Comprehensive systematic review of intelligent approaches in UAV-based intrusion detection, blockchain, and network security

Mohammed, Ahmed Burhan; Fourati, Lamia Chaari; Fakhrudeen, Ahmed M.

doi:10.1016/j.comnet.2023.110140

Cited by 8 publications

(3 citation statements)

References 81 publications

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“…This emphasizes the need of implementing a more advanced strategy, including machine vision skills to proactively determine the best sites and effectively manage resources for charging stations. [6]- [11] Machine vision utilizes its ability to scan images and recognize patterns to assess geographical data, such as population density, traffic flow, and proximity to important infrastructure. This project aims to use machine vision algorithms to determine optimal places for placing charging stations, taking into account real-time traffic statistics, peak charging hours, and local demographic data.…”

Section: Introductionmentioning

confidence: 99%

Machine Vision for Efficient Electric Vehicle Charging Station Deployment

Viktorovna Nemova,

Arun

2024

MATEC Web Conf.

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This research examines the optimization of the deployment of electric vehicle (EV) charging stations by using machine vision technology, which involves analyzing real-time data and geographical information. Geospatial data analysis reveals prospective sites for charging stations by considering population density and accessibility to roads, hence identifying regions with increased demand for electric vehicle charging. The assessment of electric vehicle (EV) traffic patterns highlights the ever-changing charging requirements at various times and places, underscoring the need of flexible deployment techniques. Furthermore, evaluating the costs of implementing the deployment and the capabilities of charging, it becomes apparent that there are compromises to be made between the initial expenditures of installation, the amount of power generated, and the quantity of charging stations. These trade-offs are essential for optimizing resources. The usage study of charging stations using machine vision reveals variations in the number of available charging points at different stations and the need for adaptive resource distribution timestamps, techniques. The examination of percentage change reveals notable fluctuations in population density, installation costs, and the availability of charging points. This information is crucial for making well-informed decisions about the deployment of charging infrastructure. Combining machine vision insights with geographical and traffic analyses presents a promising method to create data-driven strategies for the placement of EV charging stations. This approach addresses the changing needs of electric mobility and provides guidance to stakeholders for efficient and flexible charging solutions.

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Section: Introductionmentioning

confidence: 99%

Machine Vision for Efficient Electric Vehicle Charging Station Deployment

Viktorovna Nemova,

Arun

2024

MATEC Web Conf.

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“…Microgrids, which are small-scale localized power networks, have gained significant interest because of their ability to include renewable sources and improve energy resilience. [1]- [5] This research specifically examines the incorporation of renewable energy sources such as solar, wind, and biomass into microgrid systems, using neural networkassisted optimization. The goal is to tackle the issues of unpredictability and intermittency that are inherent in renewable energy sources and enhance their optimal integration within microgrids.…”

Section: Introductionmentioning

confidence: 99%

Neural network-assisted integration of renewable sources in microgrids: A case study

Vladimirovich Kotov,

Ramesh

2024

MATEC Web Conf.

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This study examines the incorporation of renewable energy sources into microgrids using neural network-assisted optimization methods. The objective is to tackle the difficulties related to the fluctuation and uncertainty of renewable energy production. An examination of the collected data over various time periods indicates encouraging patterns in the production of renewable energy. The solar energy use shows a steady rise from 120 kWh to 140 kWh, representing a 16.67% increase. Similarly, wind energy usage also demonstrates an upward trend, increasing from 80 kWh to 95 kWh, marking an 18.75% expansion. The biomass energy production has seen a substantial increase from 50 kWh to 65 kWh, representing a significant 30% rise. The examination of microgrid load consumption demonstrates the increasing energy needs in residential, commercial, and industrial sectors. The household load consumption has increased from 150 kWh to 165 kWh, representing a 10% spike. Additionally, the commercial load and industrial load have also seen a surge of 15%. The predictions made by the neural network demonstrate a high level of accuracy, closely matching the actual output of renewable energy. The accuracy rates for solar, wind, and biomass projections are 98.4%, 95.5%, and 97.3% correspondingly. The assessment of improved energy distribution emphasizes the effective usage of renewable sources, guaranteeing grid stability and optimal resource utilization. The results highlight the capacity of neural network-assisted methods to precisely predict renewable energy outputs and efficiently incorporate them into microgrids, hence promoting sustainable and resilient energy solutions. This report provides valuable insights on improving microgrid operations, decreasing reliance on traditional energy sources, and accelerating the shift towards sustainable energy systems.

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“…In other terms, there exist no reviews about the drones themselves, involving different architectures, structures, avionics, and software-to-hardware branches (i.e., the results of algorithms over the behavioral mechanism of sensors). Forwardly, as a matter of proof, and despite the extended application fields for UAVs such as concentrating solar thermal system planning and operation [64], intrusion detection [65], forest ecology [66], forensic civil engineering [67], plant-scale growth estimation [68], and photovoltaic plant diagnosis and maintenance [69,70], the available related reviews provide a less detailed informative background about the realm of UAVs. To address this issue, this paper aims to critically review and assess the different UAV facets, decomposed into five major sets including flight control and navigation, communication and remote control, computing and data management, sensing and monitoring, and power and energy management.…”

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confidence: 99%

Comprehensive Investigation of Unmanned Aerial Vehicles (UAVs): An In-Depth Analysis of Avionics Systems

Osmani,

Schulz

2024

Sensors

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The evolving technologies regarding Unmanned Aerial Vehicles (UAVs) have led to their extended applicability in diverse domains, including surveillance, commerce, military, and smart electric grid monitoring. Modern UAV avionics enable precise aircraft operations through autonomous navigation, obstacle identification, and collision prevention. The structures of avionics are generally complex, and thorough hierarchies and intricate connections exist in between. For a comprehensive understanding of a UAV design, this paper aims to assess and critically review the purpose-classified electronics hardware inside UAVs, each with the corresponding performance metrics thoroughly analyzed. This review includes an exploration of different algorithms used for data processing, flight control, surveillance, navigation, protection, and communication. Consequently, this paper enriches the knowledge base of UAVs, offering an informative background on various UAV design processes, particularly those related to electric smart grid applications. As a future work recommendation, an actual relevant project is openly discussed.

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Comprehensive systematic review of intelligent approaches in UAV-based intrusion detection, blockchain, and network security

Cited by 8 publications

References 81 publications

Machine Vision for Efficient Electric Vehicle Charging Station Deployment

Machine Vision for Efficient Electric Vehicle Charging Station Deployment

Neural network-assisted integration of renewable sources in microgrids: A case study

Comprehensive Investigation of Unmanned Aerial Vehicles (UAVs): An In-Depth Analysis of Avionics Systems

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