Node Localization In Networks Research Articles

RSSI-based fingerprint localization in LoRaWAN networks using CNNs with squeeze and excitation blocks

The ability to offer long-range, high scalability, sustainability, and low-power wireless communication, are the key factors driving the rapid adoption of the LoRaWAN technology in large-scale Internet of Things applications. This situation has created high demand to incorporate location estimation capabilities into large-scale IoT applications to meaningfully interpret physical measurements collected from IoT devices. As a result, research aimed at investigating node localization in LoRaWAN networks is on the rise. The poor localization performance of classical range-based localization approaches in LoRaWAN networks is due to the long-range nature of LoRaWAN and the rich scattering nature of outdoor environments, which affects signal transmission. Because of the ability of fingerprint-based localization methods to effectively learn useful positional information even from noisy RSSI data, this work proposes a fingerprinting-based branched convolutional neural network (CNN) localization method enhanced with squeeze and excitation (SE) blocks to localize a node in LoRaWAN using RSSI data. Results from the experiments conducted to evaluate the performance of the proposed method using a publicly available LoRaWAN dataset prove its effectiveness and robustness in localizing a node with satisfactory results even with a 30% reduction in both the principal component analysis (PCA) variances on the training data and the size of the original sample. A localization accuracy of 284.57 m mean error on the test area was achieved using the Powed data representation, which represents an 8.39% increase in localization accuracy compared to the currently best-performing fingerprint method in the literature, evaluated using the same LoRaWAN dataset.

Wireless Sensor Networks Node Localization Algorithm Based on Range Optimization and Graph Optimization

<p>To address the problem of low localization accuracy in the node localization algorithms of wireless sensor networks (WSN) based on received signal strength indication (RSSI) ranging, a WSN node localization algorithm based on ranging optimization and graph optimization is proposed. In terms of RSSI ranging, the outliers are removed using the Grubbs method, and the data are processed using a moving average smoothing-Gaussian hybrid filter to establish a Bessel function ranging model to reduce the ranging error; in terms of node localization, the signal strength data are employed to construct a distance cost term, a cost function model is built based on these cost terms, and graph optimization is adopted to minimize this function. Then, the node position is estimated to minimize the overall observation error. Simulation results indicate that the proposed algorithm has higher ranging and localization accuracy than existing ranging and localization algorithms, and it can meet the requirements of node localization in large-scale WSN.</p> <p> </p>

Optimization Algorithms for Wireless Sensor Networks Node Localization: An Overview

Optimization Algorithms for Wireless Sensor Networks Node Localization: An Overview

Method of information technology for structure analysis of urban network fire-rescue units

The subject of this study is the process of analyzing the structure of the network of fire-rescue units of the city in the context of optimizing their spatial distribution. The purpose of this work is to increase the objectivity of decisions made while forming a city network of fire-rescue units by creating an information technology (IT) method for analysis of its structure based on the use of spatially distributed data. Objectives: to find ways to improve the level of fire safety, analyze existing approaches to the formation of a network of fire-rescue units, considering the peculiarities of building and organizing a network, and adapt the classical problem of placement; to propose a method for solving it to minimize the distance between the fire-rescue unit and the possible place of fire while ensuring maximum coverage of the territory by fire service; and to develop IT structure for its implementation based on the information flow model of the process of analyzing the fire-rescue units network using a geospatial approach. The following results were obtained. The study of the classical location problem and its adaptation to real problems arising from the analysis of the urban network of fire-rescue units made it possible to represent it as a set of independent complete bipartite graphs. To search the location of network nodes while solving an adapted problem, an IT method is developed, which, based on the p-median model, combines the author’s methodology for studying information processes and methods of geospatial analysis. Summarizing the requirements of the current legislation, a set of input and output IT data and a set of operations have been formed. The representation of the IT structure in the form of a data flow diagram explains how the set of factors is processed and generalized when making decisions on the creation and / or improvement of the existing city fire department. Conclusions. The results of the bibliographic search confirm the need to consider the spatial features of the area where it is planned to create a fire-rescue unit, as well as the spatial configuration of the urban network of existing fire stations, to evaluate its effectiveness using an integrated indicator. This requires the development of specialized methods focused on the use of geo-information systems for their implementation in decision support systems. Scientific and methodological support for IT has been developed, which gives local authorities a tool for analyzing fire safety in the city to create and / or improve the existing fire protection. An experiment to study the capabilities of the proposed method based on volunteered geographic information on Kharkiv city showed the effectiveness of its use for solving classical problems of placement, considering the accepted restrictions on the spatial availability of fire-rescue units. At the same time, additional opportunities appear in the formation of options for improving the network of fire-rescue units, considering their spatial distribution, workload, accessibility, and the resulting areas of coverage / non-coverage by the fire service. For example, the fire service coverage area of the existing structure of fire stations has been assessed. During the regulated time, it reaches 70% of the Kharkiv city area, and depending on the real road traffic, it can vary from 64.61% to 73.44%. It is illustrated that the creation of two additional fire and rescue units in the northern and southern parts of Kharkiv will increase the coverage area by approximately 5%, on average increasing it to 75.1%

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.

Mobile Node Localization in Wireless Networks: Path-Loss Model, Trilateration, and Error Mitigation in a 5G Sub-6 GHz Scenario

In this paper, we proffer a novel technique designed for low-cost and computationally light localization of mobile nodes in an urban terrain, by leveraging the extended COST 231 Hata Path-Loss (PL) model and the Trilateration technique. Our approach accounts for the possibility of a Non-Line-of-Sight (NLoS) scenario in a medium-sized city, wherein one of the three reference nodes required for the trilateration approach encounters NLoS impediments. Our proposed method proceeds with localization by utilizing solely two Line-of-Sight (LoS) reference nodes, while integrating the localization system simulator with an Extended Kalman Filter (EKF). The simulation results presented herein demonstrate a marked enhancement in performance, surpassing that of trilateration in scenarios where three LoS nodes cannot be established.

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

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

RSS-Based Cooperative Localization and Edge Node Detection

The ability to determine the location of a sensor node in a wireless sensor network is essential in many applications. We propose a received signal strength (RSS) based cooperative localization technique. The target nodes, whose locations are unknown during the deployment phase, communicate with the anchor nodes (whose locations are known) and the neighboring target nodes to localize themselves. In the proposed technique the target nodes behave as pseudo anchor nodes after they have localized themselves. It is observed that in such cooperative techniques the localization error propagates from one iteration to the next. We mitigate this error propagation by eliminating the network edge nodes from updating their location information after the first iteration. We propose two distributed edge node detection techniques: quadrant method (Quad) and geometric dilution of precision (GDOP) based method. Extensive simulations have been carried out, on uniform anchor and non-uniform network topologies, to analyze the performance of the proposed edge detection techniques and the iterative localization algorithm. The proposed localization algorithm is compared with state of the art localization techniques using simulations as well as experiments, and is shown to achieve improved localization accuracy. The Cramer-Rao lower bound (CRLB) is also derived and used as a performance benchmark.

Review on Unmanned Aerial Vehicle Assisted Sensor Node Localization in Wireless Networks: Soft Computing Approaches

Node positioning or localization is a critical requisite for numerous position-based applications of wireless sensor network (WSN). Localization using the unmanned aerial vehicle (UAV) is preferred over localization using fixed terrestrial anchor node (FTAN) because of low implementation complexity and high accuracy. The conventional multilateration technique estimates the position of the unknown node (UN) based on the distance from the anchor node (AN) to UN that is obtained from the received signal strength (RSS) measurement. However, distortions in the propagation medium may yield incorrect distance measurement and as a result, the accuracy of RSS-multilateration is limited. Though the optimization based localization schemes are considered to be a better alternative, the performance of these schemes is not satisfactory if the distortions are non-linear. In such situations, the neural network (NN) architecture such as extreme learning machine (ELM) can be a better choice as it is a highly non-linear classifier. The ELM is even superior over its counterpart NN classifiers like multilayer perceptron (MLP) and radial basis function (RBF) due to its fast and strong learning ability. Thus, this paper provides a comparative review of various soft computing based localization techniques using both FTAN and aerial ANs for better acceptability.

Node Location Method of Ecological Environment Monitoring Network Based on Zigbee

In view of the poor initialization performance of ecological environment monitoring network node location, a method of ecological environment monitoring network node location based on Zigbee is proposed. The node data collection model of ecological environment monitoring network is built based on Zigbee, and the performance is stable, which is more suitable for the node location of the ecological environment monitoring network; it is hoped that this study can provide reliable value reference and help for the future ecological studies. Through the installation of different types of sensors, the data of the ecological environment monitoring network nodes are automatically collected and sent to the server. The static weight coefficient of the collected data of the ecological environment monitoring network nodes is modified. According to the modified results, the ecological environment monitoring network is modified by DV‐HOP positioning algorithm. The nodes of the ecological environment monitoring network are located by the three‐way positioning method. The experimental results show that the initialization performance of this method is better, the accuracy is about 98%, and it is stable. It is more suitable for the node location of ecological environment monitoring network, which mainly includes ZigBee wireless sensor network module, embedded ARM, and Linux.

Methodology for estimating the energy reserve of a mobile ad-hoc communication network with UV-C channel

Communication systems of the solar-blind ultraviolet range UV-C from 200 to 280 nm provide the possibility of communication in the absence of line of sight due to the strong scattering of UV radiation in the atmosphere. For the effective use of UV-C communication systems, an adequate assessment of the energy budget of the UV channel is necessary. Analytical expressions are obtained that characterize the energy reserve of the UV-C system in relation to the specified characteristics of the optical receiver, transmitter, and UV channel. Based on the analytical and numerical model of losses in the UV channel, the energy reserve of the UV-C communication system is modeled on the distance between the transmitter and receiver, the elevation angles and the azimuthal deviation of the transmitter and receiver. The results obtained can be used to calculate and optimize ad-hoc communication networks with UV-C channel, characterized by changes in the spatial location of network nodes during operation.

An Novel Method for Object Tracking in Sensor Network Combination with Trilateration and UT Transform

Positioning and tracking are the key technologies of wireless sensor networks. Trilateration is an important method for the localization of wireless sensor network nodes. This method uses three anchor nodes to determine the location of the target. In this paper, UT transform and trilateral measurement method are combined to obtain the statistics of target position, and then use it as the virtual observation of Kalman filter to realize dynamic target tracking. The simulation results verify the performance of the method proposed in this paper.

Three-Dimensional Localization Algorithm Based on Improved A and DV-Hop Algorithms in Wireless Sensor Network.

In the traditional wireless sensor networks (WSNs) localization algorithm based on the Internet of Things (IoT), the distance vector hop (DV-Hop) localization algorithm has the disadvantages of large deviation and low accuracy in three-dimensional (3D) space. Based on the 3DDV-Hop algorithm and combined with the idea of A* algorithm, this paper proposes a wireless sensor network node location algorithm (MA*-3DDV-Hop) that integrates the improved A* algorithm and the 3DDV-Hop algorithm. In MA*-3DDV-Hop, firstly, the hop-count value of nodes is optimized and the error of average distance per hop is corrected. Then, the multi-objective optimization non dominated sorting genetic algorithm (NSGA-II) is adopted to optimize the coordinates locally. After selection, crossover, mutation, the Pareto optimal solution is obtained, which overcomes the problems of premature convergence and poor convergence of existing algorithms. Moreover, it reduces the error of coordinate calculation and raises the localization accuracy of wireless sensor network nodes. For three different multi-peak random scenes, simulation results show that MA*-3DDV-Hop algorithm has better robustness and higher localization accuracy than the 3DDV-Hop, PSO-3DDV-Hop, GA-3DDV-Hop, and N2-3DDV-Hop.

Research on Production Management and Optimization of Multisensor Intelligent Clothing in 5G Era

The influence of 5G has penetrated into all aspects of people’s lives. The field of garment production management is inevitably affected by 5G. The various advanced technologies it promotes can greatly promote the production and management of clothing. Learning and understanding these technologies can help you learn how to change in the changing garment factory to obtain more intelligent and efficient production methods without being excluded by age. The garment production line management system proposed in this paper is based on the garment production line, introduces Internet technology into the garment production process, and monitors all links of the garment production process through the Zigbee network. The system improves the automation degree of enterprises, greatly expands the application scope of wireless sensor networks, and improves the application level of data acquisition, monitoring, equipment maintenance, and diagnosis in China’s industrial field. Wireless network node location technology is also an important supporting technology for managing wireless Zigbee networks. The visual display of a physical topology map can effectively help administrators manage and maintain wireless networks.

Research on wireless sensor location technology for biologic signal measuring based on intelligent bionic algorithm

Biological signal measurement system based on wireless sensor network is a combination of traditional medical monitor and modern communication technology. It is of great significance for clinical application and the development of medical instruments, especially in family medical treatment. The application of intelligent bionic algorithm in wireless sensor network node location has become a hot topic in academic research. Traditional particle swarm optimization (PSO), as a common method to solve optimization problems, has great advantages in finding the optimal solution iteratively. However, the convergence speed of PSO cannot be adjusted dynamically according to the operation degree of the algorithm, therefore it is easy to go into the situation of finding the local optimal solution. To solve these above problems, this paper proposes a DV-Hop localization algorithm based on particle swarm bionic optimization, which improves the performance of traditional PSO algorithm from three aspects: population selection, inertia weight and learning factor. The simulation results show that, the algorithm can adjust the convergence speed dynamically, and jump out of the local optimal dilemma to the maximum extent, which improves the iterative accuracy of the algorithm for the biologic signal measuring system.

A Node Location Algorithm Based on Node Movement Prediction in Underwater Acoustic Sensor Networks

Aiming at the problems of the low mobility, low location accuracy, high communication overhead, and high energy consumption of sensor nodes in underwater acoustic sensor networks, the MPL (movement prediction location) algorithm is proposed in this article. The algorithm is divided into two stages: mobile prediction and node location. In the node location phase, a TOA (time of arrival)-based ranging strategy is first proposed to reduce communication overhead and energy consumption. Then, after dimension reduction processing, the grey wolf optimizer (GWO) is used to find the optimal location of the secondary nodes with low location accuracy. Finally, the node location is obtained and the node movement prediction stage is entered. In coastal areas, the tidal phenomenon is the main factor leading to node movement; thus, a more practical node movement model is constructed by combining the tidal model with node stress. Therefore, in the movement prediction stage, the velocity and position of each time point in the prediction window are predicted according to the node movement model, and underwater location is then completed. Finally, the proposed MPL algorithm is simulated and analyzed; the simulation results show that the proposed MPL algorithm has higher localization performance compared with the LSLS, SLMP, and GA-SLMP algorithms. Additionally, the proposed MPL algorithm not only effectively reduces the network communication overhead and energy consumption, but also improves the network location coverage and node location accuracy.

3D Localization and Visualization of Sensor Nodes and Data Sensing in Underwater Wireless Sensor Networks

Underwater sensor networks (USNs) have many challenges in terms of determining network information. One of the challenges is determining 3D position coordinates of the sensor nodes in real-time in the aquatic environment. In this work, experiments have been conducted by deploying sensor nodes underwater at a shallow depth. As a case study, sensors have been used to sense water temperature and transmit to anchor (sink) node in a multi-hop fashion. Hardware experiments have been successfully conducted for localization and data transmission and results stored in industrial cloud platform smartcore for any further requirement. NS-3 with Aqua-3D - an Aqua-Sim network simulator has the required modules to visualize 3D Underwater Sensor Network. Simulation results show that 3D network visualization and node localization is obtained accurately. The Aqua-3D results are compared with the GPS coordinate values for localization accuracy.

Underwater Acoustic Sensor Networks Node Localization Based on Compressive Sensing in Water Hydrology.

Groundwater is an important source of human activities, agriculture and industry. Underwater Acoustic Sensor Networks (UASNs) is one of the important technologies for marine environmental monitoring. Therefore, it is of great significance to study the node self- localization technology of underwater acoustic sensor network. This paper mainly studies the node localization algorithm based on range-free. In order to save cost and energy consumption, only a small number of sensing nodes in sensor networks usually know their own location. How to locate all nodes accurately through these few nodes is the focus of our research. In this paper, combined with the compressive sensing algorithm, a range-free node localization algorithm based on node hop information is proposed. Aiming at the problem that connection information collected by the algorithm is an integer, the hop is modified to further improve the localization performance. The simulation analysis shows that the improved algorithm is effective to improve the localization accuracy without additional cost and energy consumption compared with the traditional method.

Considerations about the Signal Level Measurement in Wireless Sensor Networks for Node Position Estimation.

Wireless Sensor Networks (WSN) are widely used in different monitoring systems. Given the distributed nature of WSN, a constantly increasing number of research studies are concentrated on some important aspects: maximizing network autonomy, node localization, and data access security. The node localization and distance estimation algorithms have, as their starting points, different information provided by the nodes. The level of signal strength is often such a starting point. A system for Received Signal Strength Indicator (RSSI) acquisition has been designed, implemented, and tested. In this paper, experiments in different operating environments have been conducted to show the variation of Received Signal Strength Indicator (RSSI) metric related to distance and geometrical orientation of the nodes and environment, both indoor and outdoor. Energy aware data transmission algorithms adjust the power consumed by the nodes according to the relative distance between the nodes. Experiments have been conducted to measure the current consumed by the node depending on the adjusted transmission power. In order to use the RSSI values as input for distance or location detection algorithms, the RSSI values can’t be used without intermediate processing steps to mitigate with the non-linearity of the measured values. The results of the measurements confirmed that the RSSI level varies with distance, geometrical orientation of the sensors, and environment characteristics.

Node localization method for massive sensor networks based on clustering particle swarm optimization in cloud computing environment

In order to reduce the positioning error of wireless sensor network nodes and deal with the positioning of a large number of sensor network nodes, a mass sensor node positioning method based on particle swarm optimization (SNPSO) is proposed. The node location error of node distance correction value is corrected by SNPSO algorithm, and the sensing data is encoded by the index of cloud computing resources. The dynamic target strategy (DTS) algorithm is used to solve the strict deadline constraints. The algorithm focuses on optimizing execution time to meet deadline constraints, and once feasible solutions are obtained, it focuses on optimizing execution costs within deadline constraints. The performance of the algorithm is simulated and analyzed on the platform of MATLAB 2016. Compared with the sensor positioning method, SNPSO improves the positioning accuracy of mass sensor nodes. The simulation results verify the validity of SNPSO. Compared with the improved quantum genetic algorithm (IQGA) under different scale data scheduling and different deadlines constraints, the proposed algorithm can improve the positioning accuracy of mass sensor nodes. The proposed algorithm can find the optimal solution of cloud computing resource scheduling with lower execution cost under strict deadline constraints, and can more easily meet the needs of massive sensor network node location data processing.