Sensor data distribution and knowledge inference framework for a cognitive-based distributed storage sink environment

Abstract

Large-scale sensor data distributions and knowledge inferences are major challenges for cognitive-based distributed storage environments. Cognitive storage sinks play an essential role in addressing these challenges. In a data-concentrated distributed cognitive sensor environment, cognitive storage sinks regulate the data distribution operations and infer knowledge from the large amounts of sensor data that are distributed across the conventional sensors. Embedding cognitive functions in conventional sensors is unreasonable, and the knowledge-processing limitations of conventional sensors create a serious problem. To overcome this problem, we propose a cognitive co-sensor platform across a large-scale distributed environment. Further, we propose a distributed data distribution framework (DDD-framework) for effective data distributions and a distributed knowledge inference framework (DKI-framework) that infers useful patterns for building knowledge intelligence. The analysis and discussion demonstrate that these frameworks can be adequately instigated for the purpose of optimal data distribution and knowledge inference within the horizon of a real-time distributed environment.