Localization Algorithm For Networks Research Articles

An enhanced localization algorithm for 3D wireless sensor networks using group learning optimization

An enhanced localization algorithm for 3D wireless sensor networks using group learning optimization

Fingerprint-Based Localization Enabled by Low-Rank Matrix Reconstruction in Intelligent Reflective Surface-Assisted Networks

The intelligent reflective surface (IRS) is a novel network node that consists of a large-scale passive reflective array to obtain a customized reflected wave direction by modulating the amplitude phase, which can be easily deployed to change the wireless signal propagation environment and enhance the communication performance under a non-line-of-sight (NLOS) environment, where location services cannot perform accurately. In this study, a low-rank matrix reconstruction-enabled fingerprint-based localization algorithm for IRS-assisted networks is proposed. Firstly, a 5G positioning system based on IRSs is constructed using multiple IRSs deployed to reflect signals. This enables the base station to overcome the influence of NLOS and receive the positioning signal of the point to be positioned. Then, the angular domain power expectation matrix of the received signal is extracted as a fingerprint to form a partial fingerprint database. Next, the complete fingerprint database is reconstructed using the low-rank matrix fitting algorithm, thereby considerably reducing the workload of building the fingerprint database. Finally, maximal ratio combining is used to increase the gap between the fingerprint data, and the Weighted K-Nearest Neighbor (WKNN) algorithm is used to match the fingerprint data and estimate the location of the points to be located. The simulation results demonstrate the feasibility of the proposed method to achieve sub-meter accuracy in an NLOS environment.

Automatic Tracking Based on Weighted Fusion Back Propagation in UWB for IoT Devices.

The global population is progressively entering an aging phase, with population aging likely to emerge as one of the most-significant social trends of the 21st Century, impacting nearly all societal domains. Addressing the challenge of assisting vulnerable groups such as the elderly and disabled in carrying or transporting objects has become a critical issue in this field. We developed a mobile Internet of Things (IoT) device leveraging Ultra-Wideband (UWB) technology in this context. This research directly benefits vulnerable groups, including the elderly, disabled individuals, pregnant women, and children. Additionally, it provides valuable references for decision-makers, engineers, and researchers to address real-world challenges. The focus of this research is on implementing UWB technology for precise mobile IoT device localization and following, while integrating an autonomous following system, a robotic arm system, an ultrasonic obstacle-avoidance system, and an automatic leveling control system into a comprehensive experimental platform. To counteract the potential UWB signal fluctuations and high noise interference in complex environments, we propose a hybrid filtering-weighted fusion back propagation (HFWF-BP) neural network localization algorithm. This algorithm combines the characteristics of Gaussian, median, and mean filtering, utilizing a weighted fusion back propagation (WF-BP) neural network, and, ultimately, employs the Chan algorithm to achieve optimal estimation values. Through deployment and experimentation on the device, the proposed algorithm's data preprocessing effectively eliminates errors under multi-factor interference, significantly enhancing the precision and anti-interference capabilities of the localization and following processes.

A Localization Algorithm for Underwater Acoustic Sensor Networks with Improved Newton Iteration and Simplified Kalman Filter

A Localization Algorithm for Underwater Acoustic Sensor Networks with Improved Newton Iteration and Simplified Kalman Filter

Dual-Task GPR Method: Improved Generative Adversarial Clutter Suppression Network and Adaptive Target Localization Algorithm in GPR Image

Dual-Task GPR Method: Improved Generative Adversarial Clutter Suppression Network and Adaptive Target Localization Algorithm in GPR Image

Research on downhole visible light modeling and localization based on point cloud technology

In this paper, we reconstruct the downhole visible light channel model based on point cloud technology. To make up for the defect that the reflective surface element is a virtual surface, the planar projection method and the idea of point-by-point insertion are combined to turn the point cloud data on the wall surface into a triangular mesh topology. In the meantime, the point cloud data of obstacles is utilized to determine the precise range of shadows cast by light. Subsequently, shadow coefficients are defined to derive new expressions for channel gain. In addition, the localization in a 6 m × 4 m × 3 m space is achieved through the utilization of the particle swarm algorithm optimized BP neural network (PSO-BP) localization algorithm. In scenarios where the walls exhibit unevenness and barriers are present, the root mean square positioning error is found to be 6.52 cm when taking primary reflection into consideration. Conversely, when solely relying on direct power, the positioning error increases to 9.92 cm. Consequently, it is important to consider the non-line-of-light (NLOS) link when conducting downhole visible light localization.

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.

InferLoc: Hypothesis-Based Joint Edge Inference and Localization in Sparse Sensor Networks

Ranging-based localization is a fundamental problem in the Internet of Things and unmanned aerial vehicle networks. However, the nodes’ limited-ranging scope and users’ broad coverage purpose inevitably cause network sparsity or subnetwork sparsity. The performances of existing localization algorithms are extremely unsatisfactory in sparse networks. A crucial way to deal with the sparsity is to exploit the hidden knowledge provided by the unmeasured edges, which inspires this work to propose a hypothesis-based Joint Edge Inference and Localization algorithm called InferLoc . InferLoc mines the Unmeasured but Inferable Edges (UIEs). Each UIE is an unmeasured edge, but it is restricted through other edges in the network to be inside a rigid component, so it has only a limited number of possible lengths. We propose an efficient method to detect UIEs and geometric approaches to infer possible lengths for UIEs in 2D and 3D networks. The inferred possible lengths of UIEs are then treated as multiple hypotheses to determine the node locations and the lengths of UIEs simultaneously through a joint graph optimization process. In the joint graph optimization model, to make the 0/1 decision variables for hypotheses selection differentiable, differentiable functions are proposed to relax the 0/1 selections, and rounding is applied to select the final length after the optimization converges. We also prove the condition when a UIE can contribute to sparse localization. Extensive experiments show remarkably better accuracy and efficiency performances of InferLoc than the state-of-the-art network localization algorithms. In particular, it reduces the localization errors by more than 90% and speeds up the convergence time more than 100 times than that of the widely used G2O-based methods in sparse networks.

MOANS DV-Hop: An anchor node subset based localization algorithm for wireless sensor networks

Several applications have exploited wireless sensor networks (WSNs), and localization is a key WSN technology. The range-free localization technique is substantially less expensive than conventional range-based localization strategies since it does not require distance or angle measurements between the anchor and unknown nodes. The Distance Vector-Hop (DV-Hop) technique is a widespread localization solution for WSNs because of its straightforward theory and minimal cost. The coordinate estimating precision of the classic DV-Hop algorithm needs further improvement due to its large localization error. In this paper, a DV-Hop-based method utilizing modified optimum anchor node subset (MOANS DV-Hop) is proposed to improve the localization performance of the DV-Hop algorithm. A strategy for anchor node deployment is proposed. An objective function is formulated to minimize the error in estimating coordinates of unknown nodes. With the MOANS DV-Hop algorithm, each anchor node first uses other anchor nodes to locate itself, then uses the Scaled General Learning Equilibrium Optimizer (SGLEO) algorithm to create an optimal subset made up of anchor nodes other than itself. The anchor node then updates its average hop size using the anchor node subset and broadcasts both the updated hop size and the anchor node subset to the nearby unknown nodes. An unknown node then determines its location using the Equilibrium Optimizer (EO) algorithm, the anchor node subset, and the updated hop size received from the closest anchor node. Simulation findings show that MOANS DV-Hop algorithm has a greater level of localization accuracy in coordinate estimation than both the classical and other improved DV-Hop methods.

Analysis of improved particle swarm algorithm in wireless sensor network localization

Abstract Among the various WSN technologies, node localization technology is one of the core parts of WSN applications and an important component and key technology, which is also a hot spot and focus of research at this stage. In this paper, firstly, we propose a classical ion swarm localization algorithm for wireless sensor network localization, randomly assigning appropriate velocity and position to each particle in the population in order to find the global optimum in the iterative process. Based on this, a network node localization model is established to convert the functional optimization problem with constraints into an unconstrained optimization problem to solve. At the same time, the space is searched using a chaotic search strategy, which greatly improves the search efficiency. Next, the particle swarm algorithm is further optimized, and simulation experiments are set up using MATLAB software to do comparison experiments on the PSOAPF algorithm and other algorithms. The experimental results show that when the density of beacon nodes is 40%, the average localization error of the PSOAPF algorithm is 14.26%, with the smoothest decreasing trend. When the percentage of anchor nodes is 10%, the localization error is reduced by 6.89%, and the localization accuracy of the PSOAPF algorithm is also higher than other models. This study shows that the improved particle swarm algorithm can effectively improve the localization accuracy, reduce error and accelerate the convergence speed in wireless sensing network localization.

Localization algorithm for anisotropic wireless sensor networks based on the adaptive chaotic slime mold algorithm

Localization algorithm for anisotropic wireless sensor networks based on the adaptive chaotic slime mold algorithm

Research on the location of space debris impact spacecraft based on genetic neural network

The existence of space debris will pose a serious threat to the safety of spacecraft in orbit and cause serious impact damage to spacecraft. Therefore, the timely and accurate determination of the impact location of space debris is very important to improve the safety and reliability of the orbiting spacecraft in the long run. This paper proposes an intelligent back-propagation artificial neural network-based algorithm for locating the impact source of space debris. The method of building and training the algorithm is described, based on the moment of arrival of the stress waves detected by the PVDF (Polyvinylidene Fluoride) sensor array and the sensor coordinates. Based on the ABAQUS finite element simulation data, the neural network is trained. The result shows that compared with the traditional TOA (Time of Arrival) localization algorithm, the localization accuracy of the BP (Back Propagation) neural network localization algorithm is improved by 34.7%. The optimal objective is to minimize the error between the space debris impact point and the localization point. The parameters of the PVDF sensor array (diameter d of the circular PVDF thin film sensor, radius R of the PVDF sensor array) were optimized by genetic algorithm, and the optimal sensor array parameters were obtained based on the optimization algorithm.

Application of multi-target positioning algorithm in basketball teaching system and injury prevention

In today's era, the rapid spread of information and the rise of major network platforms provide good conditions for online learning and at the same time provide a new development idea for basketball teaching. The establishment of modern multimedia teaching theory provides theoretical support for the research and development of this subject. Construct and apply the online learning system of basketball basic technology, provide high-quality education, and can accommodate a large number of basketball fans to study and exchange together, and improve the physical fitness of young people. This thesis combines the multi-target localization algorithm in wireless sensor networks, first introduces the related concepts and basic theories of UWSNs, and then summarizes the related theories of node localization technology. In the course of basketball teaching, the advantages and disadvantages of the wireless sensor network multi-target positioning algorithm and the research direction are introduced. Aiming at the problem of low positioning accuracy and high complexity of the basketball motion prediction positioning algorithm, a multi-target positioning algorithm improved by backtracking search is proposed. The MBSA algorithm improves the positioning accuracy of anchor points, and uses cooperation mechanisms and basketball mobility to locate nodes for unknown actions. An improved BSA algorithm, namely the KLBSA-LoT algorithm, is proposed. The KLBSA-LoT algorithm accurately locates the anchor point and uses a cooperative mechanism to predict the position information of unknown nodes in basketball. Applying this algorithm to basketball teaching can greatly improve the accuracy of its positioning training. Sports teaching provides new methods.

Modified Rat Swarm Optimization Based Localization Algorithm for Wireless Sensor Networks

Modified Rat Swarm Optimization Based Localization Algorithm for Wireless Sensor Networks

Improved 3D localization algorithm for large scale wireless sensor networks

As localization represents the main core of various wireless sensor network applications, several localization algorithms have been suggested in wireless sensor network research. In this article, we put forward an iterative bounding box algorithm enhanced by a Kalman filter to refine the unknown node’s estimated position. In fact, several research efforts are currently in progress to extend the 2D positioning algorithm in WSNs to 3D that reflects reality and the most practical applications. Subsequently, we replace a large number of GPS-equipped anchors with a single mobile anchor. In our studies, we consider the type of range-free sensor network exploiting the wireless sensors connectivity. We assess the performance of our algorithm using exhaustive experiments on several isotropic and anisotropic topologies. Our proposed algorithm can fulfill the joint goals of algorithm transparency and accuracy for various scenarios by evaluating parameters such as localization accuracy whilst changing other simulation parameters such as the effect of communication range, mobile anchor node position and sensor node deployment topology. It has been proven by the results of the experiments that the proposed algorithm effectively reduces the location error without requiring more equipment or increasing the communication cost.

Bearing-only distributed localization for multi-agent systems with complex coordinates

This paper addresses the bearing-only distributed localization, which is more accurate and more reliable than traditional localization technologies. A new bearing-only distributed localization framework is proposed to solve localization problems for both static sensor networks and mobile multi-agent systems (MASs). Based on the complex coordinate representation, a novel bearing-only distributed localization algorithm for sensor networks is proposed. The algorithm combines the orientation estimation and the position estimation to solve the orientation alignment problem, which makes the compasses no longer needed for the localized networks. The localization for the mobile MASs is also studied and the corresponding localization algorithm is designed, which is more general and more challenging. The key to obtaining positions of moving agents, is the velocity estimator in the proposed localization algorithm which makes the estimated positions and velocities converge to the true value simultaneously. A distinctive advantage of the localization framework proposed in this paper is that the bearing-only localization algorithms can be applied to more general systems, such as sensor networks and MASs. Simulations and experiment are presented to verify the proposed algorithms.

Mobile Anchor Assisted Effective Node Localization Algorithm for Wireless Sensor Networks

Wireless Sensor Network (WSN) is collection of thousands of randomly, spatially distributed low-cost Sensor Nodes (SN) deployed normally in harsh environments. These deployed SNs collects data and delivers to Base Station (BS) located in a remote region. In a sensor network it is necessary to know about the location of sensor node and data collected by that node as it has a strong impact on overall performance of the WSN. An algorithm to transfer data using energy efficient mobile anchor based localization mechanism is presented in this paper. Firstly, the distance between anchor node and unknown node is determined using Received Signal Strength (RSS) and then using Trilateration method location of the unknown node is identified. Proposed algorithm aims to decrease the consumable energy that improves life of the network. Simulation results are compared with the existing localization technique to evaluate performance of the proposed algorithm in terms of energy consumption, packet delivery ratio and control overheads. The proposed scheme performs better than the existing Mobile Anchor Assisted Localization algorithm based on Regular Hexagon (MAALRH) for WSNs

A Performance Analysis of an Enhanced Graded Precision Localization Algorithm for Wireless Sensor Networks

Ad hoc wireless sensor networks are composed of many tiny, low-energy, low-capacity sensors that communicate ad hoc. A significant amount of research has been conducted on localization in wireless sensor networks (WSNs). In WSNs, localization or positioning is the process of determining the physical coordinates of sensor nodes, and it is an important factor in determining the origin of events in modern communication systems. Localization methods vary based on the time-critical requirements for different applications. For improving localization accuracy, several algorithms have been proposed. In the proposed scheme, a sensor node is located in a region bounded by a network of anchor nodes. Lower bound accuracy is found and an analytical framework is provided to localize the sensor. Extensive repeatable tests on numerous networks are used to illustrate the usefulness of the suggested approach. The key advantage of the proposed algorithm is independent of the environmental irregularities.

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 Trust Assessment-Based Distributed Localization Algorithm for Sensor Networks Under Deception Attacks

Dear Editor, This letter is concerned with the distributed localization issue for wireless sensor networks subject to deception attacks. It is assumed that malicious nodes randomly launch attacks to tamper the ranging information between sensors. In order to accommodate the effects of deception attacks, a trust assessment-based distributed localization algorithm is proposed. Combined with data fusion of barycentric coordinates, our algorithm can achieve accurate localization. Finally, numerical simulations are given to validate the effectiveness of the proposed localization algorithm.