Next Generation Internet of Everything Laboratory
(NGIoE Lab)

The Internet of Everything (IoE) has emerged as a new frontier of global communication framework which would revolutionise a wide range of applications and services thus transforming how we work and live. The Internet of Things (IoT) which is core driver of IoE, has grown significantly over the last few years and expected to reach 25.5 billion of connected devices and has been adopted in all types of industry vertical and consumer markets worldwide by 2030.

China is currently playing an increasingly important role in the development of the IoE, which is growing in lockstep with the technological presence of Chinese companies. Several factors are driving this trend, including private sector incentives and Chinese government policies introduced over time to increase the participation of Chinese players in the growth of IoT, including research and technology standardisation.

To address the challenges and opportunities ahead, a group of like-minded researchers has established the multi-disciplinary Next Generation of Internet of Everything (NGIoE) Lab, which aims to become the leading IoE innovation lab that accelerates knowledge creation, expertise development, and value creation for key stakeholders in the knowledge economy. The NGIoE Lab will also provide a collaborative platform where people from diverse backgrounds can come together to inspire, be inspired, and innovate the next generation of IoE technologies.

Collectively the expertise available in the NGIoE ranges from mobile communications (5G, 6G), artificial intelligence / machine learning, indoor localisation, mobile satellite networks, VANET, MANET, IoT networks, intelligent routing, content-centric networks, blockchain IoT, wireless sensor networks, low-power and reliable embedded systems, real-time scheduling, and edge AI.

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 background 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

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.

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)