This paper presents a continuous delivery/continuous verifiability (CD/CV) method for IoT dataflows in edge¿fog¿cloud. A CD model based on extraction, transformation, and load (ETL) mechanism as well as a directed acyclic graph (DAG) construction, enable end-users to create efficient schemes for the continuous verification and validation of the execution of applications in edge¿fog¿cloud infrastructures. This scheme also verifies and validates established execution sequences and the integrity of digital assets. CV model converts ETL and DAG into business model, smart contracts in a private blockchain for the automatic and transparent registration of transactions performed by each application in workflows/pipelines created by CD model without altering applications nor edge¿fog¿cloud workflows. This model ensures that IoT dataflows delivers verifiable information for organizations to conduct critical decision-making processes with certainty. A containerized parallelism model solves portability issues and reduces/compensates the overhead produced by CD/CV operations. We developed and implemented a prototype to create CD/CV schemes, which were evaluated in a case study where user mobility information is used to identify interest points, patterns, and maps. The experimental evaluation revealed the efficiency of CD/CV to register the transactions performed in IoT dataflows through edge¿fog¿cloud in a private blockchain network in comparison with state-of-art solutions.This work has been partially supported by the project “CABAHLA-CM: Convergencia Big data-Hpc: de los sensores a las Aplicaciones” S2018/TCS-4423 from Madrid Regional Government, Spain and by the Spanish Ministry of Science and Innovation Project “New Data Intensive Computing Methods for High-End and Edge Computing Platforms (DECIDE)”. Ref. PID2019-107858GB-I00; and by the project 41756 “Plataforma tecnológica para la gestión, aseguramiento, intercambio
preservación de grandes volúmenes de datos en salud construcción de un repositorio nacional de servicios de análisis de datos de salud” by the PRONACES-CONACYT, Mexic
