Derin öğrenme tabanlı görevlerin kenar bilişim yöntemiyle uzak sunucuya taşınması
Offloading tasks to remote server for deep learning based applications over edge computing system
Yazar: HÜSEYİN ENES İLHAN
Danışman: PROF. DR. HAKAN ALİ ÇIRPAN
İstanbul Teknik Üniversitesi / Lisansüstü Eğitim Enstitüsü / Elektronik ve Haberleşme Mühendisliği Ana Bilim Dalı / Telekomünikasyon Mühendisliği Bilim Dalı
Derece: Yüksek Lisans, 2022
Konu: Elektrik ve Elektronik Mühendisliği = Electrical and Electronics Engineering
Dizin: Derin öğrenme = Deep learning ; Görüntü işleme = Image processing ; Hareketli iletişim = Mobile communication ; Kablosuz iletişim = Wireless communication

Özet:
İnsansız Hava Aracı (İHA) sistemleri uzaktan veya otonom olarak kontrol edilebildikleri için erişilmesi zor ve tehlikeli durumlarda öncelikli olarak tercih edilmektedir. Uçabilmeleri, fiziksel özellikleri ve kullanım kolaylıkları sebebiyle bu cihazlar çok farklı görevlerle donatılabilmektedir. İHA sistemlerinde kameranın kullanılması ve bu cihazların havadan görüntü ve video analizi yapmaları askeri, eğlence ve gözetleme gibi bir çok alanda bu sistemlere olan ilgiyi her geçen gün artırmaktadır. Bu cihazlar sürüş kontrol ünitesi, motor, batarya, tümleşik işlemci, kamera vb. gibi birçok alt parçacıktan oluşmaktadır. Bataryalar; sürüş sistemleri ve ek uygulamalar için donatılan ekipmanların güç ihtiyaçlarının karşılanmasından sorumludur. Ergonomik koşullar ve maliyet gibi kısıtlamalar sebebiyle bataryaların mümkün olduğunca daha küçük boyutlu olması ve ucuza mal edilmesi istenmektedir. Ancak, batarya maliyetleri depolayabildikleri enerji miktarıyla orantılıdır. Ayrıca, düşük kapasiteli bataryaların kullanılması İHA'ların kısa sürede tekrar şarj edilmesi ve görevlerinin yarıda kesilmesi gibi olumsuzluklara neden olmaktadır. Çünkü, görüntü işleme temelli çalışmalarda büyük boyuttaki verilerle çalışılmaktadır. Ek olarak bu çalışmalarda kullanılan algoritmaların ağır işlem gücü gerektiren derin öğrenme tabanlı sistemlere evrilmesi sebebiyle ihtiyaç duyulan işlemci gücü ve özellikleri artmaktadır. Görüntü işleme görevi ile donatılmış İHA sistemlerinde ihtiyaç duyulan bu işlemci gücü için sürüş sistemlerine ek olarak tümleşik işlemciler (CPU veya GPU) entegre edilmiştir. Bu durum cihaz bataryalarının daha kısa sürede bitmesine ve görevlerin yarıda kesilmesine neden olmaktadır. Telekomünikasyon teknolojilerindeki gelişmeler ile birlikte büyük miktarda verinin bir noktadan diğer bir noktaya hızlı bir şekilde taşınabilmesi mümkün olmuştur. Bu gelişmeler kenar bilişim (edge computing) ve nesnelerin interneti (Internet of Things, IoT) sistemlerinin yaygınlaşmasında büyük rol oynamaktadır. Kenar bilişim, bir istemcinin kendi üzerinde, kendi kaynaklarını kullanarak veri işlemesi yerine bu istemciye yakın yerleştirilmiş sunucular üzerinde veri işlenmesi prensibine dayanan teknoloji mimarisidir.
Tez çalışmasında görüntü bölütleme görevi ile donatılmış İHA sistemlerinin kenar bilişim yöntemiyle verileri uzak sunucuya iletmesi durumu analiz edilmiştir. Farklı telekomünikasyon teknolojileri (4G ve 5G) kullanılarak bu analiz senaryosu oluşturulmuş ve kablosuz haberleşme kanalındaki bozucu etkiler ve gürültü sebebiyle alınan verilerin bozulduğu gözlenmiştir. Bu gürültülerin giderilmesi için kenar bilişim sistemine gürültü arındırma bloğu eklenmiş ve temel seviye gürültü arındırma algoritmalardan ileri seviye derin öğrenme tabanlı algoritmalara kadar birçok farklı gürültü arındırma yöntemi incelenmiştir. Önerilen gürültü arındırma bloğunun bu sisteme entegre edilmesi kenar sunucuda bulunan derin öğrenme tabanlı görüntü bölütleme algoritmasının performansını büyük ölçüde artırdığı belirlenmiştir.

Abstract:
Unmanned Aerial Vehicle (UAV) systems have recently been one of the active research topics due to their ease of use, move ability over the air, autonomous driving support and small size. These systems consist of batteries, driving systems, control systems, communication systems and co-processors. Driving systems are responsible for operations such as flying, steering and moving. Control systems, synchronise the systems of the device and managing discrete parts of the devise as an overall. The battery provides the energy needed for operations such as driving, additional tasks and control. Co-processors enable UAVs equipped for different purposes to perform tasks such as data processing and calculation in line with these purposes. Communication systems are used to enable UAVs to transfer data with the main device they are related to or to be controlled from a remote point in non-autonomous situations.
UAVs are used in many fields, especially in image and video processing applications, because they can fly, reach places that other flying vehicles cannot reach, perform many tasks without endangering human health, and are cheap.
Image processing is the whole process used to reconstruct the image according to the desired application by using different algorithms on digital images. Digital images are 2-dimensional signals whose the tiniest elements are called pixels. Thus, images can be processed by using basic signal processing techniques such as low-pass and high-pass filtering.
Nowadays, the success in deep learning tasks and making these tasks easier to implement and distribute have increased the work on these tasks. There are many features in the images that can be noticed or hidden by the human visual system. The use of deep learning-based algorithms to extract these features and identify the hidden features that cannot be detected has accelerated the use of deep learning-based algorithms in image processing.
However, deep learning-based algorithms consist of networks that have billions of parameters. These networks are complex and deep models include nodes with weight and bias values. The data applied to the input of a node is multiplied by the weight and added to the bias, and then presented to the output of the node. In this way, operations are performed on thousands of nodes, and an output value is produced for the input data. Based on the accuracy of this value, the weights are recalculated in the reverse direction.
These operations are the basic operations applied for training a deep learning-based network. In such processes, the amount of data applied from the input is increased as much as possible to generalize the network training and bring the weight values to the most appropriate values for all situations. Therefore, for the development of a deep learning-based system, large amounts of data and powerful processors are needed to perform calculations on the network.
At the same time, since images are 2-dimensional large signals, the use of deep learning-based algorithms in these applications requires high processing power. In a UAV scenario with a deep learning-based image processing task, UAVs need to be equipped with expensive co-processors with high processing power to perform these tasks. At the same time, the high energy needs of these processors cause the battery of the UAVs to run out in a short time and their mission to be interrupted. For such scenarios, the idea of edge computing has been introduced so that the image processing is done on remote edge servers, not on local co-processors.
Edge Computing systems are based on the idea that work to be done is done not by local resources, but by using resources from remote servers. To carry out these operations, first of all, the collected data must be transmitted to remote servers.
In cloud computing systems, this data transfer is done by using fixed networks, while in edge computing systems, data is transported using mobile networks. Mobile communication technology has developed from methods that enable the transmission of speech data with analogue signals from a wireless medium between a receiver and transmitter to methods where simultaneous simulations and data transfer can be made in a shorter time than human perception.
All IP phenomenon, which is started with 3G in mobile communication technologies, has led to the idea that mobile networks can be used for different devices. Later, the increase in the amount of data and data rate carried in LTE and 5G systems showed that these networks can be used in systems such as IoT and edge computing. It is recommended to use edge computing systems, as described in the UAV scenario, to eliminate the battery and cost problems in mobile devices.
However, it is known that the data carried in edge computing systems will be corrupted because the UAVs move at high speeds and the wireless communication channel used to access the mobile network is a noisy environment.
The effect of the degradation of the data carried in the edge computing system on the deep learning-based image processing algorithm in the edge server is examined in this study. Within the scope of the thesis, first of all, an LTE system was simulated using the MATLAB LTE toolbox and the data is transmitted out by using traffic data channels over a group of fading noisy wireless channels, and a pretrained deep learning-based MobileNetV2 network was placed on the Edge server. Then, by simulating UAVs travelling at different speeds in this system, Doppler shifts at different frequencies were created in the transmitted data. Wireless communication environments with varying noise levels have been created to simulate electrical noises caused by objects and signals in the experiments. Finally, images with different resolutions were transmitted to examine the effect of the amount of data transferred on this system. As a result of the first experiments, it was seen that the edge computing system is highly affected by the noises in the wireless communication channel and after a certain UAV speed, it is affected by the Doppler shift logarithmic. While the amount of data did not affect the system's success, it increased the time spent during data migration.
The system is evolved to more complex situation and a denoising block is placed at the receiver side of the system to overcome the noise components over transmitted data. 5G mobile telecommunication system is deployed rather than LTE in the new experiments by using the MATLAB 5G toolbox. Due to the increase in carrier signal frequencies in 5G, it has been observed that the success of the edge computing system has decreased. To address this issue, an additional denoising block is placed at the edge sever in these experiments. Due to the inability to determine the noise characteristic in such examples, many methods from spatial filters to deep learning-based noise removal techniques have been tested in these experiments.
It is concluded that the use of the deep learning-based multi-stage MPRNET network, which has been put forward recently, has increased the performance of the edge computing system, and it is recommended to add the denoising block to this system.