Hadi Otrok

• Ph.D. in ECE
• Professor at Khalifa University

With the widespread adoption of Artificial intelligence (AI), AI-based tools and components are becoming omnipresent in today's solutions. However, these components and tools are posing a significant threat when it comes to adversarial attacks. Mobile Crowdsensing (MCS) is a sensing paradigm that leverages the collective participation of workers an...

Medical Ultrasound (US) imaging has seen increasing demands over the past years, becoming one of the most preferred imaging modalities in clinical practice due to its affordability, portability, and real-time capabilities. However, it faces several challenges that limit its applicability, such as operator dependency, variability in interpretation,...

Cardiac ultrasound (US) scanning is a commonly used techniques in cardiology to diagnose the health of the heart and its proper functioning. Therefore, it is necessary to consider ways to automate these tasks and assist medical professionals in classifying and assessing cardiac US images. Machine learning (ML) techniques are regarded as a prominent...

Medical Ultrasound (US) imaging has seen increasing demands over the past years, becoming one of the most preferred imaging modalities in clinical practice due to its affordability, portability, and real-time capabilities. However, it faces several challenges that limit its applicability, such as operator dependency, variability in interpretation,...

Deep Reinforcement Learning (DRL) has emerged as a powerful paradigm for solving complex problems. However, its full potential remains inaccessible to a broader audience due to its complexity, which requires expertise in training and designing DRL solutions, high computational capabilities, and sometimes access to pre-trained models. This necessita...

This paper addresses the challenges of selecting relay nodes and coordinating among them in UAV-assisted Internet-of-Vehicles (IoV). The selection of UAV relay nodes in IoV employs mechanisms executed either at centralized servers or decentralized nodes, which have two main limitations: 1) the traceability of the selection mechanism execution and 2...

Multi-Agent Deep Reinforcement Learning (MDRL) is a promising research area in which agents learn complex behaviors in cooperative or competitive environments. However, MDRL comes with several challenges that hinder its usability, including sample efficiency, curse of dimensionality, and environment exploration. Recent works proposing Federated Rei...

Target localization is a critical task in sensitive applications, where multiple sensing agents communicate and collaborate to identify the target location based on sensor readings. Existing approaches investigated the use of Multi-Agent Deep Reinforcement Learning (MADRL) to tackle target localization. Nevertheless, these methods do not consider p...

This paper addresses the challenges of selecting relay nodes and coordinating among them in UAV-assisted Internet-of-Vehicles (IoV). Recently, UAVs have gained popularity as relay nodes to complement vehicles in IoV networks due to their ability to extend coverage through unbounded movement and superior communication capabilities. The selection of...

In Federated Learning (FL), the limited accessibility of data from diverse locations and user types poses a significant challenge due to restricted user participation. Expanding client access and diversifying data enhance models by incorporating diverse perspectives, thereby improving adaptability. However, in dynamic and mobile environments, the a...

The rapid evolution of communication networks in recent decades has intensified the need for advanced Network and Service Management (NSM) strategies to address the growing demands for efficiency, scalability, enhanced performance, and reliability of these networks. Large Language Models (LLMs) have received tremendous attention due to their unpara...

This paper addresses the challenges of Last Mile Delivery (LMD) in crowdsourced platforms under time and budget constraints. LMD service providers face a continuous increase in demand with limited resources, such as workers and budgets. With tasks that vary in urgency, limited resources often lead to task failures. Furthermore, the increasing numbe...

The rapid evolution of communication networks in recent decades has intensified the need for advanced Network and Service Management (NSM) strategies to address the growing demands for efficiency, scalability, enhanced performance, and reliability of these networks. Large Language Models (LLMs) have received tremendous attention due to their unpara...

Reducing the battery charging time of an electric vehicle (EV) is one of the key factors to boost the widespread adoption of EVs. The commercial, off-board high power, dc fast charging station need high initial investment and maintenance cost. On the other hand, the standard on-board type-1 and type-2 ac chargers with 3.3 $kW$ to 19 $kW$ need l...

This work addresses the problem of Last Mile Delivery (LMD) under time-critical and budget-constrained environments. Given the rapid growth of e-commerce worldwide, LMD has become a primary bottleneck to the efficiency of delivery services due to several factors, including travelling distance, service cost, and delivery time. Existing works mainly...

Electric vehicle (EV) charging infrastructure development is one of the key aspects of the electrification of transportation systems. However, incorporating EV public charging facilities has practical and financial concerns, especially in developing economies. Recently, mobile public charging stations (MPCS) have been considered an alternate, pract...

In this paper, we tackle the network delays in the Internet of Things (IoT) for an enhanced QoS through a stable and optimized federated fog computing infrastructure. Network delays contribute to a decline in the Quality-of-Service (QoS) for IoT applications and may even disrupt time-critical functions. Our paper addresses the challenge of establis...

The Metaverse offers a second world beyond reality, where boundaries are non-existent, and possibilities are endless through engagement and immersive experiences using the virtual reality (VR) technology. Many disciplines can benefit from the advancement of the Metaverse when accurately developed, including the fields of technology, gaming, educati...

In this paper, the problem of collaboration in crowdsourced last-mile delivery is addressed, where multiple crowdsourced vehicles cooperate to fulfill tasks. Collaborative crowdsourced frameworks allow recruited vehicles, referred to as workers , to perform shorter trips while expanding the geographic coverage. Existing solutions in collaborative...

Federated learning gained importance in sensitive IoT environments by creating a privacy-preserving ecosystem where participants share machine-learning models instead of raw data. However, federated learning shifts data control away from the server, exposing it to Non-Independent and Identically Distributed (non-IID) problems caused by biased clien...

Federated learning is a promising collaborative and privacy-preserving machine learning approach in data-rich smart cities. Nevertheless, the inherent heterogeneity of these urban environments presents a significant challenge in selecting trustworthy clients for collaborative model training. The usage of traditional approaches, such as the random c...

The objective of this work is to design and develop a new three port boost AC-DC converter which facilitates on-board fast charging for electric vehicles using partial power processing (PPP) at bidirectional DC-DC stage. As compared to conventional two-stage chargers with two port AC-DC and full-power processing (FPP) DC-DC converter, the three-por...

In this paper, we tackle the network delays in the Internet of Things (IoT) for an enhanced QoS through a stable and optimized federated fog computing infrastructure. Network delays contribute to a decline in Quality-of-Service (QoS) for IoT applications and may even disrupt time-critical functions. Our paper addresses the challenge of establishing...

The “black-box” nature of artificial intelligence (AI) models has been the source of many concerns in their use for critical applications. Explainable Artificial Intelligence (XAI) is a rapidly growing research field that aims to create machine learning models that can provide clear and interpretable explanations for their decisions and actions. In...

Federated Learning (FL) is a novel distributed privacy-preserving learning paradigm, which enables the collaboration among several participants (e.g., Internet of Things devices) for the training of machine learning models. However, selecting the participants that would contribute to this collaborative training is highly challenging. Adopting a ran...

Fog computing empowers the internet of vehicles (IoV) paradigm by offering computational resources near the end users. In this dynamic paradigm, users tend to move in and out of the range of fog nodes which has implications for the quality of service of the vehicular applications. To cope with these limitations, scholars addressed forming federatio...

Target tracking, a critical application in the Internet of Things (IoT) and Mobile Crowd Sensing (MCS) domains, is a complex task that involves the continuous estimation of the positions of an object by using efficient and accurate algorithms. Some potential applications of target tracking include surveillance systems, asset tracking, wildlife moni...

In this paper, we increase the availability and integration of devices in the learning process to enhance the convergence of federated learning (FL) models. To address the issue of having all the data in one location, federated learning, which maintains the ability to learn over decentralized data sets, combines privacy and technology. Until the mo...

Due to the current improvement in self-driving cars and the extensive focus and research on the topic of the Internet of Vehicles (IoV), the near future may behold a great revolution in the automotive industry as cars become fully autonomous. This change entails a considerable amount of data to be transferred from internet of things (IoT) devices,...

In target localization applications, readings from multiple sensing agents are processed to identify a target location. The localization systems using stationary sensors use data fusion methods to estimate the target location, whereas other systems use mobile sensing agents (UAVs, robots) to search the area for the target. However, such methods are...

In this work, we propose a new paradigm of Federated Learning (FL) for Internet of Things (IoT) devices called Coalitional Federated Learning . The proposed paradigm aims to address the challenges of (1) non-independent and identically distributed (non-IID) data across clients; (2) communication overhead due to the large number of messages exchan...

Federated Learning (FL) is a revolutionary privacy-preserving distributed learning framework that allows a small group of users to cooperatively build a machine-learning model using their own data locally. Smart cities are areas that can generate high volume and critical data, which has the potential to revolutionize federated learning. Nevertheles...

Driven by privacy concerns and the promise of Deep Learning, researchers have devoted significant effort to exploring the applicability of Machine Learning (ML). In the domains of communication, network, and service management, ML-based decision-making solutions are eagerly sought to replace traditional model-driven approaches, addressing the growi...

The Metaverse offers a second world beyond reality, where boundaries are non-existent, and possibilities are endless through engagement and immersive experiences using the virtual reality (VR) technology. Many disciplines can benefit from the advancement of the Metaverse when accurately developed, including the fields of technology, gaming, educati...

The black-box nature of artificial intelligence (AI) models has been the source of many concerns in their use for critical applications. Explainable Artificial Intelligence (XAI) is a rapidly growing research field that aims to create machine learning models that can provide clear and interpretable explanations for their decisions and actions. In t...

The use of Internet of Things (IoT) in environment monitoring has led to the development of Smart Environmental Monitoring (SEM) paradigm. Target or source localization, that determines the underlying cause of environmental occurrences, is an important aspect of SEM. To prevent an environmental event in becoming a potential disaster, swift and earl...

Artificial intelligence (AI) has the potential to revolutionize healthcare by automating the detection and classification of events and anomalies. In the scope of this work, events and anomalies are abnormalities in the patient’s data, where the former are due to a medical condition, such as a seizure or a fall, and the latter are erroneous data du...

This paper addresses the problem of multiple source localization (MSL) using the Internet of Things (IoT) sensors. MSL entails determining the locations of multiple unknown sources by fusing sensory data within a designated area of interest (AoI). Existing solutions suffer from limitations such as increased algorithmic complexity as the number of s...

Multi-Agent Deep Reinforcement Learning (MDRL) is a promising research area in which agents learn complex behaviors in cooperative or competitive environments. However, MDRL comes with several challenges that hinder its usability, including sample efficiency, curse of dimensionality, and environment exploration. Recent works proposing Federated Rei...

The increasing number of Internet of Things (IoT) devices and low-cost sensors have facilitated developments in large-scale monitoring applications. However, the accuracy of low-cost sensors remains questionable. Monitoring applications, such as environmental monitoring, try to detect ’interesting’ data points or patterns, known as anomalies, that...

Federated Learning (FL) is a novel distributed privacy-preserving learning paradigm, which enables the collaboration among several devices. However, selecting the participants that would contribute to this collaborative training is highly challenging. Adopting a random selection strategy would entail substantial problems due to the heterogeneity in...

In this paper, the problem of distributed, multi-perspective conformance checking for Business Process Model and Notation (BPMN) is addressed. Traditionally, conformance checking has been performed centrally by a trusted entity, however that may not be applicable in the case of collaborative processes between multiple organizations. Consortium Bloc...

Machine Learning's influence increases daily, and its application prevails in different, critical, and life-changing fields. However, collecting the needed data is challenging due to the increasing concerns about clients' privacy. In this context, federated learning addresses privacy issues by adopting an on-device model training strategy while com...

In this paper, we increase the availability and integration of devices in the learning process to enhance the convergence of federated learning (FL) models. To address the issue of having all the data in one location, federated learning, which maintains the ability to learn over decentralized data sets, combines privacy and technology. Until the mo...

Federated Learning (FL) is a novel distributed privacy-preserving learning paradigm, which enables the collaboration among several participants (e.g., Internet of Things devices) for the training of machine learning models. However, selecting the participants that would contribute to this collaborative training is highly challenging. Adopting a ran...

Numerous research recently proposed integrating Federated Learning (FL) to address the privacy concerns of using machine learning in privacy-sensitive firms. However, the standards of the available frameworks can no longer sustain the rapid advancement and hinder the integration of FL solutions, which can be prominent in advancing the field. In thi...