to replace multiple static anchors by a single mobile anchor during the localization process. This paper proposes a Kalman filter based on bounding box localization algorithm (KF-BBLA) in WSNs with mobile anchor node. We present a new mobile anchor localization strategy to minimize energy, hardware costs, and computation complexity, while improving accuracy and cost-effectiveness. Network connectivity measurement and the bounding box localization method are used in order to identify the bounded possible localization zone. The Kalman filter is then used to minimize the uncertainty produced by the connectivity process. We aim also to minimize the localization inaccuracies generated by the bounding box algorithm. Simulation results show that our proposed approach significantly reduces the localization error compared to other localization algorithms chosen from the recent literature by up to 20%. We use the cumulative distribution function (CDF) as an indicator to assess the accuracy of our proposed algorithm.

Improved Wireless Sensor Network Localization Algorithm Based on Selective Opposition Class Topper Optimization (SOCTO)

One of the most important challenges in wireless sensor networks is the issue of localization. Furthermore, it is critical to monitor and evaluate the data gathered. With the aid of a collection of constructed nodes known as beacons, the localization technique can measure node location. For a variety of factors, such as upkeep, lifespan, and breakdown, the fixed density of these beacons may be increased or decreased. Because of its robustness, flexibility, and economic viability, a well-known technique for locating wireless sensor network nodes is the distance vector-hop (DV-Hop) algorithm. As a result, researchers continue to look for ways to develop it. Based on a proposed selective opposition class topper optimization (SOCTO), an enhanced DV-Hop localization algorithm is developed. It also focuses on an optimised formulation to compute the average hop-size with weight of beacon nodes in order to reduce the localization error within the estimated distance between the beacon and the dumb node, due to improved localization accuracy. The range-free localization algorithm in uniform multi-hop wireless sensor networks is the subject of a recent study. The results show that our proposed method outperforms the DV-Hop technique and related techniques in terms of average localization error (\(0.05\%\)) versus random beacon node deployment, average localization error (\(0.04\%\)) versus circular beacon node deployment, and average localization error (\(0.08\%\)) versus spiral beacon node deployment.

A Hybrid Modified Ant Colony Optimization - Particle Swarm Optimization Algorithm for Optimal Node Positioning and Routing in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have been widely deployed in hostile locations for environmental monitoring. Sensor placement and energy management are the two main factors that should be focused due to certain limitations in WSNs. The nodes in a sensor network might not stay charged when energy draining takes place; therefore, increasing the operational lifespan of the network is the primary purpose of energy management. Recently, major research interest in WSN has been focused with the essential aspect of localization. Several types of research have also taken place on the challenges of node localization of wireless sensor networks with the inclusion of range-free and range-based localization algorithms. In this work, the optimal positions of Sensor Nodes (SNs) are determined by proposing a novel Hybrid M-ACO – PSO (HMAP) algorithm. In the HMAP method, the improved PSO utilizes learning strategies for estimating the relay nodes' optimal positions. The M-ACO assures the data conveyance. A route discovers when it relates to the ideal route irrespective of the possibility of a system that includes the nodes with various transmission ranges, and the network lifetime improves. The proposed strategy is executed based on the energy, throughput, delivery ratio, overhead, and delay of the information packets.

Distributed localization of wireless sensor network using communication wheel

We study the network localization problem, i.e., the problem of determining node positions of a wireless sensor network modeled as a unit disk graph. In an arbitrarily deployed network, positions of all nodes of the network may not be uniquely determined. Computational complexity results suggest that even if the network corresponds to a unique solution, a polynomial-time algorithm is unlikely to exist. So we are interested in algorithms that efficiently localize the network partially. A widely used technique that can efficiently localize a uniquely localizable portion of the network is trilateration: starting from three anchors (nodes with known positions), nodes having at least three localized neighbors are sequentially localized. However, the performance of trilateration can substantially differ for different choices of the initial three anchors. In this paper, we propose a distributed localization scheme with a theoretical characterization of nodes that are guaranteed to be localized. In particular, our proposed distributed algorithm starts localization from a strongly interior node and provided that the subgraph induced by the strongly interior nodes is connected, it localizes all nodes of the network except some boundary nodes and isolated weakly interior nodes.

Cube-Based Multitarget 3D Localization Using Bayesian Learning-Based Turbo Decoding in Wireless Sensor Networks

Wireless techniques have progressed beyond two-dimensional (2D) applications; in particular, three-dimensional (3D) localization in wireless sensor networks has attracted increasing attention. While many research works in 3D localization focused on the accuracy enhancement of schemes based on the sensors’ measurements, limited research works addressed the design of 3D localization schemes considering the narrowband signal restriction of sensors. Against this background, we propose a cube-based multitarget three-dimensional (3D) localization solution by exploiting sensors’ time-difference-of-arrival (TDOA) measurements. In particular, unlike the traditional TDOA based scheme, our scheme works in an asynchronous network. Our contributions are twofold. First, the distributed TDOA–based sensor arrays placed with a predefined method create a cube-based location system in 3D space. Second, we propose a turbo expectation propagation (EP)-based decoding algorithm (TED). EP computation is an efficient tool in Bayesian machine learning. With the assistance of the iterative sensor reliability correction (ISRC), we propose an improved algorithm referred to as ISRC-TED. Specifically, the ISRC-TED algorithm outperforms the TED algorithm by utilizing the decisions of the sensor array at every iteration to improve the decoding accuracy further. A reduced-complexity tree search–based decoding strategy for TED and ISRC-TED is also proposed. In simulations, the proposed TED and ISRC-TED algorithms were highly effective, even when the partial sensor network was in power-saving mode. For example, the proposed ISRC-TED algorithm had almost no localization performance loss when 10% of the sensors were in sleep mode.

Enhanced gray wolf optimization for estimation of time difference of arrival in WSNs

Purpose Intelligent prediction of node localization in wireless sensor networks (WSNs) is a major concern for researchers. The huge amount of data generated by modern sensor array systems required computationally efficient calibration techniques. This paper aims to improve localization accuracy by identifying obstacles in the optimization process and network scenarios. Design/methodology/approach The proposed method is used to incorporate distance estimation between nodes and packet transmission hop counts. This estimation is used in the proposed support vector machine (SVM) to find the network path using a time difference of arrival (TDoA)-based SVM. However, if the data set is noisy, SVM is prone to poor optimization, which leads to overlapping of target classes and the pathways through TDoA. The enhanced gray wolf optimization (EGWO) technique is introduced to eliminate overlapping target classes in the SVM. Findings The performance and efficacy of the model using existing TDoA methodologies are analyzed. The simulation results show that the proposed TDoA-EGWO achieves a higher rate of detection efficiency of 98% and control overhead of 97.8% and a better packet delivery ratio than other traditional methods. Originality/value The proposed method is successful in detecting the unknown position of the sensor node with a detection rate greater than that of other methods.

Localization in wireless sensor network using machine learning optimal trained deep neural network by parametric analysis

Sensor nodes (SNs), which are a component of wireless sensor devices, have several uses in monitoring and surveillance. To perform location-based routing, the sensor nodes must be aware of their location reference (LR). The field of localization is the subject of several studies. Future developments in mobility, 3-D localization, energy preservation, and security are yet largely neglected. Here, optimization aided DNN trained with the measured distance based features like “Angle of Arrival (AoA) and Received Signal Strength Indicator (RSSI)” gives the predicted results on location of unknown nodes. To enhance the accuracy on localization, a hybrid model termed as Lion Assisted Firefly Algorithm (LAFA) model has been introduced. In this conference, a parametric analysis is made on the proposed algorithm by varying the parameter in LAFA. This includes the performance analysis of the model under each variation.

Correction to: Improved DV-Hop based on parallel and compact whale optimization algorithm for localization in wireless sensor networks

Correction to: Improved DV-Hop based on parallel and compact whale optimization algorithm for localization in wireless sensor networks

Malicious anchor node extraction using geodesic search for survivable underwater wireless sensor network

Localization in underwater wireless sensor network (UWSN) faces an imminent threat when the triangulating anchor node starts to malfunction. Traditional geometric approaches are insufficient to cope with the survivability of UWSN topology. To address these issues, this paper presents a symplectic geometry for identification of the malicious anchor node. Consequently, a geodesic search algorithm (GSA) based Target localization is proposed which reduces the positioning error by exploiting the phase-space constancy of the underwater acoustic sensor network topology to effectively triangulate the target node despite its mobility. First, a malicious anchor node model is presented. The node movement is expressed in the form of “ripple region”. GSA is then proposed which effectively frees the node metastasis from anchor node geometry, thereby making the underwater system more survivable and resilient. Simulation results evaluate the survivability of the geodesic formalism in terms of the reduced penalty incurred by node movement, as well as the reduced impact of anchor node malfunction. An improvement of 13.46% and 9.26% reveals the utility of the geodesic technique in aquamarine sensor deployments, which would be beneficial in underwater resource exploration and defense planning.

Cooperative Localization in Wireless Sensor Networks With AOA Measurements

This paper researches the cooperative localization in wireless sensor networks (WSNs) with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\pi /\pi $ </tex-math></inline-formula> -periodic angle-of-arrival (AOA) measurements. Two types of localizers are developed from the perspectives of Bayesian inference and convex optimization. When the orientation angles are known, the positioning problem is resolved by a phase-only generalized approximate message passing (POG-AMP) algorithm with importance sampling mechanism. From the perspective of convex optimization, the positioning problem under <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\pi /\pi $ </tex-math></inline-formula> -periodic AOAs is converted as a least square (LS) problem and then resolved by the gradient-descent/projected gradient-descent method named as Type-I LS localizer. When the orientations are unknown, expectation-maximization (EM) mechanism is introduced into the POG-AMP localizer, where node positions and orientations are alternatively updated through exchanging their statistical confidences. Type-II LS localizer is constructed by alternatively executing Type-I LS and a maximum-likelihood (ML) estimator of orientation. Cramér-Rao lower bounds (CRLBs) are derived for the proposed localizers. Simulation results validate that the proposed AMP-type and LS-type localizers outperform existing localizers, AMP-type localizers successfully handle nonlinear quantization losses, and EM-framework and ML estimator handle unknown orientation problem. AMP-type localizers outperform LS-type ones, and can approach to the CRLBs even under high noise contaminations.

Wireless Sensor Network Localization in 3D using Steerable Anchors’ Antennas

Wireless sensor network localization plays an important role in mobile computing. Moreover, Sensor nodes are often deployed non-uniformly in anisotropic WSNs with holes in various applications such as monitoring area terrain. The existence of holes will invariably affect the Euclidean distances between nodes and result in low accuracy of node localization. The proposed algorithm is suitable for four different topologies, including the semi-C-shape topology, the O-shape topology, the multiple O-shape topology and the concave-shape topology and is exceedingly accurate and efficient comparing with state-of-the-art methods in anisotropic WSNs with holes. Our results show that the error in horizontal plane is less than 0.25 m while in the Z-axis is less than 0.5 m.

Cuboid-based Wireless Sensor Network Localization Algorithm

Localization is one of the key technologies in wireless sensor networks (WSNs), since it provides fundamental support for many location-aware protocols and applications. Constraints on cost and power consumption make it infeasible to equip each sensor node in the network with a global position system (GPS) unit, especially for large-scale WSNs. A promising method to localize unknown nodes is to use anchor nodes, which are equipped with GPS units among unknown nodes and broadcast their current locations to help nearby unknown nodes with localization. In this paper we can proposed a novel algorithm of cuboid localization with the help of central point precision method. Simulation shows that the results are far better then existing cuboid methods and gain accuracy of up to 83% with a localization error of 1.6m and standard deviation of 2.7.

The Improvement of DV-Hop Model and Its Application in the Security Performance of Smart Campus

In the smart campus, sensors are the basic units in the whole the Internet of Things structure, which play the role of collecting information and transmitting it. How to transmits more information at a certain power level has attracted the attention of many researchers. In this paper, the DV-Hop algorithm is optimized by combining simulated annealing-interference particle swarm optimization algorithm to improve the node localization of wireless sensor networks and enhance the security performance of smart campus. To address the problem that particle swarm optimization easily falls into local optimum, a perturbation mechanism is introduced in the basic particle swarm optimization algorithm. Meanwhile, the acceptance probability P is introduced in the simulated annealing algorithm to determine whether a particle is accepted when it “flies” to a new position, which improves the probability of finding a global optimal solution. Comparing the average localization error and optimization rate of the DV-Hop algorithm, PSO-DV-Hop algorithm, and the optimized algorithm. The results show a greater advantage of the algorithm. This will greatly enhance the safety performance and efficiency of the smart campus.

Adaptive Event-Driven Robust Set-Membership Estimation for Received-Signal-Strength-Based Moving Targets Localization

This article presents an environmentally adaptive event-driven robust set-membership fusion estimator for received-signal-strength-based moving targets localization or tracking in resource-constrained mobile wireless sensor networks under the influence of unknown but bounded modeling and measurement uncertainties. First, a new adaptive event-triggered mechanism is designed to schedule close to the desired number of anchors no matter how the anchors are distributed near the moving target, which can improve energy efficiency, ease communication burden, and reduce computational complexity. Second, to obtain the recursive fusion estimation formulas generating an ellipsoidal set containing the actual state of the moving target or mobile node by fusing the received signal strength measurements from the nearby triggered anchors, this estimator is divided into two steps: 1) the prediction update step and 2) the fusion estimation step. Each step is converted to a semidefinite or convex programming problem of dimension variation, and its computational complexity is reduced substantially by the decoupled method and the adaptive event-triggered mechanism. Finally, numerical simulations show that the proposed method has superiority in reducing energy consumption, communication, and computational complexity with guaranteed localization accuracy.

Improved DV-Hop based on parallel and compact whale optimization algorithm for localization in wireless sensor networks

AbstractImproving localization performance is one of the critical issues in Wireless Sensor Networks (WSN). As a range-free localization algorithm, Distance Vector-Hop(DV-Hop) is well-known for its simplicity but is hindered by its low accuracy and poor stability. Therefore, it is necessary to improve DV-Hop to achieve a competitive performance. However, the comprehensive performance of WSN is limited by computing and storage capabilities of sensor nodes. In this paper, we propose an algorithm with parallel and compact techniques based on Whale Optimization Algorithm (PCWOA) to improve DV-Hop performance. The compact technique saves memory consumption by reducing the original population. The parallel techniques enhance the ability to jump out of local optimization and improve the solution accuracy. The proposed algorithm is tested on CEC2013 benchmark functions and compared with some popular algorithms and compact algorithms. Experimental results show that the improved algorithm achieves competitive results over compared algorithms. Finally, simulation research is conducted to verify the localization performance of our proposed algorithm.

Modified Zeroing Neurodynamics Models for Range-Based WSN Localization From AOA and TDOA Measurements

With the rise of the internet of things, wireless sensor network (WSN) technology has gained unprecedented development and has attracted increasing attention from researchers. Due to the inherent characteristics of WSN, such as interaction with the environment, WSN localization becomes an essential and attractive topic in academia and industry. In this paper, the range-based localization problem in a mobile WSN application scenario is considered to be time-varying and modeled as a dynamic matrix equation by introducing the time parameter. Two modified zeroing neurodynamics (ZND) models are proposed and investigated to deal with range-based WSN localization problems from angle of arrival (AOA) measurement and time difference of arrival (TDOA) measurement. In addition, the convergence of the proposed models is theoretically analyzed. Furthermore, computer simulations on WSN localization are carried out to prove the effectiveness of the proposed models in terms of accuracy and robustness to the dynamic environment. Additionally, the application to underwater sensor node localization of underwater acoustic network (UAN) testbed is provided to illustrate the feasibilities of the proposed models for solving UAN localization problem.

Hybrid cumulative approach for localization of nodes with adaptive threshold gradient feature on energy minimization using federated learning

PurposeLocalization of the nodes is crucial for gaining access of different nodes which would provision in extreme areas where networks are unreachable. The feature of localization of nodes has become a significant study where multiple features on distance model are implicated on predictive and heuristic model for each set of localization parameters that govern the design on energy minimization with proposed adaptive threshold gradient feature (ATGF) model. A received signal strength indicator (RSSI) model with node estimated features is implicated with localization problem and enhanced with hybrid cumulative approach (HCA) algorithm for node optimizations with distance predicting.Design/methodology/approachUsing a theoretical or empirical signal propagation model, the RSSI (known transmitting power) is converted to distance, the received power (measured at the receiving node) is converted to distance and the distance is converted to RSSI (known receiving power). As a result, the approximate distance between the transceiver node and the receiver may be determined by measuring the intensity of the received signal. After acquiring information on the distance between the anchor node and the unknown node, the location of the unknown node may be determined using either the trilateral technique or the maximum probability estimate approach, depending on the circumstances using federated learning.FindingsImprovisation of localization for wireless sensor network has become one of the prime design features for estimating the different conditional changes externally and internally. One such feature of improvement is observed in this paper, via HCA where each feature of localization is depicted with machine learning algorithms imparting the energy reduction problem for each newer localized nodes in Section 5. All affected parametric features on energy levels and localization problem for newer and extinct nodes are implicated with hybrid cumulative approach as in Section 4. The proposed algorithm (HCA with AGTF) has implicated with significant change in energy levels of nodes which are generated newly and which are non-active for a stipulated time which are mentioned and tabulated in figures and tables in Section 6.Originality/valueLocalization of the nodes is crucial for gaining access of different nodes which would provision in extreme areas where networks are unreachable. The feature of localization of nodes has become a significant study where multiple features on distance model are implicated on predictive and heuristic model for each set of localization parameters that govern the design on energy minimization with proposed ATGF model. An RSSI model with node estimated features is implicated with localization problem and enhanced with HCA algorithm for node optimizations with distance predicting.