# A Novel Data Forwarding Strategy for a Drone Delay Tolerant Network with Range Extension
## 5 Evaluation  
The performance of intermittent networks is highly dependent on the nodes' mobility pattern. However, in current vehicular intermittent networks, nodes do not change their mobility behavior. This makes sense as changing mobility pattern costs vehicles too much. But given in the new drone network, energy costs are so minimal, for the first time, we can consider changing the mobility pattern to improve delivery ratio and reduce delay. We compare HFPP with two DTN routing protocols called Epidemic [8] and EBR [11], and a similar path planning method [16], using a java-based simulator called ONE [17]. The objective of such a comparison is to consider the impact of path planning on data delivery and energy inefficiency in a logistics application. For data delivery, the considered logistics network creates an opportunistic data network where only DTN routing protocols may work efficiently. Hence, we compare HFPP with other well-known DTN routing protocols. Please note, HFPP is the only approach that has inter-node communications as well as modifying nodes' path. Hence, we could not  
Figure 1: Original flight paths of two drones. Figure 2: Optimized flight paths of the two drones in Figure 1 based on Heuristic Flight Path Planning (HFPP). find any similar approach to compare with. However, we demonstrate that changing drones' mobility pattern can have minimum impact on the performance of a logistics network while a high performance is achieved in terms of data delivery compared with other routing protocols. Furthermore, we will show the importance of drones' density in such comparison. We also show the energy inefficiency that HFPP imposes to the network. Briefly, in Epidemic routing protocol, packets are flooded throughout the network and in EBR, the number of replicas is limited and a forwarding strategy is taken upon contacts. In [16], a greedy search algorithm simply finds the closest next way point and establish the flight path. We would like to point out that [16] was the only work related to our work though it does not support inter-drone communications. In the simulations, we consider a disjointed network of drones that are placed randomly on a field of size \(10\times 10\) km\({}^{2}\). We model the new generation of drones that have the capability of carrying up to 5 kg load. The weight of the drone with no load is 4.9 kg and moves at a speed of 100 km/h. Every drone is fully charged at the base station with 2000 k J of energy that allows it to fly at the maximum distance of 100 km. Drones are equipped with GPS and a typical Wi-Fi using IEEE 802.11 g with a radio range of 70 m and the data transmission rate is set to 54 Mbps. We also assume that the geographical location of registered destinations is known to drones. Moreover, during a contact period, drones will stay in the communication range until the process of packet exchange is fully completed. Also, each drone generates a data packet with a different size every 60 s.  
In our evaluations, we assume that the number of destinations is fixed to 50, and the number of drones varies between 50 and 250. This will give us a very important insight of how increase in the number of drones for dropping of packages will impact on data network performance. We have achieved interesting results of this experiment. In all these scenarios, we assume different deadlines for assignment delivery to consider how it affects the flight path and the delay of data delivery. We use the following metrics in our evaluation: (i) delivery ratio which is the ratio of the number of delivered packets to the number of generated packets, (ii) average data delivery delay which is the average delay that every delivered packet experience, (iii) overhead ratio which is ratio of number of delivered packets and number of relayed drones, (iv) average assignment delivery delay which is the average of time that a consignment is delivered, and (v) Energy Inefficiency which is the energy consumption's difference between direct and detour distance.