M. S. Liew’s research while affiliated with University of Nottingham Malaysia Campus and other places

In the rapidly expanding Internet of Things (IoT) landscape, the security of IoT devices is a major concern. The challenge lies in the lack of intrusion detection systems (IDS) models for these devices. This is due to resource limitations, resulting in, single point of failure, delayed threat detection and privacy issues when centralizing IDS processing. To address this, a LiteDLVC model is proposed in this paper, employing a multi-layer perceptron (MLP) in a federated learning (FL) approach to minimize the vulnerabilities in IoT system. This model manages smaller datasets from individual devices, reducing processing time and optimizing computing resources. Importantly, in the event of an attack, the LiteDLVC model targets only the compromised device, protecting the FL aggregator and other IoT devices. The model's evaluation using the BoT-IoT dataset on TensorFlow Federated (TFF) demonstrates higher accuracy and better performance with full features subset of 99.99% accuracy on test set and achieved average of 1.11sec in detecting bot attacks through federated detection. While on 10-best subset achieved 99.99 on test with 1.14sec as average detection time. Notably, this highlights that LiteDLVC model can potential secure IoT device from device level very efficiently. To improve the global model convergence, we are currently exploring the use genetic algorithm. This could lead to better performance on diverse IoT data distributions, and increased overall efficiency in FL scenes with non-IID data.

Over time, the change in the inflation rate causes cost overruns by deviating the prices of goods and services in construction projects that require practitioners to make budgeting revisions. Hence, this study aims to develop a construction rates forecasting model that can incorporate the changing impact of the inflation rate on construction rates and predict the prices in a particular year, which can be adjusted when developing the Bill of Quantities. Following the time series analysis standards, a mathematical model was developed using MATLAB for forecasting. Construction rates, building prices, labour wages and machinery rates were forecasted from 2020 to 2025 based on the data collected from 2013 to 2019. Akaike information criterion was used to validate the self-developed construction rate forecasting model. It was revealed that the model yielded better results when the construction rates were compared with the autoregressive integrated moving average time series model results. The rates forecasting model may be used for any construction project where rates are affected by the inflation effect.

VIV can cause fatigue damage to the pipeline due to excessive vibration in the long run. The effect of VIV on different pipe conditions are assessed using Three Dimensional (3D) Computational Fluid Dynamics (CFD) ANSYS software. Different geometries of pipe were modelled for different Reynolds numbers. This study presents the three-dimensional (3D) simulation result concerning the effect of pipe diameter at different pipeline span, and the effect of pipeline surface condition towards the vortex induced vibration (VIV) phenomenon on the free span subsea pipeline. In evaluating hydrodynamics forces acting on the pipe, the larger pipe diameter, longer pipe length and coarser pipe surface introduced higher hydrodynamic force on the pipe, in term of drag and lift forces. In term of the development of wake region at pipe aft, the smaller pipe diameter, shorter pipe length, and smoother pipe surface has smaller wake region size. The evaluation of different type of pipe geometries could contribute to the study of pipeline VIV suppression techniques. The study on drag reduction measure on textured pipeline as a VIV suppression device can benefits the industry whereby it can help in mitigating VIV and thus avoid pipeline failure due to fatigue from excessive cyclic loadings.

One of the essential parts of any synthetic aperture radar (SAR) based microwave imaging system for pipeline inspection is the antenna, which determines the system ability to detect defects on an object reconstructed image. As the standoff distance increases, the antenna’s ability to detect defects on a material becomes influenced by the field of operation making it less effective, thereby affecting the defect information used to characterise the reconstructed pipe sample. In this paper, we conduct a comparative analysis of various antennas performances for pipeline inspection in near and far field operations using a SAR-based microwave system. An antipodal Vivaldi antenna (AVA) and 2-AVA are designed, and their performances are compared to an open-ended waveguide (OEW). The defective pipe image is reconstructed using the autofocus range-Doppler algorithm (RDA) for the OEW and AVA and improved multiple signal classification least square estimation (MUSIC-LSE) for the 2-AVA. Simulation and experimental results showed that the three antennas exhibit different performances depending on the region of operations. For the near field inspection, the OEW has a higher signal-to-noise-ratio (SNR) of 19 dB, fewer errors squared error (SE), and COV of 15% compared to the AVA with 16 dB, and 2-AVA with 14 dB, respectively. For the far field inspection, the 2-AVA gives a better SNR of 2 dB compared to the OEW and AVA with SNRs of -5 dB and -3 dB respectively. Thus, the choice of the antenna for the inspection depends on the region of operation.

The use of wind energy is rapidly growing worldwide as wind energy systems are emerging as a promising technology for utilizing offshore wind resources for large scale generation of electricity. However, developing optimum floating wind turbine for Deepwater application is a challenging task. Thus, in this study, the dynamic analysis of floating offshore wind turbine subjected to combined Deepwater environmental loads is investigated experimentally. A spar type floating wind turbine was modeled, and a parametric study was performed to determine the critical parameters influencing the dynamic response of the platform. The outcome of the study shows that the dynamic response of the structures is affected by the wave, wind, current properties, and water depth.

To ensure Energy Efficiency (EE) and better Quality of service (QoS), it is necessary to analyze the energy saving possibilities for low resource utilization in the current networks caused by rigorous QoS requirements and implementing EE approach in the planned model for performance improvement. Distributed Denial of Service (DDoS) attacks aim to exhaust the network’s processing and communication capacity by saturating it with packets and generating malicious traffic. There are numerous advantages that make Digital Twin (DT) and Intrusion Detection technique (ID) an effective remedy for a range of (fifth generation) 5G problems. A DDoS attack must be immediately detected and stopped before a legitimate user can access the target of the attacker for the 5G network to provide an efficient energy service. Although they clearly show promise in assisting with the creation and implementation of the challenging 5G environment, Digital Twins is still a relatively new technology for 5G networks but will increase EE. In this research, a thorough examination of the materials was carried out to identify the most cutting-edge DT and ID methods. The purpose of this study was to comprehend the problems with Energy Efficiency, the need for DT, and the methods for dealing with large-scale attack by DDoS on Energy Efficient networks. Only 94 of the 1555 articles produced by the procedure were determined to be relevant using inclusion and exclusion criteria. The outcome demonstrates that in 5G networks, DT, and its fundamental approaches, like QoS and DDoS attack mitigation, can be used to regulate the network’s Energy Efficiency. Numerous practical applications focusing on 5G Systems use their own principles. The effectiveness of these strategies was evaluated using several assessment criteria, including DT, Intrusion Detection, QoS, Energy Efficiency, and 5G Systems. Each study issue is thoroughly explained, along with typical methods, advantages, disadvantages, and performance metrics. Energy economy, network reliability, privacy, and cost reduction are all considerably increased by the implementation of intrusion detection technology in 5G systems. The decision is supported by the technology’s demonstrated efficacy, scalability, real-time detection capacities, low error level, and personalized learning attributes, all of which contribute to the long-term viability of 5G networks as an entire system.

The desire for faster data speeds and increased Energy Efficiency has prompted the development of femtocells, which are short-range, low-cost, customer cellular access points. However, in a situation of Distributed Denial of Service (DDoS) which is caused by inefficient energy, distributed attack sources could be employed to amplify the assault and increase the attack's impact. By flooding the network with packets and creating malicious traffic, Distributed Denial of Service (DDoS) attacks try to deplete the network's communication and processing capability. A DDoS assault must be identified and neutralized quickly before a valid user can reach the attacker's target for 5G network to have an effective Energy Efficient service. For the next Fifth Generation (5G) Wireless Network, there is a pressing need to build an effective Energy Efficient mobile network solution. Despite their evident promise in assisting the development and deployment of the complicated 5G environment. The physical product, the digital product, and the relationship between both the physical and virtual goods are said to make up Digital Twin (DT). On the other hand, DT allows real-time communication with both the physical twins. The synergy of energy efficiency and security improvements in this research contributes to a more holistic optimization of 5G networks. This approach seeks to minimize energy consumption while fortifying the network against evolving security threats. Integrating energy-efficient practices with robust security measures enhances the overall resilience and sustainability of 5G systems. This is crucial for ensuring continuous, reliable, and secure communication in the face of dynamic challenges.

Model that's enabled reducing false alarm rate in an IoT environment