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

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, the authors propose a fuzzy logic-based routing approach called OLSR+ for FANETs. In this scheme, they 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, they 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, they 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, they 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+, the authors consider two parameters, including route energy and route lifetime, for establishing stable paths. Finally, they 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.

• Record URL:
  https://doi.org/10.1016/j.vehcom.2022.100489
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S2214209622000365
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/22142096
• Supplemental Notes:
  • © 2022 Elsevier Inc. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Rahmani, Amir Masoud
  • Ali, Saqib
  • Yousefpoor, Efat
  • Yousefpoor, Mohammad Sadegh
  • 0000-0002-9012-6138
  • Javaheri, Danial
  • Lalbakhsh, Pooia
  • Hassan Ahmed, Omed
  • Hosseinzadeh, Mehdi
  • Lee, Sang-Woong
• Publication Date: 2022-8

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: 100489
• Serial:
  • Vehicular Communications
  • Volume: 36
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 2214-2096
  • Serial URL: http://www.sciencedirect.com/science/journal/22142096/1/2

Subject/Index Terms

• TRT Terms: Artificial intelligence; Drones; Fuzzy logic; Routing; Vehicular ad hoc networks
• Subject Areas: Aviation; Data and Information Technology;

Filing Info

• Accession Number: 01855696
• Record Type: Publication
• Files: TRIS
• Created Date: Aug 24 2022 3:02PM