- Research article
- Open Access
Sensing the room: an integrated implementation process to visualize indoor temperature data on floor plans
© Shin et al.; licensee Springer. 2014
Received: 30 June 2014
Accepted: 24 October 2014
Published: 12 November 2014
Recently multi-use environmental sensors became affordable and easy to control. This improvement enabled us to measure the broad range of indoor environments by using low-priced and controllable sensor devices. As the data set acquired by widely installed environmental sensors increased significantly, the need for making effective use of the data has become of importance. We developed an integrated visualization mechanism to express larger amounts of sensor data.
The mechanism for visualization on floor plan described in this paper consists of the following modules: 1) the sensor module; 2) the data collection module; 3) the data processing module; 4) the spatial data module; 5) the sensor location module; and 6) the data visualization module. A demonstration following the mechanism is introduced in this paper for evaluating the integrated visualization approach. We set up a test-purposed and versatile toolkit that is cheaper, smaller and more controllable than conventional tools. We collect indoor environmental data set composed of sequential numeric data so as to use them as parameters for visualization. We inspected three major issues in the process: 1) indoor temperature data of a specific room collected at a second interval; 2) such a data set can be varied by subdivided spots of interest using multiple toolkits; 3) as a result, the collection of data is regarded as one type of parameter for visualization on top of the room's floor plan, e.g. a sudden change of sequential numbers.
Indoor environment such as temperature changes are shown as colors overlapped on the floor plan. In this way it may be easier to understand the state of indoor environment and factors which influence the environment. The floor plan based visualization of an indoor environmental element seems intuitive compared to just listing the numeric information.
This paper introduces and demonstrates an integrated approach for visualizing the indoor environment on a floor plan with an actual test case. Combined with the building model, the visualized data can be used for recognition of the factors affecting to the target environmental element. We expect floor plan-based visualization to be used for decision-making.
People spend most of their daily lives indoors, and this simply explains why indoor temperature is such an important environmental factor (Hyo Joo et al. ; Yoon and Spengler ). Therefore, controlling comfortable indoor environment is essential for the people to keep their emotional and physical states healthy. An additional benefit is better work productivity (Hee-geon et al. ; Moon et al. ). Conventionally, to measure and collect the indoor environmental data such as temperature data set used to be inconvenient and expensive due to several reasons (Sik and Bae ). The multi-use environmental sensors, however, became affordable and easy to control recently, and this enabled us to measure the broad range of indoor environments by using such low-priced and controllable sensor devices (Yick et al. ). However, as the data set acquired by widely installed environmental sensors increased significantly, the need for making effective use of the data has become of importance (Jisun ; Yongdai and Kwang ; Yang Koo and Keun Ho ). The more sensor data increases, the more potentiality for managing indoor environment and necessity to manage and represent amount of them grow at the same time. Conventionally, charts are frequently used for representing sensor data (Bo-ram et al. ; Chauk and Kyung-Ae ; Kwon et al. ; Sam-gil et al. ). Chart is useful to visualize and compare few sensor data but for expressing a number of sensor data, it is ineffective (Seung-heon et al. ) and hard to understand the condition of indoor environment. We, therefore, suggest an integrated mechanism to express such a number of sensor data in an effective way. This paper aims to figure out an integrated approach to visualize such conditional factors using indoor temperature changes. The baseline of this approach is to measure the temperature and analyze given conditions (Seung-Chul et al. ). This research aims to develop an integrated visualization mechanism using accumulated data from sensors, rather than the issue for data integrity or the analysis of indoor environment. To verify the feasibility of the proposed mechanism in this paper, consequently, we propose an integrated approach to the visualization of indoor temperature changes using several pragmatically implemented software and hardware modules. This is one of the graphical representations on the existing building geometry using sensor devices and sequential processing mechanism. The entire process from sensing indoor environment to processing and visualizing acquired data can be subdivided into three parts. Each part is composed of sub-modules. After dealing with the functions of each module inside three parts, this paper shows a demonstration on an actual space. The target environmental element to be visual ized is hourly variation of temperature changes.
The Sensor Module measures the target indoor environmental element by multiple sensors and generates sets of raw data. Each sensor senses the numeric value of the target indoor environment at an interval of time, and the data are collected into a set of raw data in the Data Collection Module. The set of raw data is used by visualizing not only condition of indoor environment at real-time but also specific purpose, such as the average value that the user or manager wants to know. Sensing and collecting sufficient number of datasets by installing the appropriate number of sensors is important because the number of input data is closely related with research background and effect of visualization. The greater the input data the more precise and abundant the visualization outcome. As mentioned earlier in scope, the matter of missing or erroneous data will be covered through further work. There are various types of target indoor environments that can be measured such as temperature, humidity, brightness, air quality, and movement. In this paper, the target indoor environment visualized is limited to temperature.
Data collection module
Data processing module
In order to generate input data, the Data Processing Module retrieves a particular data among the collected raw dataset and calculates the retrieved data according to the purpose of visualization. Defining the input data should be done before selecting a particular part of the data. The target and algorithm for the data processing are decided by the definition of the input data. As in selecting a particular part of the data from a dataset of measured environmental data, it is also possible to decide what environmental element is visualized when there are plural indoor environmental elements collected by the installed sensors.
Building model module
The Building Model Module loads the building geometry, which becomes the back-layer of the visualization. The loaded building model is used as two ways. One is the reference for designating the boundary of the sensor network layer and another is the data for analyzing the visualization outcome. The building model should meet three requirements: 1) drawing a floor plan with precise scale and shape of the indoor space where sensors are installed; 2) including all the factors that influence the target indoor environment within the space; and 3) designating the sensor location on the floor plan accurately. The use of loaded building model regarding designation of the boundary of the sensor network layer is described on the Sensor Network Module. When the building model is combined with the visualization outcome, the building model becomes the basis of analysis of the visualized indoor environmental data.
Sensor network module
The Sensor Network Module generates the sensor network layer which becomes the basis for visualization on building model. Based on the loaded building model, the shape and the size of the boundaries of the sensor network layer are defined. The generated sensor network layer is visualized in Visualization Module and mapped with the input data processed by Data Processing Module. Each sensor location, which is mapped with the input data, can be acquired by Indoor Positioning System (IPS). There are several possible IPS systems such as using Wi-Fi, Global Positioning System antenna, or Geo-Magnetism. Without IPS, the sensor location can be designated manually comparing given building model with the sensor location in actual space. The process of making sensor network layer consists of two steps: 1) defining the boundary of sensor network layer by designating edge of floor plan; and 2) designating the sensor points on the boundary.
Visualization Module transforms the numeric input data into visual information using sensor network layer. Each input data of an installed sensor is connected to each sensor point on the sensor network layer. Connected with the input data, the sensor points move along the z-axis. As the sensor points move depending on input data, sensor network is transformed accordingly. The transformed sensor network enables input data to be expressed in any type of graphical image including color or geometry. In this step, the visualization method is decided by the building model and the definition of the input data, considering how to visualize the interested environmental data in the most intuitive way. When the visualization method is decided and the input data is connected to each sensor point, the sensor network layer will be transformed into a certain form of visual information, according to the decided visualization method and the processed input data.
Measurement of temperature
Converting and collecting the measured data
Temperature change data processing
Importing floor plan
Generating sensor network layer
Visualizing input data
Results and discussion
This paper introduces and demonstrates an integrated approach for visualizing the indoor environment on a floor plan with an actual test case. The entire system consists of three major parts, which are able to be subdivided into 6 modules. Several prerequisites exist in each module such as precise floor plan. As shown in Section 3 Demonstration, which is visualizing temperature changes in a position for an hour, visualization mechanism proposed in this paper is able to process the numeric data and to convert processed data into visualized data. As a result, we find out that the visualized environmental data are more intuitive and easier to figure out the state of an indoor environment compared to numeric data. Combined with the building model, the visualized data can be used for recognition of the factors affecting the target environmental element. This information enables floor plan-based visualization to be used for improved decision-making. When changing the layout of an existing building, we can use not virtual data acquired by simulation but also the visualized data acquired by real sensor as evidence for design decision-making. In terms of monitoring, intuitive visualized data can help people evacuate under emergency, showing where an accident happened. Applicability can be expanded as the number of type of sensor. To visualize not only temperature changes but also other indoor environment, we need to develop the visualization mechanism, considering various kinds of available sensors and their network and visualization methods. We will continue to enhance the capability of the data visualization mechanism depicted in this paper on a given building model, based on various types of available sensor modules, their network, or alternated data collection.
This research was supported by a grant (13AUDP-B068892-01) from Architecture & Urban Development Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government.
- Arduino. Arduino Uno board. In . Accessed 10 June 2014., [http://arduino.cc/en/Main/ArduinoBoardUno]
- Bo-ram K, Soo-Hyun P, Bong-Hee H: Indoor environment monitoring system. Proceedings of the KIISE Korea Computer Congress 2012, 39(1B):57–59.Google Scholar
- Chauk K, Kyung-Ae C: Implementation of a monitoring software using environment sensor data. Proceedings of the KIISE Korea Computer Congress 2008, 35(2B):309–313.Google Scholar
- Grasshopper. Grasshopper. In . Accessed 10 June 2014., [http://www.grasshopper3d.com]
- Hee-geon P, Myeong-jin K, Yong-shik K: Measurement and assessment of indoor air quality in newly built university buildings. Journal of the Korean Society of Living Environment System 2013, 20(2):216–224.Google Scholar
- Hyo Joo K, Geun Young Y, Jeong K: A field survey of thermal comfort in office building with thermal environment standard. Journal of the Korea Institute of Ecological Architecture and Environment 2011, 11(3):37–42.Google Scholar
- Jang Sik B: Efficient energy management utilizing prediction model in the sensor network based environment monitoring system. Ph.D Dissertation, Yeungnam University; 2012.Google Scholar
- Jisun L: A Study on visualizing method and expression of information design for big data. Korean Society of Basic Design and Art 2013, 14(3):261–269.Google Scholar
- Kwon S, Park D, Cho Y, Park E, Kim S, Jeong M: Temperature and humidity monitoring using Ubiquitous Sensor Network (USN). Proceeding of the 46th Meeting of KOSAE Korean Society for Atmospheric Environment 2008, 457–458.Google Scholar
- Maxdetect. RHT03 Digital Humidity & Temperature Sensor. In . Accessed 10 June 2014., [http://www.humiditycn.com/cp22.html]
- Moon S-W, Kim T-W, Jang D-S, Hong W-H: A study on occupants' subjective evaluation and a measurement of indoor environments of an office building. Journal of the Architectural Institute of Korea 2009, 25(5):279–286.Google Scholar
- Parallax. PLX-DAQ Data Acquisition for Excel. In . Accessed 10 June 2014., [https://classic.parallax.com/tabid/393/Default.aspx]
- Rhinoceros. Rhino3D. In . Accessed 10 June 2014., [http://www.rhino3d.com]
- Sam-gil C, Ki-tael K, Dong-il K: A study of environment monitoring system based on USN. Journal of Information and Convergence Communication Engineering 2010, 14(6):1488–1492.Google Scholar
- Seung-Chul L, Sang-Joong J, Young-Dong L, Wan-Young C: Design and implementation of flooding-based query model in wireless sensor networks for indoor environmental monitoring system. Journal of the Korean Sensors Society 2008, 17(3):168–177.Google Scholar
- Seung-heon S, Kyeong-jin B, Eung-kon K: A study on visualization technology for environmental sensor data. Proceedings of Korea Institute of Information and Communication Engineering 2007, 1: 192–197.Google Scholar
- Xiaoming. GhExcel - Interface with Excel. In . Accessed 10 June 2014., [http://www.food4rhino.com/project/ghexcel]
- Yang Koo L, Keun Ho R: Historical sensor data management using temporal information. Journal of Korea Spatial Information Society 2008, 10(4):97–102.Google Scholar
- Yick J, Mukherjee B, Ghosal D: Wireless sensor network survey. Elsevier Computer Networks 2008, 52: 2292–2330. 10.1016/j.comnet.2008.04.002View ArticleGoogle Scholar
- Yongdai K, Kwang H, C: Big data and statistics. Journal of the Korean Data Information Science Society 2013, 24(5):959–974. 10.7465/jkdi.2013.24.5.959View ArticleGoogle Scholar
- Yoon D-w, Spengler JD: Standards for indoor air pollutant levels and ventilation rates. Journal of the Architectural Institute of Korea Special Issue 1995, 39(6):12–17.Google Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.