Towards interoperability of entity-based and event-based IoT platforms: The case of NGSI and EPCIS standards

With the advancement of IoT devices and thanks to the unprecedented visibility and transparency they provide, diverse IoT-based applications are being developed. With the proliferation of IoT, both the amount and type of data items captured have increased dramatically. The data generated by IoT devices reside in different organizations and systems, and a major barrier to utilizing the data is the lack of interoperability among the standards used to capture the data. To reduce this barrier, two major standards have emerged: the Global Standards One (GS1) Electronic Product Code Information Service (EPCIS) and the FIWARE Next Generation Services Interface (NGSI). However, the two standards differ not only in the data encoding but also in the underlying philosophy of representing IoT data; namely, EPCIS is event-based, and NGSI is entity-based. Interoperability between FIWARE and EPCIS is essential for system integration. This paper presents OLIOT Mediation Gateway, now one of the incubated generic enablers offered by the FIWARE Foundation, that realizes the required interoperability between NGSI and EPCIS systems. It also demonstrates the applicability and feasibility of the Gateway by applying it to a real-life case study of integrating transparency systems used in a meat supply chain

The PigWise project: a novel approach in livestock farming through synergistic performances monitoring at individual level

ABSTRACT Optimizing production systems in agriculture and farming environments can nowadays be helped by advancements developed in other domains. In the field of precision livestock farming, solutions enriched with ICT, robotics and automation components are increasingly used to improve processes' efficiency and flexibility. This paper proposes a pervasive ICT system to monitor and record eating behavior of fattening pigs, leveraging on HF RFID ear tags identifiers (to detect animal eating while the head is on the trough) and on Camera Vision Systems (to cross-validate RFID reading). In addition a Synergistic Control algorithm is applied due to analyze information, extract feeding behaviors and detect eventual issues. Finally, these information are made available on the network, to the end-user, through the Virtus Middleware: it is an Internet of Things C0100 Scalera A., Brizzi P., Tomasi R., Gregersen T., Mertens K., Maselyne J. ,Van Nuffel A., Hessel E., Van den Weghe H. "The PigWise project: a novel approach in livestock farming through synergistic performances monitoring at individual level". EFITA-WCCA-CIGR Conference "Sustainable Agriculture through ICT Innovation", Turin, Italy, 24-27 June 2013. (IoT) system enabling seamless data integration and event sharing, able to manage heterogeneous information sources and geographically-distributed, large-scale deployments

Chapter 17: Practical experiences of IoT applications for pig, broiler and cattle beef production: IoF2020 meat trial

Internet of Things (IoT) technology offers a significant potential for livestock farming, as animals and their living environments can be monitored and relevant insights can be extracted from the data gathered. As part of the IoF2020 project (Internet of Food & Farm 2020, 2017-2021, EU H2020 programme) IoT devices were tested and demonstrated in six use cases for livestock farming for meat production. The results and lessons learned give insights in how IoT devices can technically and practically be used in livestock farming and how companies can create businesses from the technologies. The use cases are part of the meat trial of the IoF2020 project and each developed IoTbased products to solve one or more specific challenges that the livestock sector faces. The products were developed in close collaboration with the end-users, who are mainly livestock farmers and meat processors, who helped to formulate requirements, provided their facilities as a testbed and gave feedback to improve the products. The use cases cover three livestock species: pigs, broilers and beef cattle. Diverse IoT solutions and products were developed, including sensors for health and welfare monitoring, environmental monitoring, location tracking and feed silo monitoring. In addition, business intelligence dashboards, transparency/traceability systems and auditing services were also developed. The use cases had installations in 64 sites (farms, slaughterhouses) in 10 different EU countries. In this chapter, we present the objectives, results, challenges and lessons learned from the use cases. We also discuss the stakeholders involved and the business models that have been developed. Feedback from end-users proved to be crucial for steering the developments in the right direction

Automated tracking to measure behavioural changes in pigs for health and welfare monitoring

Abstract Since animals express their internal state through behaviour, changes in said behaviour may be used to detect early signs of problems, such as in animal health. Continuous observation of livestock by farm staff is impractical in a commercial setting to the degree required to detect behavioural changes relevant for early intervention. An automated monitoring system is developed; it automatically tracks pig movement with depth video cameras, and automatically measures standing, feeding, drinking, and locomotor activities from 3D trajectories. Predictions of standing, feeding, and drinking were validated, but not locomotor activities. An artificial, disruptive challenge; i.e., introduction of a novel object, is used to cause reproducible behavioural changes to enable development of a system to detect the changes automatically. Validation of the automated monitoring system with the controlled challenge study provides a reproducible framework for further development of robust early warning systems for pigs. The automated system is practical in commercial settings because it provides continuous monitoring of multiple behaviours, with metrics of behaviours that may be considered more intuitive and have diagnostic validity. The method has the potential to transform how livestock are monitored, directly impact their health and welfare, and address issues in livestock farming, such as antimicrobial use