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The Ninth International Conference on AFIN 2017 |
| Tutorials |
T1.
The Role of Culture within Green IT
Prof. Dr. William Campbell, Birmingham City University, UK
T2.
Wireless Sensors and Big Data Analytics: A Focus on Health Monitoring and Civil Infrastructures
Prof. Dr. Hesham H. Ali, University of Nebraska at Omaha, USA
T3. Photo-sensing Receivers using a-SiC: based materials - Visible Light Communication Systems
Prof. Dr. Manuela Vieira, ISEL, Portugal
Details
T1.
The Role of Culture within Green IT
Prof. Dr. William Campbell, Birmingham City University, UK
Overview
This tutorial will address the impact of organizational culture on the adoption of Green IT initiatives. We will begin by exploring organizational culture and considering the nature of culture within the IT sector. An analysis of the effect of culture on sustainable use of IT will be presented, using Cameron and Quinn’s Competing Values Framework as a tool to explore organizational culture. Another theme of this tutorial will be the use of choice architectures to ‘nudge’ individuals in particular directions with a focus on adopting green IT policies.
Other themes explored will be the roles social media play in promoting green IT and the impact of culture on the use of tools which deliver green IT, such as cloud computing. We will consider the impact of globalization. Key recommendations for working with culture to support the adoption of Green IT will be provided.
Key Topics
Definition of Green IT
The scope of “Green IT” will be explored, with discussion of the difference between “Green IT” and ”Green IS” and the ways in which IT can have a positive or negative effect on the environment.
Organisational Culture
The concept of Organisational Culture and its consequences will be discussed. A widely used tool for analysing organizational culture will be presented: the Competing Values Framework (CVF) of Cameron and Quinn. This identifies two key dimensions of organizational culture: (1) Internal Focus and Integration versus External Focus and Differentiation; and (2) Stability and Control versus Flexibility and Discretion. This produces 4 key culture types: Hierarchy, Market, Adhocracy and Clan. A key strength of this tool is that it has an associated questionnaire, the Organisational Culture Assessment Instrument (OCAI), which gives numerical values for organizational culture. This permits statistical analysis and the production of intuitively understandable ‘radar diagrams’. The CVF has been used to analyse many companies, resulting in benchmarks for various industries.
IT Culture
It has been argued that IT has developed a specific culture. This will be explored, along with its consequences.
The Impact of Organizational Culture
The Competing Values Framework will be used as a tool for exploring the impact of Organizational Culture on the adoption of green measures, in particular within IT.
Nudging Theory
The concept of nudging has been highly influential in recent years. The key idea is that the best way of changing human behaviour is not legislation, but gently prodding people to change their behaviour. An example is the ‘Traffic Lights’ symbols on food packaging. The application of nudging to Green IT will be discussed.
Application of OCAI
Participants will be given the opportunity to apply the OCAI and a greenness questionnaire to a company.
Open Research Questions
Finally, open research questions such as the impact of globalization, different national cultures and social media will be explored.
T2.
Wireless Sensors and Big Data Analytics: A Focus on Health Monitoring and Civil Infrastructures
Prof. Dr. Hesham H. Ali, University of Nebraska at Omaha, USA
The last several years have witnessed major advancements in the development of sensor technologies and wearable devices with the goal of collecting various types of useful data in many application domains. Based on such technologies, many wireless devices have swamped the market and found their way on the wrists and belts of many users. In addition, various wireless sensors are now deployed in a number of bridges and smart buildings to collect all sorts of safety and performance data. Although these developments are certainly welcomed, so much is left to be done to take full-advantage of the data gathered by such devices. The most critical missing component is the lack of advanced data analytics. In the case of health monitoring, like many aspects of healthcare, the focus has been primarily on producing devices with data collection capabilities rather than developing advanced models for analyzing the available data. There is much needed balance between data gathering and data analysis. Similarly, in the case of civic infrastructure, the collected data is rarely used to support decision-making processes related to safety and performance. In this tutorial, we attempt to fill this gap by proposing various data integration and analysis models. We are interested in gathering mobility data that can be used to classify the daily activities of each individual, which in turn can be used to build a mobility pattern associated with that individual for a given time period. We also propose a graph-theoretic model based on building correlation networks to develop a big data analytics tool for analyzing the performance parameters of civil infrastructure and predict potential safety problems. We utilize a graph-theoretic mechanism to zoom in and out of the networks and extract different types of information at various granularity levels. The proposed approach can potentially be used to predict health hazards in medical applications and safety problems associated with bridges and civil infrastructures. It can also serve as the core of a decision support system to help healthcare professionals provide more advanced healthcare support and help engineers maintain safer and efficient civil infrastructures.
Keywords: wireless sensors, mobility data, mobility devices, correlation networks, predictive models, preventative healthcare, civic infrastructures, bridges safety.
TUTORIAL OBJECTIVES
The fields of Biomedical Informatics and building information systems have been attracting a lot of attention in recent years. The use of wireless devices to collect various types of critical data continues to grow both in the commercial world as well as in the research domain. The impact of such devices remains limited though, primarily due to the lack of sophisticated data analytics
tools to allow for the extraction of useful information out of the collected data. The proposed tutorial will address these issues with a particular focus on the following objectives:
1- Provide an overview of the current commercial devices and research studies associated with the use of wireless sensors in the domains of healthcare and civil infrastructure, with a focus on the advantages and disadvantages of each device and approach.
2- Introduce the main ideas associated with obtaining a mobility pattern or signature using raw data collected from wireless sensors. The goal of such pattern is to fully characterize the mobility parameters and to some degree the health level of each individual for a given time period.
3- Introduce the basic concepts of using correlation networks to store and analyze data associated with bridges and civil infrastructure and show the potential of using these networks as a key component of an advanced decision support system.
4- Introduce the audience to how graph models and integrated networks can be developed using the mobility patterns and used to estimate health levels of various user groups. The goal of the proposed model is to classify health levels of individuals and track their health variability pattern, which may to the ability to predict potential health hazards and allow for the much needed objective of predictive and preventive healthcare.
TUTORIAL OUTLINE
The proposed tutorial is designed for a two-hour session that could potentially be extended to a three-hour session if time permits. The shorter version of the tutorial focuses on four points; providing a brief background of current technologies associated with the use of wireless sensors in health monitoring and civil infrastructure; introducing the concepts of mobility and safety signatures developed using data collected from wireless sensors, using correlation networks and graph theoretic tools to properly analyze sensor data and extract critical health and safety information; and finally studying how correlation networks can be used to link mobility studies with bioinformatics and building information systems research. A longer version of the tutorial can be developed by expanding on each point above along with adding two points; how to use clustering algorithms and advanced graph theoretic tools to provide advanced big data analysis of the collected data used to build the correlation networks, and how to integrate different types of heterogeneous data including mobility data and genetic information (features of bridges/buildings) to provide a comprehensive analysis for health data for each individual (safety analysis for bridges and buildings.
1. Survey of current wireless technologies in healthcare and building infrastructures - Brief discussion on the various research studies and commercial wireless devises developed with the goal of monitor health activities and measure various mobility parameters such as number of steps, distance covered, and active periods while emphasizing the ease of use and level of trustworthiness associated with collected data. Similar models are used to analyze buildings/bridges parameters like age, material, safety ratings and satellite images.
2. How to obtain mobility signatures using raw mobility data – Algorithms for classifying various daily activities using mobility data will be introduced and used to build the characterizing models of mobility signature. Such characterizing patterns can be used to accurately measure the level of mobility associated with each individual. Similar analyses will be provide to storing and analyzing safety and performance measure in civil infrastructures.
3. Big data analytics using correlation networks – New techniques for building correlation networks from sensor data collected from multiple individuals (buildings) at different times will be presented. Big Data analysis tools will be introduced to analyze the developed correlation networks and predict health (safety) levels of various cases with a focus on how to use such tools in predicting potential health (safety) problems.
4. Data integration tools using mobility and genomic data – Correlation Networks for modeling integrating various types data will be presented. The integration model represents potential next steps in healthcare in which various types of data will be used to establish an accurate picture associated with each person’s health and the ability to track progress of recovery from injuries or medical procedures.
REQUIREMENTS AND TARGET AUDIENCE
The tutorial is intended primarily for computational scientists who are interested in wireless networks and data analytics. It is also of interest to Biomedical and Engineering researchers since the focus of the main application domains of the proposed methodology is health informatics and civil infrastructure. In particular, those interested in how wireless and network technologies can used to support the new direction of health care and maintenance of infrastructures that focused on predictive and preventative approaches. Biomedical scientists and engineers with some background in computational concepts who are interested in how new technologies can support health care and building information systems represent another group of intended audience. Basic background in computer science and wireless networks would be helpful but not necessary. The main concepts will be introduced in a highly accessible manner.
TUTORIAL DURATION AND FORMAT
The tutorial material will be presented through an interactive lecture and a demo illustrating the use of network models for integrating different mobility and wireless data and extract useful information from the input data. This includes the classification of health levels (safety of bridges and smart buildings) and the ability to provide an alarm system to predict, and consequently prevent, health hazards (safety problems).
T3. Photo-sensing Receivers using a-SiC: based materials - Visible Light Communication Systems
Prof. Dr. Manuela Vieira, ISEL, Portugal
In this talk, a double pi’n/pin a-SiC:H voltage and optical bias controlled device is presented and it behavior as image and color sensor, optical amplifier and multiplex/demultiplex device discussed. Several application for Visible Light Communication (VLC) are discussed. Namely a demonstration of an indoor localization system and Smart Vehicle Lighting System will be reported. The novelty is the use of a WDM device based on SiC technology in optical communications. The characteristics of various VCL system components, e.g., transmitter, receiver, and multiplexing techniques are analyzed and characterized. The device multiplexes the different optical channels, performs different filtering processes: amplification, switching, and wavelength conversion and at the end, it decodes the encoded signals recovering the transmitted information. It was used as a receiver in a positioning system where the location inside the unit cell, the ID address of the cell inside the network and the payload data transmitted by the three different RGB channels. Also, the use VLC for vehicle safety applications, creating a smart vehicle lighting system that combines the functions of illumination and signalling, communications, and positioning is reported.
The sensing element structure (single or tandem) and the light source properties (wavelength, intensity and frequency) are correlated with the sensor output characteristics (light-to-dark sensitivity, resolution, linearity, bit rate and S/N ratio). Depending on the application, different readout techniques are used. When a low power monochromatic scanner readout the generated carriers the transducer recognize a color pattern projected on it acting as a color and image sensor. Scan speeds up to 104 lines per second are achieved without degradation in the resolution. If the photocurrent generated by different monochromatic pulsed channels is readout directly, the information is multiplexed or demultiplexed. It is possible to decode the information from three simultaneous color channels without bit errors at bit rates per channel higher than 4000bps. Finally, when triggered by appropriated light, it can amplify or suppress the generated photocurrent working as an optical amplifier. An electrical model is presented to support the sensing methodologies. Experimental and simulated results show that the tandem devices act as charge transfer systems. They filter, store, amplify and transport the photo-generated carriers, keeping its memory (color, intensity and frequency) without adding any optical pre-amplifier or optical filter as in the standard p-i-n cells.
