# Emerging Trends in Hybrid Wireless Communication and Data Management for the Industry 4.0
## 5 Hierarchical and Multi-Tier Communication and Data Management for the Industry 4.0
### Decentralized Data Distribution  
In terms of the data management, the Orchestrator plays an important role in facilitating the development of novel smart data distribution solutions that cooperate with cloud-based service provisioning and communication technologies. Smart proactive data storage/replication techniques can be designed, ensuring that data is located where it can be accessed by appropriate decision makers in a timely manner based on the performance of the underlying communication infrastructure. Consequently, the Orchestrator serves as a great opportunity to implement different types of data-oriented automation functions at reduced costs, like interactions with external data providers or requestors, inter-cell data distribution planning, and management and coordination of the LMs. For the data management, allocation of roles to the Orchestrator, LMs, and individual devices is less precisely defined in general, and can vary significantly on a per-application and per-scenario basis. In general, we expect that the Orchestrator decides on which cells (controlled by one LM each) data  
Figure 6: Hybrid communication management: interaction between management entities. need to be available and thus replicated. Also, it decides on which cells data must not be replicated due to ownership reasons. Thus, we expect the Orchestrator to be responsible for managing the heterogeneity issues related to managing data across a number of different cells, possibly owned and operated by different entities. LMs manage individual cells. They typically decide where, inside the cell, data need to be replicated, stored, and moved dynamically, based on the requirements of the specific applications, and the resources available at the individual nodes. Note that data in general are replicated across the individual nodes, and not exclusively at the LMs, to guarantee low delays and jitters, which might be excessive if the LMs operate as unique centralized data managers. In some cases, end-devices can also participate in management functions, for example, by exploiting D2D communications to directly exchange data between them, implementing localized data replication or storage policies. In those cases, the data routing is not necessarily regulated centrally, but can be efficiently distributed, using appropriate cooperation schemes. In the architecture, therefore, the control of data management schemes can be performed centrally at the Orchestrator, locally at the LMs, or even at individual devices, as appropriate. Data management operations become distributed, and exploit devices which lie between source and destination devices, like the use of proxies for data storage and access. Note that our architectural scheme enables the storing and replication of data between (i) (potentially mobile) nodes in the factory environment (e.g., the mobile nodes of the factory operators, nodes installed in work cells, nodes attached to mobile robots, etc.) (ii) edge nodes providing storage services for the specific (areas of the) factory, and (iii) remote cloud storage services. All the three layers can be used in a synergic way, based on the properties of the data and the requirements of the users requesting it. Depending on these properties, data processing may need highly variable computational resources. Advanced scheduling and resource management strategies lie at the core of the distributed infrastructure resources usage. However, such strategies must be tailored to the particular algorithm/data combination to be managed. Differently from the past, the scheduling process, instead of looking for smart ways to adapt the application to the execution environment, now it aims at selecting and managing the computational resources available on the distributed infrastructure to fulfil some performance indicators. Our suggested architectural scheme has been used in order to efficiently deploy the data management functions over typical industrial IoT networks.