Wireless industrial monitoring and control networks: the journey so far and the road ahead

While traditional wired communication technologies have played a crucial role in industrial monitoring and control networks over the past few decades, they are increasingly proving to be inadequate to meet the highly dynamic and stringent demands of today’s industrial applications, primarily due to the very rigid nature of wired infrastructures. Wireless technology, however, through its increased pervasiveness, has the potential to revolutionize the industry, not only by mitigating the problems faced by wired solutions, but also by introducing a completely new class of applications. While present day wireless technologies made some preliminary inroads in the monitoring domain, they still have severe limitations especially when real-time, reliable distributed control operations are concerned. This article provides the reader with an overview of existing wireless technologies commonly used in the monitoring and control industry. It highlights the pros and cons of each technology and assesses the degree to which each technology is able to meet the stringent demands of industrial monitoring and control networks. Additionally, it summarizes mechanisms proposed by academia, especially serving critical applications by addressing the real-time and reliability requirements of industrial process automation. The article also describes certain key research problems from the physical layer communication for sensor networks and the wireless networking perspective that have yet to be addressed to allow the successful use of wireless technologies in industrial monitoring and control networks

Low-Power Wireless for the Internet of Things: Standards and Applications: Internet of Things, IEEE 802.15.4, Bluetooth, Physical layer, Medium Access Control,coexistence, mesh networking, cyber-physical systems, WSN, M2M

International audienceThe proliferation of embedded systems, wireless technologies, and Internet protocols have enabled the Internet of Things (IoT) to bridge the gap between the virtual and physical world through enabling the monitoring and actuation of the physical world controlled by data processing systems. Wireless technologies, despite their offered convenience, flexibility, low cost, and mobility pose unique challenges such as fading, interference, energy, and security, which must be carefully addressed when using resource-constrained IoT devices. To this end, the efforts of the research community have led to the standardization of several wireless technologies for various types of application domains depending on factors such as reliability, latency, scalability, and energy efficiency. In this paper, we first overview these standard wireless technologies, and we specifically study the MAC and physical layer technologies proposed to address the requirements and challenges of wireless communications. Furthermore, we explain the use of these standards in various application domains, such as smart homes, smart healthcare, industrial automation, and smart cities, and discuss their suitability in satisfying the requirements of these applications. In addition to proposing guidelines to weigh the pros and cons of each standard for an application at hand, we also examine what new strategies can be exploited to overcome existing challenges and support emerging IoT applications

Optimal and probabilistic resource and capability analysis for network slice as a service

Network Slice as a Service is one of the key concepts of the fifth generation of mobile networks (5G). 5G supports new use cases, like the Internet of Things (IoT), massive Machine Type Communication (mMTC) and Ultra-Reliable and Low Latency Communication (URLLC) as well as significant improvements of the conventional Mobile Broadband (MBB) use case. In addition, safety and security critical use cases move into focus. These use cases involve diverging requirements, e.g. network reliability, latency and throughput. Network virtualization and end-to-end mobile network slicing are seen as key enablers to handle those differing requirements and providing mobile network services for the various 5G use cases and between different tenants. Network slices are isolated, virtualized, end-to-end networks optimized for specific use cases. But still they share a common physical network infrastructure. Through logical separation of the network slices on a common end-to-end mobile network infrastructure, an efficient usage of the underlying physical network infrastructure provided by multiple Mobile Service Providers (MSPs) in enabled. Due to the dynamic lifecycle of network slices there is a strong demand for efficient algorithms for the so-called Network Slice Embedding (NSE) problem. Efficient and reliable resource provisioning for Network Slicing as a Service, requires resource allocation based on a mapping of virtual network slice elements on the serving physical mobile network infrastructure. In this thesis, first of all, a formal Network Slice Instance Admission (NSIA) process is presented, based on the 3GPP standardization. This process allows to give fast feedback to a network operator or tenant on the feasibility of embedding incoming Network Slice Instance Requests (NSI-Rs). In addition, corresponding services for NSIA and feasibility checking services are defined in the context of the ETSI ZSM Reference Architecture Framework. In the main part of this work, a mathematical model for solving the NSE Problem formalized as a standardized Linear Program (LP) is presented. The presented solution provides a nearly optimal embedding. This includes the optimal subset of Network Slice Instances (NSIs) to be selected for embedding, in terms of network slice revenue and costs, and the optimal allocation of associated network slice applications, functions, services and communication links on the 5G end-to-end mobile network infrastructure. It can be used to solve the online as well as the offline NSIA problem automatically in different variants. In particular, low latency network slices require deployment of their services and applications, including Network Functions (NFs) close to the user, i.e., at the edge of the mobile network. Since the users of those services might be widely distributed and mobile, multiple instances of the same application are required to be available on numerous distributed edge clouds. A holistic approach for tackling the problem of NSE with edge computing is provided by our so-called Multiple Application Instantiation (MAI) variant of the NSE LP solution. It is capable of determining the optimal number of application instances and their optimal deployment locations on the edge clouds, even for multiple User Equipment (UE) connectivity scenarios. In addition to that multi-path, also referred to as path-splitting, scenarios with a latency sensitive objective function, which guarantees the optimal network utilization as well as minimum latency in the network slice communication, is included. Resource uncertainty, as well as reuse and overbooking of resources guaranteed by Service Level Agreements (SLAs) are discussed in this work. There is a consensus that over-provisioning of mobile communication bands is economically infeasible and certain risk of network overload is accepted for the majority of the 5G use cases. A probabilistic variant of the NSE problem with an uncertainty-aware objective function and a resource availability confidence analysis are presented. The evaluation shows the advantages and the suitability of the different variants of the NSE formalization, as well as its scalability and computational limits in a practical implementation