Three-dimensional Sensor Node Localization based on AFSA-LSS VM

With the advances in new technological trends and the reduction in prices of sensor nodes, wireless sensor networks (WSNs) and their applications are proliferating in several areas of our society such as healthcare, industry, farming, and housing. Accordingly, in recent years attention on localization has increased significantly since it is one of the main facets in any WSN. In a nutshell, localization is the process in which the position of any sensor node is retrieved by exploiting measurements from and between sensor nodes. Several techniques of localization have been proposed in the literature with different localization accuracy, complexity, and hence different applicability. The localization accuracy is limited by fundamental limitations, theoretical and practical, that restrict the localization accuracy regardless of the technique employed in the localization process. In this paper, we pay special attention to such fundamental limitations from the theoretical and practical points of view and provide a comprehensive review of the state-of-the-art solutions that deal with such limitations. Additionally, discussion on the theoretical and practical limitations together with their recent solutions, remaining challenges, and perspectives are presented.

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Intelligent Three-dimensional Node Localization Algorithm Using Dynamic Path Planning

Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) ◽

10.2174/2352096514666210617125102 ◽

2021 ◽

Vol 14 ◽

Author(s):

Songhao Jia ◽

Cai Yang ◽

Xing Chen ◽

Yan Liu ◽

Fangfang Li

Keyword(s):

Path Planning ◽

Dynamic Stiffness ◽

Three Dimensional ◽

Localization Algorithm ◽

Node Localization ◽

Positioning Accuracy ◽

Dynamic Path Planning ◽

Anchor Nodes ◽

Simulation Results ◽

Dynamic Path

Background: In the applications of wireless sensor network technology, three-dimensional node location technology is crucial. The process of node localization has some disadvantages, such as the uneven distribution of anchor nodes and the high cost of the network. Therefore, the mobile anchor nodes are introduced to effectively solve accurate positioning. Objective: Considering the estimated distance error, the received signal strength indication technology is used to optimize the measurement of the distance. At the same time, dynamic stiffness planning is introduced to increase virtual anchor nodes. Moreover, the bird swarm algorithm is also used to solve the optimal location problem of nodes. Method: Firstly, the dynamic path is introduced to increase the number of virtual anchor nodes. At the same time, the improved RSSI distance measurement technology is introduced to the node localization. Then, an intelligent three-dimensional node localization algorithm based on dynamic path planning is proposed. Finally, the proposed algorithm is compared with similar algorithms through simulation experiments. Results: Simulation results show that the node coordinates obtained by the proposed algorithm are more accurate, and the node positioning accuracy is improved. The execution time and network coverage of the algorithm are better than similar algorithms. Conclusion: The proposed algorithm significantly improves the accuracy of node positioning. However, the traffic of the algorithm is increased. A little increase in traffic in exchange for positioning accuracy is worthy of recognition. The simulation results show that the proposed algorithm is robust and can be implemented and promoted in the future.

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Bayesian Localized Energy Optimized Sensor Distribution for Efficient Target Tracking

Advances in Business Information Systems and Analytics - Modeling Methods for Business Information Systems Analysis and Design ◽

10.4018/978-1-5225-5522-3.ch001 ◽

2019 ◽

pp. 1-14

Author(s):

Alonshia S. Elayaraja

Keyword(s):

Target Tracking ◽

Sensor Node ◽

Management Strategies ◽

Extended Period ◽

Sensor Nodes ◽

Wireless Sensor ◽

Sensor Management ◽

Node Localization ◽

Time Optimal ◽

Energy Consumption Rate

Many applications in wireless sensor networks perform localization of nodes over an extended period of time. Optimal selection algorithm poses new challenges to the overall transmission power levels for target detection, and thus, localized energy optimized sensor management strategies are necessary for improving the accuracy of target tracking. In this chapter, a proposal plan to develop a Bayesian localized energy optimized sensor distribution scheme for efficient target tracking in wireless sensor network is designed. The sensor node localization is done with Bayesian average, which estimates the sensor node's energy optimality. Then the sensor nodes are localized and distributed based on the Bayesian energy estimate for efficient target tracking. The sensor node distributional strategy improves the accuracy of identifying the targets to be tracked quickly. The performance is evaluated with parameters such as accuracy of target tracking, energy consumption rate, localized node density, and time for target tracking.

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

Sensors ◽

10.3390/s20020343 ◽

2020 ◽

Vol 20 (2) ◽

pp. 343 ◽

Cited By ~ 3

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.

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Sensor Node Localization with Uncontrolled Events

ACM Transactions on Embedded Computing Systems ◽

10.1145/2345770.2345777 ◽

2012 ◽

Vol 11 (3) ◽

pp. 1-25 ◽

Cited By ~ 5

Author(s):

Ziguo Zhong ◽

Tian He

Keyword(s):

Sensor Node ◽

Node Localization

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Genetic Algorithm Approach to the 3D Node Localization in TDOA Systems

Sensors ◽

10.3390/s19183880 ◽

2019 ◽

Vol 19 (18) ◽

pp. 3880 ◽

Cited By ~ 11

Author(s):

Javier Díez-González ◽

Rubén Álvarez ◽

David González-Bárcena ◽

Lidia Sánchez-González ◽

Manuel Castejón-Limas ◽

...

Keyword(s):

Genetic Algorithm ◽

Dimensional Space ◽

Three Dimensional ◽

Sensor Deployment ◽

Selection Strategy ◽

Ground Vehicles ◽

Node Localization ◽

Heuristic Solution ◽

Positioning Systems ◽

Genetic Algorithm Approach

Positioning asynchronous architectures based on time measurements are reaching growing importance in Local Positioning Systems (LPS). These architectures have special relevance in precision applications and indoor/outdoor navigation of automatic vehicles such as Automatic Ground Vehicles (AGVs) and Unmanned Aerial Vehicles (UAVs). The positioning error of these systems is conditioned by the algorithms used in the position calculation, the quality of the time measurements, and the sensor deployment of the signal receivers. Once the algorithms have been defined and the method to compute the time measurements has been selected, the only design criteria of the LPS is the distribution of the sensors in the three-dimensional space. This problem has proved to be NP-hard, and therefore a heuristic solution to the problem is recommended. In this paper, a genetic algorithm with the flexibility to be adapted to different scenarios and ground modelings is proposed. This algorithm is used to determine the best node localization in order to reduce the Cramér-Rao Lower Bound (CRLB) with a heteroscedastic noise consideration in each sensor of an Asynchronous Time Difference of Arrival (A-TDOA) architecture. The methodology proposed allows for the optimization of the 3D sensor deployment of a passive A-TDOA architecture, including ground modeling flexibility and heteroscedastic noise consideration with sequential iterations, and reducing the spatial discretization to achieve better results. Results show that optimization with 15% of elitism and a Tournament 3 selection strategy offers the best maximization for the algorithm.

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Capacity of Inhomogeneous Hybrid Wireless Networks in Three-Dimensional Space

International Journal of Online Engineering (iJOE) ◽

10.3991/ijoe.v11i9.5064 ◽

2015 ◽

Vol 11 (9) ◽

pp. 47

Author(s):

Feng Wu ◽

Jiang Zhu ◽

Yilong Tian ◽

Zhipeng Xi

Keyword(s):

Sensor Node ◽

Dimensional Space ◽

Three Dimensional ◽

Network Capacity ◽

Throughput Capacity ◽

Two Dimensional ◽

Hybrid Wireless Networks ◽

Node Distribution ◽

Analytical Expressions ◽

Three Dimensional Space

Network capacity has been widely studied in recent years. However, most of the literatures focus on the networks where nodes are distributed in a two-dimensional space. In this paper, we propose a 3D hybrid sensor network model. By setting different sensor node distribution probabilities for cells, we divide all the cells in the network into dense cells and sparse cells. Analytical expressions of the aggregate throughput capacity are obtained. We also find that suitable inhomogeneity can increase the network throughput capacity.

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