# A Novel Data Forwarding Strategy for a Drone Delay Tolerant Network with Range Extension
## 1 Introduction
To address the problem of data delivery in such a distributed network with a low node density, we take advantage of the following observation. Suppose that in a drone logistics network, every drone has its home station where a parcel is loaded on the drone. Then, each drone plans the path directly towards the parcel's destination. Now, suppose that the drone also generates data packets i.e., statistical information, geographical information, property information while they are flying to be delivered to different destinations such as homes, businesses, and other organizations. Accordingly, as the drone costs much less than other vehicles, it can plan a detour path to deliver the data while dropping off the parcel. However, the length of detour path is subjected to the drone's energy level. Now, we have two approaches: (i) when drones come within the communication range of each other, they can exchange their data packets and plan the flight path according to the arrival packets; (ii) a drone may find a very close waypoint (packet's destination) but due to the insufficient energy, the drone is unable to visit the waypoint. Hence, the drone has the chance to visit charging stations to charge enough to meet more nearby destinations before meeting the parcel's destination. If the parcel's deadline is estimated to expire before reaching the parcel's destination, the drone simply ignores adding more waypoints in their route before visiting the parcel's destination. This paper is an extension of our other work in [15] which does not consider wireless charging stations. Thus, we make the following contributions in this paper:  
We define the problem of finding a set of destinations to be visited by a drone. The objective is to exchange data packets during a contact such that the data delivery delay reduces and the delivery ratio increases. Drones also guarantee to drop off the packages before their deadline and return to the origin base such that the resulting flight path does not exceed the maximum length of the drones' flight. * We fit wireless charging stations along the flight path to charge the drones for a short period in the case of meeting more nearby waypoints (packets' destinations). This results in the data network to last longer with increased delivery ratio and reduced delay. * We propose a Heuristic Flight Path Planning (HFPP) that finds an optimal flight path that minimizes delivery delay and maximizes delivery ratio. HFPP assigns a weight to every data packet's destination and planning to include wireless charging stations followed by most weighted destinations along the path to increase flight duration and deliver more data. The weight is based on the packet's remaining time to live, priority, and size to get the chance to be fit along the drone's flight path. * We compare the properties and effectiveness of HFPP against two well-known routing protocols namely Epidemic [8], and EBR and a flight planning algorithm [16] using a Java-based simulator called ONE [17]. The results show that HFPP delivers up to 33% more data packets as compared to the existing methods. Also, HFPP reduces data delivery delays up to 85% while overhead ratio is low. The remainder of this paper has the following structure: Section 2 briefly considers related work and Section 3 formulates the problem. In Section 4, we present HFPP and in Section 5, we compare the performance of HFPP with well-known routing protocols and a movement path planning method before concluding in Section 6.