# Arithmetic Optimization AOMDV Routing Protocol for FANETs
###### Abstract  
Flying ad hoc networks (FANETs), composed of small unmanned aerial vehicles (UAVs), possess characteristics of flexibility, cost-effectiveness, and rapid deployment, rendering them highly attractive for a wide range of civilian and military applications. FANETs are special mobile ad hoc networks (MANETs), FANETs have the characteristics of faster network topology changes and limited energy. Existing reactive routing protocols are unsuitable for the highly dynamic and limited energy of FANETs. For the lithium battery-powered UAV, flight endurance lasts from half an hour to two hours. The fast-moving UAV not only affects the packet delivery rate, average throughput, and end-to-end delay but also shortens the flight endurance. Therefore, research is urgently needed into a high-performance routing protocol with high energy efficiency. In this paper, we propose a novel routing protocol called AO-AOMDV, which utilizes arithmetic optimization (AO) to enhance the ad hoc on-demand multi-path distance vector (AOMDV) routing protocol. The AO-AOMDV utilizes a fitness function to calculate the fitness value of multiple paths and employs arithmetic optimization for selecting the optimal route for routing selection. Our experiments were conducted using NS3 with three evaluation metrics: the packet delivery ratio, network lifetime, and average end-to-end delay. We compare this algorithm to routing protocols including AOMDV and AODV. The results indicate that the proposed AO-AOMDV attained a higher packet delivery ratio, network lifetime, and lower average end-to-end delay. FANETs; arithmetic optimization; AOMDV; AO-AOMDV  
2023 23 2023  
and the ad hoc on-demand distance vector routing protocol (AODV) [12; 13; 14; 15; 16]. Proactive routing protocols, on the other hand, detect changes in network topology by regularly broadcasting routing update messages to other nodes in order to update their routing tables. Well-known proactive routing protocols in recent years include the destination-sequenced distance vector (DSDV) [12; 17], optimized link state routing (OLSR) [18; 19], and the wireless routing protocol (WRP) [20; 21]. Finally, hybrid routing protocols aim to leverage the respective advantages of reactive routing and proactive routing. The zone routing protocol (ZRP) [22; 23; 24] and zone-based hierarchical link state routing (ZHLS) [25; 26] prescribe the use of proactive routing within a designated zone for each node, while employing an on-demand routing mechanism similar to DSR for routing to nodes outside the zone. However, the performance of hybrid routing is largely determined by the value of the zone radius parameter. Therefore, its adaptability is limited. Unlike topology-based routing protocols, location-based routing utilizes the location information of current and neighboring nodes for forwarding decisions as the greedy perimeter stateless routing (GPSR) [27], geographical- and energy-aware routing (GEAR) [28], and graph embedding (GEM) [29]. The premise of location-based routing protocols is that UAV nodes are able to obtain their own location information through GPS. In location-based routing, nodes do not perform global topology discovery but rely on local information for communication. However, location-based routing introduces challenges such as route failures and routing loops, requiring the design of corresponding strategies to address these issues. Meta-heuristics is an optimization method that utilizes the natural phenomena of elements to gradually discover the optimal solution from a set of initial solutions. In the ever-changing and complex environment, meta-heuristic algorithms are more efficient and stable. In MANETs, the application of meta-heuristic optimization algorithms in routing protocols has been widely studied.