In recent years,the significant growth in the Internet of Things(IoT)technology has brought a lot of attention to information and communication *** IoT paradigms like the Internet of Vehicle Things(IoVT)and the Intern...
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In recent years,the significant growth in the Internet of Things(IoT)technology has brought a lot of attention to information and communication *** IoT paradigms like the Internet of Vehicle Things(IoVT)and the Internet of Health Things(IoHT)create massive volumes of data every day which consume a lot of bandwidth and ***,to process such large volumes of data,the existing cloud computing platforms offer limited resources due to their distance from IoT ***,cloudcomputing systems produce intolerable latency problems for latency-sensitive real-time ***,a newparadigm called fog computingmakes use of computing nodes in the form of mobile devices,which utilize and process the real-time IoT devices data in orders of *** paper proposes workload-aware efficient resource allocation and load balancing in the fog-computing environment for the *** proposed algorithmic framework consists of the following components:task sequencing,dynamic resource allocation,and load *** consider electrocardiography(ECG)sensors for patient’s critical tasks to achieve maximum load balancing among fog nodes and to measure the performance of end-to-end delay,energy,network consumption and average *** proposed algorithm has been evaluated using the iFogSim tool,and results with the existing approach have been *** experimental results exhibit that the proposed technique achieves a 45%decrease in delay,37%reduction in energy consumption,and 25%decrease in network bandwidth consumption compared to the existing studies.
The advancements in sensing technologies and AI algorithms have opened up a wide range of possibilities for developing applications to meet the needs of individuals who are deaf or hard of hearing. Sign language plays...
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This work studies the urban area location privacy preserving location updates and nearby friends (NF) querying for the centralized proximity based services (PBSs). The urban area constraint forces the user mobility to...
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The present research focuses on an integrated architecture for extraction and identification of disease in the cotton plants. The deep learning architecture provided in the paper is capable of extracting the features ...
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Convolutional neural network has significantly advanced the field of image super-resolution reconstruction in recent years. The insufficient ability to model global information of hierarchical features, incomplete att...
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The classification of Alzheimer’s disease (AD) using deep learning techniques has shown promising results. However, achieving successful application in medical settings requires a combination of high precision, short...
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Human Gesture Recognition (HGR) has become an increasingly important research area in recent years, driven by the need for more natural and intuitive ways of interacting with machines. Wearable smart devices, such as ...
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Sleep apnea is a severe sleep disorder characterized by interrupted breathing during sleep. Early detection and accurate diagnosis of sleep apnea are essential to avoiding potentially dangerous health complications. T...
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Automatic classification of weather images is a crucial task in the field of meteorology. Recent advancements in Convolutional Neural Networks (CNNs) have demonstrated their effectiveness in image classification. In t...
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We apply an information-theoretic perspective to reconsider generative document retrieval (GDR), in which a document x ∈ X is indexed by t ∈ T, and a neural autoregressive model is trained to map queries Q to T. GDR...
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We apply an information-theoretic perspective to reconsider generative document retrieval (GDR), in which a document x ∈ X is indexed by t ∈ T, and a neural autoregressive model is trained to map queries Q to T. GDR can be considered to involve information transmission from documents X to queries Q, with the requirement to transmit more bits via the indexes T. By applying Shannon's rate-distortion theory, the optimality of indexing can be analyzed in terms of the mutual information, and the design of the indexes T can then be regarded as a bottleneck in GDR. After reformulating GDR from this perspective, we empirically quantify the bottleneck underlying GDR. Finally, using the NQ320K and MARCO datasets, we evaluate our proposed bottleneck-minimal indexing method in comparison with various previous indexing methods, and we show that it outperforms those methods. Copyright 2024 by the author(s)
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