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The role of the tutorials is to provide a platform for a more intensive scientific exchange amongst researchers interested in a particular topic and as a meeting point for the community. Tutorials complement the depth-oriented technical sessions by providing participants with broad overviews of emerging fields. A tutorial can be scheduled for 1.5 or 3 hours.

Wireless Sensors and Big Data Analytics: A Focus on
Health Monitoring and Civil Infrastructures


Hesham Ali
University of Nebraska at Omaha
United States
Brief Bio
Hesham H. Ali is a Professor of Computer Science and the Lee and Wilma Seaman Distinguished Dean of the College of Information Science and Technology (IS&T), at the University of Nebraska at Omaha (UNO). He currently serves as the director of the UNO Bioinformatics Core Facility that supports a large number of biomedical research projects in Nebraska. He has published numerous articles in various IT areas including scheduling, distributed systems, data analytics, wireless networks, and Bioinformatics. He has also published two books in scheduling and graph algorithms, and several book chapters in Bioinformatics. He is currently serving as the PI or Co-PI of several projects funded by NSF, NIH and Nebraska Research Initiative (NRI) in the areas of data analytics, wireless networks and Bioinformatics. He has been leading a Research Group at UNO that focuses on developing innovative computational approaches to classify biological organisms and analyze big bioinformatics data. The research group is currently developing several next generation data analysis tools for mining various types of large-scale biological data. This includes the development of new graph theoretic models for assembling short reads obtained from high throughput instruments, as well as employing a novel correlation networks approach for analyzing large heterogeneous biological data associated with various biomedical research areas, particularly projects associated with aging and infectious diseases. He has also been leading two funded projects for developing secure and energy-aware wireless infrastructure to address tracking and monitoring problems in medical environments, particularly to study mobility profiling for healthcare research.

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 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.

Aims and Learning 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.

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.

Prerequisite Knowledge of 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.

Detailed Outline

The proposed tutorial is designed for a 3-hour session. 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. Time permits, an additional topics may include 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. Detailed mentioned below:
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.

Secretariat Contacts
e-mail: sensornets.secretariat@insticc.org