Many industries now rely on drones to monitor infrastructures. In this respect, this article considers maximizing the revenue of an Industrial Internet of Things operator that provides two services: 1) data trading; and 2) drones rental. In service 1), the operator sells data of locations/points it acquired via drones. For service 2), it rents idle drones to users. The problem at hand is to determine the allocation of drones to services 1) and 2) that maximizes the operator's revenue over a given planning horizon. We outline a novel integer linear program (ILP) to solve the said problem, which can be used to determine the optimal number of drones assigned to both services. The ILP, however, requires an exhaustive collection of drone trajectories. We therefore present two heuristics called weighted-based algorithm (WBA) and genetic algorithm (GA) to generate trajectories for data collection. The results show that WBA earns 95.6% of the optimal revenue. GA is able to achieve 99% of the revenue of WBA at best.