Industry 4.0 aims to dramatically enhance the productivity of manufacturing technologies through the collection and analysis of real-time data. This combines the ubiquity of the IoT with the processing capabilities of cloud computing to generate insights that help to optimize the decision making process. The increasing demand of data and the explosion in the number of sensing devices, which might be highly constrained in terms of communication, battery and computational power, introduce new challenges that need efficient IoT-Cloud architectures. With this in mind, we extend MQTT, the de facto IoT communication protocol, using a fog computing approach that introduces a low complexity computational layer between the Cloud and IoT nodes. In this approach, the MQTT broker, which is in charge of relaying data from publishers to subscribers, is placed at the fog layer. The purpose of introducing an intermediate layer to this particular scenario is to: i) predict future data measurements through prediction techniques; ii) operate as a gateway to upper layers; and iii) provide the capability to offload computationally expensive data processing jobs from the Cloud to the Fog, minimizing additional latency and operational expenses. With this architecture, the transmissions required from IoT devices may be reduced, since the publishers would only need to update the predicted data in case of mismatching. We validate our approach with an energy consumption analysis and simulations of different Machine Learning algorithms on a real dataset, and compare it with the traditional MQTT scheme.

The introduction of the Internet of Things (IoT) is creating manifold new services and opportunities. This new technological trend enables the connection of a massive number of devices among them and with the Internet. The integration of IoT with cloud platforms also provides large storage and computing capabilities, enabling Big Data analytics and bidirectional communication between devices and users. Novel research directions are showing that Network Coding (NC) can increase the robustness and throughput of wireless networks, as well as that Homomorphic Encryption (HE) can be used to perform computations in the cloud while maintaining data privacy. In this paper, we overview the benefits of NC and HE along the entire vertical of cloud-based IoT architectures. By merging both technologies, the architecture may offer manifold advantages: First, it provides end-to-end data privacy, from end-devices to end-users. Second, sensitive data can be stored in public cloud platforms without concern about their privacy. In addition, clouds can perform advanced operations in a confidential manner, without the need to access actual data. Finally, latency can be reduced and the reliability of the system is increased. We show state-of-the-art works that demonstrate the role of both technologies in this type of architectures on a review basis. Furthermore, we describe the main characteristics of NC and HE and also discuss their benefits and limitations, as well as the emerging open challenges.

Low-Power Wide-Area Networks (LPWANs) are becoming one of the main building blocks for the Industrial IoT (IIoT) ecosystem, being an alternative to 2G/3G/4G cellular networks where long-range communication at very low speed is needed by battery powered sensors spread around the factory. Among the most promising approaches, SIGFOX stands out as an ultra-narrow band technology that allows similar signal coverage to that of cellular networks at one thousandth of its power requirements. In this paper, we analyze a SIGFOX-based heterogeneous network architecture, based on combining an ultra-low-power star network topology for short distance communications and SIGFOX as Internet Gateway for the low-power devices. By following this approach, clusters of IIoT devices are able to communicate with a nearby SIGFOX Gateway that reports the sensed data to the Internet. Extensive experimentation and energy modeling has been performed. In particular, it is found that an IoT sensor is able to autonomously operate during remarkably long periods of time, reaching up to 4 years when operating at a duty cycle of 60 minutes and powered by a 1 Ah battery.

The adoption of both Cyber–Physical Systems (CPSs) and the Internet-of-Things (IoT) has enabled the evolution towards the so-called Industry 4.0. These technologies, together with cloud computing and artificial intelligence, foster new business opportunities. Besides, several industrial applications need immediate decision making and fog computing is emerging as a promising solution to address such requirement. In order to achieve a cost-efficient system, we propose taking advantage from spot instances, a new service offered by cloud providers, which provide resources at lower prices. The main downside of these instances is that they do not ensure service continuity and they might suffer from interruptions. An architecture that combines fog and multi-cloud deployments along with Network Coding (NC) techniques, guarantees the needed fault-tolerance for the cloud environment, and also reduces the required amount of redundant data to provide reliable services. In this paper we analyze how NC can actually help to reduce the storage cost and improve the resource efficiency for industrial applications, based on a multi-cloud infrastructure. The cost analysis has been carried out using both real AWS EC2 spot instance prices and, to complement them, prices obtained from a model based on a finite Markov chain, derived from real measurements. We have analyzed the overall system cost, depending on different parameters, showing that configurations that seek to minimize the storage yield a higher cost reduction, due to the strong impact of storage cost.

Industry 4.0 has become the main source of applications of the Internet of Things (IoT), which is generating new business opportunities. The use of cloud computing and artificial intelligence is also showing remarkable improvements in industrial operation, saving millions of dollars to manufacturers. The need for time-critical decision-making is evidencing a trade-off between latency and computation, urging Industrial IoT (IIoT) deployments to integrate fog nodes to perform early analytics. In this chapter, we review next-generation IIoT architectures, which aim to meet the requirements of industrial applications, such as low-latency and highly reliable communications. These architectures can be divided into IoT node, fog, and multicloud layers. We describe these three layers and compare their characteristics, providing also different use-cases of IIoT architectures. We introduce network coding (NC) as a solution to meet some of the requirements of next-generation communications. We review a variety of its approaches as well as different scenarios that improve their performance and reliability thanks to this technique. Then, we describe the communication process across the different levels of the architecture based on NC-based state-of-the-art works. Finally, we summarize the benefits and open challenges of combining IIoT architectures together with NC techniques.