Internet of Things (IoT) is characterized as one of the leading actors for the next evolutionary stage in the computing world. IoT-based applications have already produced a plethora of novel services and are improvin...
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Internet of Things (IoT) is characterized as one of the leading actors for the next evolutionary stage in the computing world. IoT-based applications have already produced a plethora of novel services and are improving the living standard by enabling innovative and smart solutions. However, along with its rapid adoption, IoT technology also creates complex challenges regarding the management of IoT networks due to its resource limitations (computational power, energy, and security). Hence, it is urgently needed to refine the IoT-based application’s architectures to robustly manage the overall IoT infrastructure. Software-defined networking (SDN) has emerged as a paradigm that offers software-based controllers to manage hardware infrastructure and traffic flow on a network effectively. SDN architecture has the potential to provide efficient and reliable IoT network management. This research provides a comprehensive survey investigating the published studies on SDN-based frameworks to address IoT management issues in the dimensions of fault tolerance, energy management, scalability, load balancing, and security service provisioning within the IoT networks. We conducted a Systematic Literature Review (SLR) on the research studies (published from 2010 to 2021) focusing on SDN-based IoT management frameworks. We provide an extensive discussion on various aspects of SDN-based IoT solutions and architectures. We elaborate a taxonomy of the existing SDN-based IoT frameworks and solutions by classifying them into categories such as network function virtualization, middleware, OpenFlow adaptation, and blockchain-based management. We present the research gaps by identifying and analyzing the key architectural requirements and management issues in IoT infrastructures. Finally, we highlight various challenges and a range of promising opportunities for future research to provide a roadmap for addressing the weaknesses and identifying the benefits from the potentials offered by SD
For each smart home, the need of energy consumption supervision is necessary, which plays an important role to ensure the highest power quality and to enhance the stability of the whole grid. The current document impl...
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ISBN:
(纸本)9781665482622
For each smart home, the need of energy consumption supervision is necessary, which plays an important role to ensure the highest power quality and to enhance the stability of the whole grid. The current document implements a smart home supply strategy based on endless resources to reduce the electricity bill and confirm the energy balance. In this context, a proposed supervision algorithm operates in eight cases to reach optimal energy flow between renewable generators, home battery and grid in a smart home concept is presented. The system is evaluated using the framework “Business Process Model and Notation” (BPMN) Camunda basing on information stored in Firebase Cloud and results are presented in order to manifest the efficiency of this control strategy.
The Bavarian higher education environment is aiming to renew its IT strategy. The overall objective is to find an organisational solution which allows both local independence and collaborative solutions in those areas...
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Cancer is one of the top leading causes of death in the world according to the World Health Organization (WHO). Despite the continuous efforts, drug discovery often takes 10-15 years if done traditionally, and it cost...
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Cancer is one of the top leading causes of death in the world according to the World Health Organization (WHO). Despite the continuous efforts, drug discovery often takes 10-15 years if done traditionally, and it costs over $2.6 billion to finally bring a single drug to market. The integration of deep learning (DL) with these traditional methods, however, is transforming the process of drug design and prediction, evolving at high speeds, often relying on the molecular data for reference. This paper explores and compares various deep learning models, presenting a multi-model for anticancer small molecule design and bioactivity (GI50%) prediction. A fine-tuned Variation Autoencoder (VAE) model is trained on a set of anticancer molecules to generate new molecules that mimic the drug. These molecules are later fed to a meta-model based on two ensemble methods: averaging and stacking, to predict their activity against different cancer cell lines; leveraging the strengths of different Graph Neural Networks (GNNs), namely: Graph Attention Networks (GATs), Graph Convolutional Networks (GCNs), and Message Passing Neural Networks (MPNNs), based on chemical structure and a pre-trained ChemBERTa model based on the attention mechanism. The experiments were conducted on a dataset of multiple compounds across the breast cancer tumour with 6 cancer cell lines, demonstrating our model's superiority against the literature, outperforming most models; the Pearson's correlation coefficients reached up to 83% using the stacking ensemble method.
Reaching consensus -a macroscopic state where the system constituents display the same microscopic state- is a necessity in multiple complex socio-technical and techno-economic systems: their correct functioning ultim...
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A key task of data science is to identify relevant features linked to certain output variables that are supposed to be modeled or predicted. To obtain a small but meaningful model, it is important to find stochastical...
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The history of the use of mines dates back almost two centuries. The geography of their use and the associated social harm have made them, without exaggeration, a global problem. At the same time, searches were underw...
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This study investigates a Cloud–Edge-sensors infrastructure using M/M/c/K queuing theory to analyze agricultural data systems’ performance. It focuses on optimizing data handling and evaluates the system configurati...
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Background Research has repeatedly shown that exposure to suicide-related news media content is associated with suicide rates, with some content characteristics likely having harmful and others potentially protective ...
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Background Research has repeatedly shown that exposure to suicide-related news media content is associated with suicide rates, with some content characteristics likely having harmful and others potentially protective effects. Although good evidence exists for a few selected characteristics, systematic and large-scale investigations are missing. Moreover, the growing importance of social media, particularly among young adults, calls for studies on the effects of content posted on these platforms. Objective This study applies natural language processing and machine learning methods to classify large quantities of social media data according to characteristics identified as potentially harmful or beneficial in media effects research on suicide and prevention. Methods We manually labeled 3202 English tweets using a novel annotation scheme that classifies suicide-related tweets into 12 categories. Based on these categories, we trained a benchmark of machine learning models for a multi-class and a binary classification task. As models, we included a majority classifier, an approach based on word frequency (TF-IDF with a linear SVM) and two state-of-the-art deep learning models (BERT, XLNet). The first task classified postings into six main content categories, which are particularly relevant for suicide prevention based on previous evidence. These included personal stories of either (1) suicidal ideation and attempts or (2) coping and recovery, calls for action intending to spread either (3) problem awareness or (4) prevention-related information, (5) reporting of suicide cases, and (6) other tweets irrelevant to these five categories. The second classification task was binary, and separated postings in the 11 categories that refer to actual suicide, from postings in the off-topic category, which use suicide-related terms in another meaning or context. Results In both tasks, the performance of the two deep learning models was very similar and better than the majority or th
Intelligent environments work collaboratively, bringing more comfort to human beings. The intelligence of these environments comes from technological advances in sensors and communication. IoT is the model developed t...
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