In recent years, advances in signal processing have led to small, low power, inexpensive Wireless... more In recent years, advances in signal processing have led to small, low power, inexpensive Wireless Sensor Network (WSN). Target localisation and tracking problems in WSNs have received considerable attention recently, driven by the necessity to achieve higher localisation accuracy, lower cost, and the smallest form factor. This book includes five studies. The first study leads to the design and implementation of a triangulation-based localisation approach using RSS technique for indoor tracking applications. The second study concentrates on investigating a fingerprinting localisation method for indoor tracking applications. The third study focuses on designing and implementing a decentralised tracking approach for tracking multiple mobile targets with low resource requirements. The fourth study focuses on developing and designing a practical ZigBee-based tracking approach. The fifth study outlines designing and implementing an energy-efficient approach for tracking applications.
Asthma is a chronic respiratory disease that impairs breathing. Management of asthma presents a s... more Asthma is a chronic respiratory disease that impairs breathing. Management of asthma presents a significant challenge due to its inherent variability; that is, its symptoms can substantially differ among individuals, thereby complicating the prediction and management of exacerbations. Furthermore, individuals with asthma often have unique triggers that precipitate symptoms or attacks. The identification of these triggers can often prove to be a challenging, and at times, an impractical attempt. To address this, our research proposes a practical, personalized alert system, predicated on individual lung function tests conducted under varying environmental conditions classified by air-quality sensors. To validate this concept, we conducted an observational pilot study involving healthy individuals. We recruited twelve healthy participants and monitored their responses across a broad spectrum of environments, characterized by varying air quality, temperature, and humidity conditions. The lung function for each participant, assessed using peak expiratory flow (PEF) values, was recorded in each of these environments. Our results highlighted substantial variability in pulmonary responses to different environments. Utilizing these insights, we proposed a personalized alarm system that provides real-time air-quality monitoring and issues alerts when environmental conditions may potentially become unfavorable. We also explored the feasibility of employing basic machine learning techniques to predict PEF values in the aforementioned environmental conditions. This proposed system has the potential to empower individuals in actively safeguarding their respiratory health and mitigating discomfort caused by environmental conditions, especially in cases of asthma patients. By enabling timely and personalized interventions, the system aims to provide individuals with the necessary tools to minimize exposure to asthma’s possible triggers.
Asthma is a chronic respiratory disease that impairs breathing. Management of asthma presents a s... more Asthma is a chronic respiratory disease that impairs breathing. Management of asthma presents a significant challenge due to its inherent variability; that is, its symptoms can substantially differ among individuals, thereby complicating the prediction and management of exacerbations. Furthermore, individuals with asthma often have unique triggers that precipitate symptoms or attacks. The identification of these triggers can often prove to be a challenging, and at times, an impractical attempt. To address this, our research proposes a practical, personalized alert system, predicated on individual lung function tests conducted under varying environmental conditions classified by air-quality sensors. To validate this concept, we conducted an observational pilot study involving healthy individuals. We recruited twelve healthy participants and monitored their responses across a broad spectrum of environments, characterized by varying air quality, temperature, and humidity conditions. The lung function for each participant, assessed using peak expiratory flow (PEF) values, was recorded in each of these environments. Our results highlighted substantial variability in pulmonary responses to different environments. Utilizing these insights, we proposed a personalized alarm system that provides real-time air-quality monitoring and issues alerts when environmental conditions may potentially become unfavorable. We also explored the feasibility of employing basic machine learning techniques to predict PEF values in the aforementioned environmental conditions. This proposed system has the potential to empower individuals in actively safeguarding their respiratory health and mitigating discomfort caused by environmental conditions, especially in cases of asthma patients. By enabling timely and personalized interventions, the system aims to provide individuals with the necessary tools to minimize exposure to asthma’s possible triggers.
Dynamic Source Routing (DSR) is an efficient on-demand routing protocol for mobile ad-hoc network... more Dynamic Source Routing (DSR) is an efficient on-demand routing protocol for mobile ad-hoc networks (MANET). It depends on two main procedures: Route Discovery and Route Maintenance. Route discovery is the procedure used at the source of the packets to discover a route to the destination. Route Maintenance is the procedure that discovers link failures and repairs them. Route caching is the sub procedure serviceable to avoid the demand for discovering a route or to reduce route discovery delay before every data packet is sent. The goal of this paper is to evaluate the performance of DSR. Different performance expressions are investigated including, delivery ratio, end to-end delay, and throughput, depending on different cache sizes and different speeds. All of that as a study to develop a new caching strategy as a future work.
Modern wheelchairs, with advanced and robotic technologies, could not reach the life of millions ... more Modern wheelchairs, with advanced and robotic technologies, could not reach the life of millions of disabled people due to their high costs, technical limitations, and safety issues. This paper proposes a gesture-controlled smart wheelchair system with an IoT-enabled fall detection mechanism to overcome these problems. It can recognize gestures using Convolutional Neural Network (CNN) model along with computer vision algorithms and can control the wheelchair automatically by utilizing these gestures. It maintains the safety of the users by performing fall detection with IoT-based emergency messaging systems. The development cost of the overall system is cheap and is lesser than USD 300. Hence, it is expected that the proposed smart wheelchair should be affordable, safe, and helpful to physically disordered people in their independent mobility.
International Journal of Online and Biomedical Engineering (iJOE)
With the massive outbreak of coronavirus (COVID-19) disease, the demand for automatic and quick d... more With the massive outbreak of coronavirus (COVID-19) disease, the demand for automatic and quick detection of COVID-19 has become a crucial challenge for scientists around the world. Many researchers are working on finding an automated and effective system for detecting COVID-19. They have found that computed tomography (CT-scan) and X-ray images of COVID-19 infected patients can provide more accurate and faster results. In this paper, an automated system is proposed named as COV-CTX which can detect COVID-19 from CT-scan and X-ray images. The system consists of three different CNN models: VGG16, VGG16- InceptionV3-ResNet50, and Francois CNN. The models are trained with CT-scan and X-ray images individually to classify COVID-19 and non-COVID patients. Finally, the results of the models are combined to develop a voting ensemble of classifiers to ensure more accurate and precise results. The three models are trained and validated with 9412 CT-scan images (4756 numbers of COVID positive...
Robot navigation in indoor environments has become an essential task for several applications, in... more Robot navigation in indoor environments has become an essential task for several applications, including situations in which a mobile robot needs to travel independently to a certain location safely and using the shortest path possible. However, indoor robot navigation faces challenges, such as obstacles and a dynamic environment. This paper addresses the problem of social robot navigation in dynamic indoor environments, through developing an efficient SLAM-based localization and navigation system for service robots using the Pepper robot platform. In addition, this paper discusses the issue of developing this system in a way that allows the robot to navigate freely in complex indoor environments and efficiently interact with humans. The developed Pepper-based navigation system has been validated using the Robot Operating System (ROS), an efficient robot platform architecture, in two different indoor environments. The obtained results show an efficient navigation system with an aver...
Social robots have the potential to revolutionize the way we interact with technology, providing ... more Social robots have the potential to revolutionize the way we interact with technology, providing a wide range of services and applications in various domains, such as healthcare, education, and entertainment. However, most existing social robotics platforms are operated based on embedded computers, which limits the robot’s capabilities to access advanced AI-based platforms available online and which are required for sophisticated physical human–robot interactions (such as Google Cloud AI, Microsoft Azure Machine Learning, IBM Watson, ChatGPT, etc.). In this research project, we introduce a cloud-based framework that utilizes the benefits of cloud computing and clustering to enhance the capabilities of social robots and overcome the limitations of current embedded platforms. The proposed framework was tested in different robots to assess the general feasibility of the solution, including a customized robot, “BuSaif”, and commercialized robots, “Husky”, “NAO”, and “Pepper”. Our findin...
The area of localization in wireless sensor networks (WSNs) has received considerable attention r... more The area of localization in wireless sensor networks (WSNs) has received considerable attention recently, driven by the need to develop an accurate localization system with the minimum cost and energy consumption possible. On the other hand, machine learning (ML) algorithms have been employed widely in several WSN-based applications (data gathering, clustering, energy-harvesting, and node localization) and showed an enhancement in the obtained results. In this paper, an efficient WSN-based fingerprinting localization system for indoor environments based on a low-cost sensor architecture, through establishing an indoor fingerprinting dataset and adopting four tailored ML models, is presented. The proposed system was validated by real experiments conducted in complex indoor environments with several obstacles and walls and achieves an efficient localization accuracy with an average of 1.4 m. In addition, through real experiments, we analyze and discuss the impact of reference point de...
Emerging Trends in Wireless Sensor Networks [Working Title]
Usually, wireless sensor networks (WSNs) are installed in large areas to monitor various physical... more Usually, wireless sensor networks (WSNs) are installed in large areas to monitor various physical conditions of the environment and forward the collected sensed data to a base station (central node), for instance: gas monitoring, intrusion detection, tracking objects, etc. However, sensor nodes are usually deployed unattended and battery-powered with no external power source. Therefore, WSNs face the challenge of limited energy source available onboard, where packet transmission and sensing functions are the most power consumption factors in WSN. Therefore, to overcome the energy depletion in sensor nodes, it is important to study the energy management issue in WSN. In this chapter, the significance of energy management issue is discussed first, and then the possible energy management strategies for WSN are presented and illustrated.
There is increasing utilization of photovoltaic (PV) grid-connected systems in modern power netwo... more There is increasing utilization of photovoltaic (PV) grid-connected systems in modern power networks. Currently, PV grid-connected systems utilize transformerless inverters that have the advantages of being low cost, low weight, a small size, and highly efficient. Unfortunately, these inverters have an earth leakage current problem due to the absence of galvanic isolation. This phenomenon represents safety and electrical problems for those systems. Recently, the H8 transformerless inverter was introduced to eliminate the earth leakage current. The present study proposes improving the performance of an H8 transformerless inverter using model predictive control (MPC). The inverter was supplied by PV energy and attached to the grid through an LCL filter. During system modeling, the grid weakness was identified. The discrete model of the overall system, including the PV panel, the boost converter, the H8 transformerless inverter, and the controllers, was derived. Then, the introduced H8...
Using mobile sinks to collect information in wireless sensor networks is an interesting area of r... more Using mobile sinks to collect information in wireless sensor networks is an interesting area of research. As a result, a variety of mobility models were proposed by researchers over the years where each mobility model has its own properties that may affect the performance of the network in a way that differs from other models entitled with different properties. In this paper we provide a survey of mobility models that can be used in wireless sensor networks since it is important to provide a classification of the available models. Therefore, several mobility models were reviewed in the proposed work. These models were classified into two main categories namely homogenous and heterogeneous mobility. The goal of the work proposed in this paper is to provide researchers with a clear idea about the available mobility models and their properties. Additionally, we aim to provide researchers with guidelines to help them choose mobility models properly.
paper evaluates the performance of the OLSR pro-active protocol with and without backup routes un... more paper evaluates the performance of the OLSR pro-active protocol with and without backup routes under varying node densities and with different speed movements in the network. Additionally, this paper assists in ascertaining the effect of varying node densities on the connectivity's life between mobile nodes in the network. Hence, it showed the affect of a local recovery mechanism resulted in achieving a significant improvement in network performance by seeking a long life backup path between source and destination for low/high density nodes. Real time applications are required to be supported by mobile ad hoc networks. This is because of the free movement for the mobile nodes from one area to another without any notification via frequent paths. The real time applications traffics are considered a sensitive application, and it is the most affected by failure through the occurrence of delay and loss of packets. It is, therefore, not suitable for use by players. In mobile ad hoc ne...
In recent years, advances in signal processing have led to small, low power, inexpensive Wireless... more In recent years, advances in signal processing have led to small, low power, inexpensive Wireless Sensor Network (WSN). Target localisation and tracking problems in WSNs have received considerable attention recently, driven by the necessity to achieve higher localisation accuracy, lower cost, and the smallest form factor. This book includes five studies. The first study leads to the design and implementation of a triangulation-based localisation approach using RSS technique for indoor tracking applications. The second study concentrates on investigating a fingerprinting localisation method for indoor tracking applications. The third study focuses on designing and implementing a decentralised tracking approach for tracking multiple mobile targets with low resource requirements. The fourth study focuses on developing and designing a practical ZigBee-based tracking approach. The fifth study outlines designing and implementing an energy-efficient approach for tracking applications.
Asthma is a chronic respiratory disease that impairs breathing. Management of asthma presents a s... more Asthma is a chronic respiratory disease that impairs breathing. Management of asthma presents a significant challenge due to its inherent variability; that is, its symptoms can substantially differ among individuals, thereby complicating the prediction and management of exacerbations. Furthermore, individuals with asthma often have unique triggers that precipitate symptoms or attacks. The identification of these triggers can often prove to be a challenging, and at times, an impractical attempt. To address this, our research proposes a practical, personalized alert system, predicated on individual lung function tests conducted under varying environmental conditions classified by air-quality sensors. To validate this concept, we conducted an observational pilot study involving healthy individuals. We recruited twelve healthy participants and monitored their responses across a broad spectrum of environments, characterized by varying air quality, temperature, and humidity conditions. The lung function for each participant, assessed using peak expiratory flow (PEF) values, was recorded in each of these environments. Our results highlighted substantial variability in pulmonary responses to different environments. Utilizing these insights, we proposed a personalized alarm system that provides real-time air-quality monitoring and issues alerts when environmental conditions may potentially become unfavorable. We also explored the feasibility of employing basic machine learning techniques to predict PEF values in the aforementioned environmental conditions. This proposed system has the potential to empower individuals in actively safeguarding their respiratory health and mitigating discomfort caused by environmental conditions, especially in cases of asthma patients. By enabling timely and personalized interventions, the system aims to provide individuals with the necessary tools to minimize exposure to asthma’s possible triggers.
Asthma is a chronic respiratory disease that impairs breathing. Management of asthma presents a s... more Asthma is a chronic respiratory disease that impairs breathing. Management of asthma presents a significant challenge due to its inherent variability; that is, its symptoms can substantially differ among individuals, thereby complicating the prediction and management of exacerbations. Furthermore, individuals with asthma often have unique triggers that precipitate symptoms or attacks. The identification of these triggers can often prove to be a challenging, and at times, an impractical attempt. To address this, our research proposes a practical, personalized alert system, predicated on individual lung function tests conducted under varying environmental conditions classified by air-quality sensors. To validate this concept, we conducted an observational pilot study involving healthy individuals. We recruited twelve healthy participants and monitored their responses across a broad spectrum of environments, characterized by varying air quality, temperature, and humidity conditions. The lung function for each participant, assessed using peak expiratory flow (PEF) values, was recorded in each of these environments. Our results highlighted substantial variability in pulmonary responses to different environments. Utilizing these insights, we proposed a personalized alarm system that provides real-time air-quality monitoring and issues alerts when environmental conditions may potentially become unfavorable. We also explored the feasibility of employing basic machine learning techniques to predict PEF values in the aforementioned environmental conditions. This proposed system has the potential to empower individuals in actively safeguarding their respiratory health and mitigating discomfort caused by environmental conditions, especially in cases of asthma patients. By enabling timely and personalized interventions, the system aims to provide individuals with the necessary tools to minimize exposure to asthma’s possible triggers.
Dynamic Source Routing (DSR) is an efficient on-demand routing protocol for mobile ad-hoc network... more Dynamic Source Routing (DSR) is an efficient on-demand routing protocol for mobile ad-hoc networks (MANET). It depends on two main procedures: Route Discovery and Route Maintenance. Route discovery is the procedure used at the source of the packets to discover a route to the destination. Route Maintenance is the procedure that discovers link failures and repairs them. Route caching is the sub procedure serviceable to avoid the demand for discovering a route or to reduce route discovery delay before every data packet is sent. The goal of this paper is to evaluate the performance of DSR. Different performance expressions are investigated including, delivery ratio, end to-end delay, and throughput, depending on different cache sizes and different speeds. All of that as a study to develop a new caching strategy as a future work.
Modern wheelchairs, with advanced and robotic technologies, could not reach the life of millions ... more Modern wheelchairs, with advanced and robotic technologies, could not reach the life of millions of disabled people due to their high costs, technical limitations, and safety issues. This paper proposes a gesture-controlled smart wheelchair system with an IoT-enabled fall detection mechanism to overcome these problems. It can recognize gestures using Convolutional Neural Network (CNN) model along with computer vision algorithms and can control the wheelchair automatically by utilizing these gestures. It maintains the safety of the users by performing fall detection with IoT-based emergency messaging systems. The development cost of the overall system is cheap and is lesser than USD 300. Hence, it is expected that the proposed smart wheelchair should be affordable, safe, and helpful to physically disordered people in their independent mobility.
International Journal of Online and Biomedical Engineering (iJOE)
With the massive outbreak of coronavirus (COVID-19) disease, the demand for automatic and quick d... more With the massive outbreak of coronavirus (COVID-19) disease, the demand for automatic and quick detection of COVID-19 has become a crucial challenge for scientists around the world. Many researchers are working on finding an automated and effective system for detecting COVID-19. They have found that computed tomography (CT-scan) and X-ray images of COVID-19 infected patients can provide more accurate and faster results. In this paper, an automated system is proposed named as COV-CTX which can detect COVID-19 from CT-scan and X-ray images. The system consists of three different CNN models: VGG16, VGG16- InceptionV3-ResNet50, and Francois CNN. The models are trained with CT-scan and X-ray images individually to classify COVID-19 and non-COVID patients. Finally, the results of the models are combined to develop a voting ensemble of classifiers to ensure more accurate and precise results. The three models are trained and validated with 9412 CT-scan images (4756 numbers of COVID positive...
Robot navigation in indoor environments has become an essential task for several applications, in... more Robot navigation in indoor environments has become an essential task for several applications, including situations in which a mobile robot needs to travel independently to a certain location safely and using the shortest path possible. However, indoor robot navigation faces challenges, such as obstacles and a dynamic environment. This paper addresses the problem of social robot navigation in dynamic indoor environments, through developing an efficient SLAM-based localization and navigation system for service robots using the Pepper robot platform. In addition, this paper discusses the issue of developing this system in a way that allows the robot to navigate freely in complex indoor environments and efficiently interact with humans. The developed Pepper-based navigation system has been validated using the Robot Operating System (ROS), an efficient robot platform architecture, in two different indoor environments. The obtained results show an efficient navigation system with an aver...
Social robots have the potential to revolutionize the way we interact with technology, providing ... more Social robots have the potential to revolutionize the way we interact with technology, providing a wide range of services and applications in various domains, such as healthcare, education, and entertainment. However, most existing social robotics platforms are operated based on embedded computers, which limits the robot’s capabilities to access advanced AI-based platforms available online and which are required for sophisticated physical human–robot interactions (such as Google Cloud AI, Microsoft Azure Machine Learning, IBM Watson, ChatGPT, etc.). In this research project, we introduce a cloud-based framework that utilizes the benefits of cloud computing and clustering to enhance the capabilities of social robots and overcome the limitations of current embedded platforms. The proposed framework was tested in different robots to assess the general feasibility of the solution, including a customized robot, “BuSaif”, and commercialized robots, “Husky”, “NAO”, and “Pepper”. Our findin...
The area of localization in wireless sensor networks (WSNs) has received considerable attention r... more The area of localization in wireless sensor networks (WSNs) has received considerable attention recently, driven by the need to develop an accurate localization system with the minimum cost and energy consumption possible. On the other hand, machine learning (ML) algorithms have been employed widely in several WSN-based applications (data gathering, clustering, energy-harvesting, and node localization) and showed an enhancement in the obtained results. In this paper, an efficient WSN-based fingerprinting localization system for indoor environments based on a low-cost sensor architecture, through establishing an indoor fingerprinting dataset and adopting four tailored ML models, is presented. The proposed system was validated by real experiments conducted in complex indoor environments with several obstacles and walls and achieves an efficient localization accuracy with an average of 1.4 m. In addition, through real experiments, we analyze and discuss the impact of reference point de...
Emerging Trends in Wireless Sensor Networks [Working Title]
Usually, wireless sensor networks (WSNs) are installed in large areas to monitor various physical... more Usually, wireless sensor networks (WSNs) are installed in large areas to monitor various physical conditions of the environment and forward the collected sensed data to a base station (central node), for instance: gas monitoring, intrusion detection, tracking objects, etc. However, sensor nodes are usually deployed unattended and battery-powered with no external power source. Therefore, WSNs face the challenge of limited energy source available onboard, where packet transmission and sensing functions are the most power consumption factors in WSN. Therefore, to overcome the energy depletion in sensor nodes, it is important to study the energy management issue in WSN. In this chapter, the significance of energy management issue is discussed first, and then the possible energy management strategies for WSN are presented and illustrated.
There is increasing utilization of photovoltaic (PV) grid-connected systems in modern power netwo... more There is increasing utilization of photovoltaic (PV) grid-connected systems in modern power networks. Currently, PV grid-connected systems utilize transformerless inverters that have the advantages of being low cost, low weight, a small size, and highly efficient. Unfortunately, these inverters have an earth leakage current problem due to the absence of galvanic isolation. This phenomenon represents safety and electrical problems for those systems. Recently, the H8 transformerless inverter was introduced to eliminate the earth leakage current. The present study proposes improving the performance of an H8 transformerless inverter using model predictive control (MPC). The inverter was supplied by PV energy and attached to the grid through an LCL filter. During system modeling, the grid weakness was identified. The discrete model of the overall system, including the PV panel, the boost converter, the H8 transformerless inverter, and the controllers, was derived. Then, the introduced H8...
Using mobile sinks to collect information in wireless sensor networks is an interesting area of r... more Using mobile sinks to collect information in wireless sensor networks is an interesting area of research. As a result, a variety of mobility models were proposed by researchers over the years where each mobility model has its own properties that may affect the performance of the network in a way that differs from other models entitled with different properties. In this paper we provide a survey of mobility models that can be used in wireless sensor networks since it is important to provide a classification of the available models. Therefore, several mobility models were reviewed in the proposed work. These models were classified into two main categories namely homogenous and heterogeneous mobility. The goal of the work proposed in this paper is to provide researchers with a clear idea about the available mobility models and their properties. Additionally, we aim to provide researchers with guidelines to help them choose mobility models properly.
paper evaluates the performance of the OLSR pro-active protocol with and without backup routes un... more paper evaluates the performance of the OLSR pro-active protocol with and without backup routes under varying node densities and with different speed movements in the network. Additionally, this paper assists in ascertaining the effect of varying node densities on the connectivity's life between mobile nodes in the network. Hence, it showed the affect of a local recovery mechanism resulted in achieving a significant improvement in network performance by seeking a long life backup path between source and destination for low/high density nodes. Real time applications are required to be supported by mobile ad hoc networks. This is because of the free movement for the mobile nodes from one area to another without any notification via frequent paths. The real time applications traffics are considered a sensitive application, and it is the most affected by failure through the occurrence of delay and loss of packets. It is, therefore, not suitable for use by players. In mobile ad hoc ne...
This paper presents a low-cost system architecture that has been proposed for automatically monit... more This paper presents a low-cost system architecture that has been proposed for automatically monitoring air quality indoors and continuously in real-time. The designed system is in pilot phase where 4 sensor nodes are deployed in indoor environment, and data over 4 weeks has been collected and performance analysis and assessment are performed. Environmental data from sensor nodes are sent through ZigBee communication protocol. The proposed system is low in cost, and achieves low power consumption. Hardware and network architecture are presented in addition to real-world deployment.
Uploads
Books
Papers