Accuracy and timeliness of collected information by sensors (e.g., humans, unmanned vehicles, etc.) to prevent the illegal movement of drugs across the U.S. border has become critically important to create a safe and secure border environment. Efficient coordination of limited and distributed resources to gather the information necessary to (a) predict potential drug-related activities and (b) provide the protection to law enforcement authorities on the border has become paramount in this effort. We have developed a multi-perspective mathematical programming model to maximize the information gain from a team of unmanned vehicles performing surveillance around a region of interest. Routes of each vehicle are defined at its Control Station. Disruption of communication between each vehicle and its control station is minimized. Each control station considers its perspective of the environment (i.e., situational assessment) and mission, represented by a potential information gain map, resulting from the information gathered by its vehicle and received from other control stations on the same communication network. Routes of the vehicles are further constrained to be within pre-defined air corridors or shipping channels and may support (overlap in sensors coverage area) or complement (disjoint sensors coverage areas) different missions while surveilling the border. Using a small, simulated scenario, the applicability of the mathematical programming model for this type of operations is presented.