The wide proliferation of various wireless devices, communication and sensing technologies has fueled the arrival of big data era in CPS. Big data in CPS has the key features of wide variety, high volume, real-time velocity and huge value leading to the unique research challenges that are different from existing computing systems. In this project, we conduct a research on applying state-of-art big data analytics (BDA) approaches for CPS. In particular, we categorize the life cycle of BDA into four consecutive stages: Data Acquisition, Data Preprocessing, Data Storage and Data Analysis. Our approaches are involved with the above stages.

An intelligent manufacturing network (IMN) connects various manufacturing devices and intelligent control units. An IMN collects, stores and analyses data obtained from devices and control units. Intelligent decisions will then be made so that IMN controls the physical world continuously. The study in IMNs requires interdisciplinary knowledges, such wireless sensor networks, industrial control networks, and big data analytics. The Intelligent manufacturing networks are large scale, self-organised, and evolving dynamically. This project aims to investigate both theories and technologies in self-organised resilience mechanisms in Internet-of-Things enabled Intelligent manufacturing. The main research issues include: 1) the method of measuring and evaluating resilience, 2) multi-field heterogeneous network model, 3) effective strategies of promoting intelligent manufacturing with consideration of network resilience. The outcome will enhance the intelligent manufacturing network reliability.

Big Data Analytics (BDA) and Internet of Things (IoT) are rising quickly. The recent emerging Industrial IoT (IIoT), a sub-paradigm of IoT, focuses more in safety-critical industrial applications. Studies showed that the adoption of BDA increase companies’ output and productivity; IoT enables companies to have more information and control in physical resources, processes, and environments; BDA and IIoT complement each other and develop as a double “helix”. In this project, we propose a new framework integrating BDA and IIoT technologies for offshore support vessels (OSVs) based on a hybrid CPU/GPU/FPGA1 high performance computing platform for the Møre maritime cluster.

This project will achieve the following research objectives:
(1) Establishing theoretical framework to analyze the performance of Ultra-Dense Networks (UDNs);
(2) Identifying performance bottlenecks of UDNs;
(3) Designing the novel resource allocation strategies for UDNs;
(4) Proposing the novel performance optimization schemes for UDNs.

Most of current studies on the performance of large scale wireless ad hoc networks (WAHNs) only focused on a single performance metric. In fact, multiple performance metrics often interact on each other. To investigate the interaction of multiple performance metrics can help us have a better understanding of the factors affecting the performance of WAHNs. However, there is a lack of a general theoretical framework considering all the performance metrics together. In this research project, we will concentrate on the performance analysis with considering multiple performance metrics in one theoretical framework and we will also explore improving the performance of large scale WAHNs.

The performance of typical wireless networks is limited due to the collisions of possible multiple transmissions accessing the channel at the same time while the channel can accept only one transmission allowed at a time at a single interface. There are a number of studies on improving the performance of wireless networks. Among them, one group of researchers consider using multiple channels in the wireless networks equipped with only omni-directional antennas. However, the performance improvement is limited due to the high collision rates caused by omni-directional antennas. Another group of researchers consider using directional antennas in wireless networks. But their studies only apply for single-channel wireless networks, which have lower spectrum reuse and consequently lead to the lower network performance. In this project, our study will focus on a novel network (MC-MDA network) integrating both the multiple channels and the directional antennas, which potentially has a higher performance than other existing wireless networks. In particular, we  concentrate on the following research problems:

• The scalability of MC-MDA networks;
• The channel assignment of MC-MDA networks;
• The design and implementation issues with MC-MDA networks.

Blockchain has received extensive attention recently. Blockchain has the key characteristics such as decentralization, persistency, anonymity and auditability. Blockchain has a diversity of application fields far beyond Bitcoin. However, there are still a number of technical challenges in using blockchain. This project aims to find the solutions to the above challenges.

In this project, we propose GRAMS which is a resource monitoring and analysis system in grid environment. GRAMS provides an infrastructure for conducting online monitoring and performance analysis of a variety of Grid resources including computational and network devices. Based on analysis on real-time event data as well as historical performance data, steering strategies are given for users or resource scheduler to control the resources. Besides, GRAMS also provides a set of management tools as well as services portals for user not only accessing performance data but also handling these resources. Moreover, a preliminary system prototype is presented.