1,720,984 research outputs found
An Optimization Approach of IoD Deployment for Optimal Coverage Based on Radio Frequency Model
No full textRecently, Internet of Drones (IoD) has garnered significant attention due to its widespread applications. However, deploying IoD for area coverage poses numerous limitations and challenges. These include interference between neighboring drones, the need for directional antennas, and altitude restrictions for drones. These challenges necessitate the development of efficient solutions. This research paper presents a cooperative decision-making approach for an efficient IoD deployment to address these challenges effectively. The primary objective of this study is to achieve an efficient IoD deployment strategy that maximizes the coverage region while minimizing interference between neighboring drones. In deployment problem, the interference increases as the number of deployed drones increases, resulting in bad quality of communication. On the other hand, deploying a few drones cannot satisfy the coverage demand. To accomplish this, an enhanced version of a concise population-based meta-heuristic algorithm, namely Improved Particle Swarm Optimization (IPSO), is applied. The objective function of IPSO is defined based on the coverage probability, which is primarily influenced by the characteristics of the antennas and drone altitude. A radio frequency (RF) model is derived to evaluate the coverage quality, considering both Line of Sight (LOS) and Non-Line of Sight (NLOS) down-link coverage probabilities for ground communication. It is assumed that each drone is equipped with a directional antenna to optimize coverage in a given region. Extensive simulations are conducted to assess the effectiveness of the proposed approach. Results demonstrate that the proposed method achieves maximum coverage with minimum transmission power. Furthermore, a comparison is made against Collaborative Visual Area Coverage Approach (CVACA), and a game-based approach in terms of coverage quality and convergence speed. The simulation results reveal that our approach outperforms both CVACA and the gamebased schemes in terms of coverage and convergence speed. Comparisons validate the superiority of our approach over existing methods. To assess the robustness of the proposed RF model, we have considered two distinct ranges of noise: range1 spanning from -120 to -90 dBm, and range2 spanning from -90 to -70 dBm for different numbers of UAVs. In summary, this research presents a cooperative decision-making approach for efficient IoD deployment to address the challenges associated with area coverage and achieves an optimal coverage with minimal interference.This research was funded by Project Number INML2104 under the Interdisciplinary Center of Smart Mobility and Logistics at King Fahd University of Petroleum and Minerals. This study also was supported by the Special Research Fund BOF23KV17.
Authors at KFUPM would like to acknowledge the support received under University Funded Grant # INML2300. The author at Hasselt University acknowledges the support received from Special Research Fund (BOF) under Grant # BOF23KV17
IoD swarms collision avoidance via improved particle swarm optimization
No full textDrones flights have been investigated widely. In the presence of high density and complex missions, collision avoidance among swarm of drones and with environment obstacles becomes a challenging task and indispensable. This paper aims to enhance the optimality and rapidity of three dimensional IoD path generation by improving the particle swarm optimization (PSO) algorithm. The improvements include using chaos map logic to initialize the population of PSO. Also, adaptive mutation is utilized to balance local and global search. Then, the inactive particles are replaced by new fresh particles to push the solution toward global optimal. Furthermore, Monte Carlo simulation is carried out and the results are compared with slandered PSO and with recent work CIPSO. The results exhibit significant improvement in convergence speed as well as optimal solution which prove the ability of proposed method to generate safety path for IoD formation without collision with terrain obstacle and among drones.The authors would like to acknowledge the support of the department of the computer engineering at King Fahd University of Petroleum and Minerals for this work.Ahmed, G (corresponding author), King Fahd Univ Petr & Minerals, Comp Engn Dept, Dhahran, Saudi Arabia.
[email protected]; [email protected]; [email protected]; [email protected]
UAV-enabled intelligent traffic policing and emergency response handling system for the smart city
No full textAs modern cities expand and develop, the resultant increase in population density gives rise to the need for smart solutions to cope with the demands applied to the infrastructure of the city. In this paper, we investigate the shortcomings of traffic policing and emergency response handling systems; propose an intelligent, autonomous UAV-enabled solution; and describe the system in a simulated environment. Several scenarios of traffic monitoring and policing system are considered in the simulation: traffic light violations and accident detection, mobile speeding traps and automated notification, congestion detection and traffic rerouting, flagged stolen vehicles/pending arrest warrants and vehicle tracking using UAVs, and autonomous emergency response handling systems. Furthermore, smart city infrastructure enable intelligent handling of emergencies by providing traffic light prioritization for ground emergency response units to reduce delay for patient care, automated physical bollard on routes with congested points due to accidents or hazards, first responder support UAV units-medical supplies UAV, fire fighting UAV to combat or control small fires, and numerous other benefits. Lastly, we present the results of the simulated system and discuss our findings.This study received financial support from the Special Research Fund (BOF) of Hasselt University, Belgium.
Authors would like to thank the Department of Computer Engineering, King Fahd University of Petroleum and Minerals for their support in this research.Beg, A (reprint author), King Fahd Univ Petr & Minerals, Dept Comp Engn, Az Zahran, Saudi Arabia.
[email protected]; [email protected]; [email protected]; [email protected]
EATDDS: Energy-aware middleware for wireless sensor and actuator networks
No full textAccording to Object Management Group organization, Data Distribution Service (DDS) middleware is the leading technology for Industrial Internet of Things (lIoT). Therefore, using DDS-based middleware for Wireless Sensor/Actuator Networks (WSAN) will extremely ease the development and integration of WSAN applications into IloT, which has an effective impact on improving the productivity and saving the cost. However, applying such technology over WSAN significantly affects the energy consumption. In this work, an energy-aware middleware for WSAN is developed based on DDS standard, which is called EATDDS. Furthermore, developing this middleware leads to a major enhancement into TOSSIM simulator; in which an Online Energy Model (OEM) is developed to make TOSSIM capable of developing and testing energy-aware protocols. The model is validated by comparing it against POWERTOSSIM. Our results show that EATDDS is efficient and can be accommodated with limited system resources. (C) 2019 Elsevier B.V. All rights reserved.The authors would like to thank King Fand University of Petroleum and Minerals, Saudi Arabia for the support for this work
A Lightweight Intrusion Detection System Using Convolutional Neural Network and Long Short-Term Memory in Fog Computing
No full textThe Internet of Things (IoT) links various devices to digital services and significantly improves the quality of our lives. However, as IoT connectivity is growing rapidly, so do the risks of network vulnerabilities and threats. Many interesting Intrusion Detection Systems (IDSs) are presented based on machine learning (ML) techniques to overcome this problem. Given the resource limitations of fog computing environments, a lightweight IDS is essential. This paper introduces a hybrid deep learning (DL) method that combines convolutional neural networks (CNN) and long short-term memory (LSTM) to build an energy-aware, anomaly-based IDS. We test this system on a recent dataset, focusing on reducing overhead while maintaining high accuracy and a low false alarm rate. We compare CICIoT2023, KDD-99 and NSL-KDD datasets to evaluate the performance of the proposed IDS model based on key metrics, including latency, energy consumption, false alarm rate and detection rate metrics. Our findings show an accuracy rate over 92% and a false alarm rate below 0.38%. These results demonstrate that our system provides strong security without excessive resource use. The practicality of deploying IDS with limited resources is demonstrated by the successful implementation of IDS functionality on a Raspberry Pi acting as a Fog node. The proposed lightweight model, with a maximum power consumption of 6.12 W, demonstrates its potential to operate effectively on energy-limited devices such as low-power fog nodes or edge devices. We prioritize energy efficiency while maintaining high accuracy, distinguishing our scheme from existing approaches. Extensive experiments demonstrate a significant reduction in false positives, ensuring accurate identification of genuine security threats while minimizing unnecessary alerts.Funding Statement:
This work was supported by the interdisciplinary center of smart mobility and logistics at King Fahd University of Petroleum and Minerals (Grant number INML2400).
Acknowledgement:
The authors would like to acknowledge the support of the Computer Engineering Department at King Fahd University of Petroleum and Mineral for this work
3D simulation model for IoD-to-vehicles communication in IoD-assisted VANET
No full textVehicle ad hoc networks (VANETs) have gradually emerged to enhance transportation information, entertainment, safety, and other services. However, such infrastructures have certain limitations, causing intermittent network disconnection. Further, in urban areas, terrain heights act as obstacles and hinder or attenuate transmitted signals. In this study, we propose a dynamic 3D internet of drones collaborative communication approach for efficient VANET-assistance (3DIoDAV) by integrating the IoD network and VANET to support terrestrial communication. We model IoD locations as an optimization problem to optimize the IoD nodes in three-dimensional terrain. Improved particle swarm optimization is used to optimally deploy IoD nodes in 3D terrain for minimizing the number of isolated vehicles. The proposed approach considers the terrain profile influence on communication. Therefore, we propose a 3D propagation model for efficient IoD-to-vehicle (IoD2V) communication in 3D space. Experiments are performed based on the received signal from ground vehicles to examine the performance of the proposed model and the 3DIoDAV approach. Simulation results show different behaviors of IoD nodes in two-dimensional (2D) and 3D scenarios. Comparison with 2D VANET-assisted and IoDAV approaches demonstrates the proposed 3DIoDAV approach's ability to detect terrain obstacles, which guarantees the dispatching of IoD nodes into the most appropriate locations in 3D space, thereby minimizing the impact of terrain obstacles on communication.The authors declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by (the interdisciplinary center of smart mobility and
logistics at King Fahd University of Petroleum and Minerals) [Grant number (INML2033)]. This study was also supported by the Special Research Fund (BOF) number BOF23KV17.
The authors acknowledge the support project number INML2104 under the interdisciplinary center of smart mobility and logistics and the computer engineering department at King Fahd University of Petroleum and Minerals for this study
A Novel Approach for Efficient Management of Data Lifespan of IoT Devices
No full textWireless sensor networks (WSNs) are mainly data-driven networks adopted to improve the Internet of Things (IoT) in terms of data throughput, energy efficiency, and self-management. Improving the data lifespan of WSN impacts the performance of the IoT. Achieving data reliability in applications of WSNs deployed in harsh environments is challenging due to the extreme constraints in resources of sensor nodes (SNs). Motivated by the inexpensive infrastructure of WSNs, a number of distributed storage systems have been proposed focusing on achieving data survivability rather than network reliability. In this article, we focus on data storage at the things layer (wireless sensors). We evaluate the performance of a number of distributed data storage systems (DDSSs) over WSN running over the ZigBee MAC protocol. Based on our findings, we introduce a new efficient-energy data dissemination scheme called data survivability with energy efficiency (DSwEE) that outperforms the existing schemes. We compare DSwEE against two prominent protocols in data storage, namely, decentralized erasure code for data survivability (DEC-DS) and decentralized erasure code encode-and-disseminate (DEC-EaD). Results show that DSwEE achieves better performance than both DEC-DS and DEC-EaD in terms of the energy consumption and data recoverability for localized failures, which improves the lifespan of the network.This work was supported in part by the Department of Computer Engineering, King Fahd University of Petroleum and Minerals; in part by Najran University; and in part by the Special Research Fund (BOF) of Hasselt University, Belgium.Sheltami, T (corresponding author), King Fahd Univ Petr & Minerals, Dept Comp Engn, Dhahran 31261, Saudi Arabia.
[email protected]; [email protected]; [email protected]; [email protected]
Energy-Efficient UAVs Coverage Path Planning Approach
No full textUnmanned aerial vehicles (UAVs), commonly known as drones, have drawn significant consideration thanks to their agility, mobility, and flexibility features. They play a crucial role in modern reconnaissance, inspection, intelligence, and surveillance missions. Coverage path planning (CPP) which is one of the crucial aspects that determines an intelligent system's quality seeks an optimal trajectory to fully cover the region of interest (ROI). However, the flight time of the UAV is limited due to a battery limitation and may not cover the whole region, especially in large region. Therefore, energy consumption is one of the most challenging issues that need to be optimized. In this paper, we propose an energy-efficient coverage path planning algorithm to solve the CPP problem. The objective is to generate a collision-free coverage path that minimizes the overall energy consumption and guarantees covering the whole region. To do so, the flight path is optimized and the number of turns is reduced to minimize the energy consumption. The proposed approach first decomposes the ROI into a set of cells depending on a UAV camera footprint. Then, the coverage path planning problem is formulated, where the exact solution is determined using the CPLEX solver. For small-scale problems, the CPLEX shows a better solution in a reasonable time. However, the CPLEX solver fails to generate the solution within a reasonable time for large-scale problems. Thus, to solve the model for large-scale problems, simulated annealing for CPP is developed. The results show that heuristic approaches yield a better solution for large-scale problems within a much shorter execution time than the CPLEX solver. Finally, we compare the simulated annealing against the greedy algorithm. The results show that simulated annealing outperforms the greedy algorithm in generating better solution quality.This research was funded by Project Number INML2104 under the InterdisciPlinary Center of Smart Mobility and Logistics, KFUPM.
The authors would like to acknowledge the support of the Interdisciplinary Center of Smart Mobility and Logistics, and the Department of Computer Engineering at King Fahd University of Petroleum and Minerals for the support of this research
Energy-Efficient Internet of Drones Path-Planning Study Using Meta-Heuristic Algorithms
No full textThe increasing popularity of unmanned aerial vehicles (UAVs), commonly known as drones, in various fields is primarily due to their agility, quick deployment, flexibility, and excellent mobility. Particularly, the Internet of Drones (IoD)-a networked UAV system-has gained broad-spectrum attention for its potential applications. However, threat-prone environments, characterized by obstacles, pose a challenge to the safety of drones. One of the key challenges in IoD formation is path planning, which involves determining optimal paths for all UAVs while avoiding obstacles and other constraints. Limited battery life is another challenge that limits the operation time of UAVs. To address these issues, drones require efficient collision avoidance and energy-efficient strategies for effective path planning. This study focuses on using meta-heuristic algorithms, recognized for their robust global optimization capabilities, to solve the UAV path-planning problem. We model the path-planning problem as an optimization problem that aims to minimize energy consumption while considering the threats posed by obstacles. Through extensive simulations, this research compares the effectiveness of particle swarm optimization (PSO), improved PSO (IPSO), comprehensively improved PSO (CIPSO), the artificial bee colony (ABC), and the genetic algorithm (GA) in optimizing the IoD's path planning in obstacle-dense environments. Different performance metrics have been considered, such as path optimality, energy consumption, straight line rate (SLR), and relative percentage deviation (RPD). Moreover, a nondeterministic test is applied, and a one-way ANOVA test is obtained to validate the results for different algorithms. Results indicate IPSO's superior performance in terms of IoD formation stability, convergence speed, and path length efficiency, albeit with a longer run time compared to PSO and ABC.The authors would like to acknowledge the support of the Computer Engineering Department at King Fahd University of Petroleum and Minerals for the support of this work
Evaluating the Impacts of Autonomous Vehicles’ Market Penetration on a Complex Urban Freeway during Autonomous Vehicles’ Transition Period
No full textAutonomous vehicles (AVs) have been a rapidly emerging phenomenon in recent years, with some automated features already available in vehicles. AVs are expected to potentially revolutionize the existing inefficient state of urban transportation and be a step closer to environmental sustainability. This study focuses on simulation modeling in assessing the potential effects of autonomous vehicles (AVs) and on mobility and safety by developing a framework model based on traffic microsimulation for a real network located in Al-Madinah, Saudi Arabia. The market penetration rates (MPRs) will not reach 100% in the near future; instead, penetration will progressively increase. As a result, in our study, we investigated the potential effect of AV technology in five different AV market penetration rates: 0% (baseline), 25%, 50%, 75%, and 100%. The results suggest that Avs significantly improve the network’s safety and operational performance at high penetration rates. Specifically, estimated vehicle delays decreased by 26%, 34.4%, 63.7%, and 74.2% for 25%, 50%, 75%, and 100% AV penetration rates, respectively. Finally, we think this study will help decisionmakers over in the long-term in their attempts to achieve sustainable development through the optimal integration of innovative and novel technologies
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