# Data Gathering Techniques in WSN: A Cross-Layer View
## 5 Routing for Data Gathering in WSN
### Data Collection Utilizing Mobile Sink and Unmanned Aerial Vehicle (UAV)
#### 5.4.1 Routing Directly to the Mobile Sink(s) with No Intra-Node Data Forwarding  
In this section, we review papers in which the only data traffic is between the nodes and the mobile sink, and no data is forwarded between the nodes.  
[PERSON] et al. [179] consider a general fading channel model and suggest an efficient sub-optimal solution that minimizes the energy consumption of all sensor nodes (SNs) while ensuring that data is collected reliably from all SNs with bounded outage probability. The suggested solution decouples the joint optimization problem, which considers both the SNs' wake-up schedule and the unmanned aerial vehicle (UAV's) trajectory, into two separate optimization problems, ensuring that the amount of data collected from each SN reliably exceeds a threshold. One of the formulated optimization problems is non-convex due to non-convex constraints, and therefore it needs to be relaxed. The two problems are solved iteratively to obtain an approximate solution. A flight time minimization problem for a UAV that collects data from a set of energy-constrained ground sensors is studied by [PERSON] et al. [180]. The sensors are assumed to be located on a line (one dimension). Each sensor has a certain amount of data to upload. The UAV can collect data either while flying or while hovering, and only from a single sensor at a time. Accordingly, the UAV's trajectory is divided into non-overlapping data collection intervals, each dedicated to collecting data from a single sensor. The objective is to minimize the total flight time of the UAV from an initial point to a destination by jointly optimizing the division of intervals, the UAV's speed, as well as the sensors' transmission power. The flight time minimization is formulated as a dynamic programming (DP) problem, where each DP stage considers flight time minimization for a single-sensor data collection scenario. The algorithm for the single-sensor case is used to find the UAV's optimal speed and the sensor's transmission power. It is shown that the UAV's optimal speed is proportional to the given energy of the sensors and the inter-sensor distance but is inversely proportional to the data upload requirements.  
The metric addressed by [PERSON] et al. [181] is the age of the information. In particular, Ref. [181] utilizes UAV, and suggests two age-optimal trajectories for it to collect the data from the ground SNs, one that minimizes the age of the 'oldest' sensed information among the sensors, and another that minimizes the average age of the information sensed by all sensors. It is shown that both age-optimal trajectories correspond to the shortest Hamiltonian path in the wireless sensor network, in which the distance between any two sensors is represented by the amount of inter-visit time. The authors adopt dynamic programming and genetic algorithm to find the two different age-optimal trajectories. [PERSON] et al. [182] also utilize a UAV to collect the data from the ground sensors. The model suggested therein assumes that the sensors (nodes) are randomly distributed over a square area. The area is partitioned into small square cells. The UAV flying above the cells hovers above each cell to collect all the data of the sensors within the cell. The paper studies the amount of data per unit time that the UAV can collect from the ground nodes as a function of the number of cells, the height of the UAV, the number of sensors, and the energy capacity of the UAV. The paper suggests a similar analysis when multiple UAVs are utilized. It seeks the optimal number of cells to maximize the per-node capacity under the suggested model and shows that under the suggested data collection network, multiple UAVs can significantly improve the per-node capacity attained by a single UAV. The balanced network communication protocol (BNCP) that utilizes UAV as a mobile sink is suggested by [PERSON] et al. [183]. There is no inter-sensor routing in BNCP, and all sensors' communication energy is spent on the sensor-UAV transmission links. BNCP is implemented and evaluated.