3D-CHESS: Decentralized, distributed, dynamic and context-aware heterogeneous sensor systems (3D-CHESS)

The overarching goal of the 3D-CHESS Early-Stage Technology proposal is to demonstrate proof of concept (TRL 3) for a context-aware Earth observing sensor web consisting of a set of nodes with a knowledge base, heterogeneous sensors, edge computing, and autonomous decision-making capabilities. Context awareness is defined as the ability for the nodes to gather, exchange, and leverage contextual information (e.g., state of the Earth system, state and capabilities of itself and of other nodes in the network, and how those relate to the dynamic mission objectives) to improve decision making and planning. We will demonstrate the technology and characterize its performance and main trade-offs in a multi-sensor in-land hydrologic and ecologic monitoring system performing four inter-dependent missions: studying non-perennial rivers and extreme water storage fluctuations in reservoirs, and detecting and tracking ice jams and algal blooms. The Concept of Operations is as follows. Nodes in the sensor web can be ground, air or space. Nodes may be manually operated or fully autonomous. Any node can send a request for a mission to the sensor web (e.g., measuring geophysical parameter p at point x and time t+/-dT with a certain resolution dx and accuracy dp). Upon reception of a mission request, each node uses a knowledge base to decide if given its own state and capabilities it can perform part or all of the proposed mission. If so, it enters a planning phase in which based on its own goals and utility function it decides whether and how much to bid for the proposed mission. A market-based decentralized task allocation algorithm is used to coordinate assignments across nodes. To establish proof of concept for a sensor web that works as described in the previous paragraph, we will develop a multi-agent simulation tool by integrating existing tools developed by the team and apply it to a relevancy scenario focusing on global inland hydrologic science and applications. A continuous monitoring system will be simulated that will provide global, continuous measurements of water levels, inundation, and water quality for rivers and lakes using a variety of sensors and platforms. The system will start with a default scientific mission objective to study extreme water storage fluctuations in reservoirs and wetting and drying processes in non-perennial rivers (science-driven). In addition, two applications-driven missions of opportunity will be modeled and considered: ice jams and corresponding upstream flood events, and harmful algal blooms in lakes. We recognize that 3D-CHESS represents an "aggressive" vision that departs significantly from the state of practice. Therefore, in addition to comparing the value of an implementation of the full 3D-CHESS vision against the status quo (Goal 1), we will also systematically study "transition" architectures that lie somewhere in between the full 3D-CHESS concept and the status quo (Goal 2). For example, in these transition architectures, new task requests may come only from human operators, or planning may be manually done by operators for some nodes while being fully autonomous for others – although still allowing for humans to update the nodes' utility functions and intervene in case of contingency. The proposed work has direct relevance to the O1 objective of the AIST program solicitation as it develops new technologies that enable unprecedented degrees of autonomy, decentralization, and coordination to achieve new science capabilities and improved observation performance while reducing development and operational costs. The combination of the knowledge-based technologies and decentralized planning technologies integrated within a multi-agent system framework enables the EOS to respond to scientific and societal events of interest faster and more effectively.

Advance Earth system science knowledge through the Identification, develop, and demonstrate innovative information systems technologies

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