This article deals with the system for modular provisioning the IP telephony devices. Theoretical part deals with issues in mass configuration and data synchronization, followed by the exact practical implementations of the provisioning solutions. The article also describes the design and implementation of the whole platform, including the subsequent testing of functionality with possible design of further improvements.
It is estimated, that the number of connected Internet of Things (IoT) devices around the world could increase dramatically, with expectations ranging from 25 billion to 50 billion devices in 2025. As the IoT area is wider and wider and the number of connected IoT devices is higher every day, it appears that the issue of security is more up to date. The paper deals with LoRaWAN and Sigfox networks belonging to the LPWAN (Low Power Wide Area Network) category, where we focus on detection of the end device movement in a network based on the qualitative parameters of a radio signal. The result of this work is a software solution to notice the owner of the end device about the location change. As a testbed, we use LoRaWAN network, which is part of the infrastructure covering the area of Czech Republic. For Sigfox solution we use the public network provided by SimpleCell company and own solution to show actual parameters with which base station received messages. Detection serves as a measure against an attacker performing a spoofing attack or the physical movement of a statically-located end device activated by authentication methods. Based on the experimental simulation of the attacker's behavior, we have summed up the attack into individual points, according to which we subsequently constructed a countermeasure principle in the form of detection. This principle was applied to an algorithm that could be integrated into the gateway in case of LoRaWAN network and implemented as a separate element for Sigfox solution.
Software Defined Networks (SDN) are gaining attraction with the expanding use of complex data center infrastructures that accommodate the increasing demand for computational power related to much more feature-rich web applications and common use of deep learning algorithms. The increased set of features being used in the applications are reflected in the increased demands on network architectures starting with the higher network throughput, through the need for complex high-availability schemes and ending with near-perfect delay/loss communication characteristics. This increased demand resulted in the need for more flexible network architectures resulting in the major change in the networking paradigm and the related shift from traditional networks to software defined ones. The quality of service (QoS) in the networks and quality of experience (QoE) of the end-user services is a major topic of interest in the networking community resulting in several approaches implemented in the networks to ensure resource reservation or traffic prioritization. In this paper, we propose a way how to propagate the arbitrary qualitative parameter in the OpenFlow messages that would allow for easy monitoring of the quality of service and quality of experience. Moreover, we focus on the measurement of the quality of speech and the consecutive propagation of the information through the SDN network to allow SDN controllers and the OpenFlow capable switches controlled by them to react on the decreasing quality and support the services being carried through the network. The paper describes the way how the quality is measured, how the information is processed by the controller and how it is encapsulated in the OpenFlow messages. The assumptions are validated in the simulations based on the mininet simulation tool and Ryu SDN controller. The implications for the carried voice quality are discussed as well.
The significant expansion of the Internet of Things (IoT) field and unique requirements of the IoT devices bring new technologies created exclusively to provide wireless connectivity for the IoT devices. Among these technologies, we can include LoRa technology. Unlike some other LPWANs, LoRa technology is an open standard, and it allows us to build private networks. We took advantage of that and developed our gateway. The paper deals with a proposal of network infrastructure and the hardware solution of the LoRaWAN gateway based on the second generation of monolithic microcomputer Raspberry Pi model B and fully compatible LoRaWAN 868 MHz iC880A concentrator. The concentrator is connected to Raspberry Pi via Serial Peripheral Interface (SPI). In 2018, five gateways were deployed to cover a nearly entire area of Ostrava city in the Czech Republic and its surrounding areas with LoRaWAN signal. Our solution uses The Things Network platform to connect to a global open crowd-sourced IoT data network. For the end-users comfort, we implemented a web application that serves as a backend for registration of the end-devices to the LoRaWAN network, and it provides access to the history of all uplink messages transmitted by end-devices and received by LoRaWAN network. The next part of the article discusses end-device proposed for network availability testing.
Artificial neural networks affect our everyday life. But every neural network depends on the appropriate training set and setting of internal properties with hyperparameters. Even accurate and complete training set doesnt imply high performance of neural network algorithm. Tuning of hyperparameters is crucial for correct functionality, fast learning and high accuracy of neural networks. The hyperparameter selection relies on manual fine-tuning based on multiple full training trials. There are a lot of neural network implementation available for public and commercial use, but the setting of hyperparameters is often a neglected problem. Choosing the best structure and hyperparameters is the primary challenge in designing a neural network. This article describes a genetic algorithm for automatic selection of hyperparameters and their tuning for increasing the performance of neural networks without human interaction. The optimization algorithm accelerates the discovery of configuration, which is otherwise a time-consuming task. We evaluate the results of optimizations in comparison to naïve approach and compare pro and cons of different techniques.
This article deals with the system for voice control of the UAV (Unattended Aerial Vehicle) accessories using the mobile device and an advanced communication platform. The paper provides an overview of projects realized in last period in field of voice-controlled drones and explains the applied approach for automatic speech recognition using hidden markov models. Authors describes also converting speech commands instructions for UAV control and necessary steps in practical testing and optimization of the whole system. The achieved results and conclusions are given in the final chapter of the article in which authors provide their experience gained within the experimental development.
The paper deals with a speech quality monitoring system using probes placed on the individual network nodes operating
VoIP services. Information on speech quality is measured periodically and the results are then stored on the central
server which provides visualization in a form of graph respecting a topology of the probes. Article provides overall
description of the technology and algorithms used in the speech quality monitoring system and results achieved in this
applied research are verified in real operation. Contribution of the work lies in a proposal of the new multi-agent system
enabling speech quality monitoring and in own implementation and its verification in Czech academic network.