# Algorithms for Delivery of Data by Drones in an Isolated Area Divided into Squares
## 1 Introduction  
Delay tolerant networks (DTNs) allow communication in environments in which frequent transmission discontinuities are present [1, 2, 3, 4]. They have applications in numerous fields, such as space communication networks [1, 2, 3], smart cities [5], intelligent transport networks, rural networks, environmental monitoring networks, and vehicle networks [4, 6]. Within DTNs, message transmission is based on the store-carry-forward paradigm [7, 8]. Devices update their communication routes based on the topological changes of the network, and the mobility of the devices plays an important role [9, 10, 11]. The role of routing [12, 13, 14] in DTNs is to find the best path to send data through the network to reach the destination. The routing strategies used by DTNs are classified based on criteria such as connection type between nodes, the time at which the path for messages is established, the amount of information held by the nodes about the network, and the number of copies of a message that a node sends. Algorithms containing different amounts of DTN information are proposed for investigation in [14]. It has been shown that the performances of these algorithms gradually increase, depending on the amount of information about the network they use. Based onthe number of copies of a message, single-copy algorithms (forward based) and multiple-copy algorithms (flood based) have been investigated [15; 16]. The Direct Delivery algorithm only sends the message to the destination node, and is suitable for both small- and high-mobility networks, in which the probability of meeting between the nodes is high. Comparisons between classical flood-based protocols, such as Epidemic [17], Spray and Wait [18], PRoPHET [19], and MaxProp [20], were undertaken in [15]. Maximum flow with the static approach in buffer-limited delay tolerant networks was investigated in [21; 22]. In public transport networks, the connection type between nodes is based on a well-known schedule and well-defined routes. A DTN composed of pedestrians and cyclists equipped with smart devices was investigated using routing protocols in an Opportunistic Network Environment (ONE) [23]. The MaxProp protocol yielded the best results in terms of delivery probability and average latency. DTN communications in the network storage depend on the store-carry-forward mechanism. A DTN system applied in a communication network on a railway line was found to reduce the message delivery time by 20%, depending on the schedule of trains [8]. In [24], an algorithm to search for the shortest safe path on the network with a time-dependent and edge-length danger factor was proposed. This work is suitable for the optimization of heavy trucks carrying inflammable materials, poison gas, or explosive cargo, and traveling within a city. Based on the simulation in ONE software, the Scheduling-Probabilistic Routing Protocol using History of Encounters and Transitivity (PROPHET) improves the delivery rate and optimizes the delivery delay with low overhead in DTNs for IoT applications [25]. Drone networks are now used in numerous applications across domains including topography [26], aerial observation [27], delivery [28; 29], agriculture [30], communications [25; 31; 32], atmospheric sciences [33; 34; 35], and rescue missions [36]. Drone flight path planning can be categorized as off-line planning, on-line planning, or cooperative planning [37; 38]. The relatively short flight distance of drones due to their limited battery energy [39] can be extended using drone networks. Flight simulations using a topological map of hexagons, in which each hexagon contains one drone, have shown the potential for application in indoor rescue missions [36]. In [40], coverage path planning with UAVs was studied, addressing simple geometric flight patterns, such as back-and-forth and spiral, and more complex grid-based solutions considering full and partial information about the area of interest. The area was divided into squares and a drone was flown from a square to another.