# Multi-Objective Message Routing in Electric and Flying Vehicles Using a Genetics Algorithm
## 4 Performance Evaluation  
The proposed GA-based multi-objective solution is evaluated exhaustively in comparison with three existing state-of-the-art routing schemes. We believe that the energy consumption of these battery-powered mobile vehicles is a critical issue and should be considered in communication technologies. However, it is important that the numerical evaluation of the systems should be realistic (nearly) and depict the environment well. Our exhaustive comparative evaluation considers real traffic traces of New York City (4 \(\times\) 3 km\({}^{2}\)) housing 400 \(\sim\) 900 EV and 1000 UAVs. The geographical environment for the simulation is illustrated in Figure 2. Each of the EVs and UAVs is expected to be equipped with a battery and two communication modules, along with their complete functionality kits. Each vehicle is assumed to have two connectivity interfaces to transmit data (40 Mb). One communication interface for EVs has a communication range of 250 m with a 20 Mbps data rate, and another for UAVs has a 500 m range and a 30 Mbps data rate. The EV mobility pattern follows SUMO simulator commands [7], whereas the UAVs adhere to a random waypoint with a speed of 10 \(\sim\) 50 m/s [20]. A comprehensive custom-built event-driven python simulation is designed, which populates SUMO trace, containing EV with UAVs while generating 100 s of packets, each for a random source and destination. The simulation ran on a Dell PowerEdge T430 with Intel Xeon processor E5-2600 v3 product family 12-Core, with a clock speed of 3.5 GHz. The simulator accumulates all possible paths from a source to the destination. The proposed scheme and three existing solutions chose one route out of the accumulated paths, depending on their routing parameters and conditions. Finally, the simulator calculates performance metrics and repeats the process for more than 100 packets during each timestamp with a different number of total vehicles. Figure 3a shows the packet delivery ratio for the proposed scheme and three existing solutions (hop-based [9], reliable routing [7], and energy-saver [6]). The comparative analysis of the proposed scheme in Figure 3a considers hop-based only as a legacy benchmark; the actual comparison is between the latest connectivity time-based routing (reliable routing [7]) and multi-objective based routing schemes (energy-saver [6]). Each section of bars represents the evaluation of timestamps containing \(1453\sim 1871\) vehicles, where the proposed scheme achieves more than 90% success. Interestingly, with the increase in the number of vehicles, the hop-based performance degrades to 50% success, whereas reliable routing achieves better results (70% packet delivery). On the other hand, the energy-saver successfully delivers the packets for \(50\sim 70\%\) of the time. Figure 3b demonstrates that the chosen route in the proposed scheme remains connected for a longer period of \(3\sim 6\) s, whereas hop-based remained connected for \(1\sim 5\), energy-saver for \(3\sim 5\) and reliable routing for \(1\sim 4\) s. A longer connectivity time results in a higher packet delivery ratio and reduces the possibility of incomplete transmissions. The exhaustive experiments use real traces of New York City that include intersections, turns, and various road structures; however, the proposed scheme still outperforms existing solutions and achieves high packet delivery with longer connectivity. In Figure 4a, the average transmission delay per vehicle in the chosen route is shown, where almost all schemes show a delay between \(1.7\sim 2\) s. This delay depends on the transmission capability of the vehicles, and it does not include propagation and queuing delay. Nevertheless, our scheme outperforms existing solutions and achieves the lowest transmission delay per vehicle. However, the transmission delay per vehicle is a result of an extra hop in the proposed scheme's chosen route, as illustrated in Figure 4b.