A Novel Location Source Optimization Algorithm for Low Anchor Node Density Wireless Sensor Networks
<p>Different position accuracy with different anchor node density. Under low anchor node density, the number of neighbor nodes to be located in the position source increases, and the uncertainty of the localization information of the position source increases, resulting in a decrease in the position accuracy of the node to be located.</p> "> Figure 2
<p>Flow chart of cooperative localization with location source optimization.</p> "> Figure 3
<p>Flow chart of location source selection algorithm based on fuzzy comprehensive evaluation.</p> "> Figure 4
<p>(<b>a</b>) Simulation scenario. Zone 1 is the used simulation scene. (<b>b</b>) The real scene of zone 1 [<a href="#B22-sensors-21-01890" class="html-bibr">22</a>].</p> "> Figure 5
<p>Comparison of positioning accuracy of various source selection methods under different node densities. (<b>a</b>) is the comparison when the average number of anchor node connections is 3. (<b>b</b>) is the comparison when the average number of anchor node connections is 10. The red line represents the non-cooperative localization using only anchor nodes, the orange represents the cooperative localization using all neighbor nodes as the source, the purple line represents the DRSL source selection location algorithm proposed in [<a href="#B30-sensors-21-01890" class="html-bibr">30</a>], the blue line represents the MBIL algorithm in [<a href="#B23-sensors-21-01890" class="html-bibr">23</a>] and the green line represents the source optimization algorithm proposed in this article for cooperative localization.</p> "> Figure 6
<p>CDF of position error under different communication distance <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mi>c</mi> </msub> </mrow> </semantics></math>. In the figure, the purple line represents the DRSL source selection location algorithm proposed in [<a href="#B30-sensors-21-01890" class="html-bibr">30</a>], the blue line represents the MBIL algorithm in [<a href="#B23-sensors-21-01890" class="html-bibr">23</a>] and the green line represents the source optimization algorithm proposed in this article.</p> "> Figure 7
<p>CDF of position error under different initial position error. In the figure, the purple line represents the DRSL source selection location algorithm proposed in [<a href="#B30-sensors-21-01890" class="html-bibr">30</a>], the blue line represents the MBIL algorithm in [<a href="#B23-sensors-21-01890" class="html-bibr">23</a>] and the green line represents the source optimization algorithm proposed in this article.</p> "> Figure 8
<p>CDF of position error under different maximum node speed <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> </mrow> </semantics></math>. In the figure, the red line represents the position error CDF when all nodes are static, the orange line represents <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> <mo>=</mo> <mn>3</mn> <mo> </mo> <mi mathvariant="normal">m</mi> <mo>/</mo> <mi mathvariant="normal">s</mi> </mrow> </semantics></math>, and the purple line represents <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> <mo>=</mo> <mn>8</mn> <mo> </mo> <mi mathvariant="normal">m</mi> <mo>/</mo> <mi mathvariant="normal">s</mi> </mrow> </semantics></math>. The blue line indicates the situation where <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> <mo>=</mo> <mn>15</mn> <mo> </mo> <mi mathvariant="normal">m</mi> <mo>/</mo> <mi mathvariant="normal">s</mi> </mrow> </semantics></math>. In all cases, anchor node is static, the nodes to be located conform to the uniform distribution with the maximum speed <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> </mrow> </semantics></math>, and the direction of movement is random.</p> "> Figure 9
<p>CDF of position error under different number of preferred positioning sources. In the figure, the green line represents the location error CDF when the preferred location source is 15, the orange line represents the preferred location source is 9, and the red line represents the preferred location source is 6. The purple line indicates the situation where only anchor nodes are used for positioning.</p> "> Figure 10
<p>The average positioning time of each node under different numbers of neighbor nodes.</p> ">
Abstract
:1. Introduction
- (1)
- Frist of all, the system model of low anchor node density is defined. Nodes calculate location and time skew with TOA method in this model.
- (2)
- Then, a novel location source optimization algorithm is proposed for low anchor node density scenario. In the proposed method, a location source select structure is established with fuzzy comprehensive evaluation. Distribute conditional posterior Cramer-Rao lower bound (DCPCRLB), distance measurement and direction angle is considered as the most significant factors to select location source.
- (3)
- The validity and rationality of the proposed method are verified by experiments.
2. Related Work
3. Location Source Selection Algorithm Based on Fuzzy Comprehensive Evaluation
3.1. System Model
3.2. Distributed Cramer-Rao Lower Bound
3.3. Location Source Optimization Algorithm
4. Simulation Scenario and Result Analysis
4.1. Simulation Scenario Set
4.2. Simulation Result Analysis
4.3. Future Research Directions
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Location Source Selection Algorithm in Localization | Factors of Location Source Selection | Method of Fusion between Factors |
---|---|---|
MBIL [23] | the number of iterations, the rediual energy and the deviation degree error | linear combination |
LSL-DC [24] | the distance measure error | single factor |
NS-IPSO [25] | the distance measure error and the communication frequency | linear combination |
BASL [26] | the number of boundary region nodes | single factor |
GSTDOA [27] | HDOP | single factor |
enhanced three-dimensional DV-hop [28] | coplanarity | single factor |
SNA-CC [29] | closeness centrality | single factor |
DRSL [30] | the smallest least square error | single factor |
Proposed method | the DCPCRLB, the distance measurement and direction angle | the fuzzy comprehensive evaluation |
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Deng, Z.; Tang, S.; Deng, X.; Yin, L.; Liu, J. A Novel Location Source Optimization Algorithm for Low Anchor Node Density Wireless Sensor Networks. Sensors 2021, 21, 1890. https://doi.org/10.3390/s21051890
Deng Z, Tang S, Deng X, Yin L, Liu J. A Novel Location Source Optimization Algorithm for Low Anchor Node Density Wireless Sensor Networks. Sensors. 2021; 21(5):1890. https://doi.org/10.3390/s21051890
Chicago/Turabian StyleDeng, Zhongliang, Shihao Tang, Xiwen Deng, Lu Yin, and Jingrong Liu. 2021. "A Novel Location Source Optimization Algorithm for Low Anchor Node Density Wireless Sensor Networks" Sensors 21, no. 5: 1890. https://doi.org/10.3390/s21051890