This project will focus on the security issue facing Industrial Unmanned Aerial Vehicles (UAVs)-based systems.
Industrial Unmanned Aerial Vehicles (UAVs) are widely considered as the next breakaway in the ongoing Industry 4.0 revolution, thanks to their high mobility, on-demand deployment, and versatility in various industrial sectors such as manufacturing, transportation, energy, etc. However, unlike traditional industrial networks equipped with wired communications, in industrial UAV-based systems all communication links are wireless and hence UAVs wirelessly communicate to an industrial control system for data and control signal exchanges. Due to the broadcast nature of the wireless channel, such systems are vulnerable to control signal spoofing attacks and information can be easily intercepted by unauthorized receivers, giving rise to a new security challenge. More specifically, unauthorized receivers can forge the control signal and take over the UAV illegitimately, which poses a serious threat to the safety of the UAV systems. Therefore, security is a critical design issue in the implementation and operation of industrial UAVs. Although it is true that traditional encryption techniques can be used to partially address the security issues of industrial UAV networks, the security offered by such techniques can be very limited in scenarios where the eavesdropper has powerful computational capabilities. In this respect, physical layer security (PLS) can safeguard wireless data transmissions without requiring secret keys and complex algorithms, thereby making PLS a more desirable candidate to address the security issues in industrial UAV networks. In light of this, this project aims to investigate the security issue in industrial UAVs-based networks and tackle this challenge by developing and applying advanced PLS and machine learning techniques. Simulation-based modelling, backed up by mathematical analysis, will be undertaken for several practical scenarios. The project will involve theoretical analyses as well as numerical simulation results; therefore, having excellent mathematical and programming skills is necessary.
- Strong mathematical, analytical and programming skills, e.g., MATLAB.
- Strong background in communication theory and digital signal processing.
- IELTS 6.5 (or equivalent) with no element below 6.0.
- Basic knowledge of machine learning tools is a plus.
This opportunity is open to UK, EU, and International applicants
Informal enquiries can be made to:
Dr Khaled Rabie K.Rabie@mmu.ac.uk, 0161 247 1466
The quickest and most efficient way to apply for this course is to apply online. This way, you can also track your application at each stage of the process.
Please quote the reference: SciEng-KR-2019-UAV-1.
Please complete the additional Postgraduate Research Degree Supplementary Information document and upload it to the Student documents section of your online application. This collects important information about your research application and there may be delays if you do not submit this document.
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We will contact you to let you know the initial outcome of your application, and invite you to attend an interview where appropriate.
Once the university is satisfied with the following, we will send you an offer letter informing you that you have been offered a place of study:
Some offers may be conditional upon achieving certain grades in your examinations, or successfully completing a particular programme. You must satisfy these conditions before we can confirm your unconditional place.
31 August 2020