OUR research in Brief
Edge Computing
Taking account, the rising popularity of latency stringent and bandwidth hungry applications (e.g. augmented reality, virtual reality), the concept of edge computing has been introduced. Unlike cloud computing, edge computing is a small scale computing and storage facility deployed around the network access segment in order to serve the end users. Edge computing is also being part of 5G network (IMT 2020) as it would contribute in meeting performance requirements of 5G. An edge computing device could be located in a base station or embedded within an edge-network equipment (e.g. switches, edge routers). Aside from the dedicated edge computing facilities, researchers are envisioning to use the computing resources scattered around the network access segment (e.g. personal computers, computing unit of smart vehicles, robots), which will be dormant almost all the time, in order to reduce cost and benefit more users. To harness the power of such scattered and diverse computing facilities, we need to overcome several research challenges. The research scopes of this project are: (a) security and trust management in edge computing, (b) resource discovery and task scheduling and (c) energy saving in edge computing.
Optical and Wireless Integrated networks
An optical and wireless converged network gets full advantage of the merits that optical and wireless technologies offer. The major benefits of such integration are: high bandwidth and ubiquitous access. Taking account future latency stringent application requirements, the project will develop an energy efficient optical and wireless converged network architecture. To increase energy saving while meeting latency requirements of applications, in such an integrated network architecture, the scopes of the project are: (a) network traffic forwarding path management, (b) sleep mode selection for network equipment and (c) traffic scheduling. This project uses mathematical modeling and machine learning to come up with a novel solution.
5G network slicing and resource management
This research focuses on network resource virtualization and mobility management in 5G network. We aim at developing a novel mechanism for network slicing in 5G where based on the individual requirements, physical resources of a network are shared among coexisting and isolated virtual networks.
Smart Grid
Based on different time series data collected from heterogeneous data sources (both real-time and historical data), this research aims at forecasting energy demand of households.
Appliances’ load scheduling is an important concept for minimizing peak load. In this research, we aim at developing a load scheduling solution in order to reduce peak load (and thereby reducing chance of a blackout) while meeting occupants comfort requirements. The solution should take into account multiple factors, including size of energy storage systems, occupancy information and type of appliance load.
Distributed Energy Resources (DERs) uses different proprietary incompatible interfaces for communication, including Modbus, DNP3, and RS-485. Communication latency requirement between two entities in a DER could be few milliseconds so as to ensure uninterrupted power supply. In order to facilitate smooth communication among DERs with proprietary incompatible interfaces, this research aims at developing a protocol conversion mechanism with two prime objectives: (i) successful data mapping and (ii) reduce communication overhead and latency.
Disaster Management of Peatland Forest Fire in Brunei
Forest is one of Brunei’s most precious natural assets since Brunei’s land is covered with about 80% of forest and 60% of this forest has not been affected by human activities. Therefore, it is important for us to protect and conserved our forest. The climate of Brunei is tropical equatorial where the hot climate can lead to forest fire which can be a fatal threat to the preservation of our forest. At the moment, there are no forest fire detection devices or monitoring system installed in Brunei. This project will create a preventive measure that focuses on a disaster management system that will prevent peatland fire from occurring due to dry condition. This is done by advancing the restoration technique of rewetting the area by using sensors that will trigger the dam valve to release water from the dam and rewet the land to avoid any dry soil.
Application of blockchain for verification of government processes.
The application of blockchain has majorly been associated with bitcoin. As a matter of fact, bitcoin is one solution that utilized the Blockchain Technology. In fact, Blockchain technology can be applied to various other area such as smart contracts, licensing, IoTs, and smart properties. The project proposes the application of Blockchain Technology in government processes as a verification procedure. This project will study applicability of Blockchain technology, its limitation and challenges especially in area of privacy, technical constraints as well as scalability and its economics side effects.
Public Sector Risk Assessment and Evaluation framework
No one method or approach has optimum effectiveness in the identification of risks in different public organisations. Due to this risk assessments exercise are not popular and in many organisations of nonexistence. As a result, this increase the vulnerability of the organization and decrease its ability to be resilient. This project investigates undocumented current practices in risk assessment and by equating these practices with standard practices it is postulated that a better framework can be delivered.
Security Protection at the Network Layer of IoT using SDN
Security and privacy issues are one of the biggest challenges in IoT. IoT consists of heterogeneous devices and these devices need to be communicated with other devices and also, they need to be connected to the network. There are hundreds of new devices coming into the market every day due to the demand of IoT in different areas. Conventional security mechanisms are difficult to establish in the IoT environment. This research project will provide solutions using Software Defined Network (SDN) based protection for security issues and the heterogeneity problem in IoT.
Application of Machine Learning algorithms in the detection of misbehaving nodes in WSN
Wireless sensor Networks (WSN) consists of diversified autonomous, tiny, low cost and low power sensor nodes. These nodes are deployed in remote locations in M2M environment and they collect data about environment etc. and send the data to the base station for further processing. Adversaries can make use of the technology and compromise a node to attack the whole network. Machine learning algorithms can be applied to detect the misbehaving nodes and a major attack can be avoided. There are some limitations in the WSN like energy, processor and memory constrained. This research contributes to understanding the WSN, Machine learning and exploring the ML algorithms to find out the misbehaving nodes in WSN.