Flow Assignment and Processing on a Distributed Edge Computing Platform

The evolution of telecommunication networks toward the fifth generation of mobile services (5G), along with the increasing presence of cloud-native applications, and the development of Cloud and Mobile Edge Computing (MEC) paradigms, have opened up new opportunities for the monitoring and management of logistics and transportation. We address the case of distributed streaming platforms with multiple message brokers to develop an optimization model for the real-time assignment and load balancing of event streaming generated data traffic among Edge Computing facilities. The performance indicator function to be optimised is derived by adopting queuing models with different granularity (packet- and flow-level) that are suitably combined. A specific use case concerning a logistics application is considered and numerical results are provided to show the effectiveness of the optimisation procedure, also in comparison to a “static” assignment proportional to the processing speed of the brokers

IoT and UAV Integration in 5G Hybrid Terrestrial-Satellite Networks

The Fifth Generation of Mobile Communications (5G) will lead to the growth of use cases demanding higher capacity and a enhanced data rate, a lower latency, and a more flexible and scalable network able to offer better user Quality of Experience (QoE). The Internet of Things (IoT) is one of these use cases. It has been spreading in the recent past few years, and it covers a wider range of possible application scenarios, such as smart city, smart factory, and smart agriculture, among many others. However, the limitations of the terrestrial network hinder the deployment of IoT devices and services. Besides, the existence of a plethora of different solutions (short vs. long range, commercialized vs. standardized, etc.), each of them based on different communication protocols and, in some cases, on different access infrastructures, makes the integration among them and with the upcoming 5G infrastructure more difficult. This paper discusses the huge set of IoT solutions available or still under standardization that will need to be integrated in the 5G framework. UAVs and satellites will be proposed as possible solutions to ease this integration, overcoming the limitations of the terrestrial infrastructure, such as the limited covered areas and the densification of the number of IoT devices per square kilometer

UAV and IoT integration: A flying gateway

This paper introduces a new approach for Internet of Things. This approach is based on the integration of IoT and Unmanned Aerial Vehicles (UAVs) to establish a flying gateway that allows the extension of coverage of terrestrial IoT gateways. The approach is based on using several hardware devices as Arduino, Raspberry Pi boards and RAK 2445 board offering LoRa connectivity. This LoRa-based gateway is deployed on board of a drone flying over IoT nodes to gather and transmit data to a LoRa server. This system will extend the coverage of the terrestrial LoRa gateways allowing to reach remote and rural areas

Smart transportation systems (STSs) in critical conditions

In the context of smart transportation systems (STSs) in smart cities, the use of applications that can help in case of critical conditions is a key point. Examples of critical conditions may be natural-disaster events such as earthquakes, hurricanes, floods, and manmade ones such as terrorist attacks and toxic waste spills. Disaster events are often combined with the destruction of the local telecommunication infrastructure, if any, and this implies real problems to the rescue operations.The quick deployment of a telecommunication infrastructure is essential for emergency and safety operations as well as the rapid network reconfigurability, the availability of open source software, the efficient interoperability, and the scalability of the technological solutions. The topic is very hot and many research groups are focusing on these issues. Consequently, the deployment of a smart network is fundamental. It is needed to support both applications that can tolerate delays and applications requiring dedicated resources for real-time services such as traffic alert messages, and public safety messages. The guarantee of quality of service (QoS) for such applications is a key requirement.In this chapter we will analyze the principal issues of the networking aspects and will propose a solution mainly based on software defined networking (SDN). We will evaluate the benefit of such paradigm in the mentioned context focusing on the incremental deployment of such solution in the existing metropolitan networks and we will design a "QoS App" able to manage the quality of service on top of the SDN controller

Performance Evaluation of a Satellite Communication-based MEC architecture for IoT applications

New scenarios and use cases are raising following the birth of the fifth generation of mobile communications. The Internet of Things (IoT) is one of the main use cases which are growing, leading to a massive amount of data that need to be exchanged throughout the Internet. Satellite communication networks are essential in remote and isolated environments and can support fully connected environments by offloading the terrestrial infrastructure concerning delay–tolerant traffic flows. However, satellite network resources are limited and expensive, so they need to be carefully used in order to avoid waste and satisfy the required user performance. The multi-access edge computing (MEC) concept can be exploited in this context to allow data preprocessing at the edge, i.e., close to the users, so reducing the amount of data that has to traverse the backhaul satellite link and, in some cases, reducing data delivery times. This article analyses the performance of a satellite architecture in the IoT framework highlighting the advantages brought by MEC, also including data aggregation and compression techniques

Forecasting crude oil and refined products volatilities and correlations: New evidence from fractionally integrated multivariate GARCH models

The relationship between the prices of crude oil and its refined products is at the heart of the oil industry. Crude oil and refined products volatilities and correlations have been mod- elled extensively using short-memory multivariate GARCH models. This paper investigates the potential benefits from using fractionally integrated multivariate GARCH models from a fore- casting and a risk management perspective. Several models for the spot returns on three major oil-related markets are compared. In-sample results show significant evidence of long-memory decay and leverage effects in volatilities and of time-varying autocorrelations. The forecasting performance of the models is assessed by means of three approaches: the Superior Predictive Ability test, the Model Confidence Set and the Value-at-Risk. The results indicate that the multivariate models incorporating long-memory outperform the short-memory benchmarks in forecasting the conditional covariance matrix and associated risk magnitudes. The paper makes an innovative contribution to the analysis of the relationship between crude oil and its refined products providing refiners, physical oil traders, non-commercial oil traders and other energy markets agents with significant insights for hedging and risk management operations

Satellite networking integration in the 5G ecosystem: Research trends and open challenges

The envisioned 5G ecosystem will be composed of heterogeneous networks based on different technologies and communication means, including satellite communication networks. The latter can help increase the capabilities of terrestrial networks, especially in terms of higher coverage, reliability, and availability, contributing to the achievement of some of the 5G KPIs. However, technological changes are not immediate. Many current satellite communication networks are based on proprietary hardware, which hinders the integration with future 5G terrestrial networks as well as the adoption of new protocols and algorithms. On the other hand, the two main paradigms that are emerging in the networking scenario \u2014 software defined networking (SDN) and network functions virtualization \u2014 can change this perspective. In this respect, this article presents first an overview of the main research works in the field of SDN satellite networks in order to understand the already proposed solutions. Then some open challenges are described in light of the network slicing concept by 5G virtualization, along with a possible roadmap including different network virtualization levels. The remaining unsolved problems are related to the development and deployment of a complete integration of satellite components in the 5G ecosystem

Small satellites and CubeSats: survey of structures, architectures, and protocols

The space environment is still challenging but is becoming more and more attractive for an increasing number of entities. In the second half of the 20th century, a huge amount of funds was required to build satellites and gain access to space. Nowadays, it is no longer so. The advancement of technologies allows producing very small hardware components able to survive the strict conditions of the outer space. Consequently, small satellites can be designed for a wide set of missions keeping low design times, production costs, and deployment costs. One widely used type of small satellite is the CubeSat, whose different aspects are surveyed in the following: mission goals, hardware subsystems and components, possible network topologies, channel models, and suitable communication protocols. We also show some future challenges related to the employment of CubeSat networks

Reduction of the Delays within an Intrusion Detection System (IDS) based on Software Defined Networking (SDN)

Software Defined Networking (SDN) is a very useful tool not only to manage networks but also to increase network security, in particular by implementing Intrusion Detection Systems (IDS) directly into the SDN architecture. The implementation of IDS within the SDN paradigm can simplify the implementation, speed up incident responses, and, in general, allow to promptly react to cyber attacks through proper countermeasures. Nevertheless, embedding IDS within SDN also introduces delays that cannot be tolerated in specific network environments, like industrial control systems. This paper focuses on the implementation of an IDS based on Machine Learning (ML) algorithms into an SDN architecture and proposes a very practical approach to reduce the delay by using the sequential implementation of prototypes of increasing software and hardware complexity so allowing quick tests to highlight the main problems, solve them and pass to the next operative step. A fully validated performance evaluation is then shown by exploiting all the presented solutions and by using further improved hardware features. The overall performance is very good and compliant with most, even if not yet all, industrial control systems constraints. Results show how the proposed solutions provide a significant improvement of the latency so opening the door to a real implementation in the field