Wireless Local Area Network Technologies as Communication Solutions for Unmanned Surface Vehicles

As the number of research activities and practical deployments of unmanned vehicles has shown a rapid growth, topics related to their communication with operator and external infrastructure became of high importance. As a result a trend of employing IP communication for this purpose is emerging and can be expected to bring significant advantages. However, its employment can be expected to be most effective using broadband communication technologies such as Wireless Local Area Networks (WLANs). To verify the effectiveness of such an approach in a specific case of surface unmanned vehicles, the paper includes an overview of IP-based MAVLink communication advantages and requirements, followed by a laboratory and field-experiment study of selected WLAN technologies, compared to popular narrowband communication solutions. The conclusions confirm the general applicability of IP/WLAN communication for surface unmanned vehicles, providing an overview of their advantages and pointing out deployment requirements.

SplitTrie: A Fast Update Packet Classification Algorithm with Trie Splitting

Software Defined Network (SDN) currently is widely used in the implementation of new network technologies owing to its distinctive advantages. In changeable SDN environments, the update performance of SDN switches has significant importance for the overall network performance because packet processing could be interrupted by ruleset updating in SDN switches. In order to guarantee high update performance, we propose a new classification algorithm, SplitTrie, based on trie structures and trie splitting. SplitTrie splits rulesets according to the field type vectors of rules. The splitting can improve the update performance because it reduces the trie structure sizes. Experimental results demonstrated that SplitTrie could achieve 20 times of update speed in the complex rulesets comparing the method without trie splitting.

Advanced digital transformations for food security

The issues of digital transformation of the modern agricultural economy and the impact of innovative processes on its effective and sustainable functioning in order to ensure food security are analyzed in this article. The key directions for the development of digital production are shown, with the help of which the real prospects for the development of the agrarian sector of the national economy are determined. The current state of digital transformation of the agricultural sector of the economy is analyzed using modeling of the product impact and process innovations on the development of the agricultural economy. The influence of knowledge spreading in the Internet era, the development of network technologies and the use of digital platforms significantly expand the possibilities of both modern network business and business in the agricultural sector of the economy in solving problems related to the implementation of research and development in the field of digital transformation of the agricultural sector of the economy. In the conclusion, it is concluded that the development of rural areas and ensuring of food security in Russia will largely depend on the level of application of information technologies.

Review of Custom Grids for Updated Vehicles on VANET Simulators

VANET deployment and testing is time-consuming and costly. Simulation is a handy and less expensive alternative to real implementation as a workaround. It is required to develop accurate models in order to receive excellent results from a VANET simulation, which difficult operation owes to the complexity of the VANET infrastructure (for example, simulators have to model the navigation models and communication protocols). The network and navigation components, which are the building blocks of contemporary VANET simulators, are described in this section. Simulators are a useful tool for testing VANETs at a minimal cost and without endangering users. However, in order to be helpful and convey trustworthy findings, simulators must be able to simulate new technologies that enter the VANET and enable safety and security procedures. To put it another way, if simulation is a good tool for VANET development it should be enhanced. VANET simulators have been the subject of research since early 2010 [1-4]. They analyze the correctness of VANET's numerous tools like a navigation simulator and network simulator, as well as how these building blocks are connected. The introduction of new network technologies such as 5G, SDN, edge computing, and VANET research as a result of investments in autonomous cars is forcing VANET simulators to re-evaluate their support for these new capabilities. We present an updated evaluation of VANET simulators in this post, highlighting their key features and current support for emerging technologies.

A Survey on Internet of Vehicle (IoV): A pplications & Comparison of VANETs, IoV and SDN-IoV

The swift evolution of communication network technologies brings the smart connected objects into existence. The objects or things are connected and able to communicate with each other through Internet of Things (IoT). Internet of Vehicle (IoV) are the subset of the IoT technologies that has been implemented to the vehicle. IoV is the evolution of traditional Vehicular Adhoc Networks (VANETs) bringing new technologies to the smart connected vehicles. IoV network communication is purposedly to serve a real time data exchanged on roads between the vehicles and roads, vehicles and vehicles, vehicle and sensors, and vehicles and everything through different network technologies. There are significant differences between IoV, SDN-IoV and VANETs in term of network architecture, features, and applications that available. IoV and IoV-SDN is using different network framework giving different flexibility and scalability. In term of features and applications, IoV and IoV-SDN are having more wider and broad wireless connection technology in contrast with VANETs. In this survey paper, the survey will focus on the introduction to SDN, and differences of application and features in IoV, SDN-IoV and VANETs.

Development of an adaptive mass open online course in the framework of training in artificial neural network technologies

Problem and goal. The development of mass open online courses contributes to the increasing attention of students to them. At the moment, there are many large services that provide online training, but there are no clearly defined universal requirements for such courses. Also, along with this problem, there is a fairly high level of rejection of the course at various stages due to the loss of motivation to continue training. Methodology. A variant of solving these problems by using adaptive learning technologies on the example of a course on learning artificial neural network technologies was considered. Results. In the process of reviewing the issue, the topics of the online course sections were determined. As a result, a work plan was drafted and the most relevant ways to solve the identified problems were formulated. Conclusion. The developed strategy can help with further elaboration and testing of the designed course and can be applied to any mass open online course.

Application of machine learning technologies for Drone Network in logistics and port activities of Russia

Cтатья посвящена проведению исследовательского анализа особенностей практического использования беспилотных дронов и квадрокоптеров. Актуальность исследования обусловлена тем, что технологии Drone Network позволяют решить основную проблему логистики – провести оптимизацию финансовых расходов, путем сокращения затрат на реализацию цепочек поставок. Это помощь интеграции логистики, дронов и технологии машинного обучения. В рамках статьи рассмотрены теоретические аспекты понятия технологии «Drone Network». Проанализирован зарубежный опыт и международные тенденции в использовании беспилотных дронов при формировании цепочки поставок логистики морских предприятий. Рассмотрены ключевые преимущества, которые предоставляют данные технологии в совершенствовании транспортной логистики компаний. Проанализированы перспективы развития технологий Drone Network на территории Российской Федерации. Рассмотрены основные проблемы, препятствующие их практическому применению российскими компаниями. Проанализированы недостатки, с которыми сталкиваются организации в рамках использования технологий беспилотных дронов в логистике. Описаны перспективы развития технологий Drone Network в международном и российском рынке. Проанализированы перспективы применения беспилотных дронов, управляемых технологиями машинного обучения, в рамках развития портовой деятельности, внутрипортовой логистики и для поиска бедствующих кораблей. The scientific article is devoted to the research analysis of the features of the practical use of unmanned drones and frame copters in the framework of the transport logistics of goods and orders by foreign companies. The relevance of the study is due to the fact that Drone Network technologies allow solving the main problem of logistics - to optimize financial costs by reducing the cost of implementing supply chains. Perhaps this is helping the integration of logistics, drones and machine learning technology. The article discusses the theoretical aspects of the concept of the "Drone Network" technology. Analyzed foreign experience and international trends in the use of unmanned drones in the formation of the supply chain of logistics enterprises. The key advantages that these technologies provide in improving the transport logistics of companies are considered. The prospects for the development of Drone Network technologies on the territory of the Russian Federation are analyzed. The main problems that hinder their practical application by Russian companies are considered. The paper analyzes the shortcomings faced by organizations in the use of unmanned drone technologies in logistics. The prospects for the development of Drone Network technologies in the international and Russian markets are described. The prospects for the use of unmanned drones controlled by machine learning technologies in the development of port activities, intra-port logistics and for the search for distressed ships are analyzed.

Cognitive system for analysis behavioral of dynamic objects of video surveillance

The necessary measures to ensure the safety of technical structures, freight/passenger stations and other transport infrastructure facilities include continuous video monitoring with a comprehensive analysis of the scene. In conditions of high density of numerous objects continuously moving through the observation area, one of the main available signs of detecting anomalies in their behavior is their trajectory of the object. In this paper, we propose an approach to building a system for analyzing the behavior of dynamic video surveillance objects based on their tracking, implemented by means of cognitive modeling. The proposed procedures for intelligent analysis of the nature of movement of video surveillance objects are based on a combination of neural network technologies and the logical inference mechanism of the expert system, which expands the basic algorithms for technical equipment of video surveillance systems. The practical significance of the considered solutions is to increase the efficiency of detecting suspicious situations in conditions of high traffic density by conducting a parallel analysis of the movement of numerous objects of the scene, which entails the prevention of possible illegal actions in places of mass presence of people, including transport infrastructure facilities.