X3D sensor-based thermal maps for residential and commercial buildings

Maghiar

2016

Graphical Models

Self Cite

The past decade has seen the advent of numerous building energy efficiency visualization and simulation systems; however, most of them rely on theoretical thermal models to suggest building structural design for new constructions and modifications for existing ones. Sustainable methods of construction have made tremendous progress. The example of the German Energy-Plus-House technology uses a combination of (almost) zero-carbon passive heating technologies. A web-enabled X3D visualization and simulation system coupled with a cost-effective set of temperature/humidity sensors can provide valuable insights into building design, materials and construction that can lead to significant energy savings and an improved thermal comfort for residents, resulting in superior building energy efficiency. A costeffective hardware-software prototype system is proposed in this paper that can provide real-time data driven visualization or offline simulation of 3D thermal maps for residential and/or commercial buildings on the Web.

Section: Introductionmentioning

confidence: 76%

Web3D graphics enabled through sensor networks for cost-effective assessment and management of energy efficiency in buildings

Maghiar

2016

Graphical Models

Self Cite

“…BT/BLE based sensors can be deployed in many indoor environments where they collect data for large periods of time. As an example, thermal imaging can benefit from such IoT-based networks by providing a web-based 3D environment for temperature maps in commercial or residential buildings [106], as shown in Figure 23a. In order to reduce the costs, low-cost transceivers such as HC-05 (around 3 euro) and low-cost sensors such as DHT-22 (4 euro) are connected to an Arduino Board (from 3 to 7 euro) and can provide such 3D volumetric representations via Matlab.…”

Section: Iot Applicationsmentioning

confidence: 99%

Design of Wireless Sensors for IoT with Energy Storage and Communication Channel Heterogeneity

Borza

Machedon-Pisu

2019

Sensors

Autonomous Wireless Sensors (AWSs) are at the core of every Wireless Sensor Network (WSN). Current AWS technology allows the development of many IoT-based applications, ranging from military to bioengineering and from industry to education. The energy optimization of AWSs depends mainly on: Structural, functional, and application specifications. The holistic design methodology addresses all the factors mentioned above. In this sense, we propose an original solution based on a novel architecture that duplicates the transceivers and also the power source using a hybrid storage system. By identifying the consumption needs of the transceivers, an appropriate methodology for sizing and controlling the power flow for the power source is proposed. The paper emphasizes the fusion between information, communication, and energy consumption of the AWS in terms of spectrum information through a set of transceiver testing scenarios, identifying the main factors that influence the sensor node design and their inter-dependencies. Optimization of the system considers all these factors obtaining an energy efficient AWS, paving the way towards autonomous sensors by adding an energy harvesting element to them.

“…Autonomous wireless sensors (AWSs) are deployed in many indoor environments where they collect data for large periods of time. Since many thermally deficient western methods of construction hardly tap into the huge potential of applying energyefficiency technology, we proposed in [3] a prototype for generating semi-transparent (α-value) X3D thermal maps to represent temperature and humidity in large commercial and residential buildings.…”

Section: Building Interior Monitoringmentioning

confidence: 99%

Web-enabled Intelligent System for Continuous Sensor Data Processing and Visualization

The 24th International Conference on 3D Web Technology

Iacob

Khan

2019

Self Cite

A large number of sensors deployed in recent years in various setups and their data is readily available in dedicated databases or in the cloud. Of particular interest is real-time data processing and 3D visualization in web-based user interfaces that facilitate spatial information understanding and sharing, hence helping the decision making process for all the parties involved.In this research, we provide a prototype system for near realtime, continuous X3D-based visualization of processed sensor data for two significant applications: thermal monitoring for residential/commercial buildings and nitrogen cycle monitoring in water beds for aquaponics systems. As sensors are sparsely placed, in each application, where they collect data for large periods (of up to one year), we employ a Finite Differences Method and a Neural Networks model to approximate data distribution in the entire volume.