A novel Q-learning-based routing scheme using an intelligent filtering algorithm for flying ad hoc networks (FANETs)

Hosseinzadeh, Mehdi; Ali, Saqib; Ionescu-Feleaga, Liliana; Ionescu, Bogdan-Stefan; Yousefpoor, Mohammad Sadegh; Yousefpoor, Efat; Ahmed, Omed Hassan; Rahmani, Amir Masoud; Mehmood, Asif

doi:10.1016/j.jksuci.2023.101817

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…This increases production growth and improves the quality of agricultural products because FANET helps farmers manage crops and use water and fertilizer efficiently 5,6 . Additionally, the use of UAVs, equipped with cameras, sensors, and other data collection devices, allows farmers to control their product state and identify problems that are not visible on the ground surface 7,8 . Compared to other traditional methods, UAVs can be used in pesticide spraying automatically and accurately.…”

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A local filtering-based energy-aware routing scheme in flying ad hoc networks

Hosseinzadeh,

Husari,

Yousefpoor

et al. 2024

Preprint

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Flying ad hoc network (FANET) is a new technology, which creates a self-organized wireless network containing unmanned aerial vehicles (UAVs). In FANET, routing protocols deal with important challenges due to limited energy, frequent failures in communication links, high mobility of UAVs, and limited communication range of UAVs. Thus, a suitable path is always essential to transmit data between UAVs reliably. In this paper, a local filtering-based energy-aware routing scheme (LFEAR) is proposed for FANETs. LFEAR improves the template of the route request (RREQ) packet by adding three other fields, namely the energy, reliable distance, and movement similarity of the relevant route to create stable and energy-efficient paths between UAVs. In the routing process, LFEAR presents a local filtering construction technique to avoid the broadcasting storm issue. This filter limits the broadcasting range of RREQs in the network. Accordingly, only UAVs inside this local filtered area can rebroadcast RREQ and other UAVs must eliminate this packet. Upon the end of the route discovery process, the destination begins the route selection phase and extracts information about each discovered route, including the number of hops, route energy, reliable distance, and movement similarity from the relevant RREQ. Then, the destination node calculates a score for each path based on the extracted information, selects the route with the highest score, and sends a route reply (RREP) packet to the source node through this route. Finally, the simulation process of LFEAR is performed using the NS2 simulator, and two simulation scenarios, namely change in network density and change in the speed of UAVs, are defined to evaluate network performance. In the first scenario, LFEAR improves energy consumption, packet delivery rate, network lifespan, and delay by 1.33%, 1.77%, 6.74%, and 1.71%, while its routing overhead is about 16.51% more than EARVRT. In the second scenario, LFEAR optimizes energy consumption and network lifetime by 5.55% and 5.67%, respectively. However, its performance in terms of routing overhead, packet delivery rate, and delay is 23%, 2.29%, and 6.67% weaker than EARVRT, respectively.

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confidence: 99%

A local filtering-based energy-aware routing scheme in flying ad hoc networks

Hosseinzadeh,

Husari,

Yousefpoor

et al. 2024

Preprint

Self Cite

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An intelligent clustering scheme based on whale optimization algorithm in flying ad hoc networks

Hosseinzadeh,

Tanveer,

Alanazi

et al. 2024

Vehicular Communications

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A local filtering-based energy-aware routing scheme in flying ad hoc networks

Hosseinzadeh,

Husari,

Yousefpoor

et al. 2024

Sci Rep

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Flying ad hoc network (FANET) is a new technology, which creates a self-organized wireless network containing unmanned aerial vehicles (UAVs). In FANET, routing protocols deal with important challenges due to limited energy, frequent link failures, high mobility of UAVs, and limited communication range of UAVs. Thus, a suitable path is always essential to transmit data between UAVs. In this paper, a local filtering-based energy-aware routing scheme (LFEAR) is proposed for FANETs. LFEAR improves the template of the route request (RREQ) packet by adding three fields, namely the energy, reliable distance, and movement similarity of the relevant route to create stable and energy-efficient paths. In the routing process, LFEAR presents a local filtering construction technique to avoid the broadcasting storm issue. This filter limits the broadcasting range of RREQs in the network. Accordingly, only UAVs inside this local filtered area can rebroadcast RREQs and other UAVs must eliminate these packets. After ending the route discovery process, the destination begins the route selection phase and extracts information about each discovered route, including the number of hops, route energy, reliable distance, and movement similarity. Then, the destination node calculates a score for each path based on the extracted information, selects the route with the highest score, and sends a route reply (RREP) packet to the source node through this route. Finally, the simulation process of LFEAR is performed using the NS2 simulator, and two simulation scenarios, namely change in network density and change in the speed of UAVs, are defined to evaluate network performance. In the first scenario, LFEAR improves energy consumption, packet delivery rate, network lifespan, and delay by 1.33%, 1.77%, 6.74%, and 1.71%, while its routing overhead is about 16.51% more than EARVRT. In the second scenario, LFEAR optimizes energy consumption and network lifetime by 5.55% and 5.67%, respectively. However, its performance in terms of routing overhead, packet delivery rate, and delay is 23%, 2.29%, and 6.67% weaker than EARVRT, respectively.

show abstract

A novel Q-learning-based routing scheme using an intelligent filtering algorithm for flying ad hoc networks (FANETs)

Cited by 3 publications

References 21 publications

A local filtering-based energy-aware routing scheme in flying ad hoc networks

A local filtering-based energy-aware routing scheme in flying ad hoc networks

An intelligent clustering scheme based on whale optimization algorithm in flying ad hoc networks

A local filtering-based energy-aware routing scheme in flying ad hoc networks

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