Список литературы

2518-1092

Научный результат. Информационные технологии

2518-1092

10.18413/2518-1092-2022-8-1-0-2

3030

ИНФОРМАЦИОННЫЕ СИСТЕМЫ И ТЕХНОЛОГИИ

<div><strong>КЛАССИФИКАТОРЫ ГИПЕРСПЕКТРАЛЬНЫХ ИЗОБРАЖЕНИЙ В БЕСПРОВОДНЫХ СЕНСОРНЫХ СЕТЯХ В ЭКСТРЕМАЛЬНЫХ УСЛОВИЯХ</strong></div>

<strong>A HYPERSPECTRAL IMAGE CLASSIFIERS WITHIN WIRELESS SENSOR NETWORK IN EXTREME ENVIRONMENTS</strong>

Агха

Хотайфа Рабеа

Agha

Hothayfa Rabea

israagha83@gmail.com

Абдул-Джаббар

Джассим

Abdul-Jabbar

Jassim

drjssm@almaaqal.edu.iq

2023

8100

Прогресс в области компьютерных сетей породил множество областей исследований, которые проложили путь для изучения множества новых приложений. Одним из самых популярных приложений, перешедших с исследовательского уровня на уровень приложений, являются беспроводные сенсорные сети (WSN), на основе которых строятся другие передовые области, такие как Интернет вещей. Эти беспроводные сенсорные сети применялись во многих приложениях и зарекомендовали себя как эффективный инструмент для сбора информации в различных средах. С расширением круга мест, которые интересуют людей и исследуются, возникла острая необходимость в применении беспроводных сенсорных сетей в средах, с которыми людям трудно иметь дело. Это вызывает новые препятствия для реализации и работы беспроводных сенсорных сетей, таких сферах как космос, или приложения, требующие погружения датчиков в воду, промышленные среды с высоким уровнем шума или медицинские среды, а также, возможно, токсичные среды или среды, характеризующиеся очень высоким или очень низким уровнем шума, температуры. Все это, упомянутое ранее, побудило нас открыть новые горизонты исследований беспроводных сенсорных сетей для сбора данных из этих сред. Появилось много исследований, посвященных этим типам сред, и мы добавим к ним использование граничных вычислений для обработки данных в месте их сбора, особенно в космической среде и аэрофотосъемке, где данные будут обрабатываться в конце сбора и передачи данных, так как передача потребляет большую часть энергии, основную часть энергии мы уменьшим объем за счет обработки данных на границе сети. Закрытая информация отправляется только на наземную станцию, чтобы уменьшить использование полосы пропускания и выполнять вычисления в реальном времени.

Progress in the field of computer networks has produced many areas of researches which paved the way for studying many new applications. One of the most popular applications that has shifted from the research level to the application level is wireless sensor networks (WSNs), based on which other advanced fields such as the Internet of Things are built. These wireless sensor networks have been applied in many applications, and has proven to be an effective tool in collecting information in different environments, but with the expansion of the range of places that humans are interested in and trying to explore, the need has become urgent to apply wireless sensor networks to environments that are difficult to deal with by humans. This imposes new obstacles on the implementation and the performance of wireless sensor networks such as space, or applications that impose immersion of sensors in water, or industrial environments with high noise or medical environments, and perhaps toxic environments or those characterized by very high or very low temperatures. All these mentioned before prompted us to open new horizons of researches for wireless sensor networks to collect data from those environments. Many studies have appeared to deal with these types of environments, and we will add to them the use of edge computing to deal with data at the place of gathering, especially in the space environment and aerial photography, where the data will be processed at the end of data collection and transmission since the transmission consumes the major part of energy, we will decrease by processing the date at the edge of the network. Classified information is sent only to the ground station to reduce bandwidth usage and perform real-time calculations.

гиперспектральные изображенияклассификацияпограничная коммутациябеспроводная сенсорная сетьэкстремальные условия

hyperspectral imagesclassificationedge commutingwireless sensor networkextreme environments

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