Endüstriyel Ortamlarda Enerji Hasatlayan Çoğul Ortam Kablosuz Algılayıcı Ağları
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2020, 2020
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Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü
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Sert kanal koşullarına sahip olan Endüstriyel Kablosuz Algılayıcı Ağ'larda (EKAA), enerji verimli ve güvenilir kablosuz iletişim sağlamak büyük önem taşımaktadır. Ağ güvenirliğini sağlarken aynı zamanda ağın ömrünü uzatmak da zor bir problemdir. Bu çalışmanın amacı, EKAA'ların ömrünün eniyilenmesidir. Bunu yaparken, endüstriyel ortamlar için uygun olan iç mekan güneş, termal ve titreşime dayalı Enerji Hasatlama (EH) yöntemleri tanımlanmış ve bunların ağ ömrüne katkıları araştırılmıştır. Uygulama güvenilirliğini ve EH yöntemlerini birlikte değerlendirerek, ağ ömrünü eniyilemek için yeni bir Karma Tamsayılı Programlama (KTP) modeli formüle edilmiştir. Ayrıca, Kablosuz Çoğul Ortam Algılayıcı Ağ'larında (KÇOAA) iletişim, büyük veri boyutu nedeniyle fazladan enerji tüketimine sebep olur. Bu nedenle, büyük veri boyutunu iletimden önce azaltmak önemli hale gelir.Bu amaçla, iletişim ve enerji dağıtım hesaplamalarını dikkate alırken, sıkıştırıcı algılama ve görüntü sıkıştırma gibi veri boyutu küçültme yöntemlerinin endüstriyel ağ ömrü üzerindeki etkisi değerlendirilir. Öte yandan, özellikle çok sayıda algılayıcılar bulunduran ağlar için KTP modelini uygun bir zamanda çözmek bir hayli zordur. KTP'nin zaman karmaşıklığı sorununun üstesinden gelmek için sezgisel tabanlı yöntemler geliştirilmiştir.
Providing energy efficient and reliable communication for Industrial Wireless Sensor Networks (IWSNs) is of great significance when considering the harsh channel characteristics of industrial environment. However, prolonging a network lifetime while ensuring reliability becomes a major challenge. The main goal of this thesis is to maximize the network lifetime of Industrial Wireless Sensor Networks (IWSNs). The Energy Harvesting (EH) methods based on indoor solar, thermal and vibration that are suitable for industrial environments are defined and their contributions on network lifetime are investigated. A novel Mixed Integer Programming (MIP) model is formulated to maximize network lifetime by jointly considering path loss, application reliability and EH methods. Furthermore, communication in Wireless Multimedia Sensor Networks (WMSNs) causes the expense of extra energy consumption due to its huge data size. Therefore, reducing huge data size before transmission becomes important. To this end, the impact of the data size reduction methods such as compressive sensing and image compression while considering energy dissipation of both communication and computation on industrial network lifetime is evaluated. On the other hand, to solve the MIP model in a feasible time is hard especially when the large amount of sensor nodes deployed in the network. Heuristic based optimization methods are developed to overcome the time complexity of MIP problem.
Providing energy efficient and reliable communication for Industrial Wireless Sensor Networks (IWSNs) is of great significance when considering the harsh channel characteristics of industrial environment. However, prolonging a network lifetime while ensuring reliability becomes a major challenge. The main goal of this thesis is to maximize the network lifetime of Industrial Wireless Sensor Networks (IWSNs). The Energy Harvesting (EH) methods based on indoor solar, thermal and vibration that are suitable for industrial environments are defined and their contributions on network lifetime are investigated. A novel Mixed Integer Programming (MIP) model is formulated to maximize network lifetime by jointly considering path loss, application reliability and EH methods. Furthermore, communication in Wireless Multimedia Sensor Networks (WMSNs) causes the expense of extra energy consumption due to its huge data size. Therefore, reducing huge data size before transmission becomes important. To this end, the impact of the data size reduction methods such as compressive sensing and image compression while considering energy dissipation of both communication and computation on industrial network lifetime is evaluated. On the other hand, to solve the MIP model in a feasible time is hard especially when the large amount of sensor nodes deployed in the network. Heuristic based optimization methods are developed to overcome the time complexity of MIP problem.
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Computer Engineering And Computer Science And Control, Bilgisayar Mühendisliği Bilimleri-Bilgisayar Ve Kontrol
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108
Sustainable Development Goals
2
ZERO HUNGER

3
GOOD HEALTH AND WELL-BEING

7
AFFORDABLE AND CLEAN ENERGY

8
DECENT WORK AND ECONOMIC GROWTH

9
INDUSTRY, INNOVATION AND INFRASTRUCTURE

10
REDUCED INEQUALITIES
