# Multi-Objective Message Routing in Electric and Flying Vehicles Using a Genetics Algorithm
## 1 Introduction  
Applications of flying ad hoc networks (FANETs) and electric vehicular ad hoc networks (E-VANETs) have grown drastically in the past few years, spanning civil and military practices [1; 2]. The intelligent transportation system (ITS) enables communication and connectivity in both these networks; however, the constraints and differences of these networks create challenges in developing an efficient message routing protocol. Unmanned aerial vehicles (UAVs) can move freely in all possible directions due to no geographical restrictions except obstructions. An EV has a relatively independent nature compared to a swarm of UAVs. Moreover, UAVs are energy-constrained but support higher data transmission capabilities, and EVs have to rely on either dedicated short-range communications (DSRC) or cellular networks. There exist several solutions which suggest utilizing FANETs to support E-VANETs' communication, but no scheme considers both networks as a single paradigm [3; 4]. The problem at hand is efficient message routing in a co-existent paradigm, where the ideal routing protocol should prioritize faster message transmission of UAVs without hindering their residual energy, while ensuring reliability and connectivity. The authors in [4] propose a predictive message routing scheme for FANETs which identifies the current geographical coordinates of a target vehicle and deems its direction unchanged, analytically predicting the connectivity time. The scheme utilizes a quadratic equation-based time estimation but does not shed any light on the more common possibilities of no real solution or two real solutions. An interesting study in [5] uses Q-learning for a routing protocol that actively interacts with the environment and (re)adjusts its actions. A multi-objective routing scheme coined as EORB (energy-saver) in [6] considers node energy and buffer to choose the best route. The authors predict trajectory and velocity for communication and utilize a utility function for relay selection. On the other hand, ref. [3] introduced a UAV-assisted protocol for E-VANET, in which UAVs are responsiblefor a carry-and-forward mechanism for message delivery using Q-tables and rule-based fuzzy logic. An analytical connectivity time prediction-based routing scheme in [7] utilizes current geographical information and movement of the vehicles to choose a route that lasts longer. Routing in UAVs for specific applications such as public safety networks has been proposed in [8]. The authors use reinforcement learning to increase the lifespan of these aerial vehicles by considering energy as a routing metric. On the other hand, it is always possible to consider the original hop count-based routing scheme which not only ensures the minimum number of hops in a route but also results in a faster route selection [9]. Nevertheless, several existing solutions have tried utilizing SDN or a multi-objective routing for ground vehicles [10; 11; 12]. Moreover, routing in UAVs which considers metrics such as charging stations, VANET supporting networks, and trajectory-based systems has also been proposed [13; 14; 15; 16; 17; 18; 19]. An exhaustive review of existing schemes identified that a routing scheme should not consider a single objective while disregarding its byproduct's impact. It should also be noted that none of the existing routing methods have considered the vehicular domain as a single entity. A multi-objective scheme often requires evolutionary algorithms and techniques such as genetics algorithms (NSGA-I, II, etc.) which take input from a number of solutions and reduce it to the Pareto front (best possible solutions). Almost all the optimization methods consider a larger set of the population which increases the number of iterations, thus becoming space and time complex. In this paper, we propose a reduction in solution space before considering an optimization technique that identifies the Pareto front (set of optimal solutions in the objective function space) and chooses the best solution.