A Hybrid Approach Based on ACO and Firefly Algorithm for Routing in FANETs

Yadav, Amrita; Verma, Seema

doi:10.1007/978-3-030-76776-1_16

Cited by 7 publications

(1 citation statement)

References 19 publications

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“…Therefore, this issue must be considered when designing a routing scheme for this network 21 , 22 . The most common routing protocols are static routing 23 , 24 , proactive routing 23 , 24 , reactive routing 23 , 24 , hybrid routing 25 , 26 , geographical routing 23 , 24 , and multi-path routing 25 , 27 . The performance of a routing protocol depends on selecting the optimal route, which can be calculated based on various criteria, for example, speed, energy, link lifetime, network traffic, delay, link quality, and movement direction 25 , 28 .…”

Section: Introductionmentioning

confidence: 99%

An energy-aware routing method using firefly algorithm for flying ad hoc networks

Lánský

Rahmani

Malik

et al. 2023

Sci Rep

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Flying ad-hoc networks (FANETs) include a large number of drones, which communicate with each other based on an ad hoc model. These networks provide new opportunities for various applications such as military, industrial, and civilian applications. However, FANETs have faced with many challenges like high-speed nodes, low density, and rapid changes in the topology. As a result, routing is a challenging issue in these networks. In this paper, we propose an energy-aware routing scheme in FANETs. This scheme is inspired by the optimized link state routing (OLSR). In the proposed routing scheme, we estimate the connection quality between two flying nodes using a new technique, which utilizes two parameters, including ratio of sent/received of hello packets and connection time. Also, our proposed method selects multipoint relays (MPRs) using the firefly algorithm. It chooses a node with high residual energy, high connection quality, more neighborhood degree, and higher willingness as MPR. Finally, our proposed scheme creates routes between different nodes based on energy and connection quality. Our proposed routing scheme is simulated using the network simulator version 3 (NS3). We compare its simulation results with the greedy optimized link state routing (G-OLSR) and the optimized link state routing (OLSR). These results show that our method outperforms G-OLSR and OLSR in terms of delay, packet delivery rate, throughput, and energy consumption. However, our proposed routing scheme increases slightly routing overhead compared to G-OLSR.

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