LAUSR

A novel cognitive interference control framework for heterogeneous local access networks supporting computing and data processing in urban Internet of Things (IoT) systems is presented. The notion of cognitive content-aware interference control is introduced, where the transmission pattern of cognitive nodes is dynamically adapted to the state of “protected” IoT data streams. The state describes the performance degradation that interference would cause to algorithms processing the data if the cognitive nodes chose to transmit in the corresponding time period. The framework is instantiated for a case-study scenario, where device-to-device (D2D) and long-term evolution communications coexist on the same channel resource. A city-monitoring application is considered, where the state captures the type of frames within a multimedia video stream processed at the edge of the network to detect and track objects. Numerical results show that the proposed cognitive transmission strategy enables a significant throughput increase of local D2D communications for a target accuracy of the monitoring application.