Next Generation Internet of Everything Laboratory
(NGIoE Lab)

The Internet of Everything (IoE) is poised for an extraordinary evolution that will redefine the contours of our global communications framework. It promises to revolutionise a range of applications and services and reshape the dynamics of our working lives. As the primary catalyst for IoE, the Internet of Things (IoT) is experiencing a dramatic acceleration in growth. It is predicted to grow to a staggering 25.5 billion connected devices by 2030, penetrating diverse industries and consumer markets on a global scale.

China is uniquely located at the epicentre of this technological maelstrom, taking a key role in the IoE growth process. This is consistent with the technological rise of Chinese companies, creating a dynamic synergy that is driving this expanding frontier. Various elements, including impetus from the private sector and strategic actions by the government, are reinforcing this trend. Chinese companies are increasingly being drawn into the orbit of IoT, bringing their research capabilities and technological innovations to bear.

Anticipating the monumental challenges and opportunities on the horizon, a visionary group of researchers has inaugurated the Next Generation of Internet of Everything (NGIoE) Lab. This multidisciplinary centre has the ambitious goal of becoming the global hub for IoE innovation. It aims to accelerate knowledge creation, foster the development of expertise, and catalyse value creation for key players in the knowledge economy. The NGIoE Lab also envisions being a melting pot for diverse perspectives, providing a collaborative platform where ideas can emerge and grow to shape the future landscape of IoE technologies.

The expertise housed in the NGIoE Lab spans an impressive range of competencies, from mobile communications (5G, 6G), artificial intelligence/machine learning, indoor localisation, mobile satellite networks, Vehicular Ad Hoc Networks (VANET), Mobile Ad Hoc Networks (MANET), IoT networks, intelligent routing, content-centric networks, blockchain IoT, wireless sensor networks, low-power and reliable embedded systems, to real-time scheduling and edge AI. Looking to the future, the NGIoE Lab is well equipped to contribute breakthrough innovations and usher in the next era of IoE.

Mission and Vision

Mission

To be a leading IoE Innovation laboratory which accelerates knowledge creation, expertise development, and value delivery to key stakeholders in the knowledge economy. We do that by facilitating a collaborative platform where people with diverse backgrounds can come together to inspire, be inspired, and innovate.

Vision

Advancing human life, harmonising the environment with the Internet of Everything.

Key Objectives

1. Knowledge creation and expertise in the Internet of Everything domain – PhDs, Masters, FYPs
2. To provide training and consultancy especially to the local, domestic and international industries or research centres
3. Research collaborations with other domestic and international research organisations and industries

Areas of Interests

The area of research interest of NGIoE lab, non-exhaustive, is as follows:

Low-Power AI for IoT
Energy Harvesting for IoT
IoT Mobility Management
IoT Networking
IoT Smart Applications such as IoT Localisation...
IoT Data Analytics
Machine to Machine (M2M or Device to Device (D2D) communications
IoT Security and Interoperability
Cloud Environments
Edge computing
Sensor Actor Systems
Cyber-Physical Systems
Vehicle-to-everything (V2X)

Members and Expertise

Core members

Dr Chiew-Foong Kwong (Director)

Mobile networks (4G, 5G, wireless sensors), machine learning, electronic communications, radio propagation, satellite communication networks, VANET, IoT networking

Dr David Chieng (Co-Director)

IoT, mobile networks, wireless mesh networking, QoS networks, energy efficiency in wireless network, Indoor localisation, AI/ML/DL, digital transformation.

Dr Heng YU

Low-power and reliable embedded systems, Real-time scheduling, Edge AI.

Dr Pushpendu Kar

Wireless Sensor Networks, Internet of Things (IoT), Content Centric Networking, Blockchain.

Dr Sen YANG

The development of cuff-less non-invasive methods for blood pressure measurement

Dr Zheng CHU

5G/6G

Associate members

Dr Minglei You

Internet of Things, Integrated Energy System, Multi-vector Energy Systems, Machine Learning, AI, Smart Grid, Signal Processing and Wireless Communication Networks.

Dr Saeid Pourroostaei Ardakani

Internet of Things; Big Data Analysis; Distributed and Collaborative Computing; Sensory Systems; Educational Technology

Related Projects

Projects

Recent Publications

Journal Articles

Q. Liu, C. F. Kwong, S. Zhou, T. Ye, L. Li and S. P. Ardakani, (2021) "Autonomous Mobility Management for 5G Ultra-Dense HetNets via Reinforcement Learning with Tile Coding Function Approximation," IEEE Access, vol. 9, pp. 97942-97952, 2021, DOI: 10.1109/ACCESS.2021.3095555.
Q. Liu, C. F. Kwong, W. Sun, S. Zhou, L. Li and P. Kar, (2021) "Reinforcement Learning-Based Joint Self-Optimisation Method for the Fuzzy Logic Handover Algorithm in 5G HetNets", Neural Computing and Applications, ISSN: 1433-3058 (Online); 0941-0643 (Print) – Accepted for publication, In-press
L. Li, C. F. Kwong, Q. Liu, P. Kar, S. P. Ardakani, (2021) "A Novel Cooperative Cache Policy for Wireless Networks", Wireless Communications and Mobile Computing, vol. 2021. DOI:10.1155/2021/5568935
S. P. Ardakani, C. F. Kwong, P. Kar, Q. Liu, and L. Li, (2021) " CNN: A Cluster-based Named Data Routing for Vehicular Networks”, IEEE Access, ISSN: 2169-3536, vol. 9(2021), DOI: 10.1109/ACCESS.2021.3131198.
Q. Liu, C.F. Kwong, W. Sun, L. Li, S. Zhang (2021), “Intelligent Handover Triggering Mechanism in 5G Ultra-Dense Networks Via Clustering-Based Reinforcement Learning,” Mobile Network Application, vol. 26, 27–39 (2021). DOI:10.1007/s11036-020-01718-w
L. Li, C. F. Kwong, Q. Liu, and J. Wang, (2020) “A Smart Cache Content Update Policy Based on Deep Reinforcement Learning”, Wireless Communications and Mobile Computing. Wireless Communications and Mobile Computing. ISSN: 1530-8669 (Print). ISSN: 1530-8677 (Online). DOI:10.1155/2020/8836592
L. Li, C.F. Kwong, Q. Liu (2020) "A Proactive Mobile Edge Cache Policy Based on the Prediction by Partial Matching", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 1154-1161. ISSN: 2415-6698.
P. Kar, S. Misra, A. Mandal, H. Wang, (2021), "SOS: NDN Based Service-Oriented Game-Theoretic Efficient Security Scheme for IoT Networks", IEEE Transactions on Network and Service Management, DOI: 10.1109/TNSM.2021.3077632
P. Kar and H. Wang (2021), “EZPlugIn: Plug-n-Play Framework for a Heterogeneous IoT Infrastructure for Smart Home,” IEEE Internet of Things Magazine, DOI: 10.1109/IOTM.0001.2000172
S. P. Ardakani, J. Padget, M.D. Vos, A mobile agent routing protocol for data aggregation in wireless sensor networks, International Journal of Wireless Information Networks, 27-41, 2017
S. P. Ardakani, J. Padget, M.D. Vos, (2016), "CBA: A cluster-based client/server data aggregation routing protocol", Ad Hoc Networks, 68-87, 2016.
S. P. Ardakani (2018), "ACR: A Cluster-based routing protocol for VANET", International Journal of Wireless & Mobile Networks (IJWMN), 10, 2018.
S. P. Ardakani, A. Cheshmehzangi, (2021)"Reinforcement Learning-Enabled UAV Itinerary Planning for Remote Sensing Applications in Smart Farming", Telecom, 2(3), 255-270, 2021.
H. Yu, Y. Ha, B. Veeravalli, F. Chen, H. ElSayed (2021), “DVFS-Based Quality Maximization for Adaptive Applications with Diminishing Return,” IEEE Transactions on Computers, vol. 70(5), pp. 803-816, May 2021.
[Journal Article] D. Chieng, et. al. “Special Issue: Creating a Smarter Environment through the Advancement of Communication Systems, Networks and Applications”, Guest Editorial, IET Networks, Volume 4, Issue 6, November 2015.
[Journal Article] Cicconetti, C. ; De La Oliva, A. ; Chieng, D. ; Zuniga, J.C., “Extremely dense wireless networks”, Guest Editorial, IEEE Communication Magazine, Vol. 53, No. 1., January 2015.

Conference Articles

Z. Song and P. Kar (2020), “Name-Signature Look Up System: A Security Enhancement to Named Data Networking”, In proceedings of the 19th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), Guangzhou, China, Dec 2020
S. Wu, Y. Yuan, and P. Kar (2020), “Lightweight Verification and Fine-grained Access Control in Named Data Networking Based on Schnorr Signature and Hash Functions”, In proceedings of the 20th IEEE International Conference on Communication Technology (ICCT), Nanning, China, Oct 2020
W. Jiang, H. Yu, X. Liu, H. Sun, R. Li, Y. Ha (2021), “TAIT: One-Shot Full-Integer Lightweight DNN Quantization via Tunable Activation Imbalance Transfer,” IEEE/ACM SIGDA Design Automation Conference (DAC), Accepted
R. Li, H. Yu, W. Jiang, Y. Ha (2020), “DVFS-Based Scrubbing Scheduling for Reliability Maximization on Parallel Tasks in SRAM-Based FPGAs,” IEEE/ACM SIGDA Design Automation Conference (DAC), Article No. 138, pp. 1-6, July 2020.
Arosha S.M.N., Khairiyah b. H. R, Naim, A.G., Chieng, D., “Array of Things for Smart Health Solutions Injury Prevention, Performance Enhancement and Rehabilitation”, Proceedings of the Future Technologies Conference (FTC) 2018. FTC 2018/Advances in Intelligent Systems and Computing, vol 880. Springer, Cham.
K. L. A. Yau, H. G. Goh, D. Chieng, Kae Hsiang Kwong, "Application of Reinforcement Learning to Wireless Sensor Networks", Computing, Springer, InPress, 2015.
A. A. Abdulkafi, S.K. Tiong, D. Chieng, Alvin Ting, Abdulaziz M. Ghaleb and J. Koh, "Energy-Aware Load Adaptive Framework for LTE Heterogeneous Network", Transactions on Emerging Telecommunications Technologies, John Wiley & Sons, 25 April 2014.
A. Ting, D. Chieng, K. H. Kwong, I. Andonovic, K. D. Wong, “Scalability Study of Backhaul Capacity Sensitive Network Selection Scheme in LTE-WiFi HetNet”, Transactions on Emerging Telecommunications Technologies, John Wiley & Sons.

Next Generation Internet of Everything Laboratory (NGIoE Lab)