OLSR+: A new routing method based on fuzzy logic in flying ad-hoc networks (FANETs)

• Published in: Vehicular Communications 2022-08, Vol.36, p.100489, Article 100489
• Main Authors: Rahmani, Amir Masoud, Ali, Saqib, Yousefpoor, Efat, Yousefpoor, Mohammad Sadegh, Javaheri, Danial, Lalbakhsh, Pooia, Hassan Ahmed, Omed, Hosseinzadeh, Mehdi, Lee, Sang-Woong
• Format: Article
• Language: English
• Subjects: Artificial intelligence (AI); Flying ad hoc networks (FANETs); Fuzzy logic; Routing; Unmanned ariel vehicles (UAVs)
• ISSN: 2214-2096
• DOI: 10.1016/j.vehcom.2022.100489

Flying ad-hoc networks (FANETs) have many applications in military, industrial and agricultural areas. Due to specific features of FANETs, such as high-speed nodes, low density of nodes in the network, and rapid changes in the topology, most routing protocols designed for mobile ad hoc networks (MANETs) or vehicular ad hoc networks (VANETs) are not compatible with FANETs. In this paper, we propose a fuzzy logic-based routing approach called OLSR+ for FANETs. In this scheme, we seek to improve the optimized link state routing protocol (OLSR) so that it can efficiently be used in FANETs. OLSR+ includes four main phases: 1) Discovering neighboring nodes. In this phase, we propose a new and efficient technique for estimating the lifetime of the link between two unmanned ariel vehicles (UAVs) based on the link quality, distance, relative velocity, and movement direction. 2) Selecting multipoint relays (MPRs). In this phase, we present a fuzzy mechanism for selecting a set of MPR nodes. According to this mechanism, when a node has higher residual energy, higher link lifetime, and more neighborhood degree compared to others, it achieves more fitness to be selected as MPR. 3) Discovering the network topology. In this phase, we modify the format of the topology control (TC) message and add two fields, including route energy and route lifetime to this message. 4) Calculating the routing table. In OLSR+, we consider two parameters, including route energy and route lifetime, for establishing stable paths. Finally, we simulate OLSR+ using NS3 and compare its performance with two methods, namely greedy optimized link state routing (G-OLSR) and optimized link state routing (OLSR). The simulation results show that OLSR+ successfully reduces delay compared to G-OLSR and OLSR. In addition, it has higher packet delivery rate and throughput than others. Also, it improves energy consumption in the network. However, OLSR+ has more routing overhead than G-OLSR.