Node Localization In Sensor Networks Research Articles

Machine learning-based strategy for efficient node localization in wireless sensor networks

Machine learning-based strategy for efficient node localization in wireless sensor networks

Manifold learning localization based on local tangent space alignment for wireless sensor networks

At present, nodes localization in wireless sensor networks are one of the focuses of many scholars' research. But now the current localization algorithms usually face the problems of low localization accuracy and high computation. Since the information captured by wireless sensor networks is essentially nonlinear, thus this paper proposes a localization algorithm based on manifold learning algorithm local tangent space alignment for wireless sensor networks. It can process nonlinear data to achieve good positioning effect, and the variables in the algorithm have little impact on the localization effect. Two types of positioning data are considered for localization: the measurement distance and the signal strength between sensor nodes. Firstly, the local k-neighborhoods of the localized data points can be found for determining the low-dimensional geometric structure of their local tangent spaces by principal component analysis. Next, the local tangent space of the localized sample points is aligned to obtain the relative locations of the sensor nodes. Then, the physical locations of the sensor nodes can be get by lining up the relative positions with anchor points. Finally, the ending of the paper by doing simulation experiments demonstrate that the algorithm has good precision and few variables in performing nodes localization.

Node Localization Method in Wireless Sensor Networks Using Combined Crow Search and the Weighted Centroid Method.

Node localization is critical for accessing diverse nodes that provide services in remote places. Single-anchor localization techniques suffer co-linearity, performing poorly. The reliable multiple anchor node selection method is computationally intensive and requires a lot of processing power and time to identify suitable anchor nodes. Node localization in wireless sensor networks (WSNs) is challenging due to the number and placement of anchors, as well as their communication capabilities. These senor nodes possess limited energy resources, which is a big concern in localization. In addition to convention optimization in WSNs, researchers have employed nature-inspired algorithms to localize unknown nodes in WSN. However, these methods take longer, require lots of processing power, and have higher localization error, with a greater number of beacon nodes and sensitivity to parameter selection affecting localization. This research employed a nature-inspired crow search algorithm (an improvement over other nature-inspired algorithms) for selecting the suitable number of anchor nodes from the population, reducing errors in localizing unknown nodes. Additionally, the weighted centroid method was proposed for identifying the exact location of an unknown node. This made the crow search weighted centroid localization (CS-WCL) algorithm a more trustworthy and efficient method for node localization in WSNs, with reduced average localization error (ALE) and energy consumption. CS-WCL outperformed WCL and distance vector (DV)-Hop, with a reduced ALE of 15% (from 32%) and varying communication radii from 20 m to 45 m. Also, the ALE against scalability was validated for CS-WCL against WCL and DV-Hop for a varying number of beacon nodes (from 3 to 2), reducing ALE to 2.59% (from 28.75%). Lastly, CS-WCL resulted in reduced energy consumption (from 120 mJ to 45 mJ) for varying network nodes from 30 to 300 against WCL and DV-Hop. Thus, CS-WCL outperformed other nature-inspired algorithms in node localization. These have been validated using MATLAB 2022b.

RSSI-based optimization of static and mobile node combinations for dynamic node localization in wireless sensor networks

RSSI-based optimization of static and mobile node combinations for dynamic node localization in wireless sensor networks

A Study on Scan Trajectory Based Effective Localization Method Using Mobile Anchor in Wireless Sensor Networks

A Wireless Sensor Network (WSN) is a collection of different nodes working together in a networked fashion in an ordered way. For many applications, sensor location in wireless sensor networks (WSN) is crucial. It is expensive to equip each sensor node with a GPS receiver. Numerous methods, including range-based and range-free methods, have been put forth in the past to determine the locations of sensor nodes that are randomly distributed. The majority of them make use of unique nodes known as anchor nodes, which are thought to be aware of their own positions. These anchor nodes supply the information that other sensors use to calculate their locations. Massive numbers of sensor nodes are used in wireless sensor networks (WSNs) to gather data about the immediate environment. However, this data is useless until the precise location of the data collection points is known. Sensor node localization in wireless sensor networks (WSNs) is important for a number of applications. The current research study develops a path planning technique to apply mobile anchors to the localization systems that are currently in use. The two mobile anchors supported by the effective trajectories, or effective SCAN, is the focus of the proposed research project. To localise the sensor node and transmit data, this method involves setting the mobile anchors to move in the opposite direction. The advantages of this dual movable anchor are demonstrated by the research study in terms of decreased localization time, localization error, and other factors.

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>

Fuzzy Distance Jaya Algorithm based Node Localization in Anisotropic Wireless Sensor Networks

Fuzzy Distance Jaya Algorithm based Node Localization in Anisotropic Wireless Sensor Networks

Optimization Algorithms for Wireless Sensor Networks Node Localization: An Overview

Optimization Algorithms for Wireless Sensor Networks Node Localization: An Overview

Novel DV-hop algorithm-based machines learning technics for node localization in rang-free wireless sensor networks

Localization is a critical concern in many wireless sensor network (WSN) applications. Furthermore, correct information regarding the geographic placements of nodes (sensors) is critical for making the collected data valuable and relevant. Because of their benefits, such as simplicity and acceptable accuracy, the based connectivity algorithms attempt to localize multi-hop WSN. However, due to environmental factors, the precision of localisation may be rather low. This publication describes an Extreme Learning Machine (ELM) technique for minimizing localization error in range-free WSN. In this paper, we propose a Cascade Extreme Learning Machine (Cascade-ELM) to increase localization accuracy in Range-Free WSNs. We tested the proposed approaches in a variety of multi-hop WSN scenarios. Our research focused on an isotropic and irregular environment. The simulation results show that the proposed Cascade-ELM algorithm considerably improves localization accuracy when compared to previous algorithms derived from smart computing approaches. When compared to previous work, isotropic environments show improved localization results.

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.

Research on Mobile Node Localization Optimization Algorithm in Wireless Sensor Networks Based on Monte Carlo Method

Mobile node localization is one of the key technologies in wireless sensor network applications. Aiming at the shortcomings of Monte Carlo algorithm (MCL) in mobile wireless sensor network node localization in nonideal environments, such as low accuracy and low sampling rate, in the prediction phase and filtering phase of MCL, the communication radius of the unknown node is determined according to the size of the irregularity of the node. Perform layering, assign adaptive weights to anchor nodes of different layers according to the area where they are located, and propose an adaptive improved Monte Carlo algorithm. After simulation analysis, the algorithm has an average localization error of nodes under different regularity conditions. It has dropped by about 12%, and the localization error has dropped by about 10% on average under different speed conditions. Aiming at the shortcomings of the MCL algorithm in mobile wireless sensor mobile networks such as low localization accuracy, large sample demand, and long localization time, the communication radius of the node is fuzzified to reduce the sampling area of the node, and an improved Monte Carlo localization algorithm based on fuzzy theory is proposed. The improved Monte Carlo localization algorithm, after simulation analysis, is about 50% shorter than the traditional MCL algorithm in localization time, and the localization accuracy is up to about 30% higher than the traditional MCL algorithm.

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.

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.

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

Optimization for DV-Hop type of localization scheme in wireless sensor networks

Distance Vector Hop (DV-Hop) is a range-free scheme used for node localization in wireless sensor networks (WSNs). The original DV-Hop scheme localizes the unknown nodes depending on a number of anchor nodes’ known position information and the multi-hop relationship among nodes. It is very popular and can meet most application requirements as the network is isotropous. However, it becomes powerless while the network is anisotropic due to the natural defects of its ranging strategy. In view of such problems of the traditional DV-Hop, we provide a scheme aiming to improve the original DV-Hop. In our scheme, an improved cosine similarity parameter is used to measure the similarity between path pairs, and the anchor–anchor path which is most like the path from the unknown node to the target anchor is selected to compute the average hop distance of the node-anchor path independently. Then, an improved particle swarm optimization and simulated annealing hybrid algorithm is adapted to improve the position accuracy of the initial position of an unknown node, which has been derived by the trilateration algorithm used in the original DV-Hop scheme. Based on the simulation result, in comparison with the original DV-Hop scheme and another two existed improved schemes, our proposed scheme can perform much better both on the distance estimation accuracy and on the final node localization accuracy. Thereby, our proposed scheme is a feasible and optimized choice for node localization in WSNs.

Research and application of node fuzzy identification and localization in wireless sensor networks

SummaryIn recent years, sensor technology has attracted the attention of countries all over the world because of its potential huge application value, and it has played an important role in people's daily life and social production activities. This paper mainly studies the location algorithm based on ranging in the location algorithm and analyzes the advantages and disadvantages of the algorithm. In order to optimize the positioning of wireless sensor networks, a fuzzy controller is designed to fuzzify the input information of sensor nodes. Using Matlab network simulation tool, the congestion control algorithm proposed in this paper was simulated and compared with several typical congestion control protocols in wireless sensor networks. Experimental results show that the algorithm can improve network throughput, reduce network packet loss, and have a significant impact on network performance. The network throughput of the congestion control algorithm based on fuzzy logs is stable at 180 packets per second. Compared with CODA, the congestion control algorithm based on fuzzy logarithm increases the packet loss rate by 55–66%, and the SenTCP algorithm increases by 37–40%. It can be seen that congestion control algorithms based on fuzzy logic can play an important role in wireless sensor networks.

Radial basis function-based node localization for unmanned aerial vehicle-assisted 5G wireless sensor networks

Localization or positioning of wireless sensor nodes is an essential task for a wide range of applications in wireless sensor networks-based fifth generation (5G) networks. Node localization using mobile aerial beacon nodes (MABNs) provides high localization accuracy and less deployment cost compared to the localization using fixed ground beacon nodes because MABN deployed in unmanned aerial vehicles sends signals to unknown nodes (UNs) through reliable air to ground (AG) channel link. The classical received signal strength indicator (RSSI)-based multilateration and optimization-based least square localization (OLSL) schemes result high localization error because of the nonlinear distortions induced in the wireless channel. So, the highly nonlinear artificial neural network (ANN) models such as multilayer perceptron (MLP) and radial basis function (RBF) are used effectively for nonlinear node localization problems. ANN-based localization techniques are also capable of localizing mobile UNs. Further, the RBF with Gaussian activation has a little edge over the MLP with sigmoid activation because the wireless channel usually modelled with a Gaussian random variable. The simulation results included in this article also justify the efficacy of the proposed RBF localization with performance gain of about (7–70) % over MLP, OLSL and RSSI localization techniques.

Outlier node localisation in sensor networks based on double layer modified unscented Kalman filter

Outlier node localisation in sensor networks based on double layer modified unscented Kalman filter