Internet of things

Manual of Digital Earth / Editors: Huadong Guo, Michael F. Goodchild, Alessandro Annoni .- Springer, 2020 .- ISBN: 978-981-32-9915-3Digital Earth was born with the aim of replicating the real world within the digital world. Many efforts have been made to observe and sense the Earth, both from space (remote sensing) and by using in situ sensors. Focusing on the latter, advances in Digital Earth have established vital bridges to exploit these sensors and their networks by taking location as a key element. The current era of connectivity envisions that everything is connected to everything. The concept of the Internet of Things(IoT)emergedasaholisticproposaltoenableanecosystemofvaried,heterogeneous networked objects and devices to speak to and interact with each other. To make the IoT ecosystem a reality, it is necessary to understand the electronic components, communication protocols, real-time analysis techniques, and the location of the objects and devices. The IoT ecosystem and the Digital Earth (DE) jointly form interrelated infrastructures for addressing today’s pressing issues and complex challenges. In this chapter, we explore the synergies and frictions in establishing an efficient and permanent collaboration between the two infrastructures, in order to adequately address multidisciplinary and increasingly complex real-world problems. Although there are still some pending issues, the identified synergies generate optimism for a true collaboration between the Internet of Things and the Digital Earth

INTERNET OF THINGS ARCHITECTURES:MODELING AND IMPLEMENTATION CHALLENGES

Internet of Things (IoT) encompasses a broad set of technologies, hardware and software stacks.The rapid evolution and broadened scope can be attributed to the inclusion of many existing mature technologies like the wireless sensor networks, RFID and a wide variety of custom solutions and newer smart devices.There is a growing need for devices to collaborate to provide the desired service.The heterogeneity coupled with theresource constrained nature of the devices seriously limits the choices in design. The capability to onboard billions of devices on to the existing infrastructure without degrading the quality of service is robust programming frameworks are in place more crucial. Automation enables devices to act independently which can be enabled only by ensuring.Architectural modelsaddressingthe challenges like scalability, distributiveness, interoperability and programmability are the need for the hour

常時オフセンサネットワーク向け低電力同期型コード変調OOK方式トランシーバーの研究

電気通信大学201

Distributed D3: A web-based distributed data visualisation framework for Big Data

The influx of Big Data has created an ever-growing need for analytic tools targeting towards the acquisition of insights and knowledge from large datasets. Visual perception as a fundamental tool used by humans to retrieve information from the outside world around us has its unique ability to distinguish patterns pre-attentively. Visual analytics via data visualisations is therefore a very powerful tool and has become ever more important in this era. Data-Driven Documents (D3.js) is a versatile and popular web-based data visualisation library that has tended to be the standard toolkit for visualising data in recent years. However, the library is technically inherent and limited in capability by the single thread model of a single browser window in a single machine, and therefore not able to deal with large datasets. The main objective of this thesis is to overcome this limitation and address possible challenges by developing the Distributed D3 framework that employs distributed mechanism to enable the possibility of delivering web-based visualisations for large-scale data, which also allows to effectively utilise the graphical computational resources of the modern visualisation environments. As a result, the first contribution is that the integrated version of Distributed D3 framework has been developed for the Data Observatory. The work proves the concept of Distributed D3 is feasible in reality and also enables developers to collaborate on large-scale data visualisations by using it on the Data Observatory. The second contribution is that the Distributed D3 has been optimised by investigating the potential bottlenecks for large-scale data visualisation applications. The work finds the key performance bottlenecks of the framework and shows an improvement of the overall performance by 35.7% after optimisations, which improves the scalability and usability of Distributed D3 for large-scale data visualisation applications. The third contribution is that the generic version of Distributed D3 framework has been developed for the customised environments. The work improves the usability and flexibility of the framework and makes it ready to be published in the open-source community for further improvements and usages.Open Acces

Digital Twins in Industry

Digital Twins in Industry is a compilation of works by authors with specific emphasis on industrial applications. Much of the research on digital twins has been conducted by the academia in both theoretical considerations and laboratory-based prototypes. Industry, while taking the lead on larger scale implementations of Digital Twins (DT) using sophisticated software, is concentrating on dedicated solutions that are not within the reach of the average-sized industries. This book covers 11 chapters of various implementations of DT. It provides an insight for companies who are contemplating the adaption of the DT technology, as well as researchers and senior students in exploring the potential of DT and its associated technologies

Veracity in power consumption of smart home

This project is intended to develop for EDP Portugal from Cside for the newly launched smart home platform called EDP re:dy ( Remote Energy Dynamics) Home Automation devices with digital controlling platform from anywhere with optimum power consumption pattern in order to make energy efficient home.During the realization of this project,the veracity of re:dy smart meter has been analyzed with different load factors (capacitive, nductive) in order to observe the harmonics distortion pattern with various home appliances and traditional energy meters.Gathering all collected data in SDP (Service Delivery latform) online platform to analyze the sequence of energy values and according to that edp box will give effective power consumption pattern to the client's minimum tariff and energy saving pattern via reducing the power dissipation.As a final conclusion of the project, based on obtained results an effective method for smart home automation with edp re:dy box associated with MAC address of equipment added and controlled via online carrier/subscriber portal

Building a wireless mesh networked real-time electricity metering system in an MIT dormitory

Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2009."February 2009." Cataloged from PDF version of thesis.Includes bibliographical references (p. 25).A competitive, closed-loop information feedback system of wireless electricity meters was designed, tested, and implemented in seven MIT dormitory rooms. The meters utilized Allegro Hall Effect current sensors as well as ZigBee based mesh networking transceivers. A remote database stored energy use data for the community and implemented data and graph caching for an online web interface made available to the system users. The website displayed detailed statistics on energy consumption within the dormitory, rankings of the community members, and individualized pages with positive or negative normative feedback messages based upon a user's consumption level. Over the course of a three week test period, the average demand level of the seven rooms was 56.53 watts, which is equivalent to an annualized cost of $84.18 and emissions of 480 pounds of CO 2. The largest consumer used 28% of the total energy, while the bottom three consumers combined used only 26% of the overall energy. The seven rooms together demanded between 300-490 watts 95% of the time.by Austin L. Oehlerking.S.B

Lossless compression of industrial time series with direct access

[EN]The new opportunities generated by the data-driven economy in the manufacturing industry have caused many companies opt for it. However, the size of time series data that need to be captured creates the problem of having to assume high storage costs. Moreover, these costs, which are constantly growing, begin to have an impact on the profitability of companies. Thus, in this scenario, the need arises to develop techniques that allow obtaining reduced representations of the time series. In this paper, we present a lossless compression method for industrial time series that allows an efficient access. That is, our aim goes beyond pure compression, where the usual way to access the data requires a complete decompression of the dataset before processing it. Instead, our method allows decompressing portions of the dataset, and moreover, it allows direct querying the compressed data. Thus, the proposed method combines the efficient access, typical of lossy methods, with the lossless compression.For the A Coruna team: This work was supported by CITIC, as Research Center accredited by Galician University System, is funded by "Conselleria de Cultura, Educacion e Universidade from Xunta de Galicia", supported in an 80% through ERDF Funds, ERDF Operational Programme Galicia 2014-2020, and the remaining 20% by "Secretaria Xeral de Universidades" (Grant ED431G 2019/01) , Xunta de Galicia/FEDER-UE under Grants [IG240.2020.1.185; IN852A 2018/14] and Ministerio de Ciencia, Innovacion under Grants [TIN2016-78011-C4-1-R; RTC-2017-5908-7] . For the Basque team: Ministerio de Ciencia, Innovacion y Universidades under Grant [FEDER/TIN2016-78011-C4-2-R] and the Basque Government under Grant No. [IT1330-19] . Funding for open access charge: Universidade da Coruna/CISUG

Lossless Compression of Industrial Time Series With Direct Access

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract] The new opportunities generated by the data-driven economy in the manufacturing industry have causedmany companies opt for it. However, the size of time series data that need to be captured creates theproblem of having to assume high storage costs. Moreover, these costs, which are constantly growing,begin to have an impact on the profitability of companies. Thus, in this scenario, the need arises to developtechniques that allow obtaining reduced representations of the time series. In this paper, we present alossless compression method for industrial time series that allows an efficient access. That is, our aim goesbeyond pure compression, where the usual way to access the data requires a complete decompressionof the dataset before processing it. Instead, our method allows decompressing portions of the dataset,and moreover, it allows direct querying the compressed data. Thus, the proposed method combines theefficient access, typical of lossy methods, with the lossless compression.Xunta de Galicia; ED431G 2019/01Xunta de Galicia; IG240. 2020.1.185Xunta de Galicia; IN852A 2018/14Gobierno Vasco; IT1330-19For the A Coruña team: This work was supported by CITIC, as Research Center accredited by Galician University System, is funded by “Consellería de Cultura, Educación e Universidade from Xunta de Galicia”, supported in an 80% through ERDF Funds, ERDF Operational Programme Galicia 2014-2020, and the remaining 20% by “Secretaría Xeral de Universidades” (Grant ED431G 2019/01), Xunta de Galicia/FEDER-UE under Grants [IG240.2020.1.185; IN852A 2018/14] and Ministerio de Ciencia, Innovación under Grants [TIN2016-78011-C4-1-R; RTC-2017-5908-7]. For the Basque team: Ministerio de Ciencia, Innovación y Universidades under Grant [FEDER/TIN2016-78011-C4-2-R] and the Basque Government under Grant No. [IT1330-19]. Funding for open access charge: Universidade da Coruña/CISUG