Electronic mechanism of ion expulsion under UV nanosecond laser excitation of silicon: Experiment and modeling

We present experimental and modeling studies of UV nanosecond pulsed laser desorption and ablation of (111) bulk silicon. The results involve a new approach to the analysis of plume formation dynamics under high-energy photon irradiation of the semiconductor surface. Non-thermal, photo-induced desorption has been observed at low laser fluence, well below the melting threshold. Under ablation conditions, the non-thermal ions have also a high concentration. The origin of these ions is discussed on the basis of electronic excitation of Si surface states associated with the Coulomb explosion mechanism. We present a model describing dynamics of silicon target excitation, heating and harge-carrier transport

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

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

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

Reinforcement Learning-Based Load Balancing Satellite Handover Using NS-3

The Fifth-Generation of Mobile Communications (5G) is intended to meet users' growing needs for high-quality services at any time and from any location. The unique features of Low Earth Orbit (LEO) satellites in terms of higher coverage, reliability, and availability, can help expand the reach of 5G and beyond technologies to support those needs. However, because of their high speeds, a single LEO satellite is unable to provide continuous service to multiple User Equipments (UEs) spread over a large (potentially worldwide) area, resulting in the need for LEO satellite constellations with a high number of satellites and a consequent high amount of satellite handovers (HOs). Moreover, UEs can only acquire partial information about the satellite system and compete for the limited available communication resources of the satellites, requiring the implementation of a decentralized satellite HO strategy to avoid network congestion. In this paper, we propose a decentralized Load Balancing Satellite HO (LBSH) strategy based on multi-agent reinforcement Q-learning, implemented within the software Network Simulator 3 (NS-3). LBSH aims to reduce the total number of HOs and the blocking rate while balancing the load distribution among satellites. Our results show that the proposed LBSH method outperforms the state-of-the-art methods in terms of a 95% drop in the average number of HOs per user and an 84% reduction in blocking rate

Improved diffusion Monte Carlo propagators for bosonic systems using Ito calculus

The construction of importance sampled diffusion Monte Carlo (DMC) schemes accurate to second order in the time step is discussed. A central aspect in obtaining efficient second order schemes is the numerical solution of the stochastic differential equation (SDE) associated with the Fokker-Plank equation responsible for the importance sampling procedure. In this work, stochastic predictor-corrector schemes solving the SDE and consistent with It\uf4 calculus are used in DMC simulations of helium clusters. These schemes are numerically compared with alternative algorithms obtained by splitting the Fokker-Plank operator, an approach that we analyze using the analytical tools provided by It\uf4 calculus. The numerical results show that predictor-corrector methods are indeed accurate to second order in the time step and that they present a smaller time step bias and a better efficiency than second order split-operator derived schemes when computing ensemble averages for bosonic systems. The possible extension of the predictor-corrector methods to higher orders is also discussed

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

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