Smart construction companies using internet of things technologies

The digital world is enriched due to the increase in the number of things which are rapidly connecting to the Internet. The Internet of Things (IoT) facilitates and improves the work efficiency and human life in various fields. IoT was adopted extensively to male buildings more effective and extra smart. For example, buildings are consuming a considerable energy amount. In buildings, there is a critical requirement for energy efficiency, whereas one of the smart building’s aims is monitoring, reducing and managing the energy consumption of buildings without compromising the operational efficiency and the comfort of occupants. The systems of Heating, Ventilation and Air Conditioning (HVAC) are contributing to considerable consumption of energy in buildings. Also, plug loads and lighting are consuming a lot energy. Thus, smart buildings have the ability of using many IoT sensor types in HVAC along with other mechanical systems making such more adaptive and intelligent. The embedded sensors as well as their related controllers which are mounted in smart buildings are generating a huge amount of data (big data), such data might be subjected to extraction, filtration ana analyzation and utilized for the analytics of smart buildings. For example, the big data analytics might be utilized for analyzing and improving the energy efficiency in addition to the residents’ overall user experience in building. It has been verified that there is an increased focus on smart buildings and big data analytics and management. Yet, there is a requirement for identifying the problems and solutions for overcoming them in such field. With the use of a design research method and model driven architecture, this study aims to develop such system.The major aim of this work is introducing a technique with increased possibility for moving Intelligent Buildings (IBs) towards next-generation model. It depends on IoT adapted to IB for integrating smart re-configurable subsystems and components of IB into Enterprise Network Integrated Building Systems (ENIBSs), also, if possible, into ENIBS’ global networks. The study is presented in the following way. Section 2 is providing an overview of IoT, it is indicating that IoT is relatively new and no associated contribution on using the IoT on IBs or, on the ENIBSs, were indicated in such regard. Section3 is presenting the methodological model that has been used to design a generic model for the IoT with the applicability in the IBs as well as generic architectures for re-configurable smart plug-and-play control systems for quick configuration and integration regarding smart components of the IB. Section 4 provides the theory’ experimental test. The study ends up with the conclusions and some suggestions for the future work

Hierarchical video surveillance architecture: a chassis for video big data analytics and exploration

There is increasing reliance on video surveillance systems for systematic derivation, analysis and interpretation of the data needed for predicting, planning, evaluating and implementing public safety. This is evident from the massive number of surveillance cameras deployed across public locations. For example, in July 2013, the British Security Industry Association (BSIA) reported that over 4 million CCTV cameras had been installed in Britain alone. The BSIA also reveal that only 1.5% of these are state owned. In this paper, we propose a framework that allows access to data from privately owned cameras, with the aim of increasing the efficiency and accuracy of public safety planning, security activities, and decision support systems that are based on video integrated surveillance systems. The accuracy of results obtained from government-owned public safety infrastructure would improve greatly if privately owned surveillance systems ‘expose’ relevant video-generated metadata events, such as triggered alerts and also permit query of a metadata repository. Subsequently, a police officer, for example, with an appropriate level of system permission can query unified video systems across a large geographical area such as a city or a country to predict the location of an interesting entity, such as a pedestrian or a vehicle. This becomes possible with our proposed novel hierarchical architecture, the Fused Video Surveillance Architecture (FVSA). At the high level, FVSA comprises of a hardware framework that is supported by a multi-layer abstraction software interface. It presents video surveillance systems as an adapted computational grid of intelligent services, which is integration-enabled to communicate with other compatible systems in the Internet of Things (IoT)

Anturidatan lähettäminen fyysiseltä kaksoselta digitaaliselle kaksoselle

A digital twin is a digital counterpart of a physical thing such as a machine. The term digital twin was first introduced in 2010. Thereafter, it has received an extensive amount of interest because of the numerous benefits it is expected to offer throughout the product life cycle. Currently, the concept is developed by the world’s largest companies such as Siemens. The purpose of this thesis is to examine which application layer protocols and communication technologies are the most suitable for the sensor data transmission from a physical twin to a digital twin. In addition, a platform enabling this data transmission is developed. As the concept of a digital twin is relatively new, a comprehensive literature view on the definition of a digital twin in scientific literature is presented. It has been found that the vision of a digital twin has evolved from the concepts of ‘intelligent products’ presented at the beginning of the 2000s. The most widely adopted definition states that a digital twin accurately mirrors the current state of its corresponding twin. However, the definition of a digital twin is not yet standardized and varies in different fields. Based on the literature review, the communication needs of a digital twin are derived. Thereafter, the suitability of HTTP, MQTT, CoAP, XMPP, AMQP, DDS, and OPC UA for sensor data transmission are examined through a literature review. In addition, a review of 4G, 5G, NB-IoT, LoRa, Sigfox, Bluetooth, Wi-Fi, Z-Wave, ZigBee, and WirelessHART is presented. A platform for the management of the sensors is developed. The platform narrows the gap between the concept and realization of a digital twin by enabling sensor data transmission. The platform allows easy addition of sensors to a physical twin and provides an interface for their configuration remotely over the Internet. It supports multiple sensor types and application protocols and offers both web user iterface and REST API.Digitaalinen kaksonen on fyysisen tuotteen digitaalinen vastinkappale, joka sisältää tiedon sen nykyisestä tilasta. Digitaalisen kaksosen käsite otettiin ensimmäisen kerran käyttöön vuonna 2010. Sen jälkeen digitaalinen kaksonen on saanut paljon huomiota, ja sitä ovat lähteneet kehittämään maailman suurimmat yritykset, kuten Siemens. Tämän työn tarkoituksena tutkia, mitkä sovelluskerroksen protokollat ja langattomat verkot soveltuvat parhaiten anturien keräämän datan lähettämiseen fyysiseltä kaksoselta digitaaliselle kaksoselle. Sen lisäksi työssä esitellään alusta, joka mahdollistaa tämän tiedonsiirron. Digitaalisen kaksosesta esitetään laaja kirjallisuuskatsaus, joka luo pohjan työn myöhemmille osioille. Digitaalisen kaksosen konsepti pohjautuu 2000-luvun alussa esiteltyihin ajatuksiin ”älykkäistä tuotteista”. Yleisimmän käytössä olevan määritelmän mukaan digitaalinen kaksonen heijastaa sen fyysisen vastinparin tämän hetkistä tilaa. Määritelmä kuitenkin vaihtelee eri alojen välillä eikä se ole vielä vakiintunut tieteellisessä kirjallisuudessa. Kirjallisuuskatsauksen avulla johdetaan digitaalisen kaksosen kommunikaatiotarpeet. Sen jälkeen arvioidaan seuraavien sovelluskerroksen protokollien soveltuvuutta anturidatan lähettämiseen kirjallisuuskatsauksen avulla: HTTP, MQTT, CoAP, XMPP, AMQP, DDS ja OPC UA. Myös seuraavien langattomien verkkojen soveltuvuutta tiedonsiirtoon tutkitaan: 4G, 5G, NB-IoT, LoRaWAN, Sigfox, Bluetooth, Wi-Fi, Z-Wave, ZigBee ja WirelessHART. Osana työtä kehitettiin myös ohjelmistoalusta, joka mahdollistaa anturien hallinnan etänä Internetin välityksellä. Alusta on pieni askel kohti digitaalisen kaksosen käytän-nön toteutusta, sillä se mahdollistaa tiedon keräämisen fyysisestä vastinkappaleesta. Sen avulla sensorien lisääminen fyysiseen kaksoseen on helppoa, ja se tukee sekä useita sensorityyppejä että sovelluskerroksen protokollia. Alusta tukee REST API –rajapintaa ja sisältää web-käyttöliittymän

Internet of things

This is an introductory course to the IoT (Internet of things). In the early chapters the basics about the IoT are introduced. Then basics of IPv6 internet protocol that is the most used in IoT environment as well as main applications, the current state of the market and the technologies that enable the existence of the IoT are described. Finally the future challenges that are considered most important are discussed.Peer ReviewedPostprint (published version

An Experimental Study of Reduced-Voltage Operation in Modern FPGAs for Neural Network Acceleration

We empirically evaluate an undervolting technique, i.e., underscaling the circuit supply voltage below the nominal level, to improve the power-efficiency of Convolutional Neural Network (CNN) accelerators mapped to Field Programmable Gate Arrays (FPGAs). Undervolting below a safe voltage level can lead to timing faults due to excessive circuit latency increase. We evaluate the reliability-power trade-off for such accelerators. Specifically, we experimentally study the reduced-voltage operation of multiple components of real FPGAs, characterize the corresponding reliability behavior of CNN accelerators, propose techniques to minimize the drawbacks of reduced-voltage operation, and combine undervolting with architectural CNN optimization techniques, i.e., quantization and pruning. We investigate the effect of environmental temperature on the reliability-power trade-off of such accelerators. We perform experiments on three identical samples of modern Xilinx ZCU102 FPGA platforms with five state-of-the-art image classification CNN benchmarks. This approach allows us to study the effects of our undervolting technique for both software and hardware variability. We achieve more than 3X power-efficiency (GOPs/W) gain via undervolting. 2.6X of this gain is the result of eliminating the voltage guardband region, i.e., the safe voltage region below the nominal level that is set by FPGA vendor to ensure correct functionality in worst-case environmental and circuit conditions. 43% of the power-efficiency gain is due to further undervolting below the guardband, which comes at the cost of accuracy loss in the CNN accelerator. We evaluate an effective frequency underscaling technique that prevents this accuracy loss, and find that it reduces the power-efficiency gain from 43% to 25%.Comment: To appear at the DSN 2020 conferenc

Efficient Data Streaming Analytic Designs for Parallel and Distributed Processing

Today, ubiquitously sensing technologies enable inter-connection of physical\ua0objects, as part of Internet of Things (IoT), and provide massive amounts of\ua0data streams. In such scenarios, the demand for timely analysis has resulted in\ua0a shift of data processing paradigms towards continuous, parallel, and multitier\ua0computing. However, these paradigms are followed by several challenges\ua0especially regarding analysis speed, precision, costs, and deterministic execution.\ua0This thesis studies a number of such challenges to enable efficient continuous\ua0processing of streams of data in a decentralized and timely manner.In the first part of the thesis, we investigate techniques aiming at speeding\ua0up the processing without a loss in precision. The focus is on continuous\ua0machine learning/data mining types of problems, appearing commonly in IoT\ua0applications, and in particular continuous clustering and monitoring, for which\ua0we present novel algorithms; (i) Lisco, a sequential algorithm to cluster data\ua0points collected by LiDAR (a distance sensor that creates a 3D mapping of the\ua0environment), (ii) p-Lisco, the parallel version of Lisco to enhance pipeline- and\ua0data-parallelism of the latter, (iii) pi-Lisco, the parallel and incremental version\ua0to reuse the information and prevent redundant computations, (iv) g-Lisco, a\ua0generalized version of Lisco to cluster any data with spatio-temporal locality\ua0by leveraging the implicit ordering of the data, and (v) Amble, a continuous\ua0monitoring solution in an industrial process.In the second part, we investigate techniques to reduce the analysis costs\ua0in addition to speeding up the processing while also supporting deterministic\ua0execution. The focus is on problems associated with availability and utilization\ua0of computing resources, namely reducing the volumes of data, involving\ua0concurrent computing elements, and adjusting the level of concurrency. For\ua0that, we propose three frameworks; (i) DRIVEN, a framework to continuously\ua0compress the data and enable efficient transmission of the compact data in the\ua0processing pipeline, (ii) STRATUM, a framework to continuously pre-process\ua0the data before transferring the later to upper tiers for further processing, and\ua0(iii) STRETCH, a framework to enable instantaneous elastic reconfigurations\ua0to adjust intra-node resources at runtime while ensuring determinism.The algorithms and frameworks presented in this thesis contribute to an\ua0efficient processing of data streams in an online manner while utilizing available\ua0resources. Using extensive evaluations, we show the efficiency and achievements\ua0of the proposed techniques for IoT representative applications that involve a\ua0wide spectrum of platforms, and illustrate that the performance of our work\ua0exceeds that of state-of-the-art techniques