Dr Mona Jaber

Profile

Dr Mona Jaber is a Senior Lecturer on the Internet of Things at Queen Mary University of London. She is a leading expert in mobile communication with a specialisation in radio and backhaul design of cellular networks; a topic in which she published multiple articles including the top 75 most cited paper in IEEE Access. Since joining QMUL in 2019, she started a new research group that examines the intersection between IoT and machine learning in the context of sustainable living. Three fast-evolving research directions have emerged where the first investigates multi-modal data in the modelling of urban mobility, the second examines data privacy-preserving machine learning for smart energy and health, and the third elaborates the digital twin paradigm as a simulation platform for IoT-enabled sustainable living.

In 2023, she launched a new multidisciplinary research lab titled Digital Twins for Sustainable Development Goals which features a multidisciplinary team for ushering in SDG-related research through technical innovation related to IoT, AI, and Digital Twins.

Teaching

Microprocessor Systems Design (BUPT joint programme)

The course examines the structure, programming and applications of microprocessor and microcontroller devices. There will be practical design and development using microcontroller as part of the module.

Signals and Systems (BUPT joint programme)

Signals and Systems is an introduction to Signal Theory, a discipline that forms an integral part of many engineering systems, including Internet of Things systems. The concepts of continuous-time and discrete-time signals and systems will be introduced, both in the time and in the frequency domains. Fourier approaches will be presented to connect the time and frequency domains and sampling theory will be presented to connect continuous-time to discrete-time signals and systems. Analytical and computational tools will be discussed throughout the module.

Research

Research Interests:

See Mona Jaber’s research profile pages including details of research interests, publications, and live grants.

Supervision

Main supervisor:

• Chia-Yen Chang, "Mobility monitoring system in a smart city", Started Jan 2021
• Zunaira Nadeem, "Energy theft detection", Started Jan 2021
• Moudy Alshareef, "Privacy in IoT-driven e-health", Started Apr 2021
• Yuqin Liu, "Machine learning for next generation multiple access", Started Sep 2021

Second supervisor:

• Ammar Naich, "Robust Multi-Object Tracking (MOT) in scattered medium for autonomous vehicles", Started Jun 2019
• Hadeel Arubayyi, "Artificial Immune System Advances for Detecting Unknown Malware Detection in the IoT" Graduated in 2023
• Kishan Sthankiya, "Energy efficiency in the Open RAN", Started Sep 2021

Public Engagement

Senior IEEE Member

Member of IEEE ComSoc, IEEE Women in Communication Engineering (WICE), IEEE Intelligent Transportation Systems Society (ITSS)

Active member of Emerging Technology Initiative – Backhaul/fronthaul Networking & Communications (ETI-BNC)

Digital Environment Research Institute (DERI) Academic fellow

IEEE UK & Ireland Women in Engineering Affinity Group- Executive Committee leading Professional Development and Industry Links

IEEE Publis Safety Technology Initiative- Transportation Committee 

Grants

DASMATE (https://gow.epsrc.ukri.org/NGBOViewGrant.aspx?GrantRef=EP/X01262X/1)

I am excited to announce that I was awarded a New Investigator Award EPSRC grant of £506,191.16 to examine Distributed Acoustic Sensor systems for modelling Active Travel.

In a time where climate change is an imminent threat, Active Travel (AT) has become a priority in the United Kingdom (UK) and a pathway towards sustainable living. AT is defined as making a journey by physically active means, e.g., walking or cycling. In the UK, the transport sector is the highest contributor of emissions with 61% of this contribution caused by private cars and taxis. Replacing motored journeys with AT firstly promises to reduce these emissions. Moreover, AT is a form of exercise that has been shown to improve physical and mental health; hence, reduces the need of medical care and increases happiness and productivity. Interventions to promote AT include ensuring safety of commuters through cycle/pedestrian lanes, safe cycle parking, bike-sharing, cycling training, bike loan schemes, electrically assisted bikes, community/school initiatives, among others. The challenge that authorities face is the lack of insights on which type of intervention would be more effective in different areas. Indeed, the same scheme would result in different AT uptake since the latter depends on predominant trends and road infrastructure in each area. It follows that, in each area, some schemes are likely to be more effective than others.

There is a rising need to model changes in AT trends in relation to different interventions. State-of-the-art research for modelling AT trend mostly relies on video footage which is used to identify and predict the path of pedestrians. There are several drawbacks to such approaches. Firstly, video footage is negatively impacted from adverse weather conditions and lack of light. Secondly, it is cost-inhibitive to realise uninterrupted 360 degrees visibility using video cameras in a built environment. Thirdly, the video footage needs to be high resolution, hence contains private information about people. Such information challenges General Data Protection Regulation (GDPR) whilst is not required for modelling active mobility.

DASMATE aims to develop a new approach for modelling AT trends in an urban environment by leveraging the incipient advances in Distributed Acoustic Sensor (DAS) systems. DAS reuses underground fibre optic cables as distributed strain sensing where the strain is caused by moving objects above ground. Given that the sensors are underground, DAS is not affected by weather nor light. Fibre cables are often readily available and offer a continuous source for sensing along the length of the cable. Moreover, DAS systems offer a GDPR-compliant source of data that does not include private information such as face colour, gender, or clothing. DASMATE in centred on two aspects of AT modelling based on DAS analysis. The first consists of identifying the type of AT (walking, jogging, skateboarding, cycling, etc.) at any time of the day in a monitored area. The second is concerned with predicting the path of active travellers to inform on the possibility of collision with moving vehicles (which may be driver-less). This is a pioneering project that aims to establish the first framework for processing DAS data to extract samples representing AT and builds a machine learning signal processing pipeline to infer knowledge related to both aspects. The industry partner, Fotech, has facilitated the collection of a unique DAS dataset for AT modelling.