Facing the increase in world population and the stagnation in available arable land there is a high demand for optimizing the food production. Considering the worldwide and ongoing reduction of the agricultural labor force novel approaches for food production are required. Vertical farming may be such a solution where plants are being produced indoors in racks, cared by robotic appliances which will be operated by specialized software. Given the multitude of parameters which determine the ideal condition, a lot of data needs to be acquired. As this data is used to adapt the entire Cyber-Physical System to a changing environment the data has to be secure and adaptations have to consider safety aspects as well. Such systems must hence be secure, safe, scalable and self-adaptable to a high degree. We present an important element for such solutions, a cloud, IoT and robotic based smart farming framework.
Facing food insecurity and overuse of resources due to effects of climate change, humanity needs to find new ways to secure food production and produce close to consumers. Vertical farming, where plants are grown in vertical arrays inside buildings with help of Information and Communication Technology (ICT) components, could contribute to solving this issue. Such systems integrate heterogeneous devices on different computing layers and acquire a lot of data to monitor and optimize the production process. We created an indoor testing unit in which growing conditions can be monitored and controlled to optimize growth of microgreens. This setup includes an Indoor Farming Support as a Service (IFSaaS) prototype that provides safe and secure monitoring and controlling, as well as self-adaption of an indoor farming system. In this article we provide information about the combination of most suitable technologies.
In recent years the development of autonomous vehicles has increased tremendously and a variety of methodologies had been applied to make them more safe and secure. This work shows a multilevel approach combining Failure Mode, Effects and Criticality Analysis of an autonomous railway system with sociological and technical aspects to support safe operations and human-machine interactions in the field of autonomous railway systems. This approach includes all relevant technical components, as well as the assessment of measures for a safety process based on the Failure Mode, Effects and Criticality Analysis. We applied the Persona-Roberta model to assess safety aspects at the interface between humans and machines and applied both results to establish training materials. The results provide answers to questions about the avoidance of technical errors, discussions on security and safety aspects and shows organizational development tools for accident prevention. In the future the created knowledge will be used to improve trust in digital solutions and Cyber-Physical Systems.
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