Drone Base Station Trajectory Planning for Optimal Resource Scheduling in LTE Sparse M2M Networks

Providing communication services connectivity in areas out of reach of the cellular infrastructure is a very active area of research. This connectivity is particularly needed in case of deploying machine type communication devices (MTCDs) for critical purposes such as homeland security. In such applications, MTCDs may be deployed in areas that are hard to reach through regular communications infrastructure while the collected data are timely critical. Drone-supported communications constitute a new trend in complementing the reach of the terrestrial communication infrastructure. In this study, drones are used as complementary base stations to provide real-time communication services to gather critical data out of a group of MTCDs that are sparsely deployed in a marine environment. Therefore, the drone movements among the different MTCDs are to be optimized to minimize data deadline missing. We therefore compare between an ant colony-based technique that aims at optimizing the drone movements among the different MTCDs to achieve this goal, with a genetic algorithm based one. We present the results of several simulation experiments that validate the different performance aspects of both techniques.