St. Petersburg State University of Telecommunications

About: St. Petersburg State University of Telecommunications is a education organization based out in Saint Petersburg, Russia. It is known for research contribution in the topics: The Internet & Magnetic field. The organization has 408 authors who have published 465 publications receiving 2160 citations.

Future Networks 2030: Architecture & Requirements

Abstract: New technologies are approaching with big footsteps. In the decades ahead it is expected significant changes in technologies regarding to new types of devices, system and functions that they perform. We are expecting new services with various requirements, and new applications such as holographic telepresence, instant data transmission, remote surgery, minimized IoT terminals, autonomous transportation system, etc. Wide adaptation of new applications depends on how good they are supported by the network infrastructure. That is why the estimation of next generation network architecture is required. By the year 2030 the applications of new technologies are expected to generate an enormous amount of traffic. The architecture of the future networks must consider all the gaps of already existing networks in order to ensure the highest robustness, extremely low latency, Ubiquitous coverage as new applications and services require.

Advanced Deep Learning-Based Computational Offloading for Multilevel Vehicular Edge-Cloud Computing Networks

Abstract: The promise of low latency connectivity and efficient bandwidth utilization has driven the recent shift from vehicular cloud computing (VCC) towards vehicular edge computing (VEC). This paper presents an advanced deep learning-based computational offloading algorithm for multilevel vehicular edge-cloud computing networks. To conserve energy and guarantee the efficient utilization of shared resources among multiple vehicles, an integration model of computational offloading, and resource allocation is formulated as a binary optimization problem to minimize the total cost of the entire system in terms of time and energy. However, this problem is considered NP-hard and it is computationally prohibitive to solve this type of problem, particularly for large-scale vehicles, due to the curse-of-dimensionality problem. Therefore, an equivalent reinforcement learning form is generated and we propose a distributed deep learning algorithm to find the near-optimal computational offloading decisions in which a set of deep neural networks are used in parallel. Finally, simulation results show that the proposed algorithm can exhibit fast convergence and significantly reduce the overall consumption of an entire system compared to the benchmark solutions.

State of the Art and Research Challenges for Public Flying Ubiquitous Sensor Networks

Abstract: The article deals with theoretical and practical directions of Public Flying Ubiquitous Sensor Networks (FUSN-P) research. Considered the distinctive features of this type of networks from the existing ones. A wide range of issues is covered: from the methods of calculation FUSN to the new types of testing and model network structure for such networks. Presented a model network for full-scale experiment and solutions for the Internet of Things.

Multilevel cloud based Tactile Internet system

Abstract: Far from the traditional Internet in which audio and visual senses can be transferred, the Tactile Internet will introduce a way for transferring touch and actuation in real time form. However, the fifth generation of the mobile cellular system (5G) will be a great support for realizing Tactile Internet, the 1 ms round-trip-delay still a great challenge in the way of the Tactile Internet realization. Mobile edge computing (MEC) is a solution introduced to reduce the round trip latency and provide a way for offloading computation from the cellular network. In this paper we introduce a novel approach toward a multi-level cloud based cellular system, in which the small cells are connected with micro-cloud units with small capabilities to present the edge computing facilities. The micro-clouds are connected to mini-cloud units which have greater capabilities. The core network cloud connects the mini-clouds in the whole system. Introducing more levels of clouding reduces the round trip latency and the network congestion.