DataRel - Big Data for Resilient Logistics’

DATAREL aims at advancing the extant logistic knowledge with novel Internet of Things (IoT) and Big Data solutions for detecting emergence, which is used to improve the quality control and multimodal planning in terms of resilience, real time, efficiency, and dynamics. In particular, DATAREL addresses a system comprised of two main intelligent
components: the frontend Logistics Internet of Things (LIOT) and the backend Artificial Logistics Intelligence (ALI). In the frontend, LIOT investigates a radically new approach to logistic processes involving cyber-physical networks of logistic entities (e.g., goods, vehicles, infrastructure) that embed smart sensors and business logic, forming thus Smart Returnable Transport Items (SRTIs). SRTIs are capable of collecting data from extant IoT devices and social networks. LIOT can detect unexpected behaviour and do adaptive sampling of data generated by collaborating SRTIs in the region of interests. Such capabilities enable the extension of logistic processes with the real-time monitoring and dynamic planning capabilities at the point of action. In the backend, ALI is responsible for logistics planning given subsequent masses of data provided by LIOT. ALI also has learning capabilities to self-evolve with unprecedented
emergence from the dynamic environment through interacting with human planners. We argue that such an intelligent system significantly improves the real time and resilience of planning decisions under uncertainty and time pressure. Compared to existing prescriptive approaches, our online predictive/descriptive approach is much better suited to deal with current challenges of IoT and Big Data such as volume, trustworthiness,
heterogeneity, velocity, and dynamics. The implementation of this concept relies on a common cross layer service paradigm, going from enterprise applications down to the logic executed on SRTIs (e.g., sensors and actuators).