# A Novel Data Forwarding Strategy for a Drone Delay Tolerant Network with Range Extension
## 1 Introduction  
Drones or Unmanned Aerial Vehicles (UAV) are aircrafts that can autonomously fly and complete their missions unassisted [1]. Drones were initially used in military and civilian applications [2; 3] such as surveillance, policing, firefighting, and search and rescue missions [4]. More recently, retail companies such as Google, Domino's, UPS, and Amazon are developing drones in order to make fast deliveries to their customers [5]. Theoretically, this makes the customers happy as they receive the purchased item within minutes. Also, in terms of cost, drones significantly reduce the delivery cost compared with other traditional logistic networks. As an example, Amazon claims that the estimated cost per mile for a delivery drone is five cents whereas a motor vehicle with the fuel consumption of 10 L/100 km costs approximately 17 cents per mile in addition to the other costs such as vehicle depreciation and maintenance [5]. Given such a cheap and growing network of drones, it is only natural for value added services to be introduced in addition to parcel services. For example, Amazon has a patented technology that enables a delivery drone to scan and collect data from houses on its flight path while dropping off parcels [6]. It is not hard to imagine that more relevant data can be collected while performing these deliveries. For instance, drones can collect information about vehicles, the exterior of homes and any property visible from the outside and use that for marketing related products. These drones periodically generate data packets about such things as damaged roofs on homes or sick-looking trees in yards when flying over. This can be extended to more useful cases, like commercial properties,stadiums, and civilian projects that require a lot of maintenance or monitoring from big teams. In order to avoid imposing huge traffic on a centralized network, it is worthwhile to use a cost-efficient drone's network as a distributed network for data delivery. In addition to data collection, there are two other motivations behind this work. The first is that a drone network can help providers that deliver large amounts of data (such as Amazon and Google) to deliver more through their own network and secondly a drone network could act as a range extension network and alleviate reception dead spots for the coming 5G network [7] (this is the subject of new research that the authors are working on). However, data communication in such highly dynamic networks where intermittent connectivity happens frequently is a challenge. This situation becomes worse as the flight density is low (a few deliveries per hour to any particular location). Moreover, drones will quickly run out of battery as data communication significantly consume energy that makes intermittent connectivity worse. These limitations result in a network with large delays and low delivery ratio. Traditional data routing approaches in such disjointed network can be (i) flooding-based protocols [8; 9] where the number of packet replicas is not limited, and (ii) quota-based protocols [10; 11; 12] where the number of replicas is limited. To compare, flooding-based protocols achieve a high delivery ratio when the network resources such as buffer space and energy are not limited. However, this results in the network with a high overhead [10; 13]. In contrast, the quota-based protocols have a low overhead, but lower delivery ratio [14]. As we will show in our simulation studies (Section 5). Both mentioned types of protocols on our given scenario will not be efficient as the traffic density is low. This new problem is different as the previous protocols have assumed that the destinations themselves are other nodes (i.e., drones) and not fixed locations such as homes, businesses, and other customers but not drones in the network. However, we will show in Section 5 that by increasing the density of UAVs firstly, packets will have a better chance to be distributed across the UAV network and secondly, destinations have more chance to be encountered by UAVs.