Accuracy Of Node Localization Research Articles

Secure Wireless Sensor Networks: A Weighted K-NN and RNN-based Approach for Attack Detection and Localization

In the realm of wireless sensor networks (WSNs), precise localization of sensor nodes and attack detection has emerged as a significant challenge. Accurate localization of sensor nodes and detection of attacks is crucial for enhancing the network lifetime and preventing the data from unauthorized access. However, prior research has encountered various obstacles, such as high localization error, more time consumption, and poor detection performance. To overcome these difficulties, this research proposes a novel Recurrent Crossover-based Dynamic Differential (RC-DD) algorithm for effective localization and attack detection in WSN. The proposed model utilizes a weighted K-Nearest Neighbor (KNN) with Mahalanobis distance for accurately localizing the position of the sensor nodes within the network. Moreover, the Recurrent Neural Network (RNN) is employed for effectively detect multiple attacks, and Dynamic Differential Annealed Optimization (DDAO) with crossover strategy is implemented for fine-tuning the RNN’s hyperparameter, thereby enhancing the attack detection accuracy. The experimental outcome shows that the proposed RC-DD framework effectively localizes the sensor nodes and detects the attacks with a higher accuracy of 98.9% compared to existing methodologies. Additionally, the proposed framework minimizes localization error and energy consumption, underscoring its potential to enhance the security and lifetime of the network.

Enhanced Localization in Wireless Sensor Networks Using a Bat-Optimized Malicious Anchor Node Prediction Algorithm.

The accuracy of node localization plays a crucial role in the performance and reliability of wireless sensor networks (WSNs), which are widely utilized in fields like security systems and environmental monitoring. The integrity of these networks is often threatened by the presence of malicious nodes that can disrupt the localization process, leading to erroneous positioning and degraded network functionality. To address this challenge, we propose the security-aware localization using bat-optimized malicious anchor prediction (BO-MAP) algorithm. This approach utilizes a refined bat optimization algorithm to improve both the precision of localization and the security of WSNs. By integrating advanced optimization with density-based clustering and probabilistic analysis, BO-MAP effectively identifies and isolates malicious nodes. Our comprehensive simulation results reveal that BO-MAP significantly surpasses six current state-of-the-art methods-namely, the Secure Localization Algorithm, Enhanced DV-Hop, Particle Swarm Optimization-Based Localization, Range-Free Localization, the Robust Localization Algorithm, and the Sequential Probability Ratio Test-across various performance metrics, including the true positive rate, false positive rate, localization accuracy, energy efficiency, and computational efficiency. Notably, BO-MAP achieves an impressive true positive rate of 95% and a false positive rate of 5%, with an area under the receiver operating characteristic curve of 0.98. Additionally, BO-MAP exhibits consistent reliability across different levels of attack severity and network conditions, highlighting its suitability for deployment in practical WSN environments.

Accurate Range-Free Localization Using Cuckoo Search Optimization in IoT and Wireless Sensor Networks

Precise positioning of sensors is critical for the performance of various applications in the Internet of Things and wireless sensor networks. The efficiency of these networks heavily depends on the precision of sensor node locations. Among various localization approaches, DV-Hop is highly recommended for its simplicity and robustness. However, despite its popularity, DV-Hop suffers from significant accuracy issues, primarily due to its reliance on average hop size for distance estimation. This limitation often results in substantial localization errors, compromising the overall network effectiveness. To address this gap, we developed an enhanced DV-Hop approach that integrates the cuckoo search algorithm (CS). Our solution improves the accuracy of node localization by introducing a normalized average hop size calculation and leveraging the optimization capabilities of CS. This hybrid approach refines the distance estimation process, significantly reducing the errors inherent in traditional DV-Hop. Findings from simulations reveal that the developed approach surpasses the accuracy of both the original DV-Hop and multiple other current localization methods, providing a more precise and reliable localization method for IoT and WSN applications.

New Strategies for Boosting Localization Accuracy in Wireless Sensor Nodes

Wireless Sensor Networks (WSNs), accurate and energy-efficient localization of sensor nodes remains a challenging task despite significant advancements. Current geolocation algorithms often struggle with scalability, adaptability, and energy efficiency, particularly in large-scale, dynamic environments where node failures or random shifts occur. This paper proposes a novel Secure Node Localization (SABWP-NL) approach, combining Self-Adaptive Binary Waterwheel Plant Optimization (SABWP) and Bayesian optimization to enhance localization accuracy, scalability, energy efficiency, and robustness. The method evaluates node trust using Dempster-Shafer Evidence Theoryto secure localization against rogue nodes and optimizes the localization process through trilateral and multilateration systems. The SABWP-NL approach demonstrates superior performance in terms of localized nodes and localization error compared to existing techniques like BWP, ROA, and AO. Results show that SABWP-NL achieves the highest number of localized nodes and the lowest localization error, making it a promising solution for efficient and secure node localization in WSNs.

Multi-Source Information Fusion-Based Localization in Wireless Sensor Networks

In wireless sensor networks, accurate node localization is essential for ensuring the precision of data collection. The DV-Hop algorithm, a popular range-free localization method, estimates distances between nodes by multiplying hop counts with average hop distances obtained through distance vector routing. However, this algorithm often experiences localization errors in randomly distributed network environments due to considerable inaccuracies in average hop distance calculations and the approximation of actual paths by straight-line paths. This paper introduces an enhanced DV-Hop localization algorithm, which constructs a mathematical model to optimize the mean square error of the average hop distance for any anchor node. This optimization corrects the average hop distance across the network, bringing it closer to the actual value, thus reducing errors and enhancing accuracy. Simulation results indicate that with 150 nodes, a 30% beacon node ratio and a 100-m communication range, the localization error of the improved FuncDV-Hop model decreased from 0.3916 to 0.1705, and the Root Mean Square Error (RMSE) decreased from 24.78[Formula: see text]m to 14.39[Formula: see text]m, thereby improving localization accuracy by 56.46%.

An efficient hybrid bat sand cat swarm optimization‐based node localization for data quality improvement in wireless sensor networks

SummaryNode localization in wireless sensor networks (WSNs) ensures that the collected data is contextually accurate, enabling effective monitoring and management of various applications. Recently, there has been a surge in research focused on addressing node localization within WSNs. Emerging trends in this field involve the application of metaheuristic optimization techniques to refine node location determination accuracy. However, existing techniques often struggle with balancing accuracy, energy consumption, network lifetime, and computational efficiency, particularly in challenging WSN environments. Therefore, this research introduces a novel approach called efficient hybrid bat sand cat swarm optimization (EHBSCSO) to address node localization within WSNs. The hybrid method leverages the exploration capabilities of the bat optimization algorithm and the exploitation strengths of the sand cat swarm optimization algorithm. This combination allows for efficient determination of node positions, significantly improving localization accuracy while minimizing energy consumption. The EHBSCSO utilizes the received signal strength indicator (RSSI) and time of flight (ToF) approaches to assess distances among nodes accurately. Accurate node localization directly improves data quality by ensuring spatially precise data collection, reducing communication overhead, and enhancing the overall reliability of the collected data. Compared to conventional methods, the proposed EHBSCSO algorithm demonstrates superior performance, with a mean localization error of 0.18%, energy consumption of 7.2 J, computational time of 8.9 s, and localization time of 0.19 s. These metrics underscore its efficiency and precision. The research indicates that EHBSCSO not only optimizes localization accuracy but also contributes to energy efficiency and faster computational times, addressing key challenges in WSN node localization.

An improved DVHop localization algorithm using a novel angle based node reduction and optimization technique

Wireless sensor networks are becoming increasingly popular across a range of applications. One notable use is in seismic exploration and monitoring for oil and gas reservoirs. This application involves deploying numerous sensor nodes across outdoor fields to measure backscattered waves, which are then used to create an image of the subsurface. These sensor nodes remain active in the field for several days and must be accurately localized to ensure efficient reservoir detection. However, the Distance Vector-Hop (DVHop) algorithm, despite its simplicity, is not suitable for accurate node localization in exploration fields due to obstructions. In this paper, we propose a modified DVHop algorithm specifically designed for precise localization in such environments. Proposed algorithm uses angles between intermediate nodes to identify and circumvent nodes affected by obstructions. Distance estimation is performed using this reduced set of nodes. The estimated distances between these nodes are subsequently solved using Velocity Pausing Particle Swarm Optimization to determine the nodes’ locations. When evaluated in environments resembling exploration fields, our algorithm demonstrated an improvement of 25% to 63% in Average Localization Accuracy compared to other hop-based localization algorithms under similar conditions.

Optimizing federated learning approaches with hybrid Convolutional Neural Networks ‐ Bidirectional Encoder Representations from Transformers for precise estimation of average localization errors in wireless sensor networks

SummaryWireless sensor networks (WSNs) require precise node location in order to function properly, and they are essential in many different applications. In this research, we propose a unique method to maximize the accurate estimate of average localization errors (ALEs) in WSNs by utilizing federated learning (FL) approaches. The suggested approach combines the extraction of spatial features with collection of contextual data through integration of hybrid convolutional neural network–bidirectional encoder representations from transformers (CNN‐BERT) model. Effectively applying min–max normalization to input features minimizes data from flowing into test, validation, and training sets. The technique is centered around a collaborative learning architecture, wherein the weights of the model are iteratively assessed and modified on centralized server. The suggested methodology's effectiveness in an array of settings is illustrated by mean absolute error (MAE), mean squared error (MSE), and root mean square error (RMSE) values for node localization in WSN. Network B demonstrated accuracy with an MAE of 0.05, MSE of 0.03, and RMSE of 0.10; Network C demonstrated strong accuracy with an MAE of 0.09, MSE of 0.08, and RMSE of 0.12; and Network A generated an MAE of 0.04, MSE of 0.06, and RMSE of 0.08. Furthermore, the centralized server, which is crucial for collaborative learning, obtained exceptional MAE of 0.01, MSE of 0.02, and RMSE of 0.99 demonstrating the superiority of the FL‐optimized method in improving localization accuracy. The outcomes demonstrate the FL‐optimized hybrid model's superiority over conventional methods in providing accurate node localization in a variety of WSN settings.

Efficient Sink Node Position Estimation using Harris Hawks Optimization Algorithm in Wireless Sensor Networks

Wireless sensor network (WSN) was utilized widely in numerous areas owing to their accessibility in data collection, processing, and transmission, and the strength and reliability of data processing and transmission are based on the accuracy of the positions of sensor nodes (SNs) in the WSN. Sink node location estimation in WSN is a vital task intended to define the geographical position of the sink node in the network area of coverage. This procedure normally includes using numerous localization techniques that trust data like received signal strength, arrival time, time variance of arrival, or angle of arrival from adjacent SNs. The accuracy of sink node localization directly influences the efficiency of data aggregation, routing procedures, and complete performance of the network in tasks like environmental monitoring, target tracking, and event recognition. As WSNs are frequently used in remote environments where physical involvement is unusable, an effective and accurate sink node localization model plays a vital part in certifying the network's longevity and reliability. This study develops an Efficient Sink Node Position Estimation using the Harris Hawks Optimization (SNPE-HHO) Algorithm in WSN. The main intention of the SNPE-HHO technique is to recognize the optimal position of the sink node in the network. To achieve this, the SNPE-HHO technique employs the HHO system which gets inspiration from the hunting tactics of Harris Hawk. Moreover, the SNPE-HHO technique computes a fitness function that can drive the searching direction of the HHO algorithm and enhance the node estimation performance. The performance analysis of the SNPE-HHO method is verified by utilizing distinct metrics. The experimentation values confirmed the improved estimation performance of the SNPE-HHO technique over other existing methods

Enhancing Node Localization Accuracy in Wireless Sensor Networks: A Hybrid Approach Leveraging Bounding Box and Harmony Search

Enhancing Node Localization Accuracy in Wireless Sensor Networks: A Hybrid Approach Leveraging Bounding Box and Harmony Search

Dingo optimization influenced arithmetic optimization – Clustering and localization algorithm for underwater acoustic sensor networks

Underwater acoustic sensor networks (UASNs) are quickly being used in environmental monitoring, marine life observation, and underwater resource research. This paper introduces the Dingo Optimisation Influenced-Arithmetic Clustering and Localisation Algorithm. The proposed method was developed due to the above considerations. DOIAO-CLA combines Dingo Optimisation (DO) and Arithmetic Optimisation (AO), two powerful optimisation methods. DOIAO-CLA uses DO's robust exploration and exploitation features to group sensor nodes with similar propagation characteristics. It also optimises cluster formation and reduces distance discrepancies using AO. DOIAO-CLA also enables reliable localization by merging distance- and angle-based methods. DO optimises node localization by using distance and angle of arrival data. This integration enhances UASN node localization accuracy and reliability. The DOIAO-CLA was tested against other clustering and localization algorithms like Archimedes Optimisation Algorithm (AOA), Squirrel Search Algorithm (SSA), Hybrid Cat Cheetah Optimisation Algorithm (HCCOA), Ant Colony Optimisation Algorithm (ACOA), Grey Wolf Optimisation Algorithm (GWOA), and Particle Swarm Optimisation Algorithm. According to the results, DOIAO-CLA taps alternatives for network robustness, data aggregation, and localization precision. Finally, DOIAO-CLA solves the UASN clustering and localization problem in a novel and effective way.

A novel distance vector hop localization method for wireless sensor networks

Abstract Wireless sensor networks (WSNs) require accurate localization of sensor nodes for various applications. In this article, we propose the distance vector hop localization method (DVHLM) to address the node dislocation issue in real-time networks. The proposed method combines trilateration and Particle Swarm Optimization techniques to estimate the location of unknown or dislocated nodes. Our methodology includes four steps: coordinate calculation, distance calculation, unknown node position estimation, and estimation correction. To evaluate the proposed method, we conducted simulation experiments and compared its performance with state-of-the-art methods in terms of localization accuracy with known nodes, dislocated nodes, and shadowing effects. Our results demonstrate that DVHLM outperforms the existing methods and achieves better localization accuracy with reduced error. This article provides a valuable contribution to the field of WSNs by proposing a new method with a detailed methodology and superior performance.

A Study and Analysis of a New Hybrid Approach for Localization in Wireless Sensor Networks

Accurate localization of nodes in a wireless sensor network (WSN) is imperative for several important applications. The use of global positioning systems (GPS) for localization is the natural approach in most domains. In WSNs, however, the use of GPS is challenging because of the constrained nature of deployed nodes as well as the often inaccessible sites of WSN nodes deployment. Several approaches for localization without the use of GPS and harnessing the capabilities of the received signal strength indicator (RSSI) exist in literature, but each of these makes the simplifying assumption that all the WSN nodes are within the communication range of every other node. In this paper, we go beyond this assumption and propose a hybrid technique for node localization in large WSN deployments. The hybrid technique comprises a loose combination of a machine learning (ML) based approach for localization involving random forest and a multilateration approach. This hybrid approach takes advantage of the accuracy of ML localization and the iterative capabilities of multilateration. We demonstrate the efficacy of the proposed approach through experiments on a simulated set-up and follow it up with a feasibility demonstration through a prototypical implementation in the real world.

Accurate range-based distributed localization of wireless sensor nodes using grey wolf optimizer

Various ranging techniques are frequently employed in wireless sensor networks (WSNs) to determine the distance between a node and its neighboring anchor nodes. The distance measurement, as mentioned earlier is subsequently employed to estimate the location of the node whose location is unknown. The present paper presents an Accurate Localization Scheme that utilizes Grey Wolf Optimization (GWO) and is based on the Radio Signal Strength (RSS) ranging technique. The efficiency of our technique has been proved through extensive simulations, showing a consistent improvement in localization accuracy ratios and a decrease in location errors while maintaining cost-effectiveness.

Suspect fault screen assisted graph aggregation network for intra-/inter-node failure localization in ROADM-based optical networks

In optical networks, failure localization is essential to stable operation and service restoration. Several approaches have been presented to achieve accurate failure localization of nodes and inter-nodes. However, due to increasing traffic and demand for flexibility, the reconfigurable optical add/drop multiplexer (ROADM) is evolving towards a multi-degree architecture. Therefore, each ROADM is composed of multiple devices, which makes intra-node failures become more complex. In this context, intra-node failure localization can effectively reduce the pressure on network operators to further find specific devices. In this work, we redefine the intra-/inter-node failure model for multi-degree ROADM-based optical networks and propose a suspect fault screen assisted graph aggregation network (SFS-GRN) for intra-/inter-node failure localization. The SFS is responsible for screening out suspect fault devices from all devices and reducing the number of candidate devices. The GRN is used to analyze these monitoring data from an optical performance monitoring (OPM) node and network wide and to determine the most likely failure device. The proposed scheme is evaluated in a nine-node simulated network and three-node testbed network. Extensive results show that the SFS-GRN achieves higher accuracy compared with existing methods under different percentages of OPM deployment, numbers of service requests, and failure types. The SFS can remove more than 98% of devices, which is beneficial to further detection and repair for network operators. Moreover, the proposed strategy takes about 10 ms to detect a potential failure, and it has the potential to be applied to a real scenario.

Node location using cuckoo search algorithm with grouping and drift strategy for WSN

Wireless sensor networks (WSNs) have been used extensively in many fields due to their convenience in data collection, processing, and transmission, and the stability and reliability of information processing and transmission is dependent on the accuracy of the locations of sensor nodes in the WSNs. Therefore, in order to improve the accuracy of WSN node localization, an improved cuckoo search algorithm (ICS-GD) based on population grouping and drifting strategy is proposed in this manuscript for solving the WSN node localization error problem. The first step is to design a Mahalanobis distance-based method of expressing population diversity, and population clustering is also implemented on this basis in concert with individual evolution; an information interaction based drift strategy is then proposed to improve the search capability of the algorithm at the local development stage depending on the clustering; finally, the effectiveness of the ICS-GD algorithm is verified through benchmark test functions and WSN node localization error experiments and superiority of the ICS-GD algorithm through benchmark test functions and WSN node localization error experiments. Experimental results show that the ICS-GD algorithm has more clear advantages over state-of-the-art algorithms such as IAGA, BBPSO, and HGWO, and the algorithm obtains more satisfactory performance in all of the 26 benchmark test functions, and the localization error is as small as 22.21% in the solution of the WSN node localization problem.

Fluoroscopic Intraoperative Breast Neoplasm and Node Detection.

Preoperative localization is necessary for nonpalpable breast lesions. A novel procedure, fluoroscopic intraoperative neoplasm and node detection (FIND), obviates the preoperative painful and potentially expensive localization by using intraoperative visualization of the standard clip placed during diagnostic biopsy. We hypothesized FIND would improve negative margin rates. This is an IRB-approved retrospective study (September 2016 to March 2021). Electronic chart review identified breast and axillary node procedures using wire localization (WL) or FIND. Primary outcome was margin status. Secondary outcomes included re-excision rate, specimen weight, surgery time, and axillary node localization rate. We identified 459 patients, of whom 116 (25.3%) underwent FIND and 343 (74.7%) WL. Of these, 68.1% of FIND and 72.0% of WL procedures were for malignant lesions. Final margin positivity was 5.1% (4 of 79) for FIND and 16.6% (41 of 247) for WL (p = 0.008). This difference lost statistical significance on multivariable logistic regression (p = 0.652). Re-excision rates were 7.6% and 14.6% for FIND and WL (p = 0.125), with an equivalent mean specimen weight (p = 0.502), and mean surgery time of 177.5 ± 81.7 and 157.1 ± 66.8 minutes, respectively (mean ± SD; p = 0.022). FIND identified all (29 of 29) targeted axillary nodes, and WL identified only 80.1% (21 of 26) (p = 0.019). FIND has lower positive margin rates and a trend towards lower re-excision rates compared with WL, proving its value in localizing nonpalpable breast lesions. It also offers accurate localization of axillary nodes, valuable in the era of targeted axillary dissection. It is a method of visual localization, using a skill and equipment surgeons already have, and saves patients and medical systems an additional schedule-disruptive, painful procedure, especially valuable when using novel localization devices is cost-prohibitive.

A comparative analysis of localization scheme in wireless interfaced networks

Wireless networks have become an integral part of modern life, offering seamless connectivity and supporting various applications. However, achieving accurate node localization remains a significant challenge, particularly in environments where signal propagation is affected by obstacles, reflections, and interference. To address these challenges, a dynamic triangular algorithm method is proposed for node localization. This novel model aims to enhance the throughput, data rate, and efficiency of wireless communication systems in both Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) scenarios.

WSNs node localization algorithm based on multi-hop distance vector and error correction

Wireless sensor networks (WSNs) have broad application prospects in various industries, and node localization technology is the foundation of WSN applications. Recently, many range-free node localization algorithms have been proposed, but most of them suffer from low accuracy. In order to improve the localization accuracy, in this paper we proposed the node localization algorithm based on multi-hop distance vector and error correction (MDV-EC). In terms of distance estimation, firstly the MDV-EC algorithm calculates the neighbor distance according to node neighbor relationship, then estimates the distance between unknown node and anchor node in multi-hop manner, and finally calibrates the distance refer to distance correction coefficient. In view of similarity of localization errors of nodes in similar regions, an error correction scheme is also investigated, which corrects the node initial estimated locations of nodes refer to the localization error vector of nearby anchor node. Simulation results show that our proposed MDV-EC has better performance than the other two algorithms in terms of node localization accuracy, and the error correction scheme can effectively reduce the localization errors.

Machine Learning-Based Prediction of Node Localization Accuracy in IIoT-Based MI-UWSNs and Design of a TD Coil for Omnidirectional Communication

This study aims to realize Sustainable Development Goals (SDGs), i.e., SDG 9: Industry Innovation and Infrastructure and SDG 14: Life below Water, through the improvement of localization estimation accuracy in magneto-inductive underwater wireless sensor networks (MI-UWSNs). The accurate localization of sensor nodes in MI communication can effectively be utilized for industrial IoT applications, e.g., underwater gas and oil pipeline monitoring, and in other important underwater IoT applications, e.g., smart monitoring of sea animals, etc. The most-feasible technology for medium- and short-range communication in IIoT-based UWSNs is MI communication. To improve underwater communication, this paper presents a machine learning-based prediction of localization estimation accuracy of randomly deployed sensor Rx nodes through anchor Tx nodes in the MI-UWSNs. For the training of ML models, extensive simulations have been performed to create two separate datasets for the two configurations of excitation current provided to the Tri-directional (TD) coils, i.e., configuration1-case1_configuration2-case1 (c1c1_c2c1) and configuration1-case2_configuration2-case2 (c1c2_c2c2). Two ML models have been created for each case. The accuracies of both models lie between 95% and 97%. The prediction results have been validated by both the test dataset and verified simulation results. The other important contribution of this paper is the development of a novel assembling technique of a MI-TD coil to achieve an approximate omnidirectional magnetic flux around the communicating coils, which, in turn, will improve the localization accuracy of the Rx nodes in IIoT-based MI-UWSNs.