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Tutorials

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.



ZigBee Wireless Sensor and 6LowPAN


* CANCELLED *



Lecturer

Ata Elahi
Southern Connecticut State University
United States
 
Brief Bio
Dr. Elahi is a professor of computer science at Southern Connecticut State University. He received Ph.D. in Electrical Engineering from Mississippi State University in 1982. He is the author of the following books: ARM Assembly Language with Hardware Experiments published by Springer 2015, ZigBee Wireless Sensor and Control Network published by Prentice Hall 2010, Data, Network & Internet Communications Technology published by Thomson Learning 2006, and Network Communication Technology published by Delmar Thomson Learning 2000.
Abstract

Wireless Sensor and Control Networks are quickly becoming an integral part of the automation process, chemical plants, refineries, and commercial buildings. for wireless sensor and control networks is rapidly growing. Furthermore, according to a new market research report, it will be a $3.8 billion industry by the year 2017. To accommodate this burgeoning technology, numerous standards are being developed for wireless sensor and control networking such as SP100.11(Wireless Systems for Automation) by the Industrial Standard for Automation (ISA), Wireless HART (Highway Addressable Remote Transducer) by the HART organization, IPv6 over Low Rate Wireless Personal Network (6LoWPAN) by IETF (the Internet Engineering Task Force) and ZigBee by the ZigBee alliance. ZigBee is one of the most widely used technologies to connect IoT devices.

Keywords

Wireless Sensor, Wireless Technology, 6LowPAN, ZigBee

Target Audience

it is intended for educators, researchers, system designers, embedded programmers, IoT designers and anyone wishing to learn more about this technology.



Detailed Outline

1. Wireless Sensor and Control Technologies
2: ZigBee Wireless Sensor and Control Network
3: ZigBee Protocol Architecture
3: IEEE 802.15.4 Physical Layer
4: IEEE 802.15.4 Medium Access Control (MAC) Layer
5: Network Layer
6: Application Support Sublayer (APS)
7: Application Layer
8: Security
9: Address Assignment and Routing
10: ZigBee Home Automation and Smart Energy Network
11: ZigBee RF4CE
12. IPv6 over Low Rate Wireless Personal Area Network (6LowPAN)


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

Next Generation Big Data Tools for Analyzing Sensors Data in Biomedical Applications


* CANCELLED *



Lecturer

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.
Abstract

With the recent significant advancements in sensor technologies and the deployment of sensors in many application domains, the main research question now is whether the data collected from such sensors are fully analyzed and properly utilized in improving decision-making processes. In addition, with the accelerated development of Internet of Things (IoT) devices, many sensors are deployed in various domains almost on a daily basis. For example, various wireless sensors are now deployed in bridges and smart buildings to collect all sorts of safety and performance data. Although these developments are certainly welcome, so much left to be done to take full-advantage of the data gathered by such devices. An even more critical question is whether data collected from wearable devices and other personal sensors can be used to improve healthcare. The most critical missing component in these systems is the lack of advanced data analytics. In this tutorial, we propose various data integration and analysis models in an attempt to address the much-needed balance between data gathering and data analytics. We try to illustrate how the general relationship between mobility and health can be explored to assess health levels and predict potential health hazards. We are interested in gathering personal mobility data to classify daily activities, which in turn can be used to develop mobility patterns associated with individuals for large-scale population analysis. 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 smart infrastructures.












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

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