Estimating memory requirements in wireless sensor networks using social tie strengths

Mahmood, Basim; Tomasini, Marcello; Menezes, Ronaldo

doi:10.1109/lcnw.2015.7365916

Cited by 7 publications

(6 citation statements)

References 7 publications

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“…In addition to the previous findings, the modularity of the communities of Iraqi universities is also investigated. The modularity of a network reflects the strength of the communities in its structure (Tomasini and Menezes, 2015). Fig.…”

Section: Resultsmentioning

confidence: 99%

Investigating scientific collaboration networks in Iraq using cloud computing and data mining

Sultan,

Abdullah

2023

Int. J. Appl. Sci. Eng.

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The academic performance of Iraqi authors and institutions is an important aspect that needs to be investigated in the field of research. Special scientific criteria establish a researcher's level. These include citations, published articles, and others. In this study, the aim is to analyze the academic performance patterns of Iraqi authors and institutions in Google Scholar. Collaboration patterns between Iraqi scientists and universities were explored using sophisticated data collection and analysis techniques. A crawler that worked in parallel with Google Scholar was created with SerpApi, thus collecting amounts of scientist profile data stored in MongoDB Atlas. The data were processed using Spark within the Amazon Web Services (AWS) Cloud. Complex network analysis and Cytoscape software were used to comprehensively investigate the co-authorship networks of Iraqi scientists. In addition, a set of complex network criteria was applied to reveal patterns of cooperation. After visualizing the network diagram and analyzing the relationships between the nodes, the virtual nodes and communities in the network were identified, revealing critical insights into the cooperation patterns of Iraqi scientists and universities. The results have important implications for research and development policies in Iraq and demonstrate the power of complex network analysis to reveal valuable insights into academic research collaboration.

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Section: Resultsmentioning

confidence: 99%

Investigating scientific collaboration networks in Iraq using cloud computing and data mining

Sultan,

Abdullah

2023

Int. J. Appl. Sci. Eng.

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“…Deep neural network models, such as CNN and RNN, have demonstrated considerable potential in achieving favorable outcomes in the domain of text recognition problems [16], [17]. The CNN architectures are extensively employed in object recognition applications, demonstrating notable performance in character identification.…”

Section: Proposed Deep Learning Modelmentioning

confidence: 99%

A Deep Learning Based Offline Optical Character Recognition Model for Printed Ottoman Turkish

AL-KHAFFAF,

ATİLA

2023

Technium

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Developing efficient optical character recognition (OCR) systems for printed Ottoman text is a problem since current OCR models created for Arabic have restrictions that make it difficult to be performed. The performance of these models has been shown to be low when used for the recognition of Ottoman text. It has also been shown that these models that have been subjected to specialized training on Ottoman text have produced results that are not sufficient. In this study, an analysis of printed Ottoman Turkish documents in the Matbu font is conducted using a deep learning model that is proposed. Through the use of an end-to-end trainable architecture that integrates convolutional neural networks (CNNs) with bidirectional long short-term memory (BiLSTM) units, this study proposes an efficient solution to the Ottoman optical character recognition (OCR) issue. Experimental results show that the proposed model achieved overall scores for accuracy, sensitivity, and precision of 99.6%, 87.1%, and 93.3% on the test dataset respectively.

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“…The management of such giant components needs a lot of attention since it is challenging. Moreover, the management of network resources is important for many reasons such as reducing the amount of energy consumed within the networks [4], reducing the memory consumed [5], increasing the connectivity among the things (devices) [6], maximizing the utilization of resources [7], improve the scalability [6], efficiently propagate information [8], and more. All these aspects are considered issues in the IoT.…”

Section: Overviewmentioning

confidence: 99%

On the Consumption of Resources of the Hybrid Objects in the Internet of Things (Iot)

MUNDHER¹,

HADEED²,

Alramli³

2022

MINAR Journal

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The advent of the Internet of Things (IoT) introduces a variety of challenges. One of the most frequent challenges is the consumption of resources (e.g., power and memory). The consumption of resources is considered an important aspect when it comes to the general performance of the system. Therefore, it is important to consider this kind of issue before designing such systems or applications. This research aims to assess the number of resources consumed when having hybrid objects (static and dynamic) in the Internet of Things (IoT). The objects considered in this work can be devices such as sensors, smartphones, or other sensing objects that can be used in exchanging data (resources). The settings of the experiments performed in this work vary including colorful parameters (i.e., object deployment, movement patterns, and routing protocols) and a combination of them. In this work, 4 groups of experiments are designed considering different parameters. The simulations are evaluated in terms of two metrics; the amount of data exchanged and covered areas. These two metrics are used as indicators to measure the consumption of resources. The findings showed that the Gaussian strategy in deploying the static and mobile nodes in the IoT can reduce the consumption of resources (e.g., memory and power) and cover more areas within the simulation environment regardless of the movement pattern and the routing protocols used.

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Estimating memory requirements in wireless sensor networks using social tie strengths

Cited by 7 publications

References 7 publications

Investigating scientific collaboration networks in Iraq using cloud computing and data mining

Investigating scientific collaboration networks in Iraq using cloud computing and data mining

A Deep Learning Based Offline Optical Character Recognition Model for Printed Ottoman Turkish

On the Consumption of Resources of the Hybrid Objects in the Internet of Things (Iot)

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