# Future Industrial Networks in Process Automation: Goals, Challenges, and Future Directions
## 5 Research Directions  
Among the discussed challenges above, the engineering guideline for brown-field is the most important research question since it is the base for all the other functionalities to build upon. Though each brown-field site also varies from each other in terms of applications and end devices, it is adequate to derive guidelines with basic functionality to achieve IT/OT integration in process automation together with the flexibility to tune parameters in each site. An important aspect of the guidelines is to address engineering of the network performance, i.e., performance analysis models and diagnosis methods for the design of a scalable and reliable network that can detect errors and identify bot tlenecks. This process requires broad knowledge about the various services and traffics accommodated in the IT/OT network by learning their relevant parameters and important Key Performance Indicators (KPIs). The direct challenge here would be that a green-field that can evolve and adapt various TSN methods does not have any initial traffic to be investigated. Therefore, studying brown-field traffic is the closest path to understand the required performance criteria for various traffic types to provide the network performance engineering guidelines of IT/OT networks. Along with the guidelines design, security issues and solutions should come along, as the network structure and functionalities affect the security performance and the security solutions in turn affect the network performance. Another important research topic is the engineering tool, which is the key to bootstrap the system and maintain smooth operation during the production phase. Reliability is also important for control networks, and single-point failure protection should be considered besides network redundancy. For the cases that require distributed real-time systems, both configuration and synchronization are also essential, and distributed real-time system configuration requires more domain expertise besides general solutions. Related WorksAs discussed earlier, modeling network traffic to identify network structure and data flow characteristics can be seen as a prerequisite of network evolution. In this regard, there exist many research works in the IoT domain, many notably considered intrusion and anomaly detection [18; 19; 20; 21; 22; 23], but very few have focused on the profiling network traffic of industrial networks. Authors in [20] introduce communication models of various industrial networks based on traffic profiling by applying probabilistic modeling on network traffic, considering both periodic and aperiodic communication. However, the complexity of the model demands a new round of learning with any configuration changes. Markov chain model is employed in [19] to learn the regularity of packet flows by considering each packet as a state. An anomaly-based intrusion detection system that uses fuzzy logic to assess whether malicious activity is taking place on a network is presented in [23]. The system consists of a network data collector that reads raw network packets and stores them on a disk. A network data processor then performs data mining on the collected packets, and finally, the observed value is used as an input to the fuzzy analyzer. Many research papers have reviewed different aspects of IEEE TSN standards [24; 25; 26]. A comprehensive survey of queuing and scheduling mechanisms for supporting large-scale deterministic networks was discussed in [25], followed by the most recent research work [26] that presents a comprehensive survey of TSN standards and research studies addressing networking mechanisms for ultra-low-latency applications, such as in industrial control. Instead, the authors in [24] provide an extensive overview of the different fault-resilience concepts for IEEE 802.1 TSN networks. Many research works have been conducted targeting a specific feature of IEEE TSN standards, including fault tolerance [27; 28; 29], traffic planning and shaping [30; 31; 32; 33], and time synchronization [26; 34].