scholarly journals A Least Square-Based Self-Adaptive Localization Method for Wireless Sensor Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-9
Author(s):  
Baoguo Yu ◽  
Yao Wang ◽  
Chenglong He ◽  
Xiaozhen Yan ◽  
Qinghua Luo
Keyword(s):  
Radio Signal ◽  
Signal Propagation ◽  
Propagation Model ◽  
Least Square ◽  
Wireless Sensor ◽  
Localization Accuracy ◽  
Localization Method ◽  
Unknown Node ◽  
Signal Propagation Model ◽  
Self Adaptive

In the wireless sensor network (WSN) localization methods based on Received Signal Strength Indicator (RSSI), it is usually required to determine the parameters of the radio signal propagation model before estimating the distance between the anchor node and an unknown node with reference to their communication RSSI value. And finally we use a localization algorithm to estimate the location of the unknown node. However, this localization method, though high in localization accuracy, has weaknesses such as complex working procedure and poor system versatility. Concerning these defects, a self-adaptive WSN localization method based on least square is proposed, which uses the least square criterion to estimate the parameters of radio signal propagation model, which positively reduces the computation amount in the estimation process. The experimental results show that the proposed self-adaptive localization method outputs a high processing efficiency while satisfying the high localization accuracy requirement. Conclusively, the proposed method is of definite practical value.

The Hybrid HMM for RSS-Based Localization in Wireless Sensor Networks

2013 ◽  
Vol 347-350 ◽  
pp. 796-802 ◽  
Author(s):  
Yan Hong Zang ◽  
Jin Song Wang ◽  
Lin Ling ◽  
Pei Zhong Lu
Keyword(s):  
Wireless Sensor Networks ◽  
Hidden Markov ◽  
Signal Strength ◽  
Threshold Value ◽  
Signal Propagation ◽  
Propagation Model ◽  
Wireless Sensor ◽  
Node Localization ◽  
Localization Accuracy ◽  
Signal Propagation Model

We proposea method of RSS-base localization in WSN (Wireless Sensor Network), called Hybrid HMM, to improve the stabilityof node localization basedon RSS(Received Signal Strength).This model utilizesHMM(Hidden Markov Model) to takeinto account the time factor when receiving the RSS sequence, andconverts the action of ranging into an operationof classification.For the received RSS used for localization,our Hybrid HMMwill compare it withthe preset RSS threshold value, and put the result into one of two categories for subsequent processing: If the received value is higher than the threshold value, the distance value will be drawn from the signal propagation model. If lower, the information will be obtained from a trained HMM. Experimental results show that the Hybrid HMM method can greatly improve the localization accuracy.

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.

RSS-Based Outdoor Localization with Wireless Sensor Networks in Practice

2015 ◽  
pp. 225-256 ◽  
Author(s):  
Tsenka Stoyanova ◽  
Fotis Kerasiotis ◽  
George Papadopoulos
Keyword(s):  
Sensor Node ◽  
Signal Propagation ◽  
Propagation Model ◽  
Wireless Sensor ◽  
Node Localization ◽  
Internal Factors ◽  
Localization Accuracy ◽  
Factors Influencing ◽  
Outdoor Localization ◽  
Rf Signal

In this chapter the authors discuss the feasibility of sensor node localization by exploiting the inherent resources of WSN technology, such as the received signal strength (RSS) of the exchanged messages. The authors also present a brief overview of various factors influencing the RSS, including the RF-signal propagation and other topology parameters which influence the localization process and accuracy. Moreover, the RSS variability due to internal factors, related to the hardware implementation of a sensor node, is investigated in order to be considered in simulations of RSS-based outdoor localization scenarios. Localization considerations referring to techniques, topology parameters and factors influencing the localization accuracy are combined in simulation examples to evaluate their significance concerning target positioning performance. Finally, the RF propagation model and the topology parameters being identified are validated in real outdoor localization scenario.

scholarly journals A Non-Parametric Propagation Condition Identification Method and Non-Line of Sight Mitigation Algorithm for Wireless Sensor Network

2016 ◽  
Vol 10 (1) ◽  
pp. 80-87 ◽  
Author(s):  
Hao Chu ◽  
Cheng-dong Wu
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Line Of Sight ◽  
Propagation Condition ◽  
Localization Accuracy ◽  
Chi Squared ◽  
Potential Applications ◽  
Unknown Node ◽  
Non Line Of Sight

The wireless sensor network (WSN) has received increasing attention since it has many potential applications such as the internet of things and smart city. The localization technology is critical for the application of the WSN. The obstacles induce the larger non-line of sight (NLOS) error and it may decrease the localization accuracy. In this paper, we mainly investigate the non-line of sight localization problem for WSN. Firstly, the Pearson's chi-squared testing is employed to identify the propagation condition. Secondly, the particle swarm optimization based localization method is proposed to estimate the position of unknown node. Finally the simulation experiments are implemented. The simulation results show that the proposed method owns higher localization accuracy when compared with other two methods.

Sequence Localization Algorithm Based on 3D Voronoi Diagram in Wireless Sensor Network

2014 ◽  
Vol 644-650 ◽  
pp. 4422-4426 ◽  
Author(s):  
Xi Yang ◽  
Jun Liu
Keyword(s):  
Wireless Sensor Networks ◽  
Wireless Sensor Network ◽  
Voronoi Diagram ◽  
Wireless Sensor ◽  
Weighted Estimate ◽  
Localization Algorithm ◽  
Simulation Experiments ◽  
Localization Accuracy ◽  
3D Space ◽  
Unknown Node

For nodes’ self-localization in wireless sensor networks (WSN), a new localization algorithm called Sequence Localization algorithm based on 3D Voronoi diagram (SL3V) is proposed, which uses 3D Voronoi diagram to divide the localization space.It uses the polyhedron vertices as the virtual beacon nodes and constructs the rank sequence table of virtual beacon nodes. Then it computes Kendall coefficients of the ranks in the optimal rank sequence table and that of the unknown node. Finally, it realizes the weighted estimate of the unknown node by normalization processing Kendall coefficients. Simulation experiments prove that itcan obviously improve the localization accuracy compared with the traditional 2D sequence-based localization and can satisfy the need of localization for 3D space.

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