Artificial Intelligence Autonomous Unmanned Aerial Vehicle (UAV) System for Remote Sensing in Security Surveillance

Matthew, Ugochukwu Okwudili; Kazaure, Jazuli S.; Onyebuchi, Amaonwu; Daniel, O.; Muhammed, Ibrahim; Okafor, Nwamaka U.

doi:10.1109/cybernigeria51635.2021.9428862

Cited by 22 publications

(7 citation statements)

References 29 publications

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“…However, there exist several risk factors in incorporating unmanned aerial vehicles (Drones systems) into the main stream medical consumable workplace and logistics delivery management ecosystem. Taking into account of every unforeseeable circumstances and admitting that risk is an aspect of administration in mounting unmanned aerial vehicle program [38]. Therefore, comprehensive attention should be given in ensuring that every mandatory steps are followed in escaping every avoidable risk and demonstrating support for safer environment in ensuring that the program succeed.…”

Section: Methodsmentioning

confidence: 99%

IoT Drone Technology Integration in Medical Logistics Delivery

Taiwo¹,

Prisca²,

Matthew³

et al. 2022

SJPH

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Drone technology has linked up several opportunities to enable direct connection with the medical consumable donors to receive request through mobile phone communication and deliver medical consumables via Drone device. The recent development in autonomous air navigation and implementation of Drones into mainstream geographical ecosystem have been recognized for adoption in healthcare emergency responses. At the center of the innovation is the Internet of Things (IoT) technology which had necessitated advanced mission specification for Drones to operate beyond visually line of sight (BVLOS) in responding to classified healthcare assignments. The use of Drones in the healthcare management, welfare support, emergency responses and health risk management was investigated in the selected country of the Central East Africa (Rwanda). The research discovered a system of dynamic connection between the tiers of governments in active partnership with the private sector initiative to exploit Drone technology innovation to improve healthcare service delivery up to the grass root level with an outstanding productivity. The article evaluated Africa's involvement in the society healthcare extreme automation using Drone technology capabilities and deliberated on the possibilities and benefits for implementation and adoption into the national healthcare sector in Nigeria. The paper concluded that future implementation of the research will accomplish the digital health demands of Nigeria government, public and private sector health maintenance to guarantee sustainable healthcare investment and effective healthcare service delivery.

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Section: Methodsmentioning

confidence: 99%

IoT Drone Technology Integration in Medical Logistics Delivery

Taiwo¹,

Prisca²,

Matthew³

et al. 2022

SJPH

Self Cite

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“…The Drones have attracted the public attention over the years with focus on its military adoption and utility in news gathering in the recent time (Holton, Lawson, & Love, 2015). The media houses, journalists, commercial organization and corporate institutions have engaged the services of UAVs to obtain, process, transform and report most current news to establish occurrences of natural devastations which invariably would be too dangerous for human journalistic coverages, therefore Drone use could offer unique leverages that enrich news storytelling from the global perspective (Ugochukwu O Matthew, Kazaure, Onyebuchi, et al, 2021).…”

Section: The Background Of Studymentioning

confidence: 99%

“…In journalism and mass media communication, emergency responses and on spot mobilization and spotlight event coverages often times happened in some locations that are inaccessible both with limited lines of visibility such as expansive forests or locations where it's difficult for news service equipment to gain access or extremely expensive. In such scenario, a Drone equipped with an infrared camera and trained with artificial intelligence convolution neural network (CNN) to recognize human images and objects can be of great help and support(Ugochukwu O Matthew, Kazaure, Onyebuchi, et al, 2021). Drones have gained extensive popularity across a variety of industries, including healthcare, journalism & mass media, transportation & logistics, agriculture, construction, oil and gas sector, mining and for ecological management (Chamberlain, 2017).…”

Section: Research Implementationmentioning

confidence: 99%

News Reporting in Drone Internet of Things Digital Journalism

Nwanakwaugwu

Matthew

Okey

et al. 2023

International Journal of Interactive Communication Systems and Technologies

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The current study investigated several innovations for drone technology adoption in journalistic expeditions for intelligence and news gathering purposes. The necessity to leverage technologies to improve the direct involvement of eyewitnesses especially in violence-prone areas where physical and direct human involvement would be impossible or with high risk of survivability expectations is the motivating factor that directed the current research. The paper surveys the adoption of autonomous sensing drone systems in internet of things journalism and amalgamated the theoretic ingredients from the academic standpoint with realistic technological advancements from the global perspective and eventually expanded the propositions for conceivable adoption in the credible societal applications. The paper envisioned the future journalism and mass media practices and how drone innovation can revolutionize the journalism profession for the purpose of news and intelligence gathering with practical and technical realism with reduction of journalistic casualties.

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“…more sophisticated IoT systems continue to emerge and are deployed in various environments for different uses. Because of this, many people consider this technology to be a gamechanger, capable of opening up previously unimaginable possibilities for human and animal community alike [1]. These emerging technologies have created an enabling environment for creating, exchanging, collecting, storing, and distributing data within and among heterogeneous devices involving no human interaction and intervention.…”

Section: Introductionmentioning

confidence: 99%

Transfer Learning Approach to IDS on Cloud IoT Devices Using Optimized CNN

et al. 2023

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Data centralization can potentially increase Internet of Things (IoT) usage. The trend is to move IoT devices to a centralized server with higher memory capacity and a more robust management interface. Hence, a larger volume of data will be transmitted, resulting in more network security issues. Cloud IoT offers more advantages for deploying and managing IoT systems through minimizing response delays, optimal latency, and effective network load distribution. As a result, sophisticated network attack strategies are deployed to leverage the vulnerabilities in the extensive network space and exploit user information. Several attempts have been made to provide network intrusion detection systems (IDS) to the cloud IoT interface using machine learning and deep learning approaches on dedicated IDS datasets. This paper proposes a transfer learning IDS based on the Convolutional Neural Network (CNN) architecture that has shown excellent results on image classification. We use five pre-trained CNN models, including VGG16, VGG19, Inception, MobileNet, and EfficientNets, to train on two selected datasets: CIC-IDS2017 and CSE-CICIDS2018. Before the training, we carry out preprocessing, imbalance treatment, dimensionality reduction, and conversion of the feature vector into images suitable for the CNN architecture using Quantile Transformer. Three best-performing models (InceptionV3, MobileNetV3Small, and EfficientNetV2B0) are selected to develop an ensemble model called efficient-lightweight ensemble transfer learning (ELETL-IDS) using the model averaging approach. On evaluation, the findings show that the ELETL-IDS outperformed existing state-of-the-art proposals in all evaluation metrics, reaching 100% in accuracy, precision, recall, and F-score. We use Matthew's Correlation Coefficient (MCC) to validate this result and compared it to the AUC-ROC, which maintained an exact value of 0.9996. To this end, our proposed model is lightweight, efficient, and reliable enough to be deployed in cloud IoT systems for intrusion detection.

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Artificial Intelligence Autonomous Unmanned Aerial Vehicle (UAV) System for Remote Sensing in Security Surveillance

Cited by 22 publications

References 29 publications

IoT Drone Technology Integration in Medical Logistics Delivery

IoT Drone Technology Integration in Medical Logistics Delivery

News Reporting in Drone Internet of Things Digital Journalism

Transfer Learning Approach to IDS on Cloud IoT Devices Using Optimized CNN

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