Three Dimensional Optimum Node Localization in Dynamic Wireless Sensor Networks

This paper proposes a three-dimensional wireless sensor networks node localization algorithm based on multidimensional scaling anchor nodes, which is used to realize the absolute positioning of unknown nodes by using the distance between the anchor nodes and the nodes. The core of the proposed localization algorithm is a kind of repeated optimization method based on anchor nodes which is derived from STRESS formula. The algorithm employs the Tunneling Method to solve the local minimum problem in repeated optimization, which improves the accuracy of the optimization results. The simulation results validate the effectiveness of the algorithm. Random distribution of three-dimensional wireless sensor network nodes can be accurately positioned. The results satisfy the high precision and stability requirements in three-dimensional space node location.

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Range-Based Positioning with Self-Adapting Fireworks Algorithm for Wireless Sensor Networks

Mathematical Problems in Engineering ◽

10.1155/2020/9286438 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9

Author(s):

Meigen Huang ◽

Bin Yu

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Three Dimensional ◽

Basic Function ◽

Wireless Sensor ◽

Time Of Arrival ◽

Node Localization ◽

Fireworks Algorithm ◽

Optimization Task ◽

Self Adaptive

Positioning is the basic function of wireless sensor networks (WSN). At present, range-based positioning is a common method to obtain the position of a node, but its accuracy has encountered a bottleneck. The fundamental reason is that it ignores the ranging information between blind nodes. Therefore, based on the design of the positioning optimization factor to adjust the weight of the ranging information between the blind nodes, the positioning model of minimizing the error is established. On the basis of designing the blind node positioning matrix, the multiblind node localization problem is transformed into a single-objective optimization task, and the model also supports two-dimensional and three-dimensional positioning. In order to solve the model efficiently, we added a self-adaptive function that matches the positioning requirements for the explosion search mechanism of the fireworks algorithm (FWA) and then proposed a self-adaptive FWA (SA-FWA). The experimental results on the real ranging dataset show that the model has higher accuracy than other methods and achieves the current optimal positioning error, which is 1.88 m for received signal strength data and 1.02 m for time of arrival information, respectively.

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Robustness analysis for three-dimensional node localization in wireless sensor networks

2015 21st Asia-Pacific Conference on Communications (APCC) ◽

10.1109/apcc.2015.7412537 ◽

2015 ◽

Author(s):

Wei Liu ◽

Enqing Dong ◽

Yang Song

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Three Dimensional ◽

Robustness Analysis ◽

Wireless Sensor ◽

Node Localization

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A Hybrid Mobile Node Localization Algorithm Based on Adaptive MCB-PSO Approach in Wireless Sensor Networks

Wireless Communications and Mobile Computing ◽

10.1155/2020/3845407 ◽

2020 ◽

Vol 2020 ◽

pp. 1-17 ◽

Cited By ~ 2

Author(s):

Hua Wu ◽

Ju Liu ◽

Zheng Dong ◽

Yang Liu

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Three Dimensional ◽

Mobile Node ◽

Global Optimum ◽

Wireless Sensor ◽

Localization Algorithm ◽

Mobile Nodes ◽

Node Localization ◽

Localization Accuracy

In this paper, a hybrid adaptive MCB-PSO node localization algorithm is proposed for three-dimensional mobile wireless sensor networks (MWSNs), which considers the random mobility of both anchor and unknown nodes. An improved particle swarm optimization (PSO) approach is presented with Monte Carlo localization boxed (MCB) to locate mobile nodes. It solves the particle degeneracy problem that appeared in traditional MCB. In the proposed algorithm, a random waypoint model is incorporated to describe random movements of anchor and unknown nodes based on different time units. An adaptive anchor selection operator is designed to improve the performance of standard PSO for each particle based on time units and generations, to maintain the searching ability in the last few time units and particle generations. The objective function of standard PSO is then reformed to make it obtain a better rate of convergence and more accurate cost value for the global optimum position. Furthermore, the moving scope of each particle is constrained in a specified space to improve the searching efficiency as well as to save calculation time. Experiments are made in MATLAB software, and it is compared with DV-Hop, Centroid, MCL, and MCB. Three evaluation indexes are introduced, namely, normalized average localization error, average localization time, and localization rate. The simulation results show that the proposed algorithm works well in every situation with the highest localization accuracy, least time consumptions, and highest localization rates.

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