# Toward a Detailed Evaluation of Wireless Industrial Data Distribution Approaches +
## 1 Introduction  
Industry 4.0 is becoming characterized, among others, by increasing levels of automation achieved by the collaboration between robots and more in general intelligent machines. Each physical machine is likely to embed a higher and higher number of devices (e.g., industrial IoT devices) constantly generating data, to be exchanged and exploited for controlling the collective behavior of the smart factory. In this regard, optimization of the data distribution process is a major objective for industry [1]. Intelligent industrial environments operate using advanced sensors and communication technologies; therefore, large amounts of data are generated and collected in shop-floors, industrial fields and enterprise facilities, necessitating data handling technologies to provide integrated environments where the production services can run efficiently and be administered effectively [2]. The importance of data in Industry 4.0 deployments is becoming increasingly relevant. Industrial use cases produce greater amounts of data, both at the node level (for example, AV IoT devices) and at the system level, due to the augmenting numbers of devices collaborating with industrial and robotic modules. Furthermore, many key Industry4.0 applications are fused via effective data administration across networked elements. Consequently, a recently adopted approach is to view industrial networks as data-centric and to include efficient data administration as a top priority in such networking settings [3]. In the recent past, pure cloud computing was providing us with an opportunity to address emerging data-centric problems seamlessly at a central point of control [4]. In many cases, hierarchy clouds or fog-oriented approaches have also been used for providing additional usability [5]. Although there have been approaches in the literature about storing and accessing industrially relevant data on the cloud, the additional cost of moving the data among local and distant locations has already been shown as high for some application scenarios and prohibiting for others [6]. When the Industry 4.0 paradigm was born, there were no standards capable of meeting all fundamental requirements. Since the third industrial revolution, in the industries there were connected machines, but they only worked with Ethernet, which cannot guarantee the respect of all the constraints, in particular having a short and deterministic latency for a multitude of devices. The first attempt that was made to overcome the problem was the introduction of the Real Time Ethernet protocol [7] which works with common network devices (switches, bridges and routers) and guarantees deterministic latency, but there have been developed numerous incompatible implementations. Determinism cannot be guaranteed in networks that use Transmission Control Protocol (TCP) as the transport layer protocol in the TCP/IP stack as it involves retransmissions when messages are lost and this takes time. In addition, Real Time Ethernet generates a lot of traffic to send little data as messages must be encapsulated in Ethernet frames that are 64 bytes in size. The second solution proposed was Time-Sensitive Networking (TSN) [8], whose main features are synchronization among nodes, traffic shaping, the existence of shared rules for path selection and resistance to failures. The problem of TSN is that it is not a deterministic protocol, but as mentioned above, this is one of the main requirements and therefore it is not suitable for use in Industry 4.0. Ethernet is not suitable also because in an industrial environment, there can be hundreds if not thousands of nodes, and if they were all wired, it would create an environment in which it would be difficult to ensure the safety of workers. Furthermore, it is difficult to imagine the use of a mobile system connected to the Internet via a cable since the length of the cable would limit its movement.