# Energy-Efficient Swarm Behavior for Indoor UAV Ad-Hoc Network Deployment

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## Abstract

**:**

## 1. Introduction

## 2. State of the Art

#### Mobility Strategies

## 3. Swarm Mobility Behavior

#### 3.1. Platform

#### 3.2. Proposed Behavior

**f**, made of three functions (

**f**${}_{1}$,

**f**${}_{2}$,

**f**${}_{3}$). The movement to be developed by the robot is the sum of all three components with an adjustment term for each of them. To execute the movement of an agent, only the unit vector of $\widehat{f}=\frac{f}{\left|f\right|}$ is calculated, because we need to know which direction to take. Terms $\alpha $${}_{\mathit{i}}$ allow graduating the intensity of each component.

**f**${}_{1}$ is obtained as a vector pointing to the lowest intensity zone of pheromones within the vehicle perception radius, so that this intensity decreases with the square of the distance. Assuming that the vehicle position is the center of the coordinates, this component can be specified so that:

**v**is a vector that is constructed with the coordinates of the tuple (x, y) for each element of the set C. $\mathsf{\Psi}$${}_{\mathit{field}}$${}_{\mathit{x},\mathit{y}}$ is the component (x, y) of a matrix containing the pheromone reading of space coordinates (x, y) returned by the virtual sensor of stigmergy.

**f**${}_{2}$ determines the obstacle avoidance. It is defined as a vector that points to the area with fewer obstacles detected by the ultrasonic sensors:

**r**${}_{j}$ the position of the obstacle j and

**p**the current robot position.

#### 3.3. Behavior Analysis

**p**${}_{0}$ = (40, 50) of the map presented in Figure 2. New robots will be added progressively from this position each minute.

## 4. Obtaining the Energy Swarm Consumption

#### 4.1. Using the Energy Model

## 5. Conclusions

## Author Contributions

## Funding

^{3}CE de Investigación en Docencia Universitaria 2016-2017, Red ICE 3701, of the Instituto de Ciencias de la Educación of the University of Alicante.

## Acknowledgments

## Conflicts of Interest

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**Figure 2.**(

**a**) Maps used in the experimentation. The starting points of the deployment process in these maps are the following ones (top to bottom): (i) $(x,y)=(35,55)$, (ii) $(x,y)=(45,50)$, (iii) $(x,y)$ = $(35,50)$ and (iv) $(x,y)=(15,50)$. (

**b**,

**c**) Evolution of a selected execution for two sample maps, where white areas are free zones and black areas represent obstacles. Steps 0, 500, 1500, 2000 are shown. In addition, the obstacle detection area of the robots (circle) areas with pheromones (blue) and the coverage range for each agent (red) is presented.

**Figure 3.**Number of individuals in each state and percentage of area covered by an individual given a simulation step, where each step is equal to 0.5 s. The mean and standard deviation for 30 different runs are shown until the swarm has covered 85% of the environment.

**Figure 4.**In this picture, both the mathematical models and the results of the simulation are shown. We have provided the number of agents in the wander and beacon states, the instant energy consumed by the swarm, the amount of environment covered and the average energy consumed. For these simulations, the average and standard deviation of 50 different executions of our behavior are shown. As can be seen, the models presented and the simulations coincide substantially.

**Figure 5.**In this picture, both the mathematical models and the results of the simulation are shown for four different maps. The energy consumption of the swarm vs. the predicted energy consumption for four maps (

**a**–

**d**) is shown. Each map is shown on the right side of its corresponding figure. For each of them, we have provided the number of agents in the wander and beacon states, the instant energy consumed by the swarm, the amount of environment covered and the average of energy consumed. For these simulations, the average and standard deviation of 50 different executions of our behavior are shown. As can be seen, the models presented and the simulations achieve very similar results.

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**MDPI and ACS Style**

Aznar, F.; Pujol, M.; Rizo, R.; Pujol, F.A.; Rizo, C.
Energy-Efficient Swarm Behavior for Indoor UAV Ad-Hoc Network Deployment. *Symmetry* **2018**, *10*, 632.
https://doi.org/10.3390/sym10110632

**AMA Style**

Aznar F, Pujol M, Rizo R, Pujol FA, Rizo C.
Energy-Efficient Swarm Behavior for Indoor UAV Ad-Hoc Network Deployment. *Symmetry*. 2018; 10(11):632.
https://doi.org/10.3390/sym10110632

**Chicago/Turabian Style**

Aznar, Fidel, Mar Pujol, Ramon Rizo, Francisco A. Pujol, and Carlos Rizo.
2018. "Energy-Efficient Swarm Behavior for Indoor UAV Ad-Hoc Network Deployment" *Symmetry* 10, no. 11: 632.
https://doi.org/10.3390/sym10110632