There are many critical applications for remote sensing in low-resourced areas. Some of these include agricultural monitoring of crops and/or forests for fire and/or disease; wildlife monitoring, to track wildlife movement and detect poachers; free-range livestock monitoring, to track herd movement and to prevent cattle rustling; and early warning of terrorist or bandit activity. In low-resourced situations especially, the system cost is of huge importance, and spells the difference as to whether the system may be implemented or not. Sensors in area-monitoring networks typically are battery-powered, and must be replaced when the battery is exhausted. This can be both difficult and costly, particularly in inaccessible locations. For this reason, reducing the power consumption of sensors in the field is a key factor in designing such remote sensing systems. In this paper/project we consider a hybrid WSN-drone system for remote sensing. A single drone is used to collect information from wieless sensors which have fixed locations in the field. The system is designed so that the drone harvests informations from all sensors during a single tour. The system design is posed as a constrained optimization problem. Our novel solution approach involves using a Lagrange multiplier to construct a differential equation in which the Lagrange multiplier is the independent variable. Our approach has reduced computational complexity, so that real-time solution within seconds is possible even for very large systems.