Internet of X-Enabled Intelligent Unmanned Aerial Vehicles Security for Hyper-connected Societies

Almalki, Faris A.; Alsamhi, Saeed Hamood; Angelides, Marios C.

doi:10.1007/978-981-19-1960-2_5

Cited by 4 publications

(1 citation statement)

References 51 publications

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“…Temporary event or short-term large-scale events (e.g., sports, social, cultural) require an extra measure of planning, coordination, and management; thus, they can heavily benefit from CubeSat and its wide range applications such as commercial applications (e.g., telecommunications, remote sensing, highresolution aerial imagery, ship tracking and monitoring). Additionally, such small satellites help in more domains like [7][8][9]:…”

Section: Introductionmentioning

confidence: 99%

Developing CubeSat and AI Framework for Crowd Management Case of Short-Term Large-Scale Events

Almalki¹,

Mutawi²,

Turkistani³

et al. 2022

Adv. sci. technol. eng. syst. j.

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Many consequences can be resulted in mismanagement of crowd, which might get people injured or even lose their lives. Thus, crowd management helps in controlling overcrowded areas during events, and allowing authorities to monitor, manage and reduce incidents. Space science and technology have made huge leap in crowd management, let alone when this technology integrated with Artificial intelligent. Hence, space-based systems like CubeSats are seen as the best approach to help monitoring and providing various reasons. For instance, wide coverage footprint, collect specific area data simultaneously, assist in aerial photography, as well as providing wide range of wireless communications services that can integrate appropriately with AI and wearable devices. This work aims to design a CubeSat vehicle to manage crowds during short-term, large-scale events. The proposed system is, also, coupled with AI framework along with the camera and wearable devices to monitor the crowds' performance continuously by relying on two aspects: Firstly, aerial imaging (e.g., photos and videos). Secondly, using wearable devices that can be worn to monitor vital signs of crowds. Moreover, the proposed system can independently relieve congestion, as well as notify the ground controller of all problems to take further actions. The obtained results confirm that the proposed innovative solution is for crowd management with accuracy reached 95%, and MSE equal to 0.049.

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Section: Introductionmentioning

confidence: 99%

Developing CubeSat and AI Framework for Crowd Management Case of Short-Term Large-Scale Events

Almalki¹,

Mutawi²,

Turkistani³

et al. 2022

Adv. sci. technol. eng. syst. j.

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A synthesis of machine learning and internet of things in developing autonomous fleets of heterogeneous unmanned aerial vehicles for enhancing the regenerative farming cycle

Almalki,

Angelides

2024

Computing

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The use of Unmanned Aerial Vehicles (UAVs) for agricultural monitoring and management offers additional advantages over traditional methods, ranging from cost reduction to environmental protection, especially when they utilize Machine Learning (ML) methods, and Internet of Things (IoT). This article presents an autonomous fleet of heterogeneous UAVs for use in regenerative farming the result of a synthesis of Deep Reinforcement Learning (DRL), Ant Colony Optimization (ACO) and IoT. The resulting aerial framework uses DRL for fleet autonomy and ACO for fleet synchronization and task scheduling inflight. A 5G Multiple Input Multiple Output-Long Range (MIMO-LoRa) antenna enhances data rate transmission and link reliability. The aerial framework, which has been originally prototyped as a simulation to test the concept, is now developed into a functional proof-of-concept of autonomous fleets of heterogeneous UAVs. For assessing performance, the paper uses Normalized Difference Vegetation Index (NDVI), Mean Squared Error (MSE) and Received Signal Strength Index (RSSI). The 5G MIMO-LoRa antenna produces improved results with four key performance indicators: Reflection Coefficient (S11), Cumulative Distribution Functions (CDF), Power Spectral Density Ratio (Eb/No), and Bit Error Rate (BER).

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Leveraging Machine Learning for Cybersecurity Resilience in Industry 4.0: Challenges and Future Directions

Yu,

Shvetsov,

Hamood Alsamhi

2024

IEEE Access

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Internet of X-Enabled Intelligent Unmanned Aerial Vehicles Security for Hyper-connected Societies

Cited by 4 publications

References 51 publications

Developing CubeSat and AI Framework for Crowd Management Case of Short-Term Large-Scale Events

Developing CubeSat and AI Framework for Crowd Management Case of Short-Term Large-Scale Events

A synthesis of machine learning and internet of things in developing autonomous fleets of heterogeneous unmanned aerial vehicles for enhancing the regenerative farming cycle

Leveraging Machine Learning for Cybersecurity Resilience in Industry 4.0: Challenges and Future Directions

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