Sensor data sharing involves exchanging sensor data among multiple devices, systems, or platforms through various means, such as wired or wireless communication, cloud storage, and distributed computing. In Vehicle-to-Everything (V2X) communication, sensor data sharing is known as Collective Perception (CP). V2X Collective Perception is the principle of exchanging sensor data among V2X-capable stations, such as vehicles, vulnerable road users, or roadside units, by exchanging lists of perceived objects in the allocated 5.9 GHz frequency band for road safety and traffic efficiency. An object can be anything relevant to traffic safety and is described using its characteristics such as position, heading, and velocity. Objects are detected thanks to sensors such as cameras, LiDARs, and radars mounted on V2X stations. This thesis investigates the message generation rule for CP, specifically how often and with which objects a Collective Perception Message (CPM) should be generated for transmission. The contained studies focus on the challenges posed by the limited bandwidth available in the 5.9 GHz channel against the object selection for inclusion in CPMs. In the first part of the realized studies, the protocol design and the requirements of CP are comprehended from the network and application-related aspects, concluding that the process of filtering objects is necessary to control the channel usage of CP. Moreover, results show that object filtering is only beneficial in high-traffic density scenarios and should not be applied when channel resources are plenty available. In the second part, methods are developed and assessed to adapt the object filtering mechanism to the available channel resources and control information redundancy, i.e., controlling the number of vehicles transmitting updates about the same objects. Through a combination of theoretical analysis, large-scale simulations, and experimental evaluation, this thesis provides a better understanding of the requirements of CP for object filtering and shows the benefits of a developed novel algorithm to adapt object filtering to the available channel resources. Additionally, it elaborates on new metrics and provides a requirements analysis and performance assessment of selected information redundancy reduction techniques. Finally, the results show that combining both approaches enables efficient control of information redundancy while allowing efficient channel resource usage.