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Digital Twins for Sustainable Development Goals

Dr Mona Jaber


Senior Lecturer in Internet of Things

Room Number: Engineering, Eng 212


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.


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 Interests:

I am broadly interested in machine learning applications for network data analytics with focus on network automation and IoT applications. In particular, my current research interests are shaped by the emerging intersection between smart city and transport applications leading to smart urban mobility. In this context, my research explores the role machine learning and IoT data to model the interaction between the urban planning, traffic control, and urban mobility in a digital twin approach.

Similarly, my continuing research on network data analytics investigates the application of machine learning techniques to manage the performance of wireless networks and enable an agile user-centric service-provisioning scheme, dubbed as network elasticity.



Full list of publications


CY Chiang, R Zhong, J Ding, J Wood, S Bee, M Jaber, "AllTheDocks road safety dataset: A cyclist's perspective and experience" arXiv preprint arXiv:2404.10528

H Alrubayyi, MS Alshareef, Z Nadeem, AM Abdelmoniem, M Jaber, "Security Threats and Promising Solutions Arising from the Intersection of AI and IoT: A Study of IoMT and IoET Applications", Future Internet 16 (3), 85


N Badini, M Jaber, M Marchese, F Patrone, "Energy-Aware Satellite Handover Based on Deep Reinforcement Learning",  IEEE Globecom Workshops (GC Wkshps), 437-442, 2023 DOI: 10.1109/GCWkshps58843.2023.10464394

Y Liu, R Zhong, M Jaber, Y Liu, "Energy Efficient Beamforming Design for Non-Orthogonal Multiple Access Systems: A Curiosity-Driven Approach", IEEE Globecom Workshops (GC Wkshps), 884-889, 2023 DOI: 10.1109/GCWkshps58843.2023.10464579

H Alrubayyi, G Goteng, M Jaber, "AIS for Malware Detection in a Realistic IoT System: Challenges and Opportunities", Network 3 (4), 522-537    2023

MS Alshareef, M Jaber, AM Abdelmoniem, "A Differential Privacy Approach for Privacy-Preserving Multi-Modal Stress Detection", IEEE 28th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), 2023 DOI: 10.1109/CAMAD59638.2023.10478412

Jaber, M (2023) IoT and machine learning for enabling sustainable development goals -   Front. Comms. Net., 03 July 2023
Sec. IoT and Sensor Networks
Volume 4 - 2023 |

C. -Y. Chiang, M. Jaber, K. K. Chai and J. Loo, (2023) "Distributed Acoustic Sensor Systems for Vehicle Detection and Classification," in IEEE Access, vol. 11, pp. 31293-31303, 2023, doi: 10.1109/ACCESS.2023.3260780.     

Z. Nadeem, Z. Aslam, M. Jaber, A. Qayyum and J. Qadir, (2023) "Energy-aware Theft Detection based on IoT Energy Consumption Data," 2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring), Florence, Italy, 2023, doi: 10.1109/VTC2023-Spring57618.2023.10200352.


M. S. Alshareef, B. Alturki and M. Jaber, "A transformer-based model for effective and exportable IoMT-based stress detection," GLOBECOM 2022 - 2022 IEEE Global Communications Conference, Rio de Janeiro, Brazil, 2022, doi: 10.1109/GLOBECOM48099.2022.10001083.

Y. Liu, R. Zhong and M. Jaber, "A Reinforcement Learning Approach for Energy Efficient Beamforming in NOMA Systems," GLOBECOM 2022 - 2022 IEEE Global Communications Conference, Rio de Janeiro, Brazil, 2022, pp. 3827-3832, doi: 10.1109/GLOBECOM48099.2022.10000828.

Jaber, M. (2022) Machine Learning in IoT Networking and Communications. Journal of Sensor and Actuator Networks. 11, 37.

M. Ozturk, A. I. Abubakar, R. N. B. Rais, M. Jaber, S. Hussain and M. A. Imran, "Context-Aware Wireless Connectivity and Processing Unit Optimization for IoT Networks," in IEEE Internet of Things Journal, vol. 9, no. 17, pp. 16028-16043, 1 Sept.1, 2022. doi: 10.1109/JIOT.2022.3152381

Salcedo, E., Jaber, M., & Requena Carrión, J. (2022). A Novel Road Maintenance Prioritisation System Based on Computer Vision and Crowdsourced Reporting. Journal of Sensor and Actuator Networks, 11(1). doi:10.3390/jsan11010015

Ozturk, M., Abubakar, A. I., Rais, R. N. B., Jaber, M., Hussain, S., & Imran, M. A. (2022). Context-Aware Wireless Connectivity and Processing Unit Optimization for IoT Networks. IEEE Internet of Things Journal. doi:10.1109/JIOT.2022.3152381


Rizwan, A., Jaber, M., Filali, F., Imran, A., & Abu-Dayya, A. (2021). A Zero-touch Network Service Management Approach using AI-enabled CDR Analysis. IEEE Access. doi:10.1109/ACCESS.2021.3129281

Alrubayyi, H., Goteng, G., Jaber, M., & Kelly, J. (2021). Challenges of malware detection in the IoT and a review of artificial immune system approaches. Journal of Sensor and Actuator Networks, 10(4). doi:10.3390/jsan10040061

Fathy, Y., Jaber, M., & Nadeem, Z. (2021). Digital twin-driven decision making and planning for energy consumption. Journal of Sensor and Actuator Networks, 10(2). doi:10.3390/JSAN10020037

Alrubayyi, H., Goteng, G., Jaber, M., & Kelly, J. (2021). A novel negative and positive selection algorithm to detect unknown malware in the IoT. In IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021. doi:10.1109/INFOCOMWKSHPS51825.2021.9484483

Bassoy, S., Jaber, M., Onireti, O., & Imran, M. A. (2021). Radio & Backhaul Load Aware Multi-Objective Clustering in Multi-cell MIMO Cooperative Networks. IEEE Transactions on Vehicular Technology. doi:10.1109/TVT.2021.3070992


Fathy, Y., Jaber, M., & Brintrup, A. (2020). Learning with Imbalanced Data in Smart Manufacturing: A Comparative Analysis. IEEE Access, 2734-2757. doi:10.1109/ACCESS.2020.3047838

Jaber, M., & Alam, A. S. (2020). A reinforcement learning approach for wireless backhaul spectrum sharing in IoE HetNets. In IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC Vol. 2020-August. doi:10.1109/PIMRC48278.2020.9217340

Nadas, J., Jaber, M., Van Berghe, S. D., & Imran, M. A. (2020). Towards Continuous Subject Identification Using Wearable Devices and Deep CNNs. In IEEE International Conference on Communications Vol. 2020-June. Virtual conference. doi:10.1109/ICC40277.2020.9149260


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






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. 

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