Anchor Node Selection Research Articles

An Efficient Modified Black Widow Optimized Node Localization in Wireless Sensor Network

Wireless Sensor Network (WSN) is an encouraging technology in various applications like atmosphere monitoring, vehicular systems, and targeting devices. Localization in WSN is performed to identify the current position or coordinates of sensor nodes. The crucial task of localization is to find coordinates without manual configuration and expensive hardware like GPS. This work proposes a new localization algorithm using Modified Black Widow Optimization (MBWO). The proposed method involves an optimal selection of anchor nodes to reduce localization errors. Black Widow Optimization (BWO) is a naturally inspired optimization algorithm encouraged by the unique coupling behaviour of black widow spiders. The performance of black widow optimization is improved by levy flight distribution to escape from local minima. Experimental results show that the proposed MBWO-optimized localization achieves a higher localized node count with minimized localized error compared to other optimization algorithms.

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.

OptiLoc: Integrating SCSO and DV-Hop for wireless sensor network localization with application to disease forecasting in cattle farm monitoring

Disease prediction and management in cattle farming present significant challenges due to the complex interplay of environmental factors, disease transmission dynamics and the need for accurate localization of disease-carrying cattle. Traditional approaches often fall short in optimizing anchor node selection and accurately forecasting disease occurrences. To address these limitations, we propose OptiLoc, a novel framework that integrates advanced optimization techniques and disease forecasting algorithms. The proposed OptiLoc framework leverages Enhanced Sand Cat Swarm Optimization (SCSO) for optimizing anchor node selection within the farm area network. By dynamically adjusting the exploration rate and incorporating an opposition-based memory mechanism, OptiLoc ensures thorough exploration of the search space while preventing premature convergence. Furthermore, the multi-objective optimization aspect of SCSO considers factors such as coverage, energy efficiency, and connectivity, resulting in optimal anchor node placement. Additionally, OptiLoc integrates disease forecasting capabilities using logistic regression classifiers with dictionary learning. By extracting meaningful features from sensor data and training linear classifiers, OptiLoc predicts disease outbreaks among the cattle population. These predictions are seamlessly integrated into the optimization process through an expanded fitness function, which considers disease-related parameters alongside traditional optimization objectives. Furthermore, OptiLoc enhances disease management and control efforts by localizing sensor nodes using the DV-Hop method. By estimating sensor node positions based on hop distances to anchor nodes and dynamically calibrating hop distances based on real-time data on disease prevalence and environmental factors, OptiLoc achieves precise localization results. The proposed OptiLoc framework offers several advantages over traditional approaches. It optimizes anchor node placement, accurately forecasts disease outbreaks, and enhances localization accuracy, thereby empowering farmers with actionable insights to safeguard the health and well-being of their livestock. Performance evaluation reveals for the anchor nodes, it measures a mean localization error of 3.2% to 9.1% with a mean error of 5.91%. Similarly, for the sensor nodes, the mean localization error is 3.45% to 8.25% with a mean error of 5.445%. The AUC of the logistic regression classifier with dictionary learning is 0.97. Through its comprehensive approach, OptiLoc represents a significant advancement in optimizing WSNs in agricultural environments.

Hybrid positioning algorithm based on least squares and LM for 3D cluster networks

This paper proposes a hybrid positioning algorithm, LS-LM, based on least squares (LS) and Levenberg–Marquardt (LM) for position estimation of low-cost and miniaturized unmanned cluster networks in the absence of GPS. The LS method is first used to obtain an initial solution of position estimation, and the LM algorithm to iteratively refine the solution. The LS-LM algorithm has the advantages of simple computation, no complicated communication, and full distribution. It has high accuracy in the coordinated positioning algorithm without coordinate matching. In evaluating its efficacy,we have compared the root mean square error of the LS-LM algorithm to that of the LS method, tetrahedron shape measurement (TSM) algorithm and improved trilateration localization method with minimum uncertainty propagation and optimized selection of anchor nodes (ITL-MEPOSA) method. The simulation results demonstrate a varying degree of reduction in error with the application of the LS-LM algorithm.

Real-Time Anchor Node Selection for Two-Way-Ranging (TWR) Ultra-Wideband (UWB) Indoor Positioning Systems

Real-Time Anchor Node Selection for Two-Way-Ranging (TWR) Ultra-Wideband (UWB) Indoor Positioning Systems

A modified tetrahedron shape measure and its application for 3D trilateration localization in mobile cluster networks

In the error analysis of 3D trilateration localization, we constructed a new tetrahedron shape measurement method based on the condition number of the tetrahedron. This method uses algebraic operations, which is simpler than the previous methods based on complex geometric operations, and is also suitable for the shape measurement of triangle. For the trilateration localization problem in 3D space, based on tetrahedron shape measurement (TSM), we designed an algorithm of selecting anchor nodes on the hollow sphere centered at the unknown node. Extensive simulation experiments show that the tetrahedron shape measurement method proposed in this paper is effective. The anchor node selection algorithm based on tetrahedron shape measurement (TSM) can effectively suppress the iterative error problem in trilateration localization. Furthermore, the calculation of the tetrahedron condition number can be used for the deployment of anchor nodes in trilateration localization.

PO-GNN: Position-observant inductive graph neural networks for position-based prediction

Most graph neural networks (GNNs) map local neighborhood information into node embeddings but lack positional information. It results in similar node embeddings for nodes in similar local neighborhoods, leading to poor performance. We propose a position-observant inductive graph neural network (PO-GNN) architecture that captures the global positions of the nodes using the proposed novel closeness-distance metric. We demonstrate that the problem of anchor node selection, a significant component of position-aware GNNs, is NP-hard. Additionally, we utilize a heuristic anchor selection approach to obtain equitably distributed and asymmetric reference nodes over the given graph. In particular, PO-GNN significantly achieved a 50% relative improvement in the AUC metric for the Link Prediction (LP) over five different datasets and 85% for the Pairwise Node Classification (PNC) tasks over four different datasets and compared to the common GNN architectures and 1.805% and 15.29% improvement over state-of-the-art position-aware GNNs in LP and PNC tasks respectively. Furthermore, we also perform ablation studies over the length and number of random walks along with five aggregation methods and four update methods.

An Anchor Node Selection Scheme for Improving RSS-Based Localization in Wireless Sensor Network

Accurate location information is essential for various emerging applications in wireless sensor network (WSN). In order to improve localization accuracy, it is of paramount importance to reduce the effects of noisy distance measurements. This paper proposes an anchor node selection scheme for Received Signal Strength- (RSS-) based localization in WSN. In the proposed approach, the nodes are sorted firstly to select anchor nodes reasonably, and to further reduce the influence of range error, the weight is assigned to each selected anchor node. Finally, an effective modified cuckoo search algorithm is used to compute the coordinates of unknown nodes. Extensive experiments are conducted to study the effects of anchor node ratio, ranging error factor and node density on the localization accuracy performance of the proposed method. The experimental results demonstrate that the proposed method performs better in improving localization accuracy compared with the localization technique without special anchor selection scheme which selects all anchors’ information received and the localization technique selecting nearest anchors.

Channel-Quality-Evaluation-Based Anchor Node Selection for UWB Indoor Positioning

Ultra-wideband (UWB) is a widely used technology in wireless indoor positioning. However, Non-line-of-sight (NLOS) and complex multipath fading introduce positioning errors to the UWB system. In order to alleviate the influence of non-line-of-sight (NLOS) and multipath fading, a channel-quality-evaluation-based method is proposed in this paper. In the proposed method, the qualities of the channel between unknown nodes and anchor nodes are evaluated by a weighted equation related to the channel impulse response (CIR) characteristics. Anchor nodes with higher quality are selected adaptively for positioning. The experiments showed that this method can reduce the root-mean-squared error (RMSE) of the positioning results by 40.4% on average and 95.78% in some strongly degraded cases.

A Distributed Localization Method for Wireless Sensor Networks Based on Anchor Node Optimal Selection and Particle Filter.

In wireless sensor networks, due to the significance of the location information of mobile nodes for many applications, location services are the basis of many application scenarios. However, node state and communication uncertainty affect the distance estimation and position calculation of the range-based localization method, which makes it difficult to guarantee the localization accuracy and the system robustness of the distributed localization system. In this paper, we propose a distributed localization method based on anchor nodes selection and particle filter optimization. In this method, we first analyze the uncertainty of error propagation to the least-squares localization method. According to the proportional relation between localization error and uncertainty propagation, anchor nodes are selected optimally in real-time during the movement of mobile nodes. Then we use the ranging and position of the optimally selected anchor nodes to obtain the location information of the mobile nodes. Finally, the particle filter (PF) algorithm is utilized to gain the optimal estimation of the localization results. The experimental evaluation results verified that the proposed method effectively improves the localization accuracy and the robustness of the distributed system.

Dual-line data collection scheme for efficient mobile sink operation in solar-powered wireless sensor networks

Solar energy harvesting has been presented as a key solution to alleviate the lifespan problem of conventional battery-based wireless sensor networks, but it does not address the problem of uneven energy consumption across the network. In conventional sensor networks with a stationary sink node, the nodes around the sink node consume more energy than other nodes, which is known as the hotspot problem. In recent studies, mobile sink technology has been proved to be an effective solution to the problem, where a mobile sink visits sensor nodes and collects data. To reduce the energy consumption of the mobile sink, a subset of sensor nodes are designated as anchor nodes to collect data for a certain period, and the mobile sink visits only such anchor nodes. However, even with these approaches, small hotspots can still be formed near those anchor nodes, hence it is crucial to optimize the selection and management of anchor nodes. This study aims to propose a dual line-based anchor node selection scheme tailored to efficiently solve the hotspot problem in mobile sink-based solar-powered wireless sensor networks. The proposed scheme designates the areas, in which some nodes can be chosen as anchor nodes, in the form of two lines. This dual line-based anchor selection algorithm effectively operates for the best use of the solar energy harvested by each node in a balanced manner. The experimental results confirm that the proposed scheme minimizes hotspots in the network, reduces the blackout time of nodes, and improves the data collection rate.

A distributed localization method for mobile nodes

In Wireless Sensor Networks(WSNs), the location services are the basis of many application scenarios. However, for the range-based localization method, the localization accuracy and the system robustness of the distributed localization system are difficult to guarantee, due to the uncertainty of the distance estimation and position calculation are affected by the node state and communication uncertainty. In this paper, we propose the distributed localization method based on anchor node selection and Particle Filter optimization. In this method, we analyze the uncertainty of error propagation in the Least-squares method and find that there is a proportional relation between localization error and uncertainty propagation. According to this relationship, we propose the corresponding optimization criterion methods of anchor nodes. To optimize the initial localization results, we present the distributed localization method based on anchor node optimal selection and Particle Filter. Simulation results show that the methods we proposed could effectively improve the localization accuracy of the mobile nodes and the robustness of the system.

Improved 3-dimensional DV-hop localization algorithm based on information of nearby nodes

DV-Hop, a range-free localization algorithm, has been one of the most popular localization algorithm. It is easy and inexpensive to implement. Therefore, in the literature, many improved variants of this algorithm exist. However, poor location accuracy and higher power consumption by DV-Hop algorithm always open new avenues for research on this algorithm and makes it a favorite among the researchers. In this paper, we have proposed an Improved 3-Dimensional DV-Hop algorithm based on the information of nearby nodes (I3D-DVLAIN). In the algorithm, by calculating hopsize at the unknown nodes, we eliminate one communication among the nodes, which reduces power consumption in the network. The hopsize calculation and location estimation is done by using only the nearby anchor nodes, which minimizes the network usage and decreases the computational effort. For the selection of nearby anchor nodes, we introduce a new method. Further, for localization, a novel method is used for solving the system of distance equations that restricts propagation of inherent error in the distance and increases localization accuracy. Furthermore, by mathematically analyzing the propagation of error in solving the system of equations, we prove the superiority of I3D-DVLAIN over other compared algorithms. The results obtained through simulation and complexity analysis of the computation and communication further strengthens our observations about the superiority of the proposed algorithm.

Hybrid ABC-BAT Optimization Algorithm for Localization in HWSN

In this paper, a hybrid ABC-BAT optimization algorithm is designed for localization in Heterogeneous Wireless Sensor Networks (HWSN). This algorithm combines the features of both ABC and BAT optimization algorithms for effective selection of anchor nodes and localization. In this algorithm, Artificial Bee Colony (ABC) algorithm is applied for deploying and selecting the anchor nodes for coverage. The fitness function is derived in terms of node degree and residual energy parameters. Then Bat Optimization Algorithm (BOA) has been applied for localization by estimating the distance between the anchor and each non-localized node. The fitness function is estimated in terms of Expected Hop Progress (EHP) and estimated distance error parameters. By simulation results, it has been shown that the proposed ABC-BAT optimization technique minimizes the localization error, localization delay to yield better localization accuracy, when compared to ABC and BAT algorithms.

Indoor Positioning System Using Dynamic Model Estimation.

Indoor Positioning Systems (IPSs) are used to locate mobile devices in indoor environments. Model-based IPSs have the advantage of not having an exhausting training and signal characterization of the environment, as required by the fingerprint technique. However, most model-based IPSs are done using fixed model parameters, treating the whole scenario as having a uniform signal propagation. This might work for most small scale experiments, but not for larger scenarios. In this paper, we propose PoDME (Positioning using Dynamic Model Estimation), a model-based IPS that uses dynamic parameters that are estimated based on the location the signal was sent. More specifically, we use the set of anchor nodes that received the signal sent by the mobile node and their signal strengths, to estimate the best local values for the log-distance model parameters. Also, since our solution depends highly on the selected anchor nodes to use on the position computation, we propose a novel method for choosing the three best anchor nodes. Our method is based on several data analysis executed on a large-scale, Bluetooth-based, real-world experiment and it chooses not only the nearest anchor but also the ones that benefit our least-square-based position computation. Our solution achieves a position estimation error of 3 m, which is better than a fixed-parameters model from the literature.

Anchor selection for topology inference and routing in wireless sensor networks

Anchor-based ad-hoc networks utilize hop measurements to generate a virtual coordinate system for topology inference and routing applications. A common problem with such coordinate system is its sensitivity to anchor placement. We present a general formulation to the anchor node selection problem. Then, we relax the optimization problem by deriving an upper-bound of the objective function. We finally propose an iterative algorithm that consists in choosing additional anchor nodes based on the connectivity information provided by the current anchor set. Numerical simulations indicate that our anchor selection method is robust to missing measurements and improves network topology inference and routing performance.

A Rescue-Assistance Navigation Method by Using the Underground Location of WSN after Disasters.

A challenging rescue task for the underground disaster is to guide survivors in getting away from the dangerous area quickly. To address the issue, an escape guidance path developing method is proposed based on anisotropic underground wireless sensor networks under the condition of sparse anchor nodes. Firstly, a hybrid channel model was constructed to reflect the relationship between distance and receiving signal strength, which incorporates the underground complex communication characteristics, including the analytical ray wave guide model, the Shadowing effect, the tunnel size, and the penetration effect of obstacles. Secondly, a trustable anchor node selection algorithm with node movement detection is proposed, which solves the problem of high-precision node location in anisotropic networks with sparse anchor nodes after the disaster. Consequently, according to the node location and the obstacles, the optimal guidance path is developed by using the modified minimum spanning tree algorithm. Finally, the simulations in the 3D scene are conducted to verify the performance of the proposed method on the localization accuracy, guidance path effectiveness, and scalability.

UWB/BLE Tracking System for Elderly People Monitoring.

Localization systems are the source of data that allows to evaluate elderly person’s behaviour, to draw conclusions concerning his or her health status and wellbeing, and to detect emergency situations. The article contains a description of a system intended for elderly people tracking. Two novel solutions have been implemented in the system: a hybrid localization algorithm and a method for wireless anchor nodes synchronization. The algorithm fuses results of time difference of arrival and received signal strength measurements in ultrawideband (UWB) and Bluetooth Low Energy (BLE) radio interfaces, respectively. The system allows to change the intensity of UWB packets transmission to adapt localization accuracy and energy usage to current needs and applications. In order to simplify the system installation, communication between elements of the system infrastructure instead of wire interfaces is performed over wireless ones. The new wireless synchronization method proposed in the article consists in retransmission of UWB synchronization packets by selected anchor nodes. It allows for extension of the system coverage, which is limited by the short range of UWB transmission. The proposed solution was experimentally verified. The synchronization method was tested in a laboratory, and the whole system’s performance was investigated in a typical flat. Exemplary results of the tests performed with older adult participation in their own homes are also included.

An anchor node selection mechanism-based node localisation for mines using wireless sensor networks

An anchor node selection mechanism-based node localisation for mines using wireless sensor networks

Improved DV-Hop Localization Algorithm Based on Dynamic Anchor Node Set for Wireless Sensor Networks

Node localization is a key issue in wireless sensor networks (WSN) area, and distance vector hop (DV-Hop) algorithm is widely adopted in WSN localization. Existing DV-Hop based algorithms employ all anchor nodes to localize the unknown node. However there is a large error of the estimated distance from the unknown node to some anchor nodes, which also results in the final unknown node localization error large. To improve the localization accuracy, we propose an improved DV-Hop algorithm based on dynamic anchor node set (DANS IDV-Hop). Differently to the existing DV-Hop based algorithms which apply total anchor nodes, DANS IDV-Hop utilizes part of anchor nodes to participate in localization. Firstly, the selection of anchor nodes is abstracted into a combinatorial optimization problem. For selecting appropriate anchor nodes, a novel binary particle coding scheme and fitness function are designed. Subsequently, the binary particle swarm optimization (BPSO) algorithm is applied to construct the dynamic anchor node set (DANS), and the localization is carried out on the DANS. Finally, the continuous particle swarm optimization (PSO) algorithm is utilized to further optimize the unknown node coordinates. Simulation results show that DANS IDV-Hop has excellent localization accuracy than that of the original DV-Hop and other DV-Hop based improved algorithms.