# Energy Efficient Clustering Protocol for FANETS Using Moth Flame Optimization

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## Abstract

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## 1. Introduction

- A method for data clustering based on the MFO is presented;
- The quality of the solutions provided by the suggested technique is compared to three well-known algorithms to determine which is superior;
- A total of five statistical tests have been conducted using different grid sizes to evaluate the proposed approach’s statistical quality;
- The use of k-means density and the MRCQ approach for data compression has been employed to improve the CH selection process;
- Experimental and statistical graphs demonstrate the effectiveness of the suggested technique.

## 2. Background and Motivation

#### 2.1. FANETS

#### 2.2. What Links Make FANETs, Different from MANETs, and VANETs?

- There are now many ad hoc networks striving to connect. For example, Wireless sensor networks make extensive use of these technologies for gathering and transmitting data about the surrounding environment [24]. Peer-to-peer and broadcast traffic must be allowed simultaneously for FANET to work effectively.
- The Distances between FANET and FANETS nodes are much longer than the distances between the two networks [12,13,24]. Unmanned aerial vehicles (UAVs) need a more extended communication range than either MANETs or VANETs if they are to be linked together. Consequently, radio links, hardware circuits, and physical layer behaviour are all impacted.
- Multi-UAV systems may contain various types of sensors, each of which may need a separate data transmission strategy [19].

#### 2.3. What’s the Roles of Bioinspired Algorithms in FANETs?

## 3. Proposed Methodology

#### 3.1. Network Building and Nodes Positioning

#### 3.2. Cluster Formulation and CH selection with K-Means Sorted Fitness

#### 3.3. Data Compression and Network Commination

- Data compressionOur proposed protocol is used for captured data, primarily images and videos. So, to reduce the data transmission energy, we need to use a compression algorithm. Our proposed protocol uses MRCQ (multi-resolution compression and query) image-based compression approach [26]. Sensor nodes are organized in a hierarchy to establish multiresolution summaries of sensed data in the network [11]. Lower-resolution summaries are transmitted to the sink, whereas high-resolution outlines remain in the network and can be accessed for further analysis [32]. As a result, MRCQ has lower implementation costs and may be used with low-cost sensor systems [33,34].
- Node Movement and Network CommunicationsCommunication and data transfer between nodes begin when clustering is complete. Whether a node inside a cluster [35], a node across the cluster, or the base station is the intended destination for the data, the CH is responsible for getting it there [36,37]. EECP-MFO adheres to the RPGM [38] reference point group mobility model. There is a point of reference for all nodes in RPGM that they all will follow. EECP-MFO considers a reference point for the CHs, and all CMs adjust their positions under how their respective CHs move.

## 4. Experimental Results and Analysis

#### 4.1. Cluster Building Time

#### 4.2. Energy Consumption

#### 4.3. Probability of Success

#### 4.4. Cluster Lifetime

#### 4.5. Consistency of Cluster Heads

## 5. Discussion

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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Protocol | Year | Network Type | Cluster Method | Complexity | No of CH’s | No of Nodes in Cluster | Mobility | Energy Efficiency |
---|---|---|---|---|---|---|---|---|

LEACH | 2000 | Homogenous | Distributed | Low | Uncertain | Unforeseeable | Inactive | Yes |

LEACH-C | 2002 | heterogenous | Centralized | Low | Certain | Unforeseeable | Inactive | Yes |

CBLADSR | 2012 | Heterogenous | Distributed | High | Uncertain | Unforeseeable | Inactive | Yes |

CACONET | 2016 | Homogenous/heterogenous | Centralized | High | Uncertain | Unforeseeable | Inactive | Yes |

PSONET | 2011 | Homogenous/heterogenous | Centralized | Very high | Uncertain | Unforeseeable | Inactive | Yes |

GWOCNET | 2014 | Homogenous/heterogenous | Centralized | Very high | Uncertain | Unforeseeable | Inactive | Yes |

CAVDO | 2018 | Heterogenous | Distributed | Medium | Uncertain | Unforeseeable | Inactive | Yes |

Protocol | Energy model | Location Awareness | Connectivity to Bs | Link Quality Based | Connection Awareness | Collison Avoidance | Position of Base Station | Deployment Mode |
---|---|---|---|---|---|---|---|---|

LEACH | First order | No | Singe hop | Distance | No | No | Outside | Random |

LEACH-C | First order | Yes | Singe hop | Distance | No | No | Outside | Random |

CBLADSR | First order | No | Singe hop | Distance | Partially | No | Outside | Random and uniform |

CACONET | First order | Yes | Singe hop | Distance | No | No | Outside | Random |

PSONET | First order | Yes | Singe hop | Distance | No | No | Outside | Random |

GWOCNET | First order | No | Singe hop | Distance | No | No | Outside | Random |

CAVDO | First order | No | Singe hop | Distance | Partially | No | Outside | Random and non-uniform |

Parameters | Values |
---|---|

Grid Size | 1000 × 1000 m^{2}, 2000 × 2000 m^{2} and 3000 × 3000 m^{2} |

Density of Connected Nodes | 20, 30, 40, 50, 60 |

Minimum Distance Between Nodes | 5 m |

Mobility Model | Reference Point Mobility Model |

Simulation Runs | 10 |

Simulation Time | 120 s |

Position Exchange Interval | 2 s |

Node Energy Level at Start Time | 80-Watt Hour |

Transmission Range | Dynamic |

Transmission Frequency | 2.45 GHz |

Constant Bit Rate | 100 kbps |

Receiver Sensitivity | −90 dBm |

W1 +W2 + W3 | 1 |

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**MDPI and ACS Style**

Bharany, S.; Sharma, S.; Bhatia, S.; Rahmani, M.K.I.; Shuaib, M.; Lashari, S.A.
Energy Efficient Clustering Protocol for FANETS Using Moth Flame Optimization. *Sustainability* **2022**, *14*, 6159.
https://doi.org/10.3390/su14106159

**AMA Style**

Bharany S, Sharma S, Bhatia S, Rahmani MKI, Shuaib M, Lashari SA.
Energy Efficient Clustering Protocol for FANETS Using Moth Flame Optimization. *Sustainability*. 2022; 14(10):6159.
https://doi.org/10.3390/su14106159

**Chicago/Turabian Style**

Bharany, Salil, Sandeep Sharma, Surbhi Bhatia, Mohammad Khalid Imam Rahmani, Mohammed Shuaib, and Saima Anwar Lashari.
2022. "Energy Efficient Clustering Protocol for FANETS Using Moth Flame Optimization" *Sustainability* 14, no. 10: 6159.
https://doi.org/10.3390/su14106159