Aiman Erbad | Hamad Bin Khalifa University (original) (raw)

Papers by Aiman Erbad

IEEE Internet of Things Journal

arXiv (Cornell University), Apr 26, 2023

Clustered federated Multitask learning is introduced as an efficient technique when data is unbal... more Clustered federated Multitask learning is introduced as an efficient technique when data is unbalanced and distributed amongst clients in a non-independent and identically distributed manner. While a similarity metric can provide client groups with specialized models according to their data distribution, this process can be time-consuming because the server needs to capture all data distribution first from all clients to perform the correct clustering. Due to resource and time constraints at the network edge, only a fraction of devices is selected every round, necessitating the need for an efficient scheduling technique to address these issues. Thus, this paper introduces a two-phased client selection and scheduling approach to improve the convergence speed while capturing all data distributions. This approach ensures correct clustering and fairness between clients by leveraging bandwidth reuse for participants spent a longer time training their models and exploiting the heterogeneity in the devices to schedule the participants according to their delay. The server then performs the clustering depending on predetermined thresholds and stopping criteria. When a specified cluster approximates a stopping point, the server employs a greedy selection for that cluster by picking the devices with lower delay and better resources. The convergence analysis is provided, showing the relationship between the proposed scheduling approach and the convergence rate of the specialized models to obtain convergence bounds under non-i.i.d. data distribution. We carry out extensive simulations, and the results demonstrate that the proposed algorithms reduce training time and improve the convergence speed by up to 50% while equipping every user with a customized model tailored to its data distribution.

Engineering Applications of Artificial Intelligence

IEEE Transactions on Cognitive Communications and Networking

Pervasive and Mobile Computing

2022 IEEE 33rd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

arXiv (Cornell University), Aug 11, 2022

In 6G networks, unmanned aerial vehicles (UAVs) can serve as aerial flying base stations (AFBS) w... more In 6G networks, unmanned aerial vehicles (UAVs) can serve as aerial flying base stations (AFBS) with aerial mobile edge computing (AMEC) server capabilities. AFBS is an increasingly popular solution for delivering time-sensitive applications, extending network coverage, and assisting ground base stations in the healthcare systems for remote areas with limited infrastructure. Furthermore, the UAVs are deployed in the healthcare system to support the Internet of medical things (IoMT) devices in data collection, medical equipment distribution, and providing smart services. However, ensuring the privacy and security of patients' data with the limited UAV resources is a major challenge. In this paper, we present a federated deep reinforcement learning framework for resource allocation in UAV-enabled healthcare systems, where IoMT devices send their trained model parameters without transmitting sensitive raw data to the AMEC server. In the proposed framework, the IoMT device is associ...

IEEE Communications Surveys & Tutorials

2022 International Wireless Communications and Mobile Computing (IWCMC)

IEEE Transactions on Network Science and Engineering

2022 IEEE Wireless Communications and Networking Conference (WCNC)

2020 International Conference on Computational Science and Computational Intelligence (CSCI)

The internet of things (IoT) is becoming part of the infrastructure supporting various services i... more The internet of things (IoT) is becoming part of the infrastructure supporting various services in every day’s life. Due to the complex nature of IoT systems with heterogeneous devices, the needed security and privacy aspects are mostly ignored in the initial system design. One of the proposed solutions to address the security threats from the physical layer perspective is physical-layer security (PLS). We propose the use of 3-D stochastic geometry to accurately model IoT systems in a realistic scenarios, where sensors, access points, and eavesdroppers are randomly located in a 3-D space. We use our model with realistic system deployment parameters to conduct rigorous performance analysis for critical security metrics, such as the successful transmission probability (STP) and the secrecy outage probability (SOP) in different potential IoT scenarios. We finally utilize simulation to validate the theoretical analysis.

2022 IEEE Wireless Communications and Networking Conference (WCNC)

2022 Wireless Telecommunications Symposium (WTS)

This work has been submitted to the IEEE for possible publication. Copyright may be transferred w... more This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.

Surgical Endoscopy, 2022

BackgroundTele-mentoring during surgery facilitates the transfer of surgical knowledge from a men... more BackgroundTele-mentoring during surgery facilitates the transfer of surgical knowledge from a mentor (specialist surgeon) to a mentee (operating surgeon). The aim of this work is to develop a tele-mentoring system tailored for minimally invasive surgery (MIS) where the mentor can remotely demonstrate to the mentee the required motion of the surgical instruments.MethodsA remote tele-mentoring system is implemented that generates visual cues in the form of virtual surgical instrument motion overlaid onto the live view of the operative field. The technical performance of the system is evaluated in a simulated environment, where the operating room and the central location of the mentor were physically located in different countries and connected over the internet. In addition, a user study was performed to assess the system as a mentoring tool.ResultsOn average, it took 260 ms to send a view of the operative field of 1920 × 1080 resolution from the operating room to the central location...

2021 IEEE Global Communications Conference (GLOBECOM), 2021

LoRa supports the exponential growth of connected devices. In this paper, we investigate green Lo... more LoRa supports the exponential growth of connected devices. In this paper, we investigate green LoRa wireless networks powered by both the grid power and a renewable energy source. The grid power compensates for the randomness and intermittency of the harvested energy. We propose an efficient and smart resource management scheme of the limited number of channels and spreading factors (SFs) with the objective of improving the LoRa gateway (LG) energy efficiency. We formulate the problem of grid power consumption minimization while satisfying the quality of service demands. The optimal resource management problem is solved by decoupling the formulated problem into two sub-problems: channel and SF assignment problem and energy management problem. Next, we develop an adaptable resource management schemes based on Reinforcement Learning (RL) taking into account the channel and energy correlation. Simulations results show that the proposed resource management schemes offer efficient use of renewable energy in LoRa wireless networks.

2020 International Symposium on Networks, Computers and Communications (ISNCC), 2020

IEEE Internet of Things Journal

arXiv (Cornell University), Apr 26, 2023

Clustered federated Multitask learning is introduced as an efficient technique when data is unbal... more Clustered federated Multitask learning is introduced as an efficient technique when data is unbalanced and distributed amongst clients in a non-independent and identically distributed manner. While a similarity metric can provide client groups with specialized models according to their data distribution, this process can be time-consuming because the server needs to capture all data distribution first from all clients to perform the correct clustering. Due to resource and time constraints at the network edge, only a fraction of devices is selected every round, necessitating the need for an efficient scheduling technique to address these issues. Thus, this paper introduces a two-phased client selection and scheduling approach to improve the convergence speed while capturing all data distributions. This approach ensures correct clustering and fairness between clients by leveraging bandwidth reuse for participants spent a longer time training their models and exploiting the heterogeneity in the devices to schedule the participants according to their delay. The server then performs the clustering depending on predetermined thresholds and stopping criteria. When a specified cluster approximates a stopping point, the server employs a greedy selection for that cluster by picking the devices with lower delay and better resources. The convergence analysis is provided, showing the relationship between the proposed scheduling approach and the convergence rate of the specialized models to obtain convergence bounds under non-i.i.d. data distribution. We carry out extensive simulations, and the results demonstrate that the proposed algorithms reduce training time and improve the convergence speed by up to 50% while equipping every user with a customized model tailored to its data distribution.

Engineering Applications of Artificial Intelligence

IEEE Transactions on Cognitive Communications and Networking

Pervasive and Mobile Computing

2022 IEEE 33rd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

arXiv (Cornell University), Aug 11, 2022

In 6G networks, unmanned aerial vehicles (UAVs) can serve as aerial flying base stations (AFBS) w... more In 6G networks, unmanned aerial vehicles (UAVs) can serve as aerial flying base stations (AFBS) with aerial mobile edge computing (AMEC) server capabilities. AFBS is an increasingly popular solution for delivering time-sensitive applications, extending network coverage, and assisting ground base stations in the healthcare systems for remote areas with limited infrastructure. Furthermore, the UAVs are deployed in the healthcare system to support the Internet of medical things (IoMT) devices in data collection, medical equipment distribution, and providing smart services. However, ensuring the privacy and security of patients' data with the limited UAV resources is a major challenge. In this paper, we present a federated deep reinforcement learning framework for resource allocation in UAV-enabled healthcare systems, where IoMT devices send their trained model parameters without transmitting sensitive raw data to the AMEC server. In the proposed framework, the IoMT device is associ...

IEEE Communications Surveys & Tutorials

2022 International Wireless Communications and Mobile Computing (IWCMC)

IEEE Transactions on Network Science and Engineering

2022 IEEE Wireless Communications and Networking Conference (WCNC)

2020 International Conference on Computational Science and Computational Intelligence (CSCI)

The internet of things (IoT) is becoming part of the infrastructure supporting various services i... more The internet of things (IoT) is becoming part of the infrastructure supporting various services in every day’s life. Due to the complex nature of IoT systems with heterogeneous devices, the needed security and privacy aspects are mostly ignored in the initial system design. One of the proposed solutions to address the security threats from the physical layer perspective is physical-layer security (PLS). We propose the use of 3-D stochastic geometry to accurately model IoT systems in a realistic scenarios, where sensors, access points, and eavesdroppers are randomly located in a 3-D space. We use our model with realistic system deployment parameters to conduct rigorous performance analysis for critical security metrics, such as the successful transmission probability (STP) and the secrecy outage probability (SOP) in different potential IoT scenarios. We finally utilize simulation to validate the theoretical analysis.

2022 IEEE Wireless Communications and Networking Conference (WCNC)

2022 Wireless Telecommunications Symposium (WTS)

This work has been submitted to the IEEE for possible publication. Copyright may be transferred w... more This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.

Surgical Endoscopy, 2022

BackgroundTele-mentoring during surgery facilitates the transfer of surgical knowledge from a men... more BackgroundTele-mentoring during surgery facilitates the transfer of surgical knowledge from a mentor (specialist surgeon) to a mentee (operating surgeon). The aim of this work is to develop a tele-mentoring system tailored for minimally invasive surgery (MIS) where the mentor can remotely demonstrate to the mentee the required motion of the surgical instruments.MethodsA remote tele-mentoring system is implemented that generates visual cues in the form of virtual surgical instrument motion overlaid onto the live view of the operative field. The technical performance of the system is evaluated in a simulated environment, where the operating room and the central location of the mentor were physically located in different countries and connected over the internet. In addition, a user study was performed to assess the system as a mentoring tool.ResultsOn average, it took 260 ms to send a view of the operative field of 1920 × 1080 resolution from the operating room to the central location...

2021 IEEE Global Communications Conference (GLOBECOM), 2021

LoRa supports the exponential growth of connected devices. In this paper, we investigate green Lo... more LoRa supports the exponential growth of connected devices. In this paper, we investigate green LoRa wireless networks powered by both the grid power and a renewable energy source. The grid power compensates for the randomness and intermittency of the harvested energy. We propose an efficient and smart resource management scheme of the limited number of channels and spreading factors (SFs) with the objective of improving the LoRa gateway (LG) energy efficiency. We formulate the problem of grid power consumption minimization while satisfying the quality of service demands. The optimal resource management problem is solved by decoupling the formulated problem into two sub-problems: channel and SF assignment problem and energy management problem. Next, we develop an adaptable resource management schemes based on Reinforcement Learning (RL) taking into account the channel and energy correlation. Simulations results show that the proposed resource management schemes offer efficient use of renewable energy in LoRa wireless networks.

2020 International Symposium on Networks, Computers and Communications (ISNCC), 2020