scholarly journals Antenna Radiation Pattern Influence on the Localization Accuracy in Wireless Sensor Networks

2013 ◽  
Vol 13 (2) ◽  
pp. 43-46 ◽  
Author(s):  
E. COCA ◽  
V. POPA
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Radiation Pattern ◽  
Wireless Sensor ◽  
Antenna Radiation ◽  
Localization Accuracy ◽  
Antenna Radiation Pattern

scholarly journals IMPROVING LOCALIZATION ACCURACY IN WIRELESS SENSOR NETWORKS

2013 ◽  
pp. 152-156
Author(s):  
VINOD KUMAR ◽  
SATYENDRA YADAV ◽  
ASHUTOSH KUMAR SINGH
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Sequential Monte Carlo ◽  
Fundamental Problem ◽  
Geographical Location ◽  
Wireless Sensor ◽  
Localization Accuracy ◽  
Localization Algorithms ◽  
Direction Of Movement

The most fundamental problem of wireless sensor networks is localization (finding the geographical location of the sensors). Most of the localization algorithms proposed for sensor networks are based on Sequential Monte Carlo (SMC) method. To achieve high accuracy in localization it requires high seed node density and it also suffers from low sampling efficiency. There are some papers which solves this problems but they are not energy efficient. Another approach The Bounding Box method was used to reduce the scope of searching the candidate samples and thus reduces the time for finding the set of valid samples. In this paper we propose an energy efficient approach which will further reduce the scope of searching the candidate samples, so now we can remove the invalid samples from the sample space and we can introduce more valid samples to improve the localization accuracy. We will consider the direction of movement of the valid samples, so that we can predict the next position of the samples more accurately, hence we can achieve high localization accuracy.

scholarly journals Performance Evaluation of Hop-Based Range-Free Localization Methods in Wireless Sensor Networks

2011 ◽  
Vol 2011 ◽  
pp. 1-6 ◽  
Author(s):  
M. Keshtgary ◽  
M. Fasihy ◽  
Z. Ronaghi
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Hop Count ◽  
Localization Accuracy ◽  
Energy Consuming ◽  
Network Overhead ◽  
Entire Network ◽  
Evaluation Parameters ◽  
Range Free

Knowledge of nodes' locations is an important requirement for many applications in Wireless Sensor Networks. In the hop-based range-free localization methods, anchors broadcast the localization messages including a hop count value to the entire network. Each node receives this message and calculates its own distance with anchor in hops and then approximates its own location. In this paper, we review range-free localization methods and evaluate the performance of two methods: “DV-hop” and “amorphous” by simulation. We consider some parameters like localization accuracy, energy consumption, and network overhead. Recent papers that evaluate localization methods mostly concentrated on localization accuracy. But we have considered a group of evaluation parameters, energy consuming, and network overhead in addition to the location accuracy.

An Enhanced DV-Hop Localization Algorithm for Wireless Sensor Networks

2012 ◽  
Vol 2 (2) ◽  
pp. 16-35 ◽  
Author(s):  
Shrawan Kumar ◽  
D. K. Lobiyal
Keyword(s):  
Wireless Sensor Networks ◽  
Linear Programming ◽  
Sensor Networks ◽  
Problem Formulation ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Location Accuracy ◽  
Localization Accuracy ◽  
Error Terms

Obtaining precise location of sensor nodes at low energy consumption, less hardware requirement, and little computation is a challenging task. As one of the well-known range-free localization algorithm, DV-Hop can be simply implemented in wireless sensor networks, but it provides poor localization accuracy. Therefore, in this paper, the authors propose an enhanced DV-Hop localization algorithm that provides good localization accuracy without requiring additional hardware and communication messages in the network. The first two steps of proposed algorithm are similar to the respective steps of the DV-Hop algorithm. In the third step, they first separate error terms (correction factors) of the estimated distance between unknown node and anchor node. The authors then minimize these error terms by using linear programming to obtain better location accuracy. Furthermore, they enhance location accuracy of nodes by introducing weight matrix in the objective function of linear programming problem formulation. Simulation results show that the performance of our proposed algorithm is superior to DV-Hop algorithm and DV-Hop–based algorithms in all considered scenarios.

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.

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.

Localization in Wireless Sensor Networks in Beacon Drifting Scenarios

2012 ◽  
Vol 562-564 ◽  
pp. 1234-1239
Author(s):  
Ming Xia ◽  
Qing Zhang Chen ◽  
Yan Jin
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Probability Density ◽  
Sensor Nodes ◽  
Density Model ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Localization Accuracy ◽  
Scenarios Simulation ◽  
Simulation Results

The beacon drifting problem occurs when the beacon nodes move accidentally after deployment. In this occasion, the localization results of sensor nodes in the network will be greatly affected and become inaccurate. In this paper, we present a localization algorithm in wireless sensor networks in beacon drifting scenarios. The algorithm first uses a probability density model to calculate the location reliability of each node, and in localization it will dynamically choose nodes with highest location reliabilities as beacon nodes to improve localization accuracy in beacon drifting scenarios. Simulation results show that the proposed algorithm achieves its design goals.

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