IEEE 5th World Forum on Internet of Things
15-18 April 2019 – Limerick, Ireland

TOP6-Sensors and Sensor Systems

Track Description

Ranging from on-body bio-sensors, to instrumented smart homes, to sensor systems for industrial use and manufacturing, to area-wide networks of environmental sensors, “Sensor Systems” are at the core of the Internet of Things ecosystem. The data generated from sensors is an essential building block in IoT for driving the decisions and actions that create value and that enable new capabilities.

This Track is focused on examining industry and technology trends for systems that sense and transduce informational, physical, biological, and chemical phenomena in relation to highly impactful IoT applications. In keeping with the themes of the World Forum the Track will specifically address the requirements, development, implementation, and operations of Sensors and Sensor Systems for the Vertical Areas chosen for the World Forum.

  • Agriculture
  • Automotive and Transportation
  • Healthcare, Pharmaceuticals, and Medical Devices
  • Industrial IoT
  • Maritime IoT
  • Smart Cities and Public Safety

The Track is organized into three two hour Sessions. Through a series of presentations and panel discussions, thought leaders from industry, academia, and government will examine the following:

  • Emerging Sensor Technologies and the Potential Impact to IoT
  • Commercial Opportunities, Applications, and Market Challenges
  • Standardization efforts for Sensors and Sensor Networks

Representatives of the IEEE Sensors Council will be supporting the Track and provide opportunities for collaboration and standardization beyond the World Forum. We hope that you can join us and be part of the vigorous dialog and help build the future of IoT.

Track Co-Chairs

Gerard Hayes, Wireless Research Center of North Carolina

Gerard HayesDr. Hayes has nearly three decades of experience in government and commercial electromagnetic research and design.  Prior to working with the Town of Wake Forest to establish the WRCNC in 2010, Dr. Hayes was the Director of Engineering at GreenWave Scientific where he led the development of antenna and RF circuit designs for a diverse range of DoD applications.  At Sony Ericsson Mobile Communications (USA) Inc., Dr. Hayes provided global technical leadership in the Technology and Research organization with contributions to handset antenna design, technology, and radiated performance optimization.  At Lockheed Martin (formerly Lockheed Missiles and Space Co.), Dr. Hayes supported research and development efforts for space-based, phased array applications.  The scope of his experience encompasses electromagnetic theory, bioelectromagnetics, antenna design, RF circuit analysis, and material engineering. He has participated in the development of international standards for OTA, HAC, and SAR evaluation (including IEEE, IEC, CTIA, and C63 standards).  With over 70 US patents, Dr. Hayes has maintained a prominent technical role in the wireless industry.

Yu-Hsing Wang, Hong Kong University of Science and Technology (HKUST) and Data-Enabled Scalable Research (DESR) Laboratory

Yu-Hsing Wang

Prof. Yu-Hsing WANG received his B.S. and M.S. degrees in Civil Engineering from National Taiwan University and Ph.D. in Civil Engineering from Georgia Institute of Technology where he received the George F. Sowers Distinguished Graduate Student Award for Ph.D. Students. Prof. Wang is a Professional Geotechnical Engineer in Taiwan since 1996. Currently, he is Professor and Associate Department Head at the Department of Civil and Environmental Engineering and founder/director of Data-Enabled Scalable Research (DESR) Laboratory, the Hong Kong University of Science and Technology (HKUST). The DESR Lab is a Makerspace, specialized in the applications of Vertical AI, integrated with Geotechnical Internet of Things (Geo-IoT), Big Data Analytics, and Deep Learning, etc., on sustainable urban development and city resilience. In 2005, he received the ASTM International Hogentogler Award. In 2008 and 2017, he received the School of Engineering Teaching Award, HKUST. In 2013, he received the Distinguished Alumni Award from the Department of Civil Engineering, National Taiwan University. He has been invited for Keynote and theme lectures in the international conferences and served as associated editors and editorial board members in different journals.

Track Speakers

Levente Klein

Levente KleinDr. Levente Klein is a Research Staff Member in the Internet of Things and Industry solution at the IBM T.J. Watson Research Center, Yorktown Heights, NY. His work at IBM span multiple research topics from material science, nano-optics and wireless sensing solutions with strong focus to apply research technologies to industrial problems. Since joining IBM Research in 2006, he developed technologies to enable energy efficient cooling in data centers, monitoring fugitive methane gas in oil and gas industry, and application of wireless sensing solution in agriculture and healthcare.

Current research interests focus on environmental monitoring of greenhouse gases, application of wireless sensing in outdoor environment and physics based modeling and analytics. His work was recognized by 3 IBM’s Outstanding Technical Achievement award and multiple IBM Research Division Award. He published more than 50 scientific publications and has 40 patents granted in USA.

Talk Title: Intelligent Sensors for Environmental Monitoring

The ambient environment is affecting human health, productivity and overall wellbeing. Many indoor sensors were developed to asses and control the surrounding environments for human comfort but once sensors are operated outdoor they must withstand harsh environmental conditions, maintain calibration and measurement accuracy. Current intelligent solutions are combining sensing and computation to dynamically determine what to measure, dynamically adjust data acquisition rate, increase data compression and enable computation close to the edge. The next leap in research is the fusion between global scale intelligent sensor networks and satellite/aerial observations that combines the high precision sampling of the local environments with global monitoring; enabling new applications like precision agriculture, air quality monitoring and disaster management. This presentation present current effort to develop intelligent sensors including sensor fabrication, quantification of sensor reliability, and integration of control loops and decision support based on sensor data. Examples in precision irrigation of vineyards, detection of fugitive methane emission for oil and gas industry and monitoring of natural disasters will be presented including the distribution of intelligence between cloud and edge computing, automatic sensor calibrations and development of big data approaches to handle massive amount of data.

Virginia Pilloni

Virginia PilloniVirginia Pilloni is an Assistant Professor at the University of Cagliari. Her main research interests are focused on Internet of Things and Wireless ad-hoc networks, with particular attention to the improvement of their performance through task allocation. She received her PhD in Electronic and Computer Engineering with Doctor Europaeus mention from the University of Cagliari in 2013. From November 2011 to April 2012 she was a visiting PhD student at the CCSR at the University of Surrey. She was awarded with the Master Degree in Telecommunication Engineering with full marks in 2009 at the University of Cagliari. She has been involved in several national and international research projects, among which DEMANES (EU – 7FP) and QoE-NET (EU – H2020-MSCA-ITN-2014). She is an Editor of Elsevier Computer Networks. She has served as conference chair and program committee member of several international conferences.

Talk Title:  Efficient Task Allocation for Resource Management in IoT: Objects Cooperation from Mobile CrowdSensing to Smart Buildings

The Internet of Things (IoT) is characterized by heterogeneous objects that dynamically cooperate and make their resources available, with the aim of achieving a common objective. Thanks to the pervasive spread of smart objects, the IoT is expected to offer amazing improvements in collecting, processing and distributing information. With the aid of context awareness technologies, objects will gain the intelligence to provide information on their status and acquire data from other objects. This entails that devices will implement a certain level of logic to coordinate in the execution of collaborative actions so as to optimize the management of their available resources (e.g. lifetime, storage, processing capabilities) and improve the system reliability.

The objective of this talk is to analyze the opportunities and challenges related to efficient task allocation for optimal resource management in IoT scenarios.  The talk will first introduce the technologies that can be used to address the IoT challenges that affect resource management. It will later focus on the main approaches proposed by the literature for the deployment of applications to IoT objects. The talk will discuss how the analyzed approaches address the problem of optimizing different resources, and which constraints are considered in order to satisfy quality requirements. Accordingly, the application of these approaches to some typical IoT scenarios, such as Mobile CrowdSourcing (MCS) and Smart Buildings, will be investigated.

Lin Wang

Lin WangChuo Kaihatsu Corporation, Japan

L. Wang, who working at Chuo Kaihatsu Corporation, is a senior manager of geotechnical engineering with expertise in rain-induced slope instability, earthquake-induced slope instability, development of slope monitoring and alarm system. He undertook his PhD at the University of Tokyo, Japan, after graduation he is engaged in the research and development of the monitoring of landslide and slope failures in the long term.

Recently he carries out the research, supported by cabinet office, government of Japan, and finished the SIP (Cross ministerial Strategic Innovation Promotion Program) research during 2014-2017, which theme is R&D of Early Warning Monitoring System of Slope Failure Based on Multi-point Tilt Change by using IoT and MEMS technology. The results of research are now deployed to Japan, Australia, Taiwan, China and India during last ten years.

Talk Title: An Early Warning System of Landslide by Multi-Point MEMS Tilting Sensors Technology

(The case studies of IoT technology and MEMS technology which used to landslide and slope failures in Japan, Australia, India, Taiwan and China)

An early warning monitoring system is one of the most effective ways to reduce disasters induced by slope instabilities. Many large-scale disasters induced by rainfall and earthquakes occurred in the world, and many people’s life were lost by the disasters. To reduce vulnerability to such slope and landslide hazards, an early warning system becomes important, and for this purpose, a newly developed simple and low-cost early warning system for slope failure and landslides is presented here. The new system is based on a tilt sensor that is easy to install. The sensor can monitor water content and slope deformation with a tilt Micro Electro Mechanical Systems (MEMS) module embedded in the sensor unit, and it can transfer real time data via a wireless network based on IoT technology. Since 2010, the monitoring system has been used in many actual large-scale slope failure and landslide sites to validate field performance. We report on several monitoring cases by using IoT sensor technology to show that the early warning system adequately monitors the stability of landslide, slope failure fields in Japan, Australia, India, Taiwan and China. Based on the field site test results, the monitoring method is proposed for regions of increased hazard of earthquake-induced slope failure.

Acknowledgments:

This research has been supported by SIP (the Cross-ministerial Strategic Innovation Promotion Program) of the Cabinet Office of Government of Japan, Grants-in-Aid for Scientific Research of the Japan Society for the Promotion of Science (JSPS), Core-to-Core Program “B. Asia-Africa Science Platforms” (JSPS), Construction Technology Research and Development Subsidy Program of the Ministry of Land, Infrastructure, Transport and Tourism of Japan, and International Cooperation Project of the Chinese Ministry of Science and Technology (projects: 07(2007DFA21150) and 09(2009DFB20190)).

Yu-Hsing Wang

Yu-Hsing WangProf. Yu-Hsing WANG received his B.S. and M.S. degrees in Civil Engineering from National Taiwan University and Ph.D. in Civil Engineering from Georgia Institute of Technology where he received the George F. Sowers Distinguished Graduate Student Award for Ph.D. Students. Prof. Wang is a Professional Geotechnical Engineer in Taiwan since 1996. Currently, he is Professor and Associate Department Head at the Department of Civil and Environmental Engineering and founder/director of Data-Enabled Scalable Research (DESR) Laboratory, the Hong Kong University of Science and Technology (HKUST). The DESR Lab is a Makerspace, specialized in the applications of Vertical AI, integrated with Geotechnical Internet of Things (Geo-IoT), Big Data Analytics, and Deep Learning, etc., on sustainable urban development and city resilience. In 2005, he received the ASTM International Hogentogler Award. In 2008 and 2017, he received the School of Engineering Teaching Award, HKUST. In 2013, he received the Distinguished Alumni Award from the Department of Civil Engineering, National Taiwan University. He has been invited for Keynote and theme lectures in the international conferences and served as associated editors and editorial board members in different journals.

Talk Title: Vertical AI and AI-enabled sensing for Smart and Resilient City

Building a world-class smart and resilient city is an endless endeavor of the Hong Kong government. The advent of AI Era enables the feasibility and even accelerates the progress towards this goal. In this talk, we, from the Data-Enabled Scalable Research (DESR) Laboratory of the HKUST, will first showcase how the Vertical AI, an integrated solution from sensor design/deployment, data gathering, building the big data infrastructure, AI-enabled big data analytics, to the semantic integration for a better decision making, can be leveraged to spearhead the development of smart and resilient city. Then, based on the development of Vertical AI, we will focus on the next generation of sensor and sensing system for the AI Era, i.e., AI-enabled sensing. We will showcase different implementations that involved different data types garnered, including image, sound, and vibration data, to evaluate the performance and the potential impact of AI-enabled sensing from a holistic point of view.