Toward Cyber-Eco Systems: Networked Sensing, Inference, and Control for Ecological and Agricultural Systems

New applications are motivating and informing the design of sen-sor/actuator (S/A) networks, and, more broadly, distributed intelligent systems. Key to enhancing S/A networks is the Dynamic Data Driven Applications Systems (DDDAS) paradigm, characterized by the ability of an executing model of the system to ingest data and in turn steer the collection of new data. Knowledge of many physical systems is uncertain, so that sensing and actuation must be mediated by inference of the structure and parameters of physical-system models. One application domain of rapidly growing interest is ecological research and agricultural systems, motivated by the need to understand plant survival and growth as a function of genetics, environment, and climate. For these applications, cyber-eco systems must be developed that infer dynamic data-driven predictive models of plant growth dynamics in response to weather and climate drivers that allow incorporation of uncertainty. This chapter describes the cyber-eco systems algo-rithms and system architecture, including S/A node design, site-level networking, data assimilation, inference, and distributed control. Among the innovations are: a modular, parallel-processing node hardware design allowing real-time processing, energy-aware hardware/software design, and a networking protocol that builds in trade-offs between energy conservation and latency. The implementations presented in this chapter include experimental networks in an Eastern USA forest environment and an operational distributed system, the Southwest Experimental Garden Array (SEGA), consisting of geographically distributed outdoor gardens on an elevational gradient of over 1500 m in Arizona, USA. Finally, results demonstrate fine-scale inference of soil moisture for irrigation control.