arXiv subjects

Ahmad Alsharoa

Publications and source records attributed to Ahmad Alsharoa.

At least 19 recordsLinked to original sources

arXiv2026

LiDAR for Crowd Management: Applications, Benefits, and Future Directions

Light Detection and Ranging (LiDAR) technology offers significant advantages for effective crowd management. This article presents LiDAR technology and highlights its primary advantages over other monitoring technologies, including enhanced privacy, performance in various weather conditions, and precise 3D mapping. We present a general taxonomy of four key tasks in crowd management: crowd detection, counting, tracking, and behavior classification, with illustrative examples of LiDAR applications for each task. We identify challenges and open research directions, including the scarcity of dedicated datasets, sensor fusion requirements, artificial intelligence integration, and processing needs for LiDAR point clouds. This article offers actionable insights for developing crowd management solutions tailored to public safety applications.

cs.CV↗

arXiv2024

Empowering Urban Traffic Management: Elevated 3D LiDAR for Data Collection and Advanced Object Detection Analysis

The 3D object detection capabilities in urban environments have been enormously improved by recent developments in Light Detection and Range (LiDAR) technology. This paper presents a novel framework that transforms the detection and analysis of 3D objects in traffic scenarios by utilizing the power of elevated LiDAR sensors. We are presenting our methodology's remarkable capacity to collect complex 3D point cloud data, which allows us to accurately and in detail capture the dynamics of urban traffic. Due to the limitation in obtaining real-world traffic datasets, we utilize the simulator to generate 3D point cloud for specific scenarios. To support our experimental analysis, we firstly simulate various 3D point cloud traffic-related objects. Then, we use this dataset as a basis for training and evaluating our 3D object detection models, in identifying and monitoring both vehicles and pedestrians in simulated urban traffic environments. Next, we fine tune the Point Voxel-Region-based Convolutional Neural Network (PV-RCNN) architecture, making it more suited to handle and understand the massive volumes of point cloud data generated by our urban traffic simulations. Our results show the effectiveness of the proposed solution in accurately detecting objects in traffic scenes and highlight the role of LiDAR in improving urban safety and advancing intelligent transportation systems.

cs.CV↗

arXiv2024

Remote Breathing Monitoring Using LiDAR Technology

Breathing monitoring is crucial in healthcare for early detection of health issues, but traditional methods face challenges like invasiveness, privacy concerns, and limited applicability in daily settings. This paper introduces light detection and ranging (LiDAR) sensors as a remote, privacy-respecting alternative for monitoring breathing metrics, including inhalation/exhalation patterns, respiratory rates, breath depth, and detecting breathlessness. We highlight LiDARs ability to function across various postures, presenting empirical evidence of its accuracy and reliability. Our findings position LiDAR as an innovative solution in breathing monitoring, offering significant advantages over conventional methods.

eess.IV↗

arXiv2024

Leveraging 3D LiDAR Sensors to Enable Enhanced Urban Safety and Public Health: Pedestrian Monitoring and Abnormal Activity Detection

The integration of Light Detection and Ranging (LiDAR) and Internet of Things (IoT) technologies offers transformative opportunities for public health informatics in urban safety and pedestrian well-being. This paper proposes a novel framework utilizing these technologies for enhanced 3D object detection and activity classification in urban traffic scenarios. By employing elevated LiDAR, we obtain detailed 3D point cloud data, enabling precise pedestrian activity monitoring. To overcome urban data scarcity, we create a specialized dataset through simulated traffic environments in Blender, facilitating targeted model training. Our approach employs a modified Point Voxel-Region-based Convolutional Neural Network (PV-RCNN) for robust 3D detection and PointNet for classifying pedestrian activities, significantly benefiting urban traffic management and public health by offering insights into pedestrian behavior and promoting safer urban environments. Our dual-model approach not only enhances urban traffic management but also contributes significantly to public health by providing insights into pedestrian behavior and promoting safer urban environment.

cs.CV↗

arXiv2024

The Role of Intelligent Transportation Systems and Artificial Intelligence in Energy Efficiency and Emission Reduction

Despite the technological advancements in the transportation sector, the industry continues to grapple with increasing energy consumption and vehicular emissions, which intensify environmental degradation and climate change. The inefficient management of traffic flow, the underutilization of transport network interconnectivity, and the limited implementation of artificial intelligence (AI)-driven predictive models pose significant challenges to achieving energy efficiency and emission reduction. Thus, there is a timely and critical need for an integrated, sophisticated approach that leverages intelligent transportation systems (ITSs) and AI for energy conservation and emission reduction. In this paper, we explore the role of ITSs and AI in future enhanced energy and emission reduction (EER). More specifically, we discuss the impact of sensors at different levels of ITS on improving EER. We also investigate the potential networking connections in ITSs and provide an illustration of how they improve EER. Finally, we discuss potential AI services for improved EER in the future. The findings discussed in this paper will contribute to the ongoing discussion about the vital role of ITSs and AI applications in addressing the challenges associated with achieving energy savings and emission reductions in the transportation sector. Additionally, it will provide insights for policymakers and industry professionals to enable them to develop policies and implementation plans for the integration of ITSs and AI technologies in the transportation sector.

cs.CY↗

arXiv2021

Facilitating Satellite-Airborne-Balloon-Terrestrial Integration for Dynamic and Infrastructure-less Networks

This magazine investigates the potential enhancement of the data throughput of ground users by integrating ground base stations (GBS) with air stations, such as balloon, airborne, and satellite. The objective is to establish dynamic bi-directional wireless services (i.e., uplink and downlink) for ground users in congested and remote areas. The proposed integration involves satellite, high-altitude platforms (HAPs), and tethered balloons (TBs) in the exosphere, stratosphere, and troposphere, respectively, for better altitude reuse coupled with emerging optical or other high-frequency directional transceivers. This will lead to a significant enhancement in scarce spectrum aggregate efficiency. However, the air stations deployment and resource managements in this integrated system faces difficulties. This article tackles resource management challenges by (i) providing wireless services to ground users in remote areas and connecting them with metropolitan and rural areas and (ii) employing HAPs equipped with free-space-optical communication modules as back-hauling backbones. Finally, we illustrate some numerical results to show the benefit of our proposed integrated system.

eess.SY↗

arXiv2020

MirrorVLC: Optimal Mirror Placement for Multi-Element VLC Networks

Visible Light Communication (VLC) is a rapidly growing technology which can supplement the current radio frequency (RF) based wireless communication systems. VLC can play a huge part in solving the ever-increasing problem of spectrum scarcity because of the growing availability of Light Emitting Diodes (LEDs). One of the biggest advantages of VLC over other communication systems is that it can provide illumination and data communication simultaneously without needing any extra deployment. Although it is essential to provide data rate at a blazing speed to all the users nowadays, maintaining a satisfactory level in the distribution of lighting is also important. In this paper, we present a novel approach of using mirrors to enhance the illumination uniformity and throughput of an indoor multi-element VLC system architecture. In this approach, we improve the Signal-to-Interference plus Noise Ratio (SINR) of the system and overall illumination uniformity of the room by redirecting the reflected LED beams on the walls to darker spots with the use of mirrors. We formulate a joint optimization problem focusing on maximization of the SINR while maintaining a reasonable illumination uniformity across the room. We propose a two-stage solution of the optimization problem with optimization of illumination in the first stage and SINR at the second stage. We propose three different heuristic solutions for the second stage and analyze the performance of them, which demonstrates the advantages of each heuristic for different possible scenarios. We also show that about threefold increase in average illumination and fourfold increase in average throughput can be achieved when the mirror placement is applied which is a significant performance improvement.

cs.NI↗

arXiv2020

A Latency-Aware Task Offloading in Mobile Edge Computing Network for Distributed Elevated LiDAR

Recently, elevated LiDAR (ELiD) has been proposed as an alternative to local LiDAR sensors in autonomous vehicles (AV) because of the ability to reduce costs and computational requirements of AVs, reduce the number of overlapping sensors mapping an area, and to allow for a multiplicity of LiDAR sensing applications with the same shared LiDAR map data. Since ELiDs have been removed from the vehicle, their data must be processed externally in the cloud or on the edge, necessitating an optimized backhaul system that allocates data efficiently to compute servers. In this paper, we address this need for an optimized backhaul system by formulating a mixed-integer programming problem that minimizes the average latency of the uplink and downlink hop-by-hop transmission plus computation time for each ELiD while considering different bandwidth allocation schemes. We show that our model is capable of allocating resources for differing topologies, and we perform a sensitivity analysis that demonstrates the robustness of our problem formulation under different circumstances.

eess.SP↗

arXiv2019

D3S: A Framework for Enabling Unmanned Aerial Vehicles as a Service

In this paper, we consider the use of UAVs to provide wireless connectivity services, for example after failures of wireless network components or to simply provide additional bandwidth on demand, and introduce the concept of UAVs as a service (UaaS). To facilitate UaaS, we introduce a novel framework, dubbed D3S, which consists of four phases: demand, decision, deployment, and service. The main objective of this framework is to develop efficient and realistic solutions to implement these four phases. The technical problems include determining the type and number of UAVs to be deployed, and also their final locations (e.g., hovering or on-ground), which is important for serving certain applications. These questions will be part of the decision phase. They also include trajectory planning of UAVs when they have to travel between charging stations and deployment locations and may have to do this several times. These questions will be part of the deployment phase. The service phase includes the implementation of the backbone communication and data routing between UAVs and between UAVs and ground control stations.

cs.NI↗

arXiv2019

Resource and Placement Optimization for Multiple UAVs using Backhaul Tethered Balloons

This paper studies the improvement of the achievable end-to-end data rate of ground users assisted with unmanned aerial vehicles (UAVs) and tethered balloons (TBs). The goal is to maximize the end-to-end throughput of a network suffering from the absence of terrestrial infrastructure. First, we solve an integer linear programming problem to optimize the associations. Then, we solve the UAVs transmit powers optimally by converting the problem into a convex one. Subsequently, an efficient algorithm is proposed to optimize the UAVs placement. Finally, our mathematical formalism is illustrated with some selected numerical results that show the advantages provided by our proposed scheme.

cs.NI↗

arXiv2019

Facilitating Satellite-Airborne-Terrestrial Integration for Dynamic and Infrastructure-less Networks

This paper studies the potential improvement in the achievable data rate available to ground users by integrating satellite, airborne, and terrestrial networks. The goal is to establish dynamic wireless services in remote or infrastructure-less areas. This integration uses high-altitude platforms in the exosphere, stratosphere, and troposphere for better altitude reuse coupled with emerging optical or other high-frequency directional transceivers. Hence they offer a significant increases in the scarce spectrum aggregate efficiency. However, managing resource allocation with deployment in this integrated system still has some difficulties. This paper aims to tackle resource management challenges by (i) providing wireless services to ground users in remote areas and connecting them with metropolitan and rural areas, (ii) employing high-altitude platforms (HAPs) equipped with free-space-optical communication modules for back-hauling backbone. Finally, we show how our results illustrate the advantages of using the proposed scheme.

cs.NI↗

arXiv2019

Improvement of the Global Connectivity using Integrated Satellite-Airborne-Terrestrial Networks with Resource Optimization

In this paper, we propose a novel wireless scheme that integrates satellite, airborne, and terrestrial networks aiming to support ground users. More specifically, we study the enhancement of the achievable users' throughput assisted with terrestrial base stations, high altitude platforms (HAPs), and satellite station. The goal is to optimize the resource allocations and the HAPs locations in order to maximize the users' throughput. In this context, we propose to solve the optimization problem in two stages; first a short-term stage and then a long-term stage. In the short-term stage, we start by proposing a near optimal solution and low complexity solution to solve the associations and power allocations. In the first solution, we formulate and solve a binary linear optimization problem to find the best associations and then using Taylor expansion approximation to optimally determine the power allocations. While in the second solution, we propose a low complexity approach based on frequency partitioning technique to solve the associations and power allocations. One the other hand, in the long-term stage, we optimize the locations of the HAPs by proposing an efficient algorithm based on a recursive shrink-and-realign process. Finally, selected numerical results show the advantages provided by our proposed optimization scheme.

cs.NI↗

arXiv2019

Energy Efficient D2D Communications Using Multiple UAV Relays

In this paper, we propose a novel optimization model for multiple Unmanned Areal Vehicles (UAVs) working as relays and helping Device-to-Device (D2D) communications at the same time. The goal of the UAVs is to operate in an energy-efficient manner while not only optimizing the available bandwidth and power allocations of the D2D links, but also act as relays when needed to maintain the communication between ground users. We formulate an optimization problem that maximizes the energy-efficient utility while respecting the resource availability including the UAVs' energy consumption, UAV-user association, and trajectory constraints. Due to the non-convexity of the problem, we propose to solve it in three steps using Taylor series approximation to optimize the power and the bandwidth, and use a heuristic algorithm for optimizing the UAVs' trajectory and UAV-user associations.

cs.NI↗

arXiv2019

Spatial and Temporal Management of Cellular HetNets with Multiple Solar Powered Drones

This paper proposes an energy management framework for cellular heterogeneous networks (HetNets) supported by dynamic solar powered drones. A HetNet composed of a macrocell base station (BS), micro cell BSs, and drone small cell BSs are deployed to serve the networks' subscribers. The drones can land at pre-planned locations defined by the mobile operator and at the macrocell BS site where they can charge their batteries. The objective of the framework is to jointly determine the optimal trips of the drones and the MBSs that can be safely turned off in order to minimize the total energy consumption of the network. This is done while considering the cells' capacities and the minimum receiving power guaranteeing successful communications. To do so, an integer linear programming problem is formulated and optimally solved for three cases based on the knowledge level about future renewable energy statistics of the drones. A low complex relaxed solution is also developed. Its performances are shown to be close to those of the optimal solutions. However, the gap increases as the network becomes more congested. Numerical results investigate the performance of the proposed drone-based approach and show notable improvements in terms of energy saving and network capacity.

cs.NI↗

arXiv2018

Short-term and Long-term Cell Outage Compensation Using UAVs in 5G Networks

The use of Unmanned Aerial Vehicles (UAVs) has gained interest in wireless networks for its many uses and advantages such as rapid deployment and multi-purpose functionality. This is why wide deployment of UAVs has the potential to be integrated in the upcoming 5G standard. They can be used as flying base-stations, which can be deployed in case of ground Base-Stations (GBSs) failures. Such failures can be short-term or long-term. Based on the type and duration of the failure, we propose a framework that uses drones or helikites to mitigate GBS failures. Our proposed short-term and long-term cell outage compensation framework aims to mitigate the effect of the failure of any GBS in 5G networks. Within our framework, outage compensation is done with the assistance of sky BSs (UAVs). An optimization problem is formulated to jointly minimize communication power of the UAVs and maximize the minimum rates of the Users' Equipment (UEs) affected by the failure. Also, the optimal placement of the UAVs is determined. Simulation results show that the proposed framework guarantees the minimum quality of service for each UE in addition to minimizing the UAVs' consumed energy.

eess.SP↗

arXiv2018

Optimization of a Power Splitting Protocol for Two-Way Multiple Energy Harvesting Relay System

Energy harvesting (EH) combined with cooperative communications constitutes a promising solution for future wireless technologies. They enable additional efficiency and increased lifetime to wireless networks. This paper investigates a multiple-relay selection scheme for an EH-based two-way relaying (TWR) system. All relays are considered as EH nodes that harvest energy from renewable energy and radio frequency (RF) sources. Some of them are selected to forward data to the destinations. The power splitting (PS) protocol, by which the EH node splits the input RF signal into two components for EH and information transmission, is adopted at the relay nodes. The objective is to jointly optimize i) the set of selected relays, ii) their PS ratios, and iii) their transmit power levels in order to maximize data rate-based utilities over multiple coherent time slots. A joint-optimization solution based on geometric programming (GP) and binary particle swarm optimization is proposed to solve non-convex problems for two utility functions reflecting the level of fairness in the TWR transmission. Numerical results illustrate the system's behavior versus various parameters and show that the performance of the proposed scheme is very close to that of the optimal branch-and-bound method and that GP outperforms the dual problem-based method.

cs.NI↗

arXiv2018

Multi-band RF Energy and Spectrum Harvesting in Cognitive Radio Networks

This paper investigates a multi-band harvesting (EH) schemes under cognitive radio interweave framework. All secondary users are considered as EH nodes that are allowed to harvest energy from multiple bands of Radio Frequency (RF) sources. A win-win framework is proposed, where SUs can sense the spectrum to determine whether the spectrum is busy, and hence they may harvest from RF energy, or if it is idle, and hence they can use it for transmission. Only a subset of the SUs can sense in order to reduce sensing energy, and then machine learning is used to characterize areas of harvesting and spectrum usage. We formulate an optimization problem that jointly optimize number of sensing samples and sensing threshold in order to minimize the sensing time and hence maximize the amount of energy harvested. A near optimal solution is proposed using Geometric Programming (GP) to optimally solve the problem in a time-slotted period. Finally, an energy efficient approach based on multi-class Support Vector Machine (SVM) is proposed by involving only training SUs instead of all SUs.

cs.NI↗

arXiv2018

Hybrid Cell Outage Compensation in 5G Networks: Sky-Ground Approach

Unmanned Aerial Vehicles (UAVs) enabled communications is a novel and attractive area of research in cellular communications. It provides several degrees of freedom in time, space and it can be used for multiple purposes. This is why wide deployment of UAVs has the potential to be integrated in the upcoming 5G standard. In this paper, we present a novel cell outage compensation (COC) framework to mitigate the effect of the failure of any outdoor Base Station (BS) in 5G networks. Within our framework, the outage compensation is done with the assistance of sky BSs (UAVs) and Ground BSs (GBSs). An optimization problem is formulated to jointly minimize the energy of the Drone BSs (DBSs) and GBSs involved in the healing process which accordingly will minimize the number of DBSs and determine their optimal 2D positions. In addition, the DBSs will mainly heal the users that the GBS cannot heal due to capacity issues. Simulation results show that the proposed hybrid approach outperforms the conventional COC approach. Moreover, all users receive the minimum quality of service in addition to minimizing the UAVs' consumed energy.

cs.NI↗