IMPLEMENTATION OF AN IDENTIFICATION SYSTEM FOR LORA TERMINAL DEVICES IN MESH NETWORKS BASED ON THE DIGITAL OBJECTS ARCHITECTURE

2021 ◽  
Author(s):  
Д.Д. САЗОНОВ ◽  
Р.В. КИРИЧЕК
Keyword(s):  
Energy Efficient ◽  
Mesh Networks ◽  
Identification System ◽  
Practical Application ◽  
Digital Object ◽  
Digital Objects ◽  
Test Scenarios ◽  
Metadata Format

Рассмотрено практическое применение архитектуры цифровых объектов (Digital Object Architecture - DOAtв качестве системы идентификации для энергоэффективных ячеистых сетей на базе технологии LoRa. Представлены тестовые сценарии взаимодействия оконечных устройств с сервером обработки и идентификации при помощи системы Handle System, развернутой в формате локального реестра LHS (Local Handle Service). Описанные подходы и архитектура взаимодействия могут быть перенесены на любые системы интернета вещей, так как технология DOA позволяет описывать любые реальные или виртуальные сущности вещей в формате метаданных с присвоением уникальных неизменяемых идентификаторов. The article describes the practical application of Digital Objects Architecture (DOA) as an identification system for energy-efficient mesh networks based on LoRa technology. Test scenarios for the interaction of edge devices with the processing and identification server using the Handle System deployed in the form of the local registry LHS (Local Handle Service) are presented. The described approaches for implementation of identification system based on Handle System and DOA can be used in different IoT systems since DOA technology allows you to describe any real or virtual entities of things in metadata format with the assignment of unique immutable identifiers.

scholarly journals Shelf life: Identifying the abandonment of online digital humanities projects

2019 ◽  
Vol 34 (Supplement_1) ◽  
pp. i129-i134
Author(s):  
Luis Meneses ◽  
Richard Furuta
Keyword(s):  
Shelf Life ◽  
Digital Humanities ◽  
Average Length ◽  
Digital Object ◽  
Average Life ◽  
Distributed Resources ◽  
Average Life Span ◽  
Administrative Control ◽  
Digital Objects ◽  
Administrative Decentralization

Abstract A large portion of the research carried out in the digital humanities has an online digital object (usually referred as a project) as one of its components. In turn, these online digital objects can be catalogued as distributed resources, which implies that the administrative control of information related to a topic may be spread across online resources and/or collections maintained by multiple scholars in different institutions. This administrative decentralization can lead to changes in content that are often unexpected by a researcher, which can be caused by different factors or circumstances. This reasoning led us to formulate the following question: When can online digital humanities projects be considered abandoned? In this article, we carry out a study on the persistence and average life span of online projects in the digital humanities. More specifically, we will elaborate on their reliance on distributed resources and methods for measuring their shelf life: the average length of time that a digital project can endure without updates until it can ultimately be considered abandoned by its researcher.

scholarly journals FAIR Digital Objects for Science: From Data Pieces to Actionable Knowledge Units

Publications ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 21 ◽  
Author(s):  
Koenraad De Smedt ◽  
Dimitris Koureas ◽  
Peter Wittenburg
Keyword(s):  
Data Science ◽  
Open Science ◽  
Research Data ◽  
Use Cases ◽  
Digital Object ◽  
Data Interoperability ◽  
Actionable Knowledge ◽  
The Past ◽  
Research Communities ◽  
Digital Objects

Data science is facing the following major challenges: (1) developing scalable cross-disciplinary capabilities, (2) dealing with the increasing data volumes and their inherent complexity, (3) building tools that help to build trust, (4) creating mechanisms to efficiently operate in the domain of scientific assertions, (5) turning data into actionable knowledge units and (6) promoting data interoperability. As a way to overcome these challenges, we further develop the proposals by early Internet pioneers for Digital Objects as encapsulations of data and metadata made accessible by persistent identifiers. In the past decade, this concept was revisited by various groups within the Research Data Alliance and put in the context of the FAIR Guiding Principles for findable, accessible, interoperable and reusable data. The basic components of a FAIR Digital Object (FDO) as a self-contained, typed, machine-actionable data package are explained. A survey of use cases has indicated the growing interest of research communities in FDO solutions. We conclude that the FDO concept has the potential to act as the interoperable federative core of a hyperinfrastructure initiative such as the European Open Science Cloud (EOSC).

Energy efficient CPM waveforms for satellite mesh networks

2012 ◽  
Author(s):  
R. Suffritti ◽  
F. Lombardo ◽  
A. Piemontese ◽  
A. Vanelli-Coralli ◽  
E.A. Candreva ◽  
...  
Keyword(s):  
Energy Efficient ◽  
Mesh Networks

scholarly journals Energy-Efficient Inference on the Edge Exploiting TinyML Capabilities for UAVs

Drones ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 127
Author(s):  
Wamiq Raza ◽  
Anas Osman ◽  
Francesco Ferrini ◽  
Francesco De Natale
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Data Security ◽  
Energy Efficient ◽  
Cost Effective ◽  
Practical Application ◽  
Flight Duration ◽  
Micro Aerial Vehicle ◽  
Aerial Vehicle ◽  
Crowded Environment ◽  
Mission Objective

In recent years, the proliferation of unmanned aerial vehicles (UAVs) has increased dramatically. UAVs can accomplish complex or dangerous tasks in a reliable and cost-effective way but are still limited by power consumption problems, which pose serious constraints on the flight duration and completion of energy-demanding tasks. The possibility of providing UAVs with advanced decision-making capabilities in an energy-effective way would be extremely beneficial. In this paper, we propose a practical solution to this problem that exploits deep learning on the edge. The developed system integrates an OpenMV microcontroller into a DJI Tello Micro Aerial Vehicle (MAV). The microcontroller hosts a set of machine learning-enabled inference tools that cooperate to control the navigation of the drone and complete a given mission objective. The goal of this approach is to leverage the new opportunistic features of TinyML through OpenMV including offline inference, low latency, energy efficiency, and data security. The approach is successfully validated on a practical application consisting of the onboard detection of people wearing protection masks in a crowded environment.

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