In mobile crowd computing(MCC),people’s smart mobile devices(SMDs)are utilized as computing resources.Considering the ever-growing computing capabilities of today’s SMDs,a collection of them can offer significantly ...In mobile crowd computing(MCC),people’s smart mobile devices(SMDs)are utilized as computing resources.Considering the ever-growing computing capabilities of today’s SMDs,a collection of them can offer significantly high-performance computing services.In a localMCC,the SMDs are typically connected to a local Wi-Fi network.Organizations and institutions can leverage the SMDs available within the campus to form local MCCs to cater to their computing needs without any financial and operational burden.Though it offers an economical and sustainable computing solution,users’mobility poses a serious issue in the QoS of MCC.To address this,before submitting a job to an SMD,we suggest estimating that particular SMD’s availability in the network until the job is finished.For this,we propose a convolutional GRU-based prediction model to assess how long an SMD is likely to be available in the network from any given point of time.For experimental purposes,we collected real users’mobility data(in-time and outtime)with respect to a Wi-Fi access point.To build the prediction model,we presented a novel feature extraction method to be applied to the time-series data.The experimental results prove that the proposed convolutional GRU model outperforms the conventional GRU model.展开更多
Most of the digital image watermarking techniques are susceptible to geometric attacks such as cropping,rotation and scaling.These attacks are the easiest yet most successful in rendering the survival of watermark dif...Most of the digital image watermarking techniques are susceptible to geometric attacks such as cropping,rotation and scaling.These attacks are the easiest yet most successful in rendering the survival of watermark difficult.Such geometric operations alter the pixel orientation in the cover thereby rendering the watermark difficult to locate and extract.However,if the alterations produced by the geometric attacks such as scaling,cropping and rotation can be modeled in terms of the change in the image geometry,it is possible to relocate the watermark even after the original cover has suffered an attack.This paper contributes to the state of the art by proposing an image watermarking technique that attempts to model the attacks like cropping,scaling and rotation in terms of the image geometry.The proposed scheme is acceptably resistant to common geometric attacks and common image processing attacks.The watermark embedding is also done efficiently to offer resistance to image processing attacks.The watermark detection procedure is blind and key based,also not requiring the original cover work for watermark extraction.Efforts have been given to ensure that the proposed scheme conforms to robustness against attacks and exhibits high visual fidelity of the watermarked cover.展开更多
基金This research was supported by Taif University Researchers Supporting Project Number(TURSP-2020/10),Taif University,Taif,Saudi Arabia.
文摘In mobile crowd computing(MCC),people’s smart mobile devices(SMDs)are utilized as computing resources.Considering the ever-growing computing capabilities of today’s SMDs,a collection of them can offer significantly high-performance computing services.In a localMCC,the SMDs are typically connected to a local Wi-Fi network.Organizations and institutions can leverage the SMDs available within the campus to form local MCCs to cater to their computing needs without any financial and operational burden.Though it offers an economical and sustainable computing solution,users’mobility poses a serious issue in the QoS of MCC.To address this,before submitting a job to an SMD,we suggest estimating that particular SMD’s availability in the network until the job is finished.For this,we propose a convolutional GRU-based prediction model to assess how long an SMD is likely to be available in the network from any given point of time.For experimental purposes,we collected real users’mobility data(in-time and outtime)with respect to a Wi-Fi access point.To build the prediction model,we presented a novel feature extraction method to be applied to the time-series data.The experimental results prove that the proposed convolutional GRU model outperforms the conventional GRU model.
文摘Most of the digital image watermarking techniques are susceptible to geometric attacks such as cropping,rotation and scaling.These attacks are the easiest yet most successful in rendering the survival of watermark difficult.Such geometric operations alter the pixel orientation in the cover thereby rendering the watermark difficult to locate and extract.However,if the alterations produced by the geometric attacks such as scaling,cropping and rotation can be modeled in terms of the change in the image geometry,it is possible to relocate the watermark even after the original cover has suffered an attack.This paper contributes to the state of the art by proposing an image watermarking technique that attempts to model the attacks like cropping,scaling and rotation in terms of the image geometry.The proposed scheme is acceptably resistant to common geometric attacks and common image processing attacks.The watermark embedding is also done efficiently to offer resistance to image processing attacks.The watermark detection procedure is blind and key based,also not requiring the original cover work for watermark extraction.Efforts have been given to ensure that the proposed scheme conforms to robustness against attacks and exhibits high visual fidelity of the watermarked cover.
