5G Infrastructure Network Slicing: E2E Mean Delay Model and Effectiveness Assessment to Reduce Downtimes in Industry 4.0

This work has been partially funded by the H2020 project 5G-CLARITY (Grant No. 871428) and the Spanish national project TRUE-5G (PID2019-108713RB-C53).Fifth Generation (5G) is expected to meet stringent performance network requisites of the Industry 4.0. Moreover, its built-in network slicing capabilities allow for the support of the traffic heterogeneity in Industry 4.0 over the same physical network infrastructure. However, 5G network slicing capabilities might not be enough in terms of degree of isolation for many private 5G networks use cases, such as multi-tenancy in Industry 4.0. In this vein, infrastructure network slicing, which refers to the use of dedicated and well isolated resources for each network slice at every network domain, fits the necessities of those use cases. In this article, we evaluate the effectiveness of infrastructure slicing to provide isolation among production lines (PLs) in an industrial private 5G network. To that end, we develop a queuing theory-based model to estimate the end-to-end (E2E) mean packet delay of the infrastructure slices. Then, we use this model to compare the E2E mean delay for two configurations, i.e., dedicated infrastructure slices with segregated resources for each PL against the use of a single shared infrastructure slice to serve the performance-sensitive traffic from PLs. Also we evaluate the use of Time-Sensitive Networking (TSN) against bare Ethernet to provide layer 2 connectivity among the 5G system components. We use a complete and realistic setup based on experimental and simulation data of the scenario considered. Our results support the effectiveness of infrastructure slicing to provide isolation in performance among the different slices. Then, using dedicated slices with segregated resources for each PL might reduce the number of the production downtimes and associated costs as the malfunctioning of a PL will not affect the network performance perceived by the performance-sensitive traffic from other PLs. Last, our results show that, besides the improvement in performance, TSN technology truly provides full isolation in the transport network compared to standard Ethernet thanks to traffic prioritization, traffic regulation, and bandwidth reservation capabilities.H2020 project 5G-CLARITY 871428Spanish Government PID2019-108713RB-C53TRUE-5

Challenges, applications and future of wireless sensors in Internet of Things: a review

The addition of massive machine type communication (mMTC) as a category of Fifth Generation (5G) of mobile communication, have increased the popularity of Internet of Things (IoT). The sensors are one of the critical component of any IoT device. Although the sensors posses a well-known historical existence, but their integration in wireless technologies and increased demand in IoT applications have increased their importance and the challenges in terms of design, integration, etc. This survey presents a holistic (historical as well as architectural) overview of wireless sensor (WS) nodes, providing a classical definition, in-depth analysis of different modules involved in the design of a WS node, and the ways in which they can be used to measure a system performance. Using the definition and analysis of a WS node, a more comprehensive classification of WS nodes is provided. Moreover, the need to form a wireless sensor network (WSN), their deployment, and communication protocols is explained. The applications of WS nodes in various use cases have been discussed. Additionally, an overlook of challenges and constraints that these WS nodes face in various environments and during the manufacturing process, are discussed. Their main existing developments which are expected to augment the WS nodes, to meet the requirements of the emerging systems, are also presented

A Survey on Resource Management in IoT Operating Systems

Recently, the Internet of Things (IoT) concept has attracted a lot of attention due to its capability to translate our physical world into a digital cyber world with meaningful information. The IoT devices are smaller in size, sheer in number, contain less memory, use less energy, and have more computational capabilities. These scarce resources for IoT devices are powered by small operating systems (OSs) that are specially designed to support the IoT devices' diverse applications and operational requirements. These IoT OSs are responsible for managing the constrained resources of IoT devices efficiently and in a timely manner. In this paper, discussions on IoT devices and OS resource management are provided. In detail, the resource management mechanisms of the state-of-the-art IoT OSs, such as Contiki, TinyOS, and FreeRTOS, are investigated. The different dimensions of their resource management approaches (including process management, memory management, energy management, communication management, and file management) are studied, and their advantages and limitations are highlighted

Network Slicing in 5G: Admission, Scheduling, and Security

In the past few decades, there was an increase in the number of devices that have wireless capabilities such as phones, televisions, and home appliances. With the high demand for wireless networking, the fifth generation (5G) of mobile networks was designed to support the different services of new applications. In addition, one of the technical issues that 5G would evolve is the increase in traffic and the need to satisfy the user’s experience. With the evolution of wireless networking and 5G, Network Slicing has been introduced to accommodate the diverse requirements of the applications. Thus, network slicing is the concept of partitioning the physical network infrastructure into multiple self-contained logical pieces which can be identified as slices. Each slice can be customized to serve and meet different network requirements and characteristics. In terms of security, network security has allowed for new security vulnerabilities such as Distributed Denial of Service (DDoS) and resource exhaustion. However, slices can be isolated to provide better resource isolation. In addition, each slice is considered an end-to-end virtual network, operators would be able to allocate resources to the tenants which are the service providers. The isolated resources are controlled by the tenants; each tenant has control over how to use them to meet the requirements of the clients. One of the challenges in network slicing is RAN slicing. The target of RAN Slicing is to meet the QoS requirements of different services for each end-user. However, the coexistence of different services is challenging because each service has its requirements. Each slice must estimate its network demands based on the QoS requirements and control the admission to the slice. To solve this issue, we consider the scenario for the enhanced mobile broadband (eMBB) and the ultra-reliable-low-latency communication (URLLC) use cases’ coexistence, and we slice the RAN based on the priority of the user applicatio

Tyrimai įterptines Operacines sistemas skirtus belaidžio korinio daiktų interneto (DI) sistemoms

The master degree final project focusses on Embedded Operating Systems for a Wireless Cellular IoT System. Internet of Things (IoT) is a great chance for upcoming devices to be intelligent, more robust and efficient. This tremendous pathway has become available due to regular cost degradation of various separate systems and accessories like sensors, computing devices, communication methods, the cloud and the big data paradigms. Connectivity is the base for IoT and the type of access needed will focus on the nature of the application. Thus, the target is now on Narrow Band IoT, which is a Low Power Wide Area Network (LPWAN) radio technology standard that has been developed to contribute a wide range of devices and services to be connected via cellular telecommunications bands. Accordingly, the thesis works on ARM MBED OS, an embedded operating system, which is a platform as well as operating system for internet connected devices for 32-bit ARM cortex-M microcontrollers which is needed for NB-IoT system. First, MBED OS is designed and implemented on ARM cortex -M prototyping system MPS2+ as a real-time operating system by bringing latest version of CMSIS-RTOS with RTX as kernel on Cortex-M4 as well as its successor Cortex-M33 contained on MPS2+ hardware board to examine different RTOS parameters such as memory, heap, stack, hardware and software impacts. Next, these obtained parameters for MBED OS is compared with other RTOS, say FreeRTOS on MPS2+ board. Thus, the final outcome would be how cellular IoT system will change when a new embedded operating system will be incorporated into Corelink SSE 200 IoT subsytem and fulfil the requirements for NB-IoT standard

Conception d'un modèle architectural collaboratif pour l'informatique omniprésente à la périphérie des réseaux mobiles

Le progrès des technologies de communication pair-à-pair et sans fil a de plus en plus permis l’intégration de dispositifs portables et omniprésents dans des systèmes distribués et des architectures informatiques de calcul dans le paradigme de l’internet des objets. De même, ces dispositifs font l'objet d'un développement technologique continu. Ainsi, ils ont toujours tendance à se miniaturiser, génération après génération durant lesquelles ils sont considérés comme des dispositifs de facto. Le fruit de ces progrès est l'émergence de l'informatique mobile collaborative et omniprésente, notamment intégrée dans les modèles architecturaux de l'Internet des Objets. L’avantage le plus important de cette évolution de l'informatique est la facilité de connecter un grand nombre d'appareils omniprésents et portables lorsqu'ils sont en déplacement avec différents réseaux disponibles. Malgré les progrès continuels, les systèmes intelligents mobiles et omniprésents (réseaux, dispositifs, logiciels et technologies de connexion) souffrent encore de diverses limitations à plusieurs niveaux tels que le maintien de la connectivité, la puissance de calcul, la capacité de stockage de données, le débit de communications, la durée de vie des sources d’énergie, l'efficacité du traitement de grosses tâches en termes de partitionnement, d'ordonnancement et de répartition de charge. Le développement technologique accéléré des équipements et dispositifs de ces modèles mobiles s'accompagne toujours de leur utilisation intensive. Compte tenu de cette réalité, plus d'efforts sont nécessaires à la fois dans la conception structurelle tant au matériel et logiciel que dans la manière dont il est géré. Il s'agit d'améliorer, d'une part, l'architecture de ces modèles et leurs technologies de communication et, d'autre part, les algorithmes d'ordonnancement et d'équilibrage de charges pour effectuer leurs travaux efficacement sur leurs dispositifs. Notre objectif est de rendre ces modèles omniprésents plus autonomes, intelligents et collaboratifs pour renforcer les capacités de leurs dispositifs, leurs technologies de connectivité et les applications qui effectuent leurs tâches. Ainsi, nous avons établi un modèle architectural autonome, omniprésent et collaboratif pour la périphérie des réseaux. Ce modèle s'appuie sur diverses technologies de connexion modernes telles que le sans-fil, la radiocommunication pair-à-pair, et les technologies offertes par LoPy4 de Pycom telles que LoRa, BLE, Wi-Fi, Radio Wi-Fi et Bluetooth. L'intégration de ces technologies permet de maintenir la continuité de la communication dans les divers environnements, même les plus sévères. De plus, ce modèle conçoit et évalue un algorithme d'équilibrage de charge et d'ordonnancement permettant ainsi de renforcer et améliorer son efficacité et sa qualité de service (QoS) dans différents environnements. L’évaluation de ce modèle architectural montre des avantages tels que l’amélioration de la connectivité et l’efficacité d’exécution des tâches. Advances in peer-to-peer and wireless communication technologies have increasingly enabled the integration of mobile and pervasive devices into distributed systems and computing architectures in the Internet of Things paradigm. Likewise, these devices are subject to continuous technological development. Thus, they always tend to be miniaturized, generation after generation during which they are considered as de facto devices. The success of this progress is the emergence of collaborative mobiles and pervasive computing, particularly integrated into the architectural models of the Internet of Things. The most important benefit of this form of computing is the ease of connecting a large number of pervasive and portable devices when they are on the move with different networks available. Despite the continual advancements that support this field, mobile and pervasive intelligent systems (networks, devices, software and connection technologies) still suffer from various limitations at several levels such as maintaining connectivity, computing power, ability to data storage, communication speeds, the lifetime of power sources, the efficiency of processing large tasks in terms of partitioning, scheduling and load balancing. The accelerated technological development of the equipment and devices of these mobile models is always accompanied by their intensive use. Given this reality, it requires more efforts both in their structural design and management. This involves improving on the one hand, the architecture of these models and their communication technologies, and, on the other hand, the scheduling and load balancing algorithms for the work efficiency. The goal is to make these models more autonomous, intelligent, and collaborative by strengthening the different capabilities of their devices, their connectivity technologies and the applications that perform their tasks. Thus, we have established a collaborative autonomous and pervasive architectural model deployed at the periphery of networks. This model is based on various modern connection technologies such as wireless, peer-to-peer radio communication, and technologies offered by Pycom's LoPy4 such as LoRa, BLE, Wi-Fi, Radio Wi-Fi and Bluetooth. The integration of these technologies makes it possible to maintain the continuity of communication in the various environments, even the most severe ones. Within this model, we designed and evaluated a load balancing and scheduling algorithm to strengthen and improve its efficiency and quality of service (QoS) in different environments. The evaluation of this architectural model shows payoffs such as improvement of connectivity and efficiency of task executions