An Internet of Things Platform Based on Microservices and Cloud Paradigms for Livestock

With the growing adoption of the Internet of Things (IoT) technology in the agricultural sector, smart devices are becoming more prevalent. The availability of new, timely, and precise data offers a great opportunity to develop advanced analytical models. Therefore, the platform used to deliver new developments to the final user is a key enabler for adopting IoT technology. This work presents a generic design of a software platform based on the cloud and implemented using microservices to facilitate the use of predictive or prescriptive analytics under different IoT scenarios. Several technologies are combined to comply with the essential features¿scalability, portability, interoperability, and usability¿that the platform must consider to assist decision-making in agricultural 4.0 contexts. The platform is prepared to integrate new sensor devices, perform data operations, integrate several data sources, transfer complex statistical model developments seamlessly, and provide a user-friendly graphical interface. The proposed software architecture is implemented with open-source technologies and validated in a smart farming scenario. The growth of a batch of pigs at the fattening stage is estimated from the data provided by a level sensor installed in the silo that stores the feed from which the animals are fed. With this application, we demonstrate how farmers can monitor the weight distribution and receive alarms when high deviations happen.This research was partially supported by the Intelligent Energy Europe (IEE) program and the Ministerio de Economía y Competitividad under contract TIN2017-84553-C2-2-R, by the European Union FEDER (CAPAP-H6 network TIN2016-81840-REDT) and the demonstration activity financed by the Operation 01.02.01 of Technological Transfer from the Program of Rural Development in Catalunya 2014–2020 cofinanced by DARP and FEDER

A scalable parallel Progressive Hedging Algorithm for stochastic cluster-scenario-based mixed-integer models

This work presents a general parallelisation of the Progressive Hedging algorithm to coordinate the resolution of two-stage and multi-stage stochastic mixed-integer problems without (binary or integer) variables in the first stage. We report a benchmark study between the computational improvements using our proposal and the parallel version (using pyro) of the Pyomo integrated Progressive Hedging. Moreover, we study the influence of a quadratic term to accelerate the convergence, different scenario-cluster formation and several step update policies by solving different instances using our proposal