scholarly journals H-Best Particle Swarm Optimization Based Localization Algorithm for Wireless Sensor Network

2019 ◽  
Vol 9 (1) ◽  
pp. 2769-2778
Keyword(s):  
Sensor Node ◽  
Euclidean Distance ◽  
Sensor Nodes ◽  
Circular Path ◽  
Mobile Sensor ◽  
Anchor Node ◽  
Mobile Anchor ◽  
Anchor Nodes ◽  
Mobile Sensor Node ◽  
Mobile Anchor Node

Discussion of the work, which proposed the idea of virtual anchor nodes for the localization of the sensor nodes, with having the movement of single sensor node in the circular movement with being optimized by the HPSO. For the ranging the RSSI model has been proposed in the algorithm. As a reference node, single anchor node has been used for the localization of whole network. As of the random deployment of the sensor nodes (target nodes), when the target nodes fall under the range of the mobile anchor node, the Euclidean distance between the target node and mobile anchor node is being calculated. After the calculation of the Euclidean distance the two anchor nodes are being deployed with a difference of 600 angle. Using the directional information the projecting of virtual anchor nodes is done, to which the virtual anchor nodes helps in the calculation of the 2D coordinates. While the calculation the mobile sensor node follow ups the circular path. The mobile sensor node considering at a center of the area marks up distance of its maximum range, and with that distance as a radius its goes for other circular path movement if all sensor nodes don’t fell to its range. With its movement at constant velocity the algorithm runs again and again. The performance of the algorithm are done on the factors of the average localization error and convergence time. The problem as of the LoS, with the virtual anchor nodes have been minimized.

scholarly journals A Mobile Anchor Node Assisted RSSI Localization Scheme in Underwater Wireless Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4369 ◽  
Author(s):  
Yanlong Sun ◽  
Yazhou Yuan ◽  
Qimin Xu ◽  
Changchun Hua ◽  
Xinping Guan
Keyword(s):  
Sensor Node ◽  
Measured Data ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Underwater Wireless Sensor Networks ◽  
Anchor Node ◽  
Linear Trajectory ◽  
Localization Scheme ◽  
Mobile Anchor ◽  
Mobile Anchor Node

In this paper, a mobile anchor node assisted RSSI localization scheme in underwater wireless sensor networks (UWSNs) is proposed, which aims to improve location accuracy and shorten location time. First, to improve location accuracy, we design a support vector regression (SVR) based interpolation method to estimate the projection of sensor nodes on the linear trajectory of the mobile anchor node. The proposed method increases the accuracy of the nonlinear regression model of noisy measured data and synchronously decreases the estimation error caused by the discreteness of measured data. Second, to shorten location time, we develop a curve matching method to obtain the perpendicular distance from sensor nodes to the linear trajectory of the mobile anchor node. The location of the sensor node can be calculated based on the projection and the perpendicular distance. Compared with existing schemes that require the anchor node to travel at least two trajectories, the proposed scheme only needs one-time trajectory to locate sensor nodes, and the location time is shortened with the reduction in the number of trajectories. Finally, simulation results prove that the proposed scheme can obtain more accurate sensor node location in less time compared with the existing schemes.

M-Curves Path Planning for Mobile Anchor Node and Localization of Sensor Nodes Using DSA

2020 ◽  
pp. 99-120
Author(s):  
Damodar Reddy Edla ◽  
Mahesh Chowdary Kongara ◽  
Amruta Lipare ◽  
Venkatanareshbabu Kuppili ◽  
K Kannadasan
Keyword(s):  
Path Planning ◽  
Sensor Nodes ◽  
Anchor Node ◽  
Mobile Anchor ◽  
Mobile Anchor Node

Research on Mobile Anchor Node Localization Method Based on Hierarchical

2014 ◽  
Vol 654 ◽  
pp. 362-365
Author(s):  
Hong Li Yuan ◽  
Jian Yin Lu ◽  
Xiao Ming Zhang
Keyword(s):  
Wireless Sensor ◽  
Node Localization ◽  
Position Information ◽  
Anchor Node ◽  
Localization Accuracy ◽  
Sensor Applications ◽  
Mobile Anchor ◽  
Anchor Nodes ◽  
Unknown Node ◽  
Mobile Anchor Node

Localization technology is enabling technology for wireless sensor applications. In order to improve the localization accuracy of wireless sensor network node, this paper proposes a mobile anchor node localization based on a layered approach. First area for wireless sensor networks slicing. Each layer consists of a number of equilateral triangles. Anchor nodes in each layer moves along the edge of the equilateral triangle to locate unknown node. After receiving the position information of the anchor node, unknown node to determine their own position by the triangular positioning.

scholarly journals Fault Tolerant Mobile Sensor Node Traversal Schemes Based on Hexagonal Coverage

2014 ◽  
Vol 2014 ◽  
pp. 1-12
Author(s):  
Ganala Santoshi ◽  
R. J. D’Souza
Keyword(s):  
Sensor Node ◽  
Fault Tolerant ◽  
Region Of Interest ◽  
Life Time ◽  
Sensor Nodes ◽  
Mobile Sensor ◽  
Great Role ◽  
Equal Distance ◽  
Pattern Movement ◽  
Mobile Sensor Node

Mobile sensor nodes (MSNs) are equipped with locomotive can move around after having been deployed. They are equipped with limited energy. A large portion of energy is drained during the traversal. In order to extend the life time of a MSN, the traveling distance must be minimized. Region of interest (ROI) is covered with multiple MSNs using coverage based pattern movement. When a group of MSNs are deployed to cover a given ROI, all the deployed MSNs should travel an approximately equal distance. Otherwise, the MSN which travels longer distance depletes more energy compared to the MSN which travels a shorter distance. In this work we show that, ROI partition plays great role in hole free coverage and makes the MSNs have optimized movement cost with fault tolerant support.

scholarly journals Anchor Movement Strategy for Conjecture Geometry Based Localization Scheme in Wireless Sensor Network

2016 ◽  
Vol 5 (4) ◽  
pp. 255
Author(s):  
Niraj Bhupal Kapase ◽  
Santosh P Salgar ◽  
Mahesh K Patil ◽  
Prashant P Zirmite
Keyword(s):  
Wireless Sensor Network ◽  
Path Planning ◽  
Sensor Network ◽  
Sensor Node ◽  
Wireless Sensor ◽  
Localization Error ◽  
Anchor Node ◽  
Mobile Anchor ◽  
Movement Strategy ◽  
Mobile Anchor Node

<p class="Abstract"><em>Abstract</em>—Localization of sensor node with least error is one of the major concern in wireless sensor network as some of the application require sensor node to know their location with high degree of precision. For mobile anchor based localization many of the path planning schemes already developed which includes scan, double scan, Circles &amp; S- Curves. These path planning schemes have some limitations like localization error, Number of sensor nodes covered in the network, Trajectory length of mobile anchor node.  This paper represents anchor movement strategy which is based on Scan path, with modifications are made in such a way that it satisfies the requirements of localization scheme.  This movement strategy ensures that trajectory of mobile anchor node will minimize localization error and also will cover majority of sensor node in the environment. The localization error yielded by Modified Scan algorithm is in the range of 0.2 to 0.4m which is quite lower than the other existing mentioned path planning strategies producing localization error in the range 0.6 to 1.8m</p><p class="keywords">Keywords—Localization; Mobile anchor node; Wireless sensor network; Modified Scan algorithm</p>

M-Curves path planning model for mobile anchor node and localization of sensor nodes using Dolphin Swarm Algorithm

2019 ◽  
Vol 26 (4) ◽  
pp. 2769-2783 ◽  
Author(s):  
K. Kannadasan ◽  
Damodar Reddy Edla ◽  
Mahesh Chowdary Kongara ◽  
Venkatanareshbabu Kuppili
Keyword(s):  
Path Planning ◽  
Sensor Nodes ◽  
Planning Model ◽  
Anchor Node ◽  
Mobile Anchor ◽  
Swarm Algorithm ◽  
Mobile Anchor Node

A Novel Static Path Planning Method for Mobile Anchor-Assisted Localization in Wireless Sensor Networks

2020 ◽  
Vol 10 ◽  
Author(s):  
Abdelhady M. Naguib ◽  
Shahzad Ali
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Path Planning ◽  
Sensor Node ◽  
Large Scale ◽  
Performance Metrics ◽  
Wireless Sensor ◽  
Anchor Node ◽  
Mobile Anchor ◽  
Planning Methods

Background: Many applications of Wireless Sensor Networks (WSNs) require awareness of sensor node’s location but not every sensor node can be equipped with a GPS receiver for localization, due to cost and energy constraints especially for large-scale networks. For localization, many algorithms have been proposed to enable a sensor node to be able to determine its location by utilizing a small number of special nodes called anchors that are equipped with GPS receivers. In recent years a promising method that significantly reduces the cost is to replace the set of statically deployed GPS anchors with one mobile anchor node equipped with a GPS unit that moves to cover the entire network. Objectives: This paper proposes a novel static path planning mechanism that enables a single anchor node to follow a predefined static path while periodically broadcasting its current location coordinates to the nearby sensors. This new path type is called SQUARE_SPIRAL and it is specifically designed to reduce the collinearity during localization. Results: Simulation results show that the performance of SQUARE_SPIRAL mechanism is better than other static path planning methods with respect to multiple performance metrics. Conclusion: This work includes an extensive comparative study of the existing static path planning methods then presents a comparison of the proposed mechanism with existing solutions by doing extensive simulations in NS-2.

Mobile Anchor-Assisted Localization Using Invasive Weed Optimization Algorithm

2020 ◽  
pp. 415-436
Author(s):  
Vaishali Raghavendra Kulkarni ◽  
Veena Desai ◽  
Akash Sikarwar ◽  
Raghavendra V. Kulkarni
Keyword(s):  
Path Planning ◽  
Computing Time ◽  
Sensor Nodes ◽  
Circular Path ◽  
Cultural Algorithm ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Mobile Anchor ◽  
Planning Methods ◽  
Planning Algorithm

Sensor localization in wireless sensor networks has been addressed using mobile anchor (MA) and a metaheuristic algorithm. The path of a MA plays an important role in localizing maximum number of sensor nodes. The random and circle path planning methods have been presented. Each method has been evaluated for number of localized nodes, accuracy, and computing time in localization. The localization has been performed using trilateration method and two metaheuristic stochastic algorithms, namely invasive weed optimization (IWO) and cultural algorithm (CA). Experimental results indicate that the IWO-based localization outperforms the trilateration method and the CA-based localization in terms of accuracy but with higher computing time. However, the computing speed of trilateration localization is faster than the IWO- and CA-based localization. In the path-planning algorithms, the results show that the circular path planning algorithm localizes more nodes than the random path.

scholarly journals Sensing Coverage Algorithm of Sparse Mobile Sensor Node with Trade-Off between Packet Loss Rate and Transmission Delay

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Kehua Zhao ◽  
Yourong Chen ◽  
Siyi Lu ◽  
Banteng Liu ◽  
Tiaojuan Ren ◽  
...  
Keyword(s):  
Packet Loss ◽  
Data Transmission ◽  
Loss Rate ◽  
Packet Loss Rate ◽  
Transmission Delay ◽  
Sensor Nodes ◽  
Mobile Sensor ◽  
Sensing Coverage ◽  
Mobile Sensor Node ◽  
Monitoring Area

To solve the problem of sensing coverage of sparse wireless sensor networks, the movement of sensor nodes is considered and a sensing coverage algorithm of sparse mobile sensor node with trade-off between packet loss rate and transmission delay (SCA_SM) is proposed. Firstly, SCA_SM divides the monitoring area into several grids of same size and establishes a path planning model of multisensor nodes’ movement. Secondly, the social foraging behavior of Escherichia coli in bacterial foraging is used. A fitness function formula of sensor nodes’ moving paths is proposed. The optimal moving paths of all mobile sensor nodes which can cover the entire monitoring area are obtained through the operations of chemotaxis, replication, and migration. The simulation results show that SCA_SM can fully cover the monitoring area and reduce the packet loss rate and data transmission delay in the process of data transmission. Under certain conditions, SCA_SM is better than RAND_D, HILBERT, and TCM.

Adaptive neural network with hybrid optimization oriented localization in wireless sensor network: A multi-objective model

2020 ◽  
pp. 2050064
Author(s):  
P. Purusothaman ◽  
M. Gunasekaran
Keyword(s):  
Neural Network ◽  
Optimization Algorithm ◽  
Sensor Node ◽  
Optimization Algorithms ◽  
Sensor Nodes ◽  
Hybrid Optimization ◽  
Wireless Sensor ◽  
Hybrid Optimization Algorithm ◽  
Localization Model ◽  
Anchor Nodes

The localization strategy is broadly utilized in Wireless Sensor Networks (WSNs) to detect the present location of the sensor nodes. A WSN comprises of multiple sensor nodes, which makes the employment of GPS on each sensor node costly, and GPS does not give accurate localization outcomes in an indoor environment. The process of configuring location reference on each sensor node manually is also not feasible in the case of a large dense network. Hence, this proposal plans to develop an intelligent model for developing localization pattern in WSN with a group of anchor nodes, rest nodes, and target nodes. The initial step of the proposed node localization model is the selection of the optimal location of anchor nodes towards the target nodes using the hybrid optimization algorithm by concerning the constraints like the distance between the nodes. The second step is to optimally determine the location of the rest node by reference to the anchor nodes using the same hybrid optimization algorithm. Here, the weight has to be determined for each anchor sensor node based on its Received Signal Strength (RSS), and RSS threshold value with the assistance of Neural Network. The hybrid optimization algorithms check the direction to where the concerned node has to be moved by merging the beneficial concepts of two renowned optimization algorithms named as Rider Optimization Algorithm (ROA), and Chicken Swarm Optimization Algorithm (CSO) to solve the localization problem in WSN. The newly developed hybrid algorithm is termed as Rooster Updated Attacker-based ROA (RUA-ROA). Finally, the comparative evaluation indicates a significant improvement in the proposed localization model by evaluating the convergence and statistical analysis.

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