A Smart Clustering Based Approach To Dynamic Bandwidth Allocation In Wireless Networks

Awad, Mohammed; Abuhasan, Abdelmunem

doi:10.5121/ijcnc.2016.8106

Cited by 11 publications

(5 citation statements)

References 15 publications

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“…This work is mainly focused on improving the QoS through user QoS and context QoS inputs. A smart routing methodology [31] has been implemented in the literature where the routing mechanism chooses cluster heads which adapt to changing bandwidth conditions depending on the number of users which are found to dynamically vary from time to time. K-means clustering methodology has been utilized which also helps in identify the peak bandwidth needs.…”

Section: Related Workmentioning

confidence: 99%

A Metaheuristic Approach Towards Enhancement of Network Lifetime in Wireless Sensor Networks

2023

KSII TIIS

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Sensor networks are now an essential aspect of wireless communication, especially with the introduction of new gadgets and protocols. Their ability to be deployed anywhere, especially where human presence is undesirable, makes them perfect choices for remote observation and control. Despite their vast range of applications from home to hostile territory monitoring, limited battery power remains a limiting factor in their efficacy. To analyze and transmit data, it requires intelligent use of available battery power. Several studies have established effective routing algorithms based on clustering. However, choosing optimal cluster heads and similarity measures for clustering significantly increases computing time and cost. This work proposes and implements a simple two-phase technique of route creation and maintenance to ensure route reliability by employing nature-inspired ant colony optimization followed by the fuzzy decision engine (FDE). Benchmark methods such as PSO, ACO and GWO are compared with the proposed HRCM's performance. The objective has been focused towards establishing the superiority of proposed work amongst existing optimization methods in a standalone configuration. An average of 15% improvement in energy consumption followed by 12% improvement in latency reduction is observed in proposed hybrid model over standalone optimization methods.

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Section: Related Workmentioning

confidence: 99%

A Metaheuristic Approach Towards Enhancement of Network Lifetime in Wireless Sensor Networks

2023

KSII TIIS

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“…e author developed a new scheduling scheme to avoid the drawbacks of the reclustering method. A hyper round can be calculated with fuzzy inputs [18,19]. e author [20] uses a greedy algorithm for the proposed protocol, forms clusters, and establishes links based on the Hamilton path for routing.…”

Section: Literature Reviewmentioning

confidence: 99%

Hierarchical Routing with Optimal Clustering Using Fuzzy Approach for Network Lifetime Enhancement in Wireless Sensor Networks

Kumaran¹,

Bagwari

Nagarajan

et al. 2022

Mobile Information Systems

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Wireless sensor network (WSN) is one of the major emerging fields in real-time scenarios due to the features of sensors. Sensors are used in remote areas because of their usability. Energy is the major issue to be addressed in sensor networks. This energy affects the entire network lifetime. To resolve the energy issues of WSN, clustering and proper routing are the two major procedures that have to be focused on. In this paper, hierarchical routing with optimal clustering using a fuzzy system (HROCF) is proposed for network lifetime enhancement in WSNs. This proposed scheme uses two phases: selection of a cluster leader with a fuzzy inference system and the hierarchical routing strategy are implemented for routing to consume the energy of the sensors resourcefully. During the first phase, a proper cluster leader can be nominated through the fuzzy inputs, namely: residual energy, cost, and position. In the second phase, hierarchical routing can communicate the information from the lower region to the sink node through the cluster leaders in higher regions. These two phases help to prolong the lifetime. The simulation analysis demonstrates that the HROCF scheme reduces the overall energy spent in the network and supports an increase in the lifespan in terms of more alive sensors compared to the existing schemes.

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“…As in the below Fig. 3 shows the standard three layers of RBFNN, which is the input layer that supplies the high dimensional data inputs to the network, the hidden layer that re-distributes the inputs per the concept of clustering [21] to a set of centers, those centers that mainly utilize the Gaussian function as activation as it represents the radian functionality, by this the problem transformed into linearly separable, and the output layer define the separation and estimation of the outputs as single or many possible values [22].…”

Section: Radial Basis Function Neural Network Rbfnnmentioning

confidence: 99%

Classification and Prediction of Low-Density Lipoprotein Cholesterol LDL-C in The Palestinian Patients Using Machine Learning Techniques

2022

IJIES

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Cholesterol is one of the major causes of health problems in Palestine and globally in the world, with percentages of 31.5% and 31.4%, respectively. Cholesterol has four main values which are the Total Cholesterol TCH, Triglycerides TG, Low-Density Lipoprotein Cholesterol LDL-C, and High-Density Lipoprotein Cholesterol HDL-C. The main level that is a major factor for Cardiovascular Disease CVD is LDL-C that is called bad cholesterol, it builds upon the arteries walls narrowing them and slowing the blood flow feeding the heart and brain, causing heart attacks and strokes. Machine learning (ML) techniques support recognizing and diagnosing the LDL-C, which is based on the past medical history and heuristic data. This research utilized ML techniques for classifying and predicting the LDL-C. Additionally, the techniques applied to the HDL-C classification and prediction. The utilized techniques are the Artificial Neural Networks ANNs, Recurrent Neural Networks RNN, Radial Basis Function Neural Networks RBFNN, Fuzzy Logic, Support Vector Machines SVM, Decision Tress DT, Logistic Regression LR, and a hybrid model of combining the ANNs with Fuzzy Logic for optimizing the results accuracy and reducing the classifying error. The dataset was collected from Palestine with cooperation with the Palestinian Ministry of Health MoH. Another additional supportive international dataset is utilized, which is collected by the Korean National Health and Nutritional Examination Survey KNHANES, it's used to generalize the results and compare with the other efforts. The obtained result outperformed the other efforts done on the same idea with a significant difference, it reached the accuracy of 97.10% in the international dataset and 95.55% in the national dataset.

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A Smart Clustering Based Approach To Dynamic Bandwidth Allocation In Wireless Networks

Cited by 11 publications

References 15 publications

A Metaheuristic Approach Towards Enhancement of Network Lifetime in Wireless Sensor Networks

A Metaheuristic Approach Towards Enhancement of Network Lifetime in Wireless Sensor Networks

Hierarchical Routing with Optimal Clustering Using Fuzzy Approach for Network Lifetime Enhancement in Wireless Sensor Networks

Classification and Prediction of Low-Density Lipoprotein Cholesterol LDL-C in The Palestinian Patients Using Machine Learning Techniques

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