QNRF Projects

Promoting intelligent services for industrial automation and E-health is an integral part of the Qatar National Vision 2030. Providing smart, efficient, and secure services for improving Qatari citizens quality of life has been a top national interest in Qatar. Coinciding with this increasing interest, Tactile Internet has been envisioned to revolutionize several societal, economic, and industrial aspects of our future by enabling real-time machine-to-machine and human-to-machine interactions in the 5G era and beyond. The rapid evolution of Artificial Intelligence (AI), Internet of Things (IoT), and Big data is paving the path towards a plethora of Tactile Internet applications. Tactile Internet is considered as the next evolution of IoT that will enable the full potential of industrial 4.0 revolution. However, to enable such applications and build real-time interactive systems, we must provide a latency that matches the speed of our natural reactions (with a typical value of end-to-end latency being 1ms). Furthermore, the underlying network must support ultra-reliable services, since many critical tasks will be executed remotely. This calls for integrating multiple technologies at the network and application level. We envision that bringing the intelligence close to the users, using multi-access edge computing (MEC), along with transmitting the data over a heterogeneous 5G network is a key for supporting tactile applications and next generation systems’ requirements. Accordingly, this collaborative project aims at developing novel, intelligent platform, and algorithms for supporting reliable and effective services for diverse types of applications. The proposed platform and AI algorithms will adopt recent technologies of 5G to support a wide range of heterogeneous services in industrial automation and E-health. Leveraging ubiquitous sensing, heterogeneous network, intelligent processing and control schemes, the proposed platform can in real-time monitor people's daily life and provide intelligent services to both residents and travelers. In this project, we will consider several use cases from E-health applications (e.g., emergency detection and notification, remote health monitoring, epidemic prediction, and occupational safety), as representative applications for Tactile Internet. However, the proposed algorithms and methodologies can be easily applied to different types of applications.

To support diverse-intelligent services with strict requirements, our project aims to adopt the recent 5G technologies. In particular, we propose the use of edge computing and network function virtualization (NFV) to fulfil diverse applications and services requirements. In this context, the proposed platform will integrate AI schemes in 5G infrastructures to enable a paradigm shift for improving efficiency of the healthcare systems in Qatar. In contrast to the previous literature on 5G and network virtualization, the adopted platform will consider context-aware MEC approaches to be implemented over 5G networks, in order to optimize the data delivery and support diverse services, along with their requirements. Specifically, this project aims to answer the following main questions:

1.How to integrate the wireless network infrastructure and application-layer characteristics to optimize the performance of the supported services?

2.Where and how to allocate the computational and network resources across the infrastructure (i.e., from the edge to the cloud) to fulfill diverse service requirements?

3.How to leverage the spectrum across multiple networks to fulfill diverse application Quality of Service (QoS) requirements? And, how to transfer the data across user devices and infrastructure with the ultra-high reliability and ultra-low latency levels needed for Tactile Internet?

4.How to reduce the incurred cost in supporting the targeted services, while ensuring the above fulfillment of the above requirements?

Participating institutions:

1- Hamad Bin Khalifa University

2- Qatar University

3- Politecnico di Torino, Italy

4- Ooredoo Qatar

5-Droobi Health (Trio Investment)

The percentage of chronic disease (CD) patients is climbing to reach almost one-fifth globally, posing immediate threats on healthcare expenditure (in Qatar it reaches ~2.5% of the national GDP), and rising causes of death (more than 76% of deaths in Qatar are owed to CDs). This calls for transforming healthcare systems away from one-on-one patient treatment, to improve services, access and scalability, while reducing costs.

The deployment of mobile and wearable devices in healthcare along with the advances of wireless networking and cloud-based technologies will enable mobile health (mHealth) systems to operate in conjunction with invaluable sets of data that are amassed from wide range of sources. Smart Health (sHealth) can be considered the evolution of mHealth, where collected medical and physiological data can be efficiently mined at the patient level to better understand the patient context, hence optimizing the transmission and integration of diagnostic information from healthcare professionals at the cloud level, while facilitating immediate response in cases of emergency. Such concept is set to revolutionize healthcare delivery by giving CD patients better control of their medical situation and allow the public to continuously and smartly monitor their health conditions.

Recent innovations are paving the way towards an even more interactive connectivity level with the ongoing developments towards the 5G cellular technology and the Tactile Internet, especially with the 5G massive machine type communications (mMTC) and ultra-reliable low latency communications (URLLC) use case scenarios. Combining extremely low latency with high availability and reliability, the Tactile Internet, based on 5G URLLC, will facilitate real-time, synchronous, haptic feedback with remote-control solutions. This will give rise to a great variety of opportunities for emerging technologies and services especially in the field of tele-health. With the prospective evolution towards next generation 5G networks, such services will benefit from faster connections, greater mobility, increased inter-device compatibility, and harmonization between different communications standards.

Remarkably, in sHealth systems for neurological applications, continuous sensory data acquisition and rapid diagnostic feedback and medical intervention have the potential to positively impact the lives of a large portion of the global population who suffer from chronic brain illnesses and disorders. Yet, these impose strict constraints on data storage, capabilities of networking technologies, processing speed, algorithmic complexity due to the high number of active sensors, the massive volume of collected biomedical data, and the need for premium service quality in terms of reliability and delay. For example, a wireless electroencephalography (EEG) headset that measures the electrical activity of the brain with 29 channels, sampled at 512 Hz with two bytes per channel, generates approximately 107 MB of data per hour for one single patient, which requires to be processed, transmitted, stored and analyzed. Despite the promising features of 5G in terms of large spectrum bandwidth and fast transmission, the challenge remains in how to leverage such features efficiently in terms of cost and energy consumption. This motivates the use of the edge computing paradigm, which promotes smart ways to process medical data close to where they are collected (i.e., as close as possible to the edge of the network), in order to optimize the amount and quality of data communicated over 5G, hence, minimizing the delay, energy consumption, and cost associated with data delivery.

A very important use case of using EEG measurements to monitor patients is epilepsy. In fact, characterized by recurrent seizures, epilepsy is a common brain disorder affecting around 1% of the global population. Potentially harmful consequences of epileptic seizures and the lack of a definite cure for a large number of patients encouraged alternative solutions that allow subjects to actively monitor their brain health through intelligent mobile sensing devices with seizure detection or prediction capabilities. EEG monitoring is the primary tool for the assessment of epileptic seizures and it involves analyzing the change in EEG activity before, during, and after seizure onsets. The purpose of seizure prediction is to alert the patient normally few minutes beforehand of a seizure occurrence in order to take precautionary measures. However, the problem of prediction based on EEG signals is highly challenging, especially in reducing the rate of false alarms. On the other hand, seizure detection tries to identify the onset of the seizure and possibly its offset. In this case, ultra-low delay is essential as a seizure can typically have a duration of few seconds and, thus, every fraction of a second matters. This constitutes an important and impactful, yet challenging, application scenario for 5G URLLC.

In this project, we present challenges and develop solutions for designing effective sHealth systems for neurological applications using a holistic framework that captures end-user sensing, mobile device processing, radio access network connectivity, mobile edge computing, remote cloud computing and storage, combined with advanced signal analysis and machine learning intelligence. We further illustrate the presented framework via a detailed experimental use case on mobile epileptic seizure detection and predication as an example of a globally common neurological disease. In addition, we investigate efficient resource allocation over 5G/5G+ networks in order to transmit the measured health information in real-time, and to alert the user in case of an imminent seizure. We showcase our achievements via a testbed based on real measurement data, and implemented in collaboration with Hamad Medical Center (HMC) and Huawei in Qatar.

Participating institutions:

1- Qatar University

2-Hamad Medical Corporation, Qatar

3-Huawei Qatar

4-University of British Columbia, Canada

5-American University in Beirut, Lebanon

Overview: The goal of this applied research is to develop a telemedicine system to support intraoperative collaboration between a specialty and an operating surgeon during minimally invasive surgery (MIS). It will entail development of immersive, augmented reality-based, enabling core technologies that would provide operating surgeon realistic audio-visual cues from remotely located specialty surgeon. The cues will assist operating surgeon to perform precise movements of the highly-actuated surgical instruments required during a MIS for tool-tissue interaction. As the telemedicine system will provide real-time, interactive intraoperative guidance during the surgery, we hypothesis usage of such system will facilitate efficient transfer of surgical knowledge from the specialist surgeon to the operating surgeon. This will improve the outcome in case of specialized surgeries, expand the reachability, and broaden the spectrum of surgical services, thus potentially transforming the surgical care offered by a hospital.

Intellectual Merit: The proposed application-driven research will advance the field of telemedicine for MIS by developing new enabling core technologies. Specifically, this research will develop and advance:

- Human-machine interfacing technologies to capture and convert surgeon’s hand gesture into virtual surgical tool motion.

- Augmentation methods to enhance surgical view by overlaying virtual surgical tool motion.

- Secure networking protocols to transmit live audio-video streams over the network with packets of augmentation data.

- Computational frameworks to integrate the software modules with the hardware components into a single synchronized telemedicine system.

The developed technological components will be evaluated and clinically assessed by surgeons. This would improve the understanding of the utilization of telemedicine technology across disciplines related to tele-mentoring, tele-conferencing, and tele-collaboration during a surgery.

Broader Impacts: The proposed research, by advancing telemedicine technology for MIS, can potentially have significant benefits to both patients and healthcare system in regards to surgical treatment. The technology will behave as a training tool for surgeon to get trained on new or improved existing MIS techniques / surgical workflows. This would expand the range of surgical services offered by the hospital to its patients and improve the capacity building of healthcare system.

Participating Institutions:

1- Hamad Medical Corporation, Qatar

2- Qatar University

3- Hamad Bin Khalifa University

4- University of Houston, Houston, Texas, USA

Wireless medical devices have been used to treat various diseases and to hold patients’ medical records. There are many different types of wireless medical devices, such as wireless insulin pumps, pacemakers, cardiac defibrillators, neuro stimulators, drug delivery systems, and so on. In the United States alone , 4.6 million pacemakers and implantable cardiac defibrillators (ICDs) were sold between 2006 and 2011. In 2007, there were approximately 375,000 pump users sold in the U.S., with this market expected to grow 9% annually between 2009 and 2016. Unfortunately, most of the existing wireless medical devices lack sufficient security mechanisms to protect patients from malicious attacks. With the rise in use of medical devices, security becomes a critical issue because attacks on wireless medical devices may harm or even kill patients. A number of attacks could be launched on these devices. Researchers from several groups (IBM, McAfee, UMass, Harvard, etc.) have demonstrated various attacks that can penetrate these devices. Actually, an insulin pump overdose injection can cause injury and/or death. Hence, it is critical to design effective security schemes to prevent attacks on the medical devices and therefore protect patients.

Securing wireless medical devices is a challenging task due to their limited resources in terms of energy supply, processing power, storage space, and so on. For example, a medical device with a small ion-lithium battery storing about 3000 joules of energy is expected to operate for several months or even years. A medical device manufactured in 2002 that is still being used today, contains as low as 8 KB storage. Due to the above reasons, traditional crypto-based security schemes cannot be applied to many of these medical devices. This project proposes fundamental research that is aimed to make medical devices secure and safe. We plan to develop light-weight and effective security schemes that will be portable and efficient when used in such small devices.

The proposed project will advance the state-of-the-art of medical device security. The expected outcomes include (1) light-weight and effective security (safety) schemes that can detect malicious (accidental) overdoses from wireless infusion devices, (2) effective security schemes when patients are in emergency situations (e.g., coma), (3) near-field-communication-based security schemes for general wireless medical devices, and (4) other security schemes to defend various attacks on general wireless medical devices.

Social Impacts: This project addresses the critical medical device security and safety issues, as well as patient data privacy issues. Our research will generate immediate impacts on medical device security and safety, wireless security, (medical) data privacy, and other related areas. The broader impacts extend to academia via publications and demonstrations. The PIs will actively seek technical transfer through Workshop organizations and contribute to the national economic growth.

We remark that the decisions made to answer the above questions are typically entangled in ways that lead to having complex and often counteractive phenomena. Thus, a key aspect of our work is to develop intelligent and flexible platform and algorithms that account for: (i) the different requirements of the supported services, (ii) the capabilities of the network and computing infrastructure, as well as (iii) the privacy of the acquired data. Such aspects will be thoroughly investigated and fully developed, also thanks to the collaboration with one of the top universities worldwide, Politecnico di Torino, Italy, which will further strengthen the scientific excellence in Qatar. Furthermore, we will depict the feasibility of the proposed platform and algorithms via detailed experimental use cases. Our demonstrations will be implemented using real-world data sets that will be acquired from our collaborators in Droobi Health, and Ooredoo Qatar.

Participating Institutions:

1- Qatar University

2- University of Idaho, USA

3- Temple university, USA