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

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2173 ◽  
Author(s):  
Shuo Li ◽  
Tiancheng Guo ◽  
Ran Mo ◽  
Xiaoshuai Zhao ◽  
Feng Zhou ◽  
...  
Keyword(s):  
Minimum Spanning Tree ◽  
Channel Model ◽  
Localization Accuracy ◽  
Penetration Effect ◽  
Optimal Guidance ◽  
Node Location ◽  
Shadowing Effect ◽  
Anchor Nodes ◽  
3D Scene ◽  
Tunnel Size

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.

scholarly journals Advanced location-based IPv6 address for the node of wireless sensor network

2020 ◽  
Vol 10 (3) ◽  
pp. 2474
Author(s):  
Mohammed Nazar Hussein ◽  
Raed Abdulla ◽  
Thomas O’ Daniel ◽  
Maythem Abbas
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Smart Cities ◽  
Ground Truth ◽  
Wireless Sensor ◽  
Localization Accuracy ◽  
Location Data ◽  
Ipv6 Address ◽  
Node Location ◽  
Anchor Nodes

Fields such as military, transportation applications, human services, smart cities and many others utilized Wireless Sensor Network (WSN) in their operations. Despite its beneficial use, occurrence of obstacles is inevitable. From the sensed data, the randomly nodes distribution will produce multiple benefits from self-configuration and regular positioning reporting. Lately, localization and tracking issues have received a remarkable attention in WSNs, as accomplishing high localization accuracy when low energy is used, is much needed. In this paper, a new method and standards-compliant scheme according to the incorporation of GPS location data into the IPv6 address of WSN nodes is suggested. The suggestion is likewise others which depends on ground-truth anchor nodes, with a difference of using the network address to deliver the information. The findings from the results revealed that perfect GPS coordinates can be conducted in the IPv6 address as well as with the transmission radius of the node, and the information is significantly adequate to predict a node’s location. The location scheme performance is assessed in OMNet++ simulation according to the positioning error and the power metrics used. Moreover, some improvement practices to increase the precision of the node location are suggested.

Nelder Mead with Grasshopper Optimization Algorithm for Node Localization in Wireless Sensor Networks

2020 ◽  
Vol 17 (12) ◽  
pp. 5409-5421
Author(s):  
M. Santhosh ◽  
P. Sudhakar
Keyword(s):  
Optimization Algorithm ◽  
Wireless Sensor ◽  
Target Area ◽  
Node Localization ◽  
Localization Accuracy ◽  
Simplex Search ◽  
Grasshopper Optimization Algorithm ◽  
Anchor Nodes ◽  
Series Of Experiments ◽  
Grasshopper Optimization

Node localization in wireless sensor network (WSN) becomes essential to calculate the coordinate points of the unknown nodes with the use of known or anchor nodes. The efficiency of the WSN has significant impact on localization accuracy. Node localization can be considered as an optimization problem and bioinspired algorithms finds useful to solve it. This paper introduces a novel Nelder Mead with Grasshopper Optimization Algorithm (NMGOA) for node localization in WSN. The Nelder-Mead simplex search method is employed to improve the effectiveness of GOA because of its capability of faster convergence. At the beginning, the nodes in WSN are arbitrarily placed in the target area and then nodes are initialized. Afterwards, the node executes the NMGOA technique for estimating the location of the unknown nodes and become localized nodes. In the subsequent round, the localized nodes will be included to the collection of anchor nodes to perform the localization process. The effectiveness of the NMGOA model is validated using a series of experiments and results indicated that the NMGOA model has achieved superior results over the compared methods.

scholarly journals Research on RSS Data Optimization and DFL Localization for Non-Empty Environments

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4419
Author(s):  
Wenyu Mao ◽  
Rongxuan Shen ◽  
Ke Wang ◽  
Guoliang Gong ◽  
Yi Xiao ◽  
...  
Keyword(s):  
Target Location ◽  
Learning Model ◽  
Random Disturbance ◽  
Wavelet Filtering ◽  
Localization Accuracy ◽  
Generalization Ability ◽  
Localization Model ◽  
Random Disturbances ◽  
Shadowing Effect ◽  
A New Technique

Device-free localization (DFL) is a new technique which can estimate the target location through analyzing the shadowing effect on surrounding radio frequency (RF) links. In a relatively complex environment, the influences of random disturbance and the multipath effect are more serious. There are kinds of noises and disturbances in the received signal strength (RSS) data of RF links and the data itself can even be distorted, which will seriously affect the DFL accuracy. Most of the common filtering methods adopted in DFL field are not targeted and the filtering effects are unstable. This paper researches the characteristics of RSS data with random disturbances and proposes two-dimensional double correlation (TDDC) distributed wavelet filtering. It can filter out the random disturbances and noise while preserving the RSS fluctuations which are helpful for the DFL, thus improving the quality of RSS data and localization accuracy. Furthermore, RSS variation rules for the links are different in complex environments and hence, it is difficult for the collected training samples to cover all possible patterns. Therefore, a single machine learning model with poor generalization ability finds it difficult to achieve ideal localization results. In this paper, the Adaboost.M2 ensemble learning model based on the Gini decision tree (GDTE) is proposed to improve the generalization ability for unknown patterns. Extensive experiments performed in two different drawing rooms demonstrate that the TDDC distributed wavelet filtering and the GDTE localization model have obvious advantages compared with other methods. The localization accuracy rates of 87% and 95% can be achieved respectively in the two environments.

scholarly journals UWB/BLE Tracking System for Elderly People Monitoring

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1574 ◽  
Author(s):  
Jerzy Kolakowski ◽  
Vitomir Djaja-Josko ◽  
Marcin Kolakowski ◽  
Katarzyna Broczek
Keyword(s):  
Elderly People ◽  
Tracking System ◽  
Localization Algorithm ◽  
People Tracking ◽  
Localization Accuracy ◽  
Adult Participation ◽  
Emergency Situations ◽  
Synchronization Method ◽  
System A ◽  
Anchor Nodes

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.

A Collaborative Multilateral Localization Algorithm for Wireless Sensor Networks

2013 ◽  
Vol 303-306 ◽  
pp. 201-205
Author(s):  
Shao Ping Zhang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Optimization Method ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Localization Accuracy ◽  
Localization Method ◽  
Anchor Nodes ◽  
Simulation Results ◽  
Unknown Node

Localization technology is one of the key supporting technologies in wireless sensor networks. In this paper, a collaborative multilateral localization algorithm is proposed to localization issues for wireless sensor networks. The algorithm applies anchor nodes within two hops to localize unknown nodes, and uses Nelder-Mead simplex optimization method to compute coordinates of the unknown nodes. If an unknown node can not be localized through two-hop anchor nodes, it is localized by anchor nodes and localized nodes within two hops through auxiliary iterative localization method. Simulation results show that the localization accuracy of this algorithm is very good, even in larger range errors.

scholarly journals Anchor-Node-Based Distributed Localization with Error Correction in Wireless Sensor Networks

2012 ◽  
Vol 8 (4) ◽  
pp. 975147 ◽  
Author(s):  
Taeyoung Kim ◽  
Minhan Shon ◽  
Mihui Kim ◽  
Dongsoo S. Kim ◽  
Hyunseung Choo
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Estimation Error ◽  
Initial Position ◽  
Wireless Sensor ◽  
Anchor Node ◽  
Localization Accuracy ◽  
Message Transmission ◽  
Transmission Cost ◽  
Anchor Nodes

This paper proposes a scheme to enhance localization in terms of accuracy and transmission overhead in wireless sensor networks. This scheme starts from a basic anchor-node-based distributed localization (ADL) using grid scan with the information of anchor nodes within two-hop distance. Even though the localization accuracy of ADL is higher than that of previous schemes (e.g., DRLS), estimation error can be propagated when the ratio of anchor nodes is low. Thus, after each normal node estimates the initial position with ADL, it checks whether the position needs to be corrected because of the insufficient anchors within two-hop distance, that is, the node is in sparse anchor area. If correction needs, the initial position is repositioned using hop progress by the information of anchor nodes located several hops away so that error propagation is reduced (REP); the hop progress is an estimated hop distance using probability based on the density of sensor nodes. Results via in-depth simulation show that ADL has about 12% higher localization accuracy and about 10% lower message transmission cost than DRLS. In addition, the localization accuracy of ADL with REP is about 30% higher than that of DRLS, even though message transmission cost is increased.

scholarly journals THE GLOBAL OPTIMAL PLACEMENT OF BLE BEACON FOR LOCALIZATION BASED ON INDOOR MAP

2018 ◽  
Vol XLII-4 ◽  
pp. 529-534
Author(s):  
L. Ruan ◽  
L. Zhang ◽  
F. Cheng ◽  
Y. Long
Keyword(s):  
Indoor Positioning ◽  
Optimal Placement ◽  
Prototype System ◽  
Localization Accuracy ◽  
Space Objects ◽  
Positioning Method ◽  
Anchor Nodes ◽  
Global Optimal ◽  
The Cost ◽  
Indoor Positioning System

<p><strong>Abstract.</strong> Anchor Nodes in a localization system obviously play a crucial role in determining the system’s quality. Their placement directly affects the localization accuracy and their number directly impacts the total cost of the system. Nowadays, the deployment of Bluetooth nodes in industry generally relies on the experience knowledge of engineers and the cost of positioning beacon does not considered the global level. In this paper, we put forward a method to extract the number and location of BLE beacon automatically and ensure a high positioning accuracy of the indoor positioning system based the rules of indoor positioning, which use all kinds of space objects and structure characteristics of indoor map. The triangulation method was selected to study the global optimal placement of BLE beacon for localization based on indoor map. The impacts and requirements of BLE beacon placement were systematic analysed from the triangulation positioning method, indoor positioning environment and indoor user distribution characteristics. According to the characteristics of indoor environment structure and user distribution, we built an optimization model of BLE beacon placement method based on genetic algorithm which can generate the number and the location of BLE beacon. At last, the Bluetooth indoor positioning prototype system is developed to compare the experience method deployment scheme and the global optimization deployment scheme in the real indoor positioning environment.</p>

scholarly journals Performance Analysis of UAV-Assisted Wireless Powered Sensor Network over Shadowed κ − μ Fading Channels

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Ivan Milovanovic ◽  
Caslav Stefanovic
Keyword(s):  
Sensor Network ◽  
Fading Channels ◽  
Time Allocation ◽  
Channel Model ◽  
Sensor Nodes ◽  
Special Cases ◽  
Channel Parameters ◽  
Shadowing Effect ◽  
Aerial Vehicle ◽  
The Impact

In this work, we analyze performances of the unmanned aerial vehicle- (UAV-) assisted wireless powered sensor communication, where sensor transmission ability is supported by the UAV. Harvested energy from the UAV-broadcasted signal is further used at the sensor nodes for uplink information transmission to the UAV, over assumed shadowed κ − μ fading channels. Here, we observe a general scenario in which due to the flight conditions of the UAV, the channel’s content include the LOS components affected by the shadowing effect, modeled by the general shadowed κ − μ channel model, which can be reduced to other well-known channel models as its special cases. We derive closed-form expressions for the outage probability (OP) of such wireless sensor network (WSN) operating in shadowed κ − μ fading environments. Further, we analyze the optimization of time allocation to minimize OP subjected to UAV’s energy constraints. The impact of channel parameters on observed performance measures is analyzed, and obtained results are numerically validated.

scholarly journals Wireless Sensor Network Target Localization Algorithm Based on Two- and Three-Dimensional Delaunay Partitions

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Chenguang Shao
Keyword(s):  
Three Dimensional ◽  
Target Localization ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Target Node ◽  
Localization Accuracy ◽  
Localization Method ◽  
Anchor Nodes ◽  
2D And 3D ◽  
The Relationship

The target localization algorithm is critical in the field of wireless sensor networks (WSNs) and is widely used in many applications. In the conventional localization method, the location distribution of the anchor nodes is fixed and cannot be adjusted dynamically according to the deployment environment. The resulting localization accuracy is not high, and the localization algorithm is not applicable to three-dimensional (3D) conditions. Therefore, a Delaunay-triangulation-based WSN localization method, which can be adapted to two-dimensional (2D) and 3D conditions, was proposed. Based on the location of the target node, we searched for the triangle or tetrahedron surrounding the target node and designed the localization algorithm in stages to accurately calculate the coordinate value of the target. The relationship between the number of target nodes and the number of generated graphs was analysed through numerous experiments, and the proposed 2D localization algorithm was verified by extending it the 3D coordinate system. Experimental results revealed that the proposed algorithm can effectively improve the flexibility of the anchor node layout and target localization accuracy.

scholarly journals REAL TIME SEARCH OF AGRICULTURAL MACHINERY BASED ON MATRIX SEQUENCE SENSOR

2021 ◽  
pp. 425-433
Author(s):  
Jiaxin Zheng ◽  
Yanyu Gao ◽  
Zhengdong Lei ◽  
Changhu Yang ◽  
Chongjin Wang ◽  
...  
Keyword(s):  
Spread Spectrum ◽  
Three Dimensional ◽  
Agricultural Machinery ◽  
Position Information ◽  
Coordinate Position ◽  
Matrix Sequence ◽  
Node Location ◽  
Anchor Nodes ◽  
Kalman Filter Algorithm ◽  
Positioning Algorithm

Omni-directional vision sensor can provide information within the sensor range, and the directional angle of an object can be accurately obtained through omni-directional images. Based on this characteristic, an automatic navigation and positioning system for agricultural machinery is developed, and a three-dimensional positioning algorithm for agricultural wireless sensor networks based on cross particle swarm optimization is proposed. The method mainly includes three stages: convergence node selection, measurement distance correction and node location. Using the idea of crossover operation of genetic algorithm for reference, the diversity of particles is increased, and the influence of ranging error and the number of anchor nodes on positioning results is effectively improved. The location algorithm has the ability of global search. On the positioning node, the symmetric bidirectional ranging algorithm based on LFM (Linear frequency modulation) spread spectrum technology is used to calculate the distance between the positioning node and each beacon node, and the trilateral centroid positioning algorithm is used to calculate the coordinate position information of unknown nodes. Finally, the Kalman filter algorithm is used to superimpose the observed values of the target state to solve the influence of measurement noise on the positioning accuracy.

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