Future industrial applications will encompass compelling new use cases requiring stringent performance guarantees over multiple key performance indicators (KPI) such as reliability, dependability, latency, time synchronization, security, etc. Achieving such stringent and diverse service requirements necessitates the design of a special-purpose Industrial Internet of Things (IIoT) network comprising a multitude of specialized functionalities and technological enablers. This article proposes an innovative architecture for such a special-purpose 6 G IIoT network incorporating seven functional building blocks categorized into: special-purpose functionalities and enabling technologies. The former consists of Wireless Environment Control, Traffic/Channel Prediction, Proactive Resource Management and End-to-End Optimization functions; whereas the latter includes Synchronization and Coordination, Machine Learning and Artificial Intelligence Algorithms, and Auxiliary Functions. The proposed architecture aims at providing a resource-efficient and holistic solution for the complex and dynamically challenging requirements imposed by future 6 G industrial use cases. Selected test scenarios are provided and assessed to illustrate cross-functional collaboration and demonstrate the applicability of the proposed architecture in a wireless IIoT network.

Industry 4.0 is generalizing the use of wireless connectivity in manufacturing and logistics. Specifically, in Smart Logistics, novel Industry 4.0 technologies are used to enable agile supply chains, with reduced management, energy and storage costs. Cellular networks allow connectivity throughout all the scenarios where logistics processes take place, each having their own challenges. This paper explores such scenarios and challenges, and proposes 5G technology as a global unified connectivity solution. Moreover, this paper proposes a system for exploiting the application-specific optimization capabilities of 5G networks to better cater for the needs of Smart Logistics. An application traffic modeling process is proposed, along with a proactive approach to network optimization that can improve the Quality of Service and reduce connectivity costs.

Mobile networks have rapidly evolved in recent years due to the increase in multimedia traffic and offered services. This has led to a growth in the volume of control data and measurements that are used by self-healing systems. To maintain a certain quality of service, self-healing systems must complete their tasks in a reasonable time. The conjunction of a big volume of data and the limitation of time requires a big data approach to the problem of self-healing. This article reviews the data that self-healing uses as input and justifies its classification as big data. Big data techniques applied to mobile networks are examined, and some use cases along with their big data solutions are surveyed.

Self-healing networks aim to detect cells with service degradation, identify the fault cause of their problem, and execute compensation and repair actions. The development of this type of automatic system presents several challenges to be confronted. The first challenge is the scarce number of historically reported faults, which greatly complicates the evaluation of novel self-healing techniques. For this reason, in this paper, a system model to simulate faults in Long-Term Evolution (LTE) networks, along with their most significant key performance indicators, is proposed. Second, the expert knowledge required to build a self-healing system is usually not documented. Therefore, in this paper, a methodology to extract this information from a collection of reported cases is proposed. Finally, following the proposed methodology, an automatic fuzzy-logic-based system for fault identification in LTE networks is designed. Evaluation results show that the fuzzy system provides fault identification with a high success rate.

The recent developments in cellular networks, along with the increase in services, users and the demand of high quality have raised the Operational Expenditure (OPEX). Self-Organizing Networks (SON) are the solution to reduce these costs. Within SON, self-healing is the functionality that aims to automatically solve problems in the radio access network, at the same time reducing the downtime and the impact on the user experience. Self-healing comprises four main functions: fault detection, root cause analysis, fault compensation and recovery. To perform the root cause analysis (also known as diagnosis), Knowledge-Based Systems (KBS) are commonly used, such as fuzzy logic. In this paper, a novel method for extracting the Knowledge Base for a KBS from solved troubleshooting cases is proposed. This method is based on data mining techniques as opposed to the manual techniques currently used. The data mining problem of extracting knowledge out of LTE troubleshooting information can be considered a Big Data problem. Therefore, the proposed method has been designed so it can be easily scaled up to process a large volume of data with relatively low resources, as opposed to other existing algorithms. Tests show the feasibility and good results obtained by the diagnosis system created by the proposed methodology in LTE networks. (C) 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Self-organizing network (SON) mechanisms reduce operational expenditure in cellular networks while enhancing the offered quality of service. Within a SON, self-healing aims to autonomously solve problems in the radio access network and to minimize their impact on the user. Self-healing comprises automatic fault detection, root cause analysis, fault compensation, and recovery. This paper presents a root cause analysis system based on fuzzy logic. A genetic algorithm is proposed for learning the rule base. The proposed method is adapted to the way of reasoning of troubleshooting experts, which ease knowledge acquisition and system output interpretation. Results show that the obtained results are comparable or even better than those obtained when the troubleshooting experts define the rules, with the clear benefit of not requiring the experts to define the system. In addition, the system is robust, since fine tuning of its parameters is not mandatory.