# A Novel Data Forwarding Strategy for a Drone Delay Tolerant Network with Range Extension
## 3 Problem Formulation  
Let us consider a logistics network of drones as described in the introduction; The drones deliver data as an added feature to dropping off parcels. Hence, drones can communicate with each other and exchange data if they are within the communication range. Also, there are wireless charging stations distributed across the network environment that enables the drone to refill their energy budget. Briefly, we outline the following assumptions (Table 1):  
* There are v drones represented by the set \(N=[n_{1},\ldots,n_{v}]\) and q registered destinations including item buyers and data destinations indicated by \(D=\{d_{1},\ldots,d_{q}\}\). * Drones are responsible to pick up purchased items from m bases indicated by \(B=\{b_{1},\ldots,b_{m}\}\) that belong to different retail or logistic companies and deliver them to the registered destination in D.
* Every drone \(i\) has a maximum flight duration, \(\delta_{i}\), to return to the origin disregarding of the remaining energy. This parameter implies that there is another package to be picked up within \(\delta_{\rm i}\) time period for drone \(i\). This parameter may change over the flight as the package may be allocated to another drone or being cancelled. * There are \(k\) wireless charging stations represented by the set \(C=\{c_{1},\ldots,\ c_{k}\}\) that transfer up to 500 Watt. * Each drone may stay on the charging pad for different time periods Recall, max time to return to the base for every drone \(i\) is \(\delta_{\rm i}\) regardless of the remaining energy. * Charging stations are assumed to have enough wireless charging panels to avoid waiting time. * Each drone has a maximum flying distance, \(l_{max}\), that is determined based on the remaining energy. * For every packet \(i\) in the network, the priority of \(\mu_{i}\) is assigned by the source, meaning how important is the packet. The value of \(\mu_{i}\) is from 1 to 10 meaning maximum to minimum importance respectively. * Every packet \(i\) has time to live, \(\overline{\rm t}_{i}\), that if it expires, the packet will be dropped from the buffer. * Each drone has an infinite buffer space to store data packets. * Drones stay within the communication range of sources, destinations, and other drones until all forwarding packets are transferred. * Packet destinations wireless charging stations are registered in logistic companies, retailers and regulators, meaning that each drone is aware of the location of the destinations, wireless charging stations, and the location of every other drone. * All the nodes communicate at Wi-Fi frequencies with the radio range of \(R\). * Every drone moves independently with a fixed speed of \(S\). The objective is to find a flight path \(M=m_{0}\), \(m_{1},\ m_{2}\)... \(m_{q}\), \(m_{0}\) where \(m_{0}\in B\), \(m_{i\
eq 0}\in D\cup C\) such that the flight path is no longer than \(l_{max}\) and energy consumption of drones is minimized. Our algorithm is NP-hard by a reduction from a Travelling Salesman Problem (TSP) [27] where every drone finds the shortest possible route to visit the waypoints (packets' destinations) and return its base of origin. However, the minimum energy consumption occurs when the drones' mobility converges and matches the direct path towards parcel's destination. This is because the drone will not consume more energy due to detour for data delivery. Henceforth, in the following, we propose a novel heuristic algorithm to efficiently select the next destinations and charging station to be along the flight path for each drone.