Optimal Route Planning of an Unmanned Aerial Vehicle for Data Collection of Agricultural Sensors

The development of the Internet of Things is essential in agriculture to meet the challenges of the agro-ecological transition. However, forwarding the measurements from a massive number of IoT-based sensors distributed in the fields to the internet can be a challenging task, all the more in case of energy, cost, latency, data rate or connectivity constraints. The conventional technologies, as the connection to a cellular network, a gateway or a nano-satellite at low earth orbit, may not always met these constraints. Another approach consists to use a data collector embedded on an Unmanned Aerial Vehicle (UAV). This approach has numerous advantages, as its flexibility and the possibility to reduce the transmit power of the sensor nodes to communicate. However, the trajectories of the UAV have to be optimized beforehand. This paper addresses this issue by modeling the communication ranges of the sensor nodes by hemispheres and by considering the Close Enough Traveling Salesman Problem (CE-TSP) at different flying heights. To solve this problem, an algorithm based on three successive parts, a graph reduction, a partheno-genetic algorithm and heuristic rules, is proposed. This algorithm is first tested on data sets involving a massive number of communicating sensors with various communication ranges, and next on a real agricultural case study. The results highlight the performances of the method proposed and open the way to future perspectives for data collection of IoT-based sensors by means of UAVs.