Abstract: A method for observing a predetermined monitoring area, wherein one or more sensing platform nodes are employed to observe a predetermined number of sub-areas of the monitoring area, includes observing the sub-areas of the monitoring area using the sensing platform nodes so as to collect measuring data for the sub-areas. A prediction model is provided for analyzing predictability of measuring data for the sub-areas based on the collected measuring data. Future measuring data is calculated for the sub-areas and uncertainty of the future measuring data over time is calculated using the prediction model. The sensing platform nodes are scheduled for observation of the sub-areas according to a scheduling mechanism. The scheduling of the sensing platform nodes is dependent on the calculated uncertainty of the future measuring data predicted for the sub-areas.

Abstract: A method for verifying authenticity of a monitoring signal includes employing a multitude of actuators to impact a physical environment with individual signals, wherein the individual signals originate from the actuators and are directed to the physical environment; observing, via at least one sensor device, the physical environment so as to record the monitoring signal, wherein the monitoring signal represents a combined impact of the individual signals on the physical environment; and comparing the monitoring signal with an expected signal to determine a degree of similarity between the monitoring signal and the expected signal, wherein the expected signal is computed on the basis of one or more predetermined template.

Abstract: A method for observing a predetermined monitoring area, wherein one or more sensing platform nodes are employed to observe a predetermined number of sub-areas of the monitoring area, includes observing the sub-areas of the monitoring area using the sensing platform nodes so as to collect measuring data for the sub-areas. A prediction model is provided for analyzing predictability of measuring data for the sub-areas based on the collected measuring data. Future measuring data is calculated for the sub-areas and uncertainty of the future measuring data over time is calculated using the prediction model. The sensing platform nodes are scheduled for observation of the sub-areas according to a scheduling mechanism. The scheduling of the sensing platform nodes is dependent on the calculated uncertainty of the future measuring data predicted for the sub-areas.

Abstract: A method for verifying authenticity of a monitoring signal includes employing a multitude of actuators to impact a physical environment with individual signals, wherein the individual signals originate from the actuators and are directed to the physical environment; observing, via at least one sensor device, the physical environment so as to record the monitoring signal, wherein the monitoring signal represents a combined impact of the individual signals on the physical environment; and comparing the monitoring signal with an expected signal to determine a degree of similarity between the monitoring signal and the expected signal, wherein the expected signal is computed on the basis of one or more predetermined template.

Abstract: A method for verifying authenticity of a monitoring signal includes employing a multitude of actuators to impact a physical environment with individual signals, wherein the individual signals originate from the actuators and are directed to the physical environment; observing, via at least one sensor device, the physical environment so as to record the monitoring signal, wherein the monitoring signal represents a combined impact of the individual signals on the physical environment; and comparing the monitoring signal with an expected signal to determine a degree of similarity between the monitoring signal and the expected signal, wherein the expected signal is computed on the basis of one or more predetermined templates, wherein the predetermined templates are previously generated in a secret initialization procedure in such a way that the impact on the physical environment for each of the individual signals is separately recorded as a template by the sensor device.

Abstract: A method for verifying authenticity of a monitoring signal includes employing a multitude of actuators to impact a physical environment with individual signals, wherein the individual signals originate from the actuators and are directed to the physical environment; observing, via at least one sensor device, the physical environment so as to record the monitoring signal, wherein the monitoring signal represents a combined impact of the individual signals on the physical environment; and comparing the monitoring signal with an expected signal to determine a degree of similarity between the monitoring signal and the expected signal, wherein the expected signal is computed on the basis of one or more predetermined templates, wherein the predetermined templates are previously generated in a secret initialization procedure in such a way that the impact on the physical environment for each of the individual signals is separately recorded as a template by the sensor device.

Abstract: A method for crowd detection in an area includes determining moving patterns of persons in the area and the number of persons within and/or moving from and/or to the area over a certain time period to obtain model training data sets; assigning each model training data set to represent one of one or more predefined crowd levels in the area; generating a crowd detection model based on the model training data sets; and estimating an actual crowd level for the area using the generated crowd detection model with actual data of moving profiles and/or the actual number of persons within and/or moving from and/or to the area over a certain time period.

Abstract: Disclosed is a method for identifying content, preferably media content for publishing including the steps of a) Providing a content element for a user, b) Marking the content element with a global mark, c) Marking the content element with a local mark, d) Presenting the marked content element, e) Detecting at least part of the presented content element, f) Extracting the global mark and the local mark from the detected part of the content element, g) Identifying the extracted local mark and the global mark, and h) Identifying the content element based on the global and local mark. The invention relates also to a corresponding system and a use of the method and the system.

Abstract: A computer-implemented method for assigning tasks to a plurality of agents includes determining, by a processor, a stance for an agent of the plurality of agents; and, when an unassigned task is available, assigning, by the processor, the unassigned task to an agent in the second stance. The stance is selected from the group consisting of a first stance and a second stance. Determining the stance is based on whether an unassigned task is available. The first stance relates to balancing tasks among the plurality of agents and the second stance relates to maximizing an amount of tasks assigned to a subset of the plurality of agents.

Abstract: Disclosed is a method for identifying content, preferably media content for publishing including the steps of a) Providing a content element for a user, b) Marking the content element with a global mark, c) Marking the content element with a local mark, d) Presenting the marked content element, e) Detecting at least part of the presented content element, f) Extracting the global mark and the local mark from the detected part of the content element, g) Identifying the extracted local mark and the global mark, and h) Identifying the content element based on the global and local mark. The invention relates also to a corresponding system and a use of the method and the system.