Localization In Sensor Networks Research Articles

How Connected Cars Could Capture Cloud Dynamics for Solar Forecasting—Evidence from Two Simulation Scenarios

The rapidly increasing share of fluctuating electricity from photovoltaics calls for accurate approaches to estimate cloud motion, the primary source for the varying power supply. While local sensor networks are prominent in targeting forecast horizons too short for image‐based methods, they have minimal spatial coverage. This work presents the first step towards expanding those approaches to spatially scalable sensor networks: With the motivation of using automotive light sensors as a sensor network, two excerpts from a microscopic traffic simulation serve as simulative sensor networks. A fractal‐based cloud shadow pattern passes the sensor network areas with defined velocities and directions, which shall be estimated using the cumulative mean absolute error method. The evaluation results indicate that the more extensive observation areas compensate for the dynamics in the sensor network when compared to a reference work with a static sensor grid. Furthermore, this work shows how the estimates deteriorate with lower vehicle penetration rates (PR) and longer building shadows due to a lower solar elevation angle. At a penetration rate of 40%, the root mean square errors for both sensor networks are still below 5 ms−1. In conclusion, the spatiotemporal characteristics of a vehicle network offer a potential for estimating cloud movements.

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

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

Wireless sensor network localization based on Drop Mongoose Optimization Algorithm

Wireless sensor network localization based on Drop Mongoose Optimization Algorithm

RACLN: Reconfigurable Intelligent Surface as Anchors for Cooperative Localization of Wireless Sensor Network

RACLN: Reconfigurable Intelligent Surface as Anchors for Cooperative Localization of Wireless Sensor Network

Distance-Based Localization in Wireless Sensor Network Using Exponential Grey Prediction Model

Localization in wireless sensor network is the key for many applications in which location of occurrence of event is very important. Distance based localization techniques have proved to be more accurate and feasible for the location calculation of the sensor nodes. Moreover, Received Signal Strength Indicator (RSSI) based techniques are well adopted by the researcher to calculate the distance between the sensor nodes. The optimized value of RSSI may estimate the location more precisely. In this paper, Exponential Grey prediction model and weighted predicted RSSI values are utilized to provide estimated values of RSSI which are then optimized using an objective function. The decreasing term used in the differential equation of the Grey prediction model provides better prediction accuracy which if optimized provides efficient result. The conventional Grey prediction model is limited by the sample space and scope of applications. The Exponential Grey prediction model used in the proposed algorithm removes those drawbacks and can be applied in real time applications with larger sample space. The objective function used in the proposed algorithm uses weighting factor to be applied to Grey predicted RSSI and weighted predicted RSSI to estimate the RSSI value more accurately. The objective function calculates more near values of RSSI to localize the node more accurately. The simulation results obtained are compared with the latest techniques of RSSI calculation like weighted centroid method, RSSI quantization, TMA and FRBW algorithm. The results show that the proposed algorithm outperform the existing methods for RSSI estimation.

Optimized intelligent 3D localization in wireless sensor networks for better data sharing

WSNs are networks of small, autonomous devices called sensors or nodes that are equipped with sensors, processing capabilities, and wireless communication capabilities. These networks are designed to monitor and collect data from their surrounding environments and transmit that data wirelessly to a central location for analysis. However, challenges such as packet loss and limited throughput have impeded their efficiency. To address these issues, this research presents an innovative approach known as Squirrel-based Elman Neural Localization (SbENL) to optimize 3D localization in WSNs. Squirrel fitness tracks the node's location in the sensor network environment. This study initially configures WSN nodes with constrained energy resources, and data rates are continually monitored and predicted. The outcome is improved data sharing with higher throughput rates. The research assesses the performance of SbENL against existing localization techniques, demonstrating its effectiveness in minimizing data loss, reducing energy consumption, and maximizing data transfer rates. This optimized intelligent 3D localization approach holds promise for enhancing data sharing and communication efficiency in WSNs, benefiting a wide range of applications. Finally, the communication metrics were measured and valued with recent existing approaches. A SbENL has minimized the data loss energy consumption and maximized the data transferring rate.

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.

Optimizing Wireless Sensor Network Localization: Hybrid Natural Inspired with Bat Swarm Algorithm

In wireless sensor networks, the data received from sensor nodes is processed and communicated to the next node or cluster head. Sensed data are meaningful only when location information is embedded with the data. Therefore, the sensor should be able to estimate its location information and embed the location information with the sensed data. This research work proposes three hybrid nature-inspired localization optimization algorithms for location estimation of sensor nodes, namely the Hybrid genetic –Bat Localization Algorithm (GA-BAT LA), Hybrid Bat- Particle Swarm Optimization Localization Algorithm (BAT-PSO LA), and Hybrid genetic -Bat- Particle Swarm Optimization Localization Algorithm (GA-BAT-PSO LA). The localization algorithms are developed, implemented, and compared to ensure the accuracy of the estimation of location information. A fixed number of location-known anchor nodes with a variable location-unknown sensor node are considered. The algorithms are compared for a minimum number of iterations, average number of iterations, standard deviation, maximum number of iterations, time complexity, and accuracy to estimate the location of the sensor nodes.

Deep Learning-Based Transmitter Localization in Sparse Wireless Sensor Networks.

In the field of wireless communication, transmitter localization technology is crucial for achieving accurate source tracking. However, the extant methodologies for localization face numerous challenges in wireless sensor networks (WSNs), particularly due to the constraints posed by the sparse distribution of sensors across large areas. We present DSLoc, a deep learning-based approach for transmitter localization in sparse WSNs. Our method is based on an improved high-resolution network model in neural networks. To address localization in sparse wireless sensor networks, we design efficient feature enhancement modules, and propose to locate transmitter locations in the heatmap using an image centroid-based method. Experiments conducted on WSNs with a 0.01% deployment density demonstrate that, compared to existing deep learning models, our method significantly reduces the transmitter miss rate and improves the localization accuracy by more than double. The results indicate that the proposed method offers more accurate and robust performance in sparse WSN environments.

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.

An Anti-Jamming Technique by Jammer Localization for Multi-Channel Wireless Sensor Networks

An Anti-Jamming Technique by Jammer Localization for Multi-Channel Wireless Sensor Networks

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.

Periodic Energy Optimization Using IOT and ML

The rapid expansion of Internet of Things (IoT) applications across various sectors generates an enormous volume of continuous time-series data. However, transmitting this massive amount of sensor data from energy constrained IoT nodes poses a significant challenge. The continuous transmission of such data consumes vast amounts of energy.In this work, we present a solution to this problem by predicting the periodic behavior of sensor data through a higher-level view of continuous transmission data from nodes in IoT at server side. Our system is composed of an IoT sensor network and a data processing unit. The local sensor network: temperature and humidity data is collected using 4 different nodes, as well, which afterward this info is transferred into a data processing unit built on the Raspberry Pi device. We use the machine learning model Autoregressive Integrated Moving Average (ARIMA) on the processing unit. This model is then applied individually to the data from each of the four nodes, predicting processed sensor values in the future accurately. In short, after getting highly accurate prediction, then we settle down proper energy saving pattern which reduces the data transmission requirements hence results in energy saving pattern.By utilizing the predictive capabilities of the ARIMA model, we minimize the need for constant transmission of raw sensor data. Instead, we transmit only essential updates or deviations from the predicted values. This approach substantially reduces energy consumption by eliminating the transmission of redundant information. In summary, our project aims to overcome the energy limitations of IoT sensor nodes by leveraging predictive modelling techniques, specifically the ARIMA model. By accurately predicting periodic patterns in sensor data, we can optimize energy usage by transmitting only the necessary information, while still ensuring effective monitoring of temperature and humidity in the IoT network.

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.

Enhancing 3D localization in wireless sensor network: a differential evolution method for the DV-Hop algorithm

Enhancing 3D localization in wireless sensor network: a differential evolution method for the DV-Hop algorithm

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

Low-cost sensor-based damage localization for large-area monitoring of FRP composites

In recent years, there has been growing interest in self-sensing structural materials across research and industry sectors. Detecting and locating structural damage typically requires numerous sensors wired to a data acquisition (DAQ) circuit, rendering implementation impractical in real structures. This paper proposes an innovative, cost-effective sensor network for damage detection and localization in fiber-reinforced polymer composites. The innovation encompasses three key elements: (1) utilizing carbon fiber tows within the composite as piezoresistive sensors, eliminating the need for additional foreign sensor devices; (2) introducing a novel sensor layout wherein sensor tow branches with varied resistance values are connected in parallel, reducing the number of connections to the DAQ circuit and cutting manufacturing costs significantly; (3) developing a practical sensor terminal fabrication technique to minimize manufacturing expenses. The proposed design methodology for the branch resistance values is first validated using a demonstration panel. Subsequently, the overall strategy is assessed by conducting impact tests on carbon and glass fiber-reinforced composite specimens. Results validate the sensor’s ability to accurately detect and locate structural damage.

Proposal of a wireless sensor network for footstep localization and optimization of its location using bio-inspired metaheuristics

This paper introduces a proposal for a wireless network of smart sensors designed to capture the vibrations produced by the impact of a person’s footsteps on a dispersive and damped floor. These vibrations are utilized to pinpoint the location of impacts using a heuristic algorithm, enabling operation at a low data transmission/reception rate. Traditionally, the placement of sensors within a room has not been given significant consideration in the context of localization; however, our findings indicate that a detailed analysis of both the number and location of sensors can substantially enhance the accuracy of footstep localization. In this study, we have optimized the arrangement and quantity of sensors through the application of bio-inspired metaheuristics, aiming to minimize localization errors across the room. Upon evaluating various bio-inspired metaheuristic optimization algorithms, we identified the one that yielded the lowest estimation errors for the room as a whole. Our experimental tests demonstrate that such optimization significantly enhances the efficacy of the localization algorithm, resulting in a reduction of localization error ranging from 18.24% to 46.78% across different trajectories.

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.

Dealing with Outliers in Wireless Sensor Networks Localization: An Iterative and Selection-Minimization Strategy

In recent years, there has been considerable interest in robust range-based Wireless Sensor Network (WSN) localization due to the increasing importance of accurately locating sensors in various WSN applications. However, achieving precise localization is often hampered by the presence of outliers or underestimations in range measurements, particularly when employing the RSS technique. To tackle these issues, we introduce a Two-Step Localization (namely, the SelMin approach). In the initial phase, the approach utilizes Second-Order Cone Programming (SOCP) to minimize distance discrepancies. It does this by comparing a reference Euclidean Distance Matrix (EDM) with a weighted one derived from imprecise distances between sensor nodes. In the subsequent phase, a heuristic method is employed to identify a specific number of imprecise distances, referred to as outliers, that will be disregarded in the first phase, and this two-phase process continues iteratively. The experimental results demonstrate that the SelMin strategy performs better than the DSCL method when evaluated using the Root Mean Square Error (RMSE) metric. This superior performance is maintained even in challenging conditions, such as when there are many outliers (i.e, around 30%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document}) in the network. This indicates that SelMin is a reliable and robust choice for these environments.