Our group work actively in the following four areas:
A short presentation of our research: https://www.bilibili.com/video/BV1VZ4y1M7BF/.
1. Wireless Time-Sensitive Networking (Wireless Control in Industrial IoT)
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The Internet has reshaped our world and revolutionized the way human live, learn and interact. The coming digital revolution is the Industrial Internet of Things (IIoT) or Industry 4.0. McKinsey estimated that by 2025, the IIoT will create a total value of more than 7 trillion US dollars on an annual basis and will be worth more than twice the consumer Internet [1].
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As defined by the industrial giant GE, IIoT refers to “the network of a multitude of industrial devices connected by communications technologies that results in systems that can monitor, collect, exchange, analyse, and deliver valuable new insights likenever before, which can then help drive smarter, faster business decisions for industrial companies” [2].
Connectivity in industrial environment has until now been dominated by wired communication [3]. Replacing the currently used wired infrastructure in industry by efficient wireless solutions will bring significant benefits—wireless solutions will reduce complex cabling, offer flexible communication approaches and enable new fields of application that wired systems cannot serve at all or only inadequately, such as the use of flying drones, digital twins, mobile assistance systems with man/machine interaction or mobile tools and robots. However, the simple installation of current available wireless technologies like WiFi and 4G in industrial environment will not lead to satisfactory performance. This is due to the fact that typical industrial applications require the exchange of small amount of data (e.g., only a single measurement or control command) with ultrahigh reliability and low latency, whereas modern wireless communication systems have been consistently engineered with a focus on exchanging large amount of data in the low-reliability, moderate-latency regime [4].
Critical IIoT applications like motion control and mobile robot control demands packet error rate as low as 10^(-9)-10^(-5) and latency down to sub-millisecond level, which are several orders of magnitude better than what is achievable by today’s wireless technologies [5]. There is a general consensus that developing ultrareliable and low-latency wireless communications (URLLWC) is essential to fully unlock the potential of the IIoT.
In this project, we aim to develop and validate through building prototypes, novel wireless communication technologies (including both radio wireless and optical wireless) for URLLWC that is tailored for critical IIoT applications. Note that in the literatue, URLLWC is also referred to as wireless time-sensitive networking (WTSN).
[1] McKinsey Global Institute, The internet of things: mapping the value beyond the hype, 2015.
[2] GE Digital, everything you need know about Industrial Internet of Things, https://www.ge.com/digital/blog/everything-you-need-know-about-industrial-internet-things
[3] V. K. L. Huang, Z. Pang, C. J. A. Chen, and K. F. Tsang, “New trends in the practical deployment of industrial wireless: From noncritical to critical use cases,” IEEE Ind. Electron. Mag., vol. 12, pp. 50–58, June 2018.
[4] H. Chen, R. Abbas, P. Cheng, M. Shirvanimoghaddam, W. Hardjawana, W. Bao, Y. Li, and B. Vucetic, “Ultra-reliable low latency cellular
networks: Use cases, challenges and approaches,” IEEE Communications Magazine, vol. 56, no. 12, pp. 119-125, December 2018.
[5] M. Bennis, M. Debbah and H. V. Poor, "Ultrareliable and Low-Latency Wireless Communication: Tail, Risk, and Scale," in Proceedings of the IEEE, vol. 106, no. 10, pp. 1834-1853, Oct. 2018.
Connectivity in industrial environment has until now been dominated by wired communication [3]. Replacing the currently used wired infrastructure in industry by efficient wireless solutions will bring significant benefits—wireless solutions will reduce complex cabling, offer flexible communication approaches and enable new fields of application that wired systems cannot serve at all or only inadequately, such as the use of flying drones, digital twins, mobile assistance systems with man/machine interaction or mobile tools and robots. However, the simple installation of current available wireless technologies like WiFi and 4G in industrial environment will not lead to satisfactory performance. This is due to the fact that typical industrial applications require the exchange of small amount of data (e.g., only a single measurement or control command) with ultrahigh reliability and low latency, whereas modern wireless communication systems have been consistently engineered with a focus on exchanging large amount of data in the low-reliability, moderate-latency regime [4].
Critical IIoT applications like motion control and mobile robot control demands packet error rate as low as 10^(-9)-10^(-5) and latency down to sub-millisecond level, which are several orders of magnitude better than what is achievable by today’s wireless technologies [5]. There is a general consensus that developing ultrareliable and low-latency wireless communications (URLLWC) is essential to fully unlock the potential of the IIoT.
In this project, we aim to develop and validate through building prototypes, novel wireless communication technologies (including both radio wireless and optical wireless) for URLLWC that is tailored for critical IIoT applications. Note that in the literatue, URLLWC is also referred to as wireless time-sensitive networking (WTSN).
[1] McKinsey Global Institute, The internet of things: mapping the value beyond the hype, 2015.
[2] GE Digital, everything you need know about Industrial Internet of Things, https://www.ge.com/digital/blog/everything-you-need-know-about-industrial-internet-things
[3] V. K. L. Huang, Z. Pang, C. J. A. Chen, and K. F. Tsang, “New trends in the practical deployment of industrial wireless: From noncritical to critical use cases,” IEEE Ind. Electron. Mag., vol. 12, pp. 50–58, June 2018.
[4] H. Chen, R. Abbas, P. Cheng, M. Shirvanimoghaddam, W. Hardjawana, W. Bao, Y. Li, and B. Vucetic, “Ultra-reliable low latency cellular
networks: Use cases, challenges and approaches,” IEEE Communications Magazine, vol. 56, no. 12, pp. 119-125, December 2018.
[5] M. Bennis, M. Debbah and H. V. Poor, "Ultrareliable and Low-Latency Wireless Communication: Tail, Risk, and Scale," in Proceedings of the IEEE, vol. 106, no. 10, pp. 1834-1853, Oct. 2018.
2. Wireless Sensing = Wireless + Artificial Intelligence (AI)
(Wireless Sensing for Workers or Elderly People Monitoring in Private Spaces)
(Wireless Sensing for Workers or Elderly People Monitoring in Private Spaces)
The worldwide population over 65 is expected to grow to one billion in 2030 [1]. Every year 33% of elderly people over the age of 65 will fall, and the percentage increases for the elderlies living in care institutions. The fall could cause injuries and a reduction of the quality of life. Fall represents one of the main reasons for the death of elderly people. Many elderlies cannot get up by themselves after the fall, and even without any direct injuries, 50% of those who had a long time of being on the floor (longer than one hour) died within six months after the falling [2]. Therefore, it is vital to continuously monitor the health conditions of the elderly in their living places. The invasive clinical monitoring methods do not lend itself to everyday use in home and community settings. Furthermore, previous studies have reported that the elderly are reluctant to put on wearable devices on a daily basis [3].
In this project, we will develop a comprehensive wireless sensing system, which leverages the current information-carrying RF signals for the contactless and continuous monitoring of the breathing, heart rates, sleeping quality, amounts of exercise (e.g., the time length of standing and sitting), falling of the seniors.
Based on these collected big data, we will construct a mathematical model to identify the key reasons for the falling of the elderly or even predict their falling. In doing this, we can provide customized suggestions for individual elderly persons with distinct health conditions to effectively avoid potential falling. This project is featured with wireless signal analysis, activity classification and mathematical modeling, and computer programming. This is a cross-disciplinary project that will involve professors and research students from Information Engineering, and Sports Science and Physical Education.
[1] Amin, M. G., Zhang, Y. D., Ahmad, F., Ho, K.D.: ‘Radar signal processing for elderly fall detection: The future for in-home monitoring’, IEEE Signal Processing Magazine, 33, (2), 2016, pp.71-80.
[2] A. Khalili, A.-H. Soliman, M. Asaduzzaman, and A. Griffiths, “Wi-Fi Sensing: Applications and challenges,” CoRR, vol. abs/1901.00715, 2019. [Online]. Available: http://arxiv.org/abs/1901.00715.
[3] H Gokalp and M Clarke. Monitoring activities of daily living of the elderly and the potential for its use in telecare and telehealth: a review. Telemedicine journal and e-health: the official journal of the American Telemedicine Association, 19(12):910, 2013.
In this project, we will develop a comprehensive wireless sensing system, which leverages the current information-carrying RF signals for the contactless and continuous monitoring of the breathing, heart rates, sleeping quality, amounts of exercise (e.g., the time length of standing and sitting), falling of the seniors.
Based on these collected big data, we will construct a mathematical model to identify the key reasons for the falling of the elderly or even predict their falling. In doing this, we can provide customized suggestions for individual elderly persons with distinct health conditions to effectively avoid potential falling. This project is featured with wireless signal analysis, activity classification and mathematical modeling, and computer programming. This is a cross-disciplinary project that will involve professors and research students from Information Engineering, and Sports Science and Physical Education.
[1] Amin, M. G., Zhang, Y. D., Ahmad, F., Ho, K.D.: ‘Radar signal processing for elderly fall detection: The future for in-home monitoring’, IEEE Signal Processing Magazine, 33, (2), 2016, pp.71-80.
[2] A. Khalili, A.-H. Soliman, M. Asaduzzaman, and A. Griffiths, “Wi-Fi Sensing: Applications and challenges,” CoRR, vol. abs/1901.00715, 2019. [Online]. Available: http://arxiv.org/abs/1901.00715.
[3] H Gokalp and M Clarke. Monitoring activities of daily living of the elderly and the potential for its use in telecare and telehealth: a review. Telemedicine journal and e-health: the official journal of the American Telemedicine Association, 19(12):910, 2013.
3. Wireless Monitoring in IIoT
(Optimzation of Information Freshness (Age of Information))
(Optimzation of Information Freshness (Age of Information))
The freshness and timeliness of information is of significant importance in the dissemination of real-time data in many IIoT applications. Applications that rely on information freshness are pervasive nowadays, including but not limited to, networked monitoring, tactical networks, sensor networks, and airplane/vehicular control, etc. Other commercial applications include information-update and data analytics, e.g., crowdsourcing, financial trading, social networks, and Internet of Things. Recent results showed that conventional performance measures like throughput or delay are no longer suitable for quantifying the timeliness and freshness of information [1], [2]. In this context, the concept of AoI has been proposed to measure the information freshness from the receiver’s perspective. Simply put, AoI is defined as the time elapsed since the generation (at the source) of the last successfully received message (at the receiver) [1].
In this project, we design and optimize wireless communications and networking protocols to maximize the information freshness, quantified by age of information, in various time-critical networks. This includes, but is not limited to, multiuser scheduling, cooperative relaying, random access, MIMO, cognitive radio, etc.
[1] A. Kosta, N. Pappas and V. Angelakis, "Age of Information: A New Concept, Metric, and Tool", Foundations and Trends® in Networking: Vol. 12: No. 3, pp 162-259, 2017.
[2] Y. Sun, I. Kadota, R. Talak, and E. Modiano, “Age of information: A new metric for information freshness,” Synthesis Lectures on Communication Networks, vol. 12, no. 2, pp. 1–224, 2019.
In this project, we design and optimize wireless communications and networking protocols to maximize the information freshness, quantified by age of information, in various time-critical networks. This includes, but is not limited to, multiuser scheduling, cooperative relaying, random access, MIMO, cognitive radio, etc.
[1] A. Kosta, N. Pappas and V. Angelakis, "Age of Information: A New Concept, Metric, and Tool", Foundations and Trends® in Networking: Vol. 12: No. 3, pp 162-259, 2017.
[2] Y. Sun, I. Kadota, R. Talak, and E. Modiano, “Age of information: A new metric for information freshness,” Synthesis Lectures on Communication Networks, vol. 12, no. 2, pp. 1–224, 2019.
4. Prototyping Wireless IIoT Systems based on Software-Defined Radio Platforms
In this project, we leverage the state-of-the-art software defined radio (SDR) available in the lab to develop wireless IoT system prototypes for experimental evaluatation and test of the algorithms developed in the previous projects within our group.
The SDR platforms that we have include NI USRP X310, B210, N210 and Xilinx SoC ZC706 Evaluation Kits.
The SDR platforms that we have include NI USRP X310, B210, N210 and Xilinx SoC ZC706 Evaluation Kits.
Research/Teaching Grants
- PI, "Development & Evaluation of a Mesh WiFi-based In-Home Health Monitoring System for Older Adults", Worldwide Universities Network – Research Development Fund plus CUHK matching, UK£20,000 (~HK$209,000), 2021-2022.
- PI, "Development of A Simulink-based Software-Defined Experimental Platform for A Series of Communication Courses in Information Engineering Program", Teaching Grant, The Chinese University of Hong Kong, HK$197,850, 2020-21.
- Sole PI, Departmental matching grant for RGC General Research Fund, The Chinese University of Hong Kong, HK$200,000, 2020-2022,
- Sole PI, "Enabling Ultrareliable Low-latency Wireless Communications for Industrial IoT: A Noncoherent Multiuser Massive MIMO Framework", Hong Kong Research Grants Council (RGC) General Research Fund, HK$845,055, 2021-2023.
- PI, "Close-to-Product Prototype Development and Trial Run of a High-Performance Industrial Wi-Fi System", Technology and Business Development Fund, The Chinese University of Hong Kong, HK$173,301, 2020-2021
- Sole PI, "An "Online" Experiment Platform for IERG4100 and IEMS5701", Teaching Grant, The Chinese University of Hong Kong, HK$99,630, 2020.
- Sole PI, "Design and Optimization of Information Freshness-Oriented Distributed Multiple Access for Large-Scale IoT Networks", Direct Grant, The Chinese University of Hong Kong, HK$150,000, 2020-2022.
Research Collaborators
- Prof Soung Liew, Chinese University of Hong Kong, IEEE Fellow
- Prof Branka Vucetic, University of Sydney, IEEE Fellow
- Prof Yonghui Li, University of Sydney, IEEE Fellow
- Prof Petar Popovski, Aalborg University, IEEE Fellow
- Dr. Zhibo Pang, Senior Principle Scientist, ABB Corporate Research, Sweden
- A/Prof. Nikolaos Pappas, Linköping University, Sweden
- Dr Zheng Dong, Research Professor, Shandong University, China
- Dr Chao Zhai, Research Associate Professor, Shandong University, China
- Dr. Yong Zhou, Assistant Professor, ShanghaiTech University, China
This page was last modified 28 June 2020 by [He Chen].
