A Pipeline of a Resilient Workforce that integrates Advanced Analytics to the Agriculture, Food and Energy Supply Chain

Existing blockchains support a limited set of first-class transactions and rely on user-defined code for extended programmability. This creates challenges with robustness, performance and usability for applications using blockchains. To address these problems, we will develop the conceptual and implementation foundations for ���declarative blockchain transactions��� that will enable the explicit capture as first-class blockchain transactions, various transactional behaviors common to many blockchain applications. These first-class primitives will serve as building blocks that can be composed by users into more complex blockchain transaction workflows and can be supported by automated reasoning in the blockchain to avoid aforementioned challenges.

Inconsistent quality and aesthetics in agricultural crops can result in increased consumer and producer food waste, reduced industry resiliency and decreased farmers������������������ and growers������������������ profit, poor consumer satisfaction, and inefficiencies across the supply chain. Although there are opportunities to characterize and quantify sources of phenotypic variability across the agricultural supply chain - from cultural practices of growers and producers to storage and handling by distributors - the data available to allow for assessment of horticultural quality drivers are disparate and disconnected. The absence of data integration platforms that link heterogeneous datasets across the supply chain precludes the development of strategies and solutions to constrain variability in produce quality. This project������������������s central hypothesis is that multi-dimensional produce data can be securely integrated and used to optimize management practices in the field while simultaneously adding value across the entire food supply chain. We propose to develop multi-modal sensing platform along with a trust-based, data management, integration, and analytics framework for systematic organization and dynamic abstraction of heterogeneous data across the supply chain of agricultural crops. The projects short term goals are to (1) engage growers to refine research and extension priorities; (2) develop a first-of-its-kind modular imaging system that responds to grower needs by analyzing existing and novel multi-dimensional data; (3) establish the cyberinfrastructure, including analytics and blockchain, to make meaningful inference of the acquired data as related to management practices while ensuring data security; (4) deploy the sensing system at NCSU������������������s Horticultural Crops Research Station in Clinton, NC and on a large-scale system at a major commercial farm and distribution facility, and (5) extend findings to producers and regulators through NC Cooperative Extension. The proposed sensing and cyberinfrastructure platforms will be crop-agnostic and our findings will be transferable to other horticultural crops produced in NC and beyond.

This project seeks to develop a platform-SmartChainDB for supporting Smart Marketplaces in trustless environments. Such marketplaces should enable efficient assessment of bids in response to service requests without a-priori trust establishment between parties. In domains like Digital Manufacturing, job bid assessments are very time consuming efforts that take order of months. The platform will be developed by extending a BlockChain database for managing trust, with transaction types necessary to support a protocol for service requests and response bids. Another key extension will be semantic-enablement of the BlockChain database. A proof-of-concept prototype in Smart Manufacturing will be developed using SmartChainDB.

We propose this planning grant to fund activities that will crystallize the engineering research theme and further define the research thrusts that are needed to accomplish the targeted societal impact of the Engineering Research Center for Accelerating Agricultural Sustainability from Seed to Table. This Engineering Research Center proposes integrative systems solutions and innovative strategies that will address the challenges associated with food security in the 21st century. We anticipate that the engineering solutions and decision support systems that are developed as part of this ERC will accelerate the discovery breeding and management strategies for increasing crop yield under current resource constraints and enhance crop robustness to minimize losses that occur at various stages of the food supply chain. The ERC will have four synergistic research thrusts: 1) Sensor Development, Calibration, and Integration; 2) Data Mining, Machine Learning, and Multiscale Modeling for Improving Plant Yield, Robustness, and Development; 3) Heterogenous Testbeds for Inducing and Monitoring Complex Growth Conditions; and 4) Data Management Cyberinfrastructure and High Speed Computing Architecture Development.

The separation of control and data plane in SDN architectures helps merge packet and circuit paradigms into a single architecture and enables logical centralization of the control function. This enables new thinking about solutions to path optimization problems frequently encountered in networking, from routing to traffic engineering. The SERPENT project proposes to develop effective solutions for representing, storing and manipulating network state using rich semantic models such that path and topology embedding problems can be solved using a semantic database framework. This will simplify creation of novel network control and management systems able to cope with increasingly complex user requirements.

Achieving scalable processing of the increasing amount of publicly-available Semantic Web data will hinge on parallelization. The Map-Reduce programming paradigm recently emerged as a de-facto parallel data processing standard and has demonstrated effectiveness with respect to structured and unstructured data. However, Semantic Web data presents challenges not adequately addressed by existing techniques due to its flexible, fine-grained data model and the need to reason beyond explicitly represented data. This project will investigate optimization techniques that address these unique challenges based on rethinking Semantic Web data processing on Map-Reduce platforms from the ground, up - from query algebra to query execution.

Knowledge graphs are information networks with a specific topology that can be modeled as algebraic data types in a type system called Flutes, created by Datanova Scientific to rigorously analyze formal approaches to semantic integration. This project will demonstrate the capability of Flutes typing for summarizing knowledge graphs, collecting and collapsing high-dimensional data into low-dimensional data. Typing will help optimize queries based on column values similar to NOSQL databases. By using Flutes to implement a tactical knowledge base for DoD and other agencies, front-end applications can avoid including code for data validation, checking integrity constraints, or mission data extraction

DO3 Task Order 2.9 KRM

BOSH performs test and evaluation of different wireless transmission links, particularly data links transporting Full Motion Video. Transmission may be air-to- ground, air-to-air, ground-to-ground, or ground-to-ground from different ranges and line-of-sight altitudes, such as from aircraft at different attitudes, and under different environmental conditions. Most of the post-test analysis is based on capture IP traffic and is manpower intensive. BOSH is seeking ways to improve data capture, identify capture parameters, and automate data analysis, and is considering a research partnership with some NCSU faculty to that end. In the proposed project, we would undertake a reasoned and in-depth analysis of their data analyses problems and assess the suitability of available open-source big data frameworks as a foundation for BOSH's IP data analytics platform. The result of this study will be recorded in a whitepaper.