# An Arrow-Curve Path Planning Model for Mobile Beacon Node Aided Localization in Air Pollution Monitoring System IoT

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## Abstract

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## 1. Introduction

- Introducing two path models for mobile anchor-based localization in WSN.
- Solving the problem of having three pausing points on one straight line for the mobile anchor node (collinearity problem).
- Finding the average localization error, standard deviation of error, power consumption, and path length as evaluation metrics for the proposed path models.
- Finding the average localization error, standard deviation of error, power consumption, and path length as evaluation metrics for the proposed path models.
- Comparing the proposed models with previously proposed models in literature using simulation and a hardware realtime model.

## 2. Related Work

## 3. Path Model Design

- High localization accuracy;
- Full coverage area;
- Low power consumption;
- Solving the problem of collinearity.

- The unknown nodes are spread in fixed locations.
- Each node has a determined communication range which differs depending on the surrounding criteria.
- The mobile anchor node traverses through the network deterministic path. It can move around any obstacle it meets through its journey.
- The mobile anchor sends messages at each pausing point it rests at, and the unknown nodes in its scope receive the messages from it.
- The mobile anchor node can determine its position at each pausing point.
- The unknown node can compute its position via the position information from three different mobile node positions.

#### 3.1. R-Curve Path Model

#### 3.1.1. Stage 1: Experiment Preparation

#### 3.1.2. Stage 2: Mobility Motion Description

#### 3.2. Arrow-Curve Path Model

#### Stage 2: Mobility Motion Description

#### 3.3. Obstacle—Reluctant Path

## 4. Performance Evaluation

#### 4.1. Performance Parameters

- Average localization error.

- 2.
- Standard deviation of position error.

- 3.
- Coverage ratio.

- 4.
- Number of pausing points.

- 5.
- Path length.

- 6.
- Energy or power consumption.

#### 4.2. Performance Evaluation Using MATLAB Simulation

- Average localization error.

- 2.
- Standard deviation of position error.

- 3.
- Coverage ratio.

- 4.
- Path length.

- 5.
- Energy consumption.

#### 4.3. Performance Evaluation Using Node MCU

- Average localization error.

- 2.
- Standard deviation of position error.

- 3.
- Coverage ratio.

- 4.
- Number of pausing points.

- 5.
- Path length.

- 6.
- Power consumption.

#### 4.4. Performance Evaluation Comparison

- Average localization error

- 2.
- Energy Consumption

#### 4.5. Replacing One Mobile Anchor Node by Fixed Anchor Nodes

## 5. Conclusions and Future Work

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 10.**Mobile anchor node with MAX471 Voltage and Current Sensor Module Consume Voltage Load Detection Module.

**Figure 11.**Average localization error (m) corresponding to resolution (L) in Arrow-Curve model, R-Curve model, and H-Curve.

**Figure 12.**Average localization error (m) corresponding to sub-field (S) number at resolution (L): (

**a**) L = 10.; (

**b**) L = 12.5; (

**c**) L = 15; (

**d**) L = 17.5.

**Figure 13.**Standard deviation of error (SDE) corresponding to resolution (L) in Arrow-Curve model, R-Curve path model, and H-Curve.

**Figure 14.**Localization coverage ratio corresponding to resolution (L) in Arrow-Curve path model, R-Curve path model, and H-Curve.

**Figure 15.**Path length (PL) corresponding to resolution (L) in Arrow-Curve model, R-Curve path model, and H-Curve.

**Figure 16.**Power consumption (mw) corresponding to resolution (L) in Arrow-Curve model, R-Curve path model, and H-Curve.

**Figure 18.**Average localization error (m) corresponding to resolution (L) in Arrow-Curve model, R-Curve model, and H-Curve (outdoor environment): (

**a**) (outdoor environment); (

**b**) (indoor environment).

**Figure 19.**Average localization error (m) corresponding to sub-field (S) number at resolution (L) in outdoor environment: (

**a**) L = 20; (

**b**) L = 17.5; (

**c**) L = 15; (

**d**) L = 12.5; (

**e**) L = 10; (

**f**) L = 7.5.

**Figure 20.**Average localization error (m) corresponding to sub-field (S) number at resolution (L) in indoor environment: (

**a**) L = 7; (

**b**) L = 6; (

**c**) L = 5; (

**d**) L = 4; (

**e**) L = 3.

**Figure 21.**Standard deviation of error (SDE) corresponding to resolution (L) in Arrow-Curve model, R-Curve path model, and H-Curve: (

**a**) outdoor environment; (

**b**) indoor environment.

**Figure 22.**Localization coverage ratio corresponding to resolution (L) in Arrow-Curve path model, R-Curve path model, and H-Curve: (

**a**) outdoor environment; (

**b**) indoor environment.

**Figure 23.**Number of pausing points (PP) corresponding to resolution (L) in Arrow-Curve model, R-Curve path model, and H-Curve: (

**a**) outdoor environment; (

**b**) indoor environment.

**Figure 24.**Path length (PL) corresponding to resolution (L) in Arrow-Curve model, R-Curve path model, and H-Curve (outdoor environment): (

**a**) outdoor environment; (

**b**) indoor environment.

**Figure 25.**Power consumption (mW) corresponding to resolution (L) in Arrow-Curve model, R-Curve path model, and H-Curve: (

**a**) outdoor environment; (

**b**) indoor environment.

**Figure 26.**Average localization error (m) corresponding to resolution (L) in both simulation and real environment: (

**a**) Arrow-Curve path model; (

**b**) R-Curve path model.

**Figure 27.**Energy dissipated (µJ) corresponding to resolution (L) in both simulation and real environment: (

**a**) Arrow-Curve path model; (

**b**) R-Curve path model.

**Figure 28.**Energy dissipated (µJ) corresponding to resolution (L) in both one mobile anchor node and fixed anchor nodes: (

**a**) Arrow-Curve path model; (

**b**) R-Curve path model.

Parameter | Value |
---|---|

Network area size | 50 × 50 ${\mathrm{m}}^{2}$ |

Number of mobile anchor nodes | 1 |

Number of unknown nodes | 40 |

Resolution L | L = 10, 12.5, 15, 17.5 |

Path loss exponent (${\mathrm{n}}_{\mathrm{p}}$) | 3.3 |

${\mathrm{RSSI}}_{0}$ (${\mathrm{D}}_{0})$ | −59 |

${\mathrm{D}}_{0}$ | 1 m |

Standard deviation of noise (σ) | 4 |

Communication range | 15 |

Simulation run times | 10 |

Parameter | Value |
---|---|

Network area size | 20 × 20 ${\mathrm{m}}^{2}$(outdoor); 8 × 8 ${\mathrm{m}}^{2}$ (indoor) |

Number of mobile anchor nodes | 1 |

Number of unknown nodes | 20 (outdoor); 10 (indoor) |

Resolution L | L = 20, 17.5, 15, 12.5, 10, 7.5 (outdoor); L = 8, 7, 6, 5, 4, 3 (indoor) |

Path loss exponent (${\mathrm{n}}_{\mathrm{p}}$) | 2.3 (outdoor); 5.2 (indoor) |

${\mathrm{RSSI}}_{0}$ (${\mathrm{D}}_{0})$ | −54. |

${\mathrm{D}}_{0}$ | 0.4 m |

Number of beacons received at each pausing point. | 50 |

Communication range | 28 m outdoor; 9 m indoor with obstacles |

Parameter | Value |
---|---|

Network area size | 20 × 20 ${\mathrm{m}}^{2}$ |

Number of mobile anchor nodes | 1 |

Number of unknown nodes | 20 (outdoor) |

Resolution L | L = 10, 12.5, 15, 17.5, |

Path loss exponent (${\mathrm{n}}_{\mathrm{p}}$) | 2.3 |

${\mathrm{RSSI}}_{0}$ (${\mathrm{D}}_{0})$ | −54 |

${\mathrm{D}}_{0}$ | 0.4 m |

Number of beacons received at each pausing point (Node MCU) | 50 |

Simulation run times (MATLAB) | 10 |

Communication range | 28 m |

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**MDPI and ACS Style**

Ahmed, E.M.; Hady, A.A.; El-Kader, S.M.A.; Khalil, A.T.; Mohamed, W.A.
An Arrow-Curve Path Planning Model for Mobile Beacon Node Aided Localization in Air Pollution Monitoring System IoT. *Electronics* **2021**, *10*, 2757.
https://doi.org/10.3390/electronics10222757

**AMA Style**

Ahmed EM, Hady AA, El-Kader SMA, Khalil AT, Mohamed WA.
An Arrow-Curve Path Planning Model for Mobile Beacon Node Aided Localization in Air Pollution Monitoring System IoT. *Electronics*. 2021; 10(22):2757.
https://doi.org/10.3390/electronics10222757

**Chicago/Turabian Style**

Ahmed, Enas M., Anar A. Hady, Sherine M. Abd El-Kader, Abeer Twakol Khalil, and Wael A. Mohamed.
2021. "An Arrow-Curve Path Planning Model for Mobile Beacon Node Aided Localization in Air Pollution Monitoring System IoT" *Electronics* 10, no. 22: 2757.
https://doi.org/10.3390/electronics10222757