DoD’s Data-to-Decisions (D2D) program aims to develop an open-source architecture system that enables rapid integration of existing and future data exploitation tools, creating a new paradigm in data management and analysis. Automated sensor planning and management based on shared situation awareness (SA) would be useful. Designing an overall systematic framework for cloud-based sensor planning that can ingest real-time SA data, compute the application and mission-dependent quality of data and metadata, and make near-real time decisions for the sensors, is challenging. To address this need, IAI has been awarded a contract entitled, “Data-Centric Sensor Planning in a Cloud Environment.” A systematic approach based on cloud computing will be used to provide scalable data mining and analysis algorithms, and tools and platforms for ingesting real-time sensor data for shared SA and predictive processing, and to drive the sensor planning loop. A scalable workflow will be implemented with various components of Hadoop ecosystem to mature an information-theoretic sensor management framework, and integrate it with DoD data and metadata to achieve structural and semantic interoperability. A user interface layer will be implemented using Ozone Widgets. Based on a selected application scenario and mission, actions of sensors will be determined and sensor outputs will be emulated based on these decisions. One key innovation is implementing and integrating sensor planning and management loop in the cloud. Further, a mission and application-driven framework systematically computes the information quality as a fusion of both a set of relevant information characteristics and entropy. A prototype of the system will be tested on IAI’s private cloud DRACO, using IAI’s Hadoop distribution Elastic LocoMotive, which already has many Hadoop services running in a Virtual Machine (VM) and will be configured for this project.