The use of fog computing in the Internet of Things(IoT)has emerged as a crucial solution,bringing cloud services closer to end users to process large amounts of data generated within the *** its advantages,the increas...
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The use of fog computing in the Internet of Things(IoT)has emerged as a crucial solution,bringing cloud services closer to end users to process large amounts of data generated within the *** its advantages,the increasing task demands from IoT objects often overload fog devices with limited resources,resulting in system delays,high network usage,and increased energy *** of the major challenges in fog computing for IoT applications is the efficient deployment of services between fog *** address this challenge,we propose a novel Optimal Foraging Algorithm(OFA)for task placement on appropriate fog devices,taking into account the limited resources of each fog *** OFA algorithm optimizes task sharing between fog devices by evaluating incoming task requests based on their types and allocating the services to the most suitable fog *** our study,we compare the performance of the OFA algorithm with two other popular algorithms:Genetic Algorithm(GA)and Randomized Search Algorithm(RA).Through extensive simulation experiments,our findings demonstrate significant improvements achieved by the OFA ***,it leads to up to 39.06%reduction in energy consumption for the Elektroensefalografi(EEG)application,up to 25.86%decrease in CPU utilization for the Intelligent surveillance through distributed camera networks(DCNS)application,up to 57.94%reduction in network utilization,and up to 23.83%improvement in runtime,outperforming other *** a result,the proposed OFA algorithm enhances the system’s efficiency by effectively allocating incoming task requests to the appropriate fog devices,mitigating the challenges posed by resource limitations and contributing to a more optimized IoT ecosystem.
The safety of the child in the stroller is at risk when there is a deviation in the parent's focus. The automatic movement in the baby strollers have led to many accidents and have been fatal to the baby. To overc...
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Passersby often appear in photos taken at popular scenic spots. To remove the unwanted passersby in photos and fill in the empty areas, a typical approach is manually labeling passersby and using software (Photoshop, ...
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In this paper the Assembly, Integration and Verification campaign for the 2U CubeSat KvarkenSat will be presented. This CubeSat mission is part of the Kvarken Space Center, a joint venture between six universities and...
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Electric arc furnaces (EAF) can cause various power quality problems in power systems. Because of that, it is important to investigate the electric arc phenomena and deepen the knowledge related to the modeling of suc...
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We focuses on the challenges and advances in molecular property prediction within the field of drug discovery, particularly when addressing small sample sizes. In seeking to enhance predictive accuracy while reducing ...
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According to the International Data Corporation (IDC), by the year 2025, over 40 billion Internet of Things (IoT) devices will be connected to the internet. However, these devices face challenges such as insufficient ...
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In IoT applications, battery-constrained devices face an important and crucial challenge that their lifespan depends solely on their batteries. Extending this lifespan is a key challenge. Fortunately, various energy h...
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Recently, transformer-based techniques incorporating superpoints have become prevalent in 3D instance segmentation. However, they often encounter an over-segmentation problem, especially noticeable with large objects....
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Colorization is the practice of adding appropriate chromatic values to monochrome photographs or videos.A real-valued luminance image can be mapped to a three-dimensional color ***,it is a severely ill-defined problem...
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Colorization is the practice of adding appropriate chromatic values to monochrome photographs or videos.A real-valued luminance image can be mapped to a three-dimensional color ***,it is a severely ill-defined problem and not has a single *** this paper,an encoder-decoder Convolutional Neural Network(CNN)model is used for colorizing gray images where the encoder is a Densely Connected Convolutional Network(DenseNet)and the decoder is a conventional *** DenseNet extracts image features from gray images and the conventional CNN outputs a^(*)b^(*)color *** to a large number of desaturated color components compared to saturated color components in the training images,the saturated color components have a strong tendency towards desaturated color components in the predicted a^(*)b^(*)*** solve the problems,we rebalance the predicted a^(*)b^(*)color channel by smoothing every subregion individually using the average filter.2 stage k-means clustering technique is applied to divide the *** we apply Gamma transformation in the entire a^(*)b^(*)channel to saturate the *** compare our proposed method with several existing *** the experimental results,we see that our proposed method has made some notable improvements over the existing methods and color representation of gray-scale images by our proposed method is more plausible to ***,our suggested approach beats other approaches in terms of Peak Signal-to-Noise Ratio(PSNR),Structural Similarity Index Measure(SSIM)and Histogram.
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