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.

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 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.

scholarly journals Enhancing the Sensor Node Localization Algorithm Based on Improved DV-Hop and DE Algorithms in Wireless Sensor Networks

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 343 ◽  
Author(s):  
Dezhi Han ◽  
Yunping Yu ◽  
Kuan-Ching Li ◽  
Rodrigo Fernandes de Mello
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Sensor Node ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Node Localization ◽  
Random Mutation ◽  
Localization Accuracy ◽  
De Algorithm ◽  
Distance Vector

The Distance Vector-Hop (DV-Hop) algorithm is the most well-known range-free localization algorithm based on the distance vector routing protocol in wireless sensor networks; however, it is widely known that its localization accuracy is limited. In this paper, DEIDV-Hop is proposed, an enhanced wireless sensor node localization algorithm based on the differential evolution (DE) and improved DV-Hop algorithms, which improves the problem of potential error about average distance per hop. Introduced into the random individuals of mutation operation that increase the diversity of the population, random mutation is infused to enhance the search stagnation and premature convergence of the DE algorithm. On the basis of the generated individual, the social learning part of the Particle Swarm (PSO) algorithm is embedded into the crossover operation that accelerates the convergence speed as well as improves the optimization result of the algorithm. The improved DE algorithm is applied to obtain the global optimal solution corresponding to the estimated location of the unknown node. Among the four different network environments, the simulation results show that the proposed algorithm has smaller localization errors and more excellent stability than previous ones. Still, it is promising for application scenarios with higher localization accuracy and stability requirements.

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

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4179 ◽  
Author(s):  
Stelian Dolha ◽  
Paul Negirla ◽  
Florin Alexa ◽  
Ioan Silea
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Signal Strength ◽  
Data Access ◽  
Received Signal Strength ◽  
Position Estimation ◽  
Wireless Sensor ◽  
Node Localization ◽  
Received Signal Strength Indicator ◽  
Starting Point

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.

Range-Free Node Localization Schemes for Wireless Sensor Networks

2012 ◽  
Vol 457-458 ◽  
pp. 825-833
Author(s):  
Qin Qin Shi ◽  
Jian Ping Zhang ◽  
Yun Xiang Liu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Node Localization ◽  
Free Node ◽  
Localization Accuracy ◽  
Network Connection ◽  
Original Scheme ◽  
Node Position ◽  
Range Free

Two range-free node localization schemes modified from the conventional DV-Hop scheme are presented in this work. Different node position derivation algorithms are used to enhance the localization accuracy of DV-Hop. The principle of the algorithms and the improvement approach are illustrated. Simulation shows that the modified schemes outperform the original scheme in terms of the localization accuracy as the network connection topology varies.

scholarly journals SVM+KF Target Tracking Strategy Using the Signal Strength in Wireless Sensor Networks

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3832
Author(s):  
Xing Wang ◽  
Xuejun Liu ◽  
Ziran Wang ◽  
Ruichao Li ◽  
Yiguang Wu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Target Tracking ◽  
Signal Strength ◽  
Threshold Value ◽  
Wireless Sensor ◽  
Support Vector ◽  
Tracking Accuracy ◽  
Multiple Paths ◽  
The Stability

Target Tracking (TT) is a fundamental application of wireless sensor networks. TT based on received signal strength indication (RSSI) is by far the cheapest and simplest approach, but suffers from a low stability and precision owing to multiple paths, occlusions, and decalibration effects. To address this problem, we propose an innovative TT algorithm, known as the SVM+KF method, which combines the support vector machine (SVM) and an improved Kalman filter (KF). We first use the SVM to obtain an initial estimate of the target’s position based on the RSSI. This enhances the ability of our algorithm to process nonlinear data. We then apply an improved KF to modify this estimated position. Our improved KF adds the threshold value of the innovation update in the traditional KF. This value changes dynamically according to the target speed and network parameters to ensure the stability of the results. Simulations and real experiments in different scenarios demonstrate that our algorithm provides a superior tracking accuracy and stability compared to similar algorithms.

MDS-BASED METHODS FOR AD HOC NETWORK LOCALIZATION

2006 ◽  
Vol 07 (01) ◽  
pp. 5-19 ◽  
Author(s):  
AHMED A. AHMED ◽  
YI SHANG ◽  
HONGCHI SHI ◽  
BEI HUA
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Ad Hoc ◽  
Computational Cost ◽  
Wireless Sensor ◽  
Node Localization ◽  
Localization Accuracy ◽  
Scaling Methods ◽  
Hoc Networks ◽  
Metric Scaling

Recently, multidimensional scaling (MDS) techniques have been successfully applied in the MDS-MAP method to the node localization problem of ad hoc networks, such as wireless sensor networks. MDS-MAP uses MDS to compute a local, relative map at each node from the distance or proximity information of its neighboring nodes. Based on the local maps and the locations of a set of anchor nodes with known locations, the absolute positions of unknown nodes in the network can be computed. In this paper, we investigate the effects of several variants of MDS on the accuracy of localization in wireless sensor networks. We compare metric scaling and non-metric scaling methods, each with several different optimization criteria. Simulation results show that different optimization models of metric scaling achieve comparable localization accuracy for dense networks and non-metric scaling achieves more accurate results than metric scaling for sparse networks at the expense of higher computational cost.

A Refined Concentric Anchor-Beacons Location Algorithm for Wireless Sensor Networks Based on Received Signal Strength Indicator

2013 ◽  
Vol 347-350 ◽  
pp. 1860-1863
Author(s):  
Kun Zhang ◽  
Can Zhang ◽  
Chen He ◽  
Xiao Hu Yin
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Received Signal Strength Indicator ◽  
Localization Accuracy ◽  
Location Algorithm ◽  
Algorithm Base

As the development of technology, the wireless sensor networks (WSN) have a wide spread usage. And people pay more attention on the localization algorithm, as the key technology of WSN, there have been many method of self-localization. The concentric anchor-beacons (CAB) location algorithm is one of the most practical one, which is a range-free WSN localization algorithm. In order to further improve the accuracy of localizing nodes, an improved CAB location algorithm base on Received Signal Strength Indicator (RSSI) is proposed. The RSSI is used to measure the distance between two anchors and compare with the practical distance. Then the environment between two anchors can be simulated. At last the communication radius of anchors can be optimized. And the common area of the anchors in the process of localizing nodes can be reduced. Then the accuracy is improved. By simulation, the localization accuracy is improved when the anchors numbers is more than a certain percentage.

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