# Performance Analysis of Latency-Aware Data Management in Industrial IoT Networks +
## 1 Introduction  
Industry 4.0 refers to the fourth industrial revolution that transforms industrial manufacturing systems into Cyber-Physical Production Systems (CPPS) by introducing emerging information and communication paradigms, such as the Internet of Things (IoT) [1]. Two technological enablers of the Industry 4.0 are (i) the communication infrastructure that will support the ubiquitous connectivity of CPPS [2] and (ii) the data management schemes built upon the communication infrastructure that will enable efficient data distribution within the factories of the future [3]. Industrial IoT networks (Figure 1) are typically used, among others, for condition monitoring, manufacturing processes and predictive maintenance [4]. To maintain the stability and to control the performance, those industrial applications impose stringent end-to-end latency requirements on data communication between hundreds or thousands of network nodes [5], e.g., end-to-end latencies of 1-100 ms [6]. Missing or delaying important data may severely degrade the quality of control [7]. Edge computing, also referred to as fog computing, implements technical features that are typically associated with advanced networking and can satisfy those requirements [8]. Fog computing differs from cloud computing with respect to the actual software and hardware realizations, as well as in being located in spatial proximity to the data consumer (for example, the user could be a device inthe industrial IoT case). In particular, components used to realize the fog computing architecture can be characterized by their non-functional properties. Such non-functional properties are, for example, real-time behavior, reliability and availability. Furthermore, fog nodes can follow industry-specific standards (e.g., IEEE 802.15.4e [9] or WirelessHART [10]) that demand the implementation, as well as verification and validation of software and/or hardware to follow formal rules. Distributed data management, a key component of fog computing, can be a very suitable approach to cope with these issues [11]. In the context of industrial networks, one could leverage the set of nodes present at the edge of the network to distribute functions that are currently being implemented by a central controller [12]. Many flavors of distributed data management exist in the networking literature, depending on which edge devices are used. In this paper, we consider a rather extreme definition of distributed data management and use the multitude of sensor nodes present in an industrial physical environment (e.g., a specific factory) to implement a decentralized data distribution, whereby sensor nodes cache data they produce and provide these data to each other upon request. In this case, the choice of the sensor nodes where data are cached must be done to guarantee a maximum delivery latency to nodes requesting those data. In this paper, we exploit the Data Management Layer (DML), which operates independently of and complements the routing process of industrial IoT networks. Assuming that applications in such networks require that there is (i) a set of producers generating data (e.g., IoT sensors), (ii) a set of consumers requiring those data in order to implement the application logic (e.g., IoT actuators) and (iii) a maximum latency \(L_{\text{max}}\) that consumers can tolerate in receiving data after they have requested them, the DML offers an efficient method for regulating the data distribution among producers and consumers. The DML selectively assigns a special role to some of the network nodes, that of the proxy. Each node that can become a proxy potentially serves as an intermediary between producers and consumers, even though the node might be neither a producer, nor a consumer. If properly selected, proxy nodes can significantly reduce the access latency; however, when a node is selected as a proxy, it has to increase its storing, computational and communication activities.