Modified Flower Pollination Algorithm based Resource Management Model for Clustered IoT Network

Tarek; Chin-Shiuh; Yuh-Chung; Fatma

doi:https://doi.org/10.54216/IJWAC.040205

International Journal of Wireless and Ad Hoc Communication | 2022

A Novel Metaheuristic Optimization based Clustering with Routing Scheme for IoT Mobile Edge Computing Platform

Largescale IoT applications with thousands of geo distributed IoT gadgets making huge volumes of data impose immense challenges to designing transmission mechanisms that offer data transfer has less latency and great scalability. In this work, an investigation of a hierarchical Edge-Cloud publishes or subscribe brokers method was performed with the help of an effective two-tier routing structure for alleviating such problems whenever sending event notices in large scale IoT mechanisms. In this technique, IoT gadgets use the benefits of nearby edge brokers deliberately positioned in edge network for data supplying services for minimizing latency. This manuscript introduces a Novel Metaheuristic Optimization based Clustering with Routing Scheme for IoT Mobile Edge Computing Platform, named MOCRS-IoTMEC model. The projected MOCRS-IoTMEC model is mainly concentrated on the identification of optimal routes in the IoT assisted MEC environment by the use of pigeon inspired optimization (PIO) algorithm. Also, the LEACH protocol is applied to initially cluster the IoT devices. The PIO algorithm is applied to determine the fitness function to choose optimal routes. To depict the enhanced performance of the MOCRS-IoTMEC model, a detailed comparison study is made. The experimental outcomes reported the enhanced execution of the MOCRS-IoTMEC method over other approaches.

groups

Ahmed Abdelaziz mail -

Alia N. Mahmoud mail

link https://doi.org/10.54216/IJWAC.040202

Volume & Issue

Vol. Volume 4 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2022

Clustered IoT Based Data Fusion model for Smart Healthcare Systems

Futuristic sustainable computing solutions in e-healthcare applications were depends on the Internet of Things (IoT) and cloud computing (CC), has provided several features and realistic services. IoT-related medical devices gather the necessary data like recurrent transmissions in health limitations and upgrade the exactness of health limitations all inside a standard period. These data can be generated from different types of sensors in different formats. As a result, the data fusion is a big challenge to handle these IoT-based data. Moreover, IoT gadgets and medical parameters based on sensor readings are deployed for detecting diseases at the correct time beforehand attaining the rigorous state. Machine learning (ML) methods play a very significant task in determining decisions and managing a large volume of data. This manuscript offers a new Hyperparameter Tuned Deep learning Enabled Clustered IoT Based Smart Healthcare System (HPTDLEC-SHS) model. The presented HPTDLEC-SHS technique mainly focuses on the clustering of IoT devices using weighted clustering scheme and enables disease diagnosis process. At the beginning level, the HPTDLEC-SHS technique exploits min-max data normalization technique to convert the input data into compatible format. Besides, the gated recurrent unit (GRU) model is utilized to carry out the classification process. Finally, Jaya optimization algorithm (JOA) is exploited to fine tune the hyperparameters related to the GRU model. To demonstrate the enhanced performance of the HPTDLEC-SHS technique, an extensive comparative outcome highlighted its supremacy over other models.

groups

Ahmed Abdelaziz mail -

Alia N. Mahmoud mail

link https://doi.org/10.54216/JISIoT.060202

Volume & Issue

Vol. Volume 6 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2022

Early Detection of Cardiovascular Diseases using Deep Learning Feature Fusion and MRI Image Analysis

Deaths from cardiovascular disease (CVD) are more common than any other kind of mortality in the world. Electrocardiograms, two-dimensional echocardiograms, and stress tests are only a few of the diagnostic tools available to combat the rising incidence of cardiovascular disease. Since the electrocardiogram (ECG) is a clinical therapeutic agent that does not need any intrusive procedures, it may be used to diagnose cardiovascular disease (CVD) early and prescribe the appropriate treatment to prevent its fatal consequences. However, it may be time-consuming and demanding for a physical examination to interpret all these signals from various pieces of equipment, especially if they are non-stationary and repeating. It is necessary to use a computer-assisted model for rapid and precise prediction of CVDs since the Heart Signal from an ECG machine is not a stationary sign, the differences may not be repeated and may manifest at different intervals. In this paper, we offer a fully deep convolutional neural network-based automated diagnosis technique for cardiovascular illness. In order to extract those form characteristics from the Kaggle cardio-vascular disease dataset, CVD-MRI is employed in this detection method. In this case, the risk of cardiovascular disease is estimated using a completely deep convolution neural network and deep learning convolution filters (CVD). The suggested operation's main goal is to "improve the accuracy of completely deep convolution neural network while simultaneously reducing the complexity of the computation and the cost function." Accuracy of 88 percent is achieved by the proposed fully deep convolutional neural network.

groups

Abedallah Z. Abualkishik mail -

Rasha Almajed mail -

Saleh A. Almutairi mail

link https://doi.org/10.54216/FPA.080202

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

Details open_in_new

American Journal of Business and Operations Research | 2022

An Innovative Multi-Criteria Decision-Making (MCDM) Framework for Picking the Right Used Chemical Tankers: A Classified Model-Based Discussion

Because chemical tanker boats are so expensive to build and maintain, shipping firms may not be able to supply their clients with fair transportation pricing. As a result, shipping businesses may find various benefits and chances by purchasing second-hand chemical tanker vessels. But picking a chemical tanker is a hard task that requires overcoming numerous misunderstandings and weighing several conflicting factors.  A novel MCDM technique has been proposed in this study for this aim. EDAS approach is used in the proposed model, to handle uncertainty. In order to demonstrate efficacy, relevance, and robustness, the model was used to address decision-making issues involving the selection of suitable second-hand chemical tankers from a pool of 10 (alternatives). The chemical tanker boats were evaluated using 14 distinct choice criteria in the present article. The findings show that the most important factor is "CTC6′′ Maintenance cost," and the best and most preferred chemical tanker is "CTA6"

groups

Abedallah Z. Abualkishik mail -

Rasha Almajed mail -

William Thompson mail

link https://doi.org/10.54216/AJBOR.070201

Volume & Issue

Vol. Volume 7 / Iss. Issue 2

Details open_in_new

International Journal of Wireless and Ad Hoc Communication | 2022

Photovoltaic Charging Station Site Selection using a Multi-Criteria Decision Making (MCDM) Framework with a Novel Criterion Identification

Charging points on islands are becoming highly essential due to growing environmental concerns and an increase in the number of electric ships that need to be recharged. Site choice is the first step, but there is not enough research on island photovoltaic charging station site selection (IPVCS). To select the best IPVCS site, a multi-criteria decision-making framework (MCDM) is proposed. As a result of this structure, a new set of criteria for evaluating ships is formed, and current criteria are used to suggest two new ones: "Likelihood of adverse weather" and "Charging distance of the ship." Simultaneously time, the correlation among criteria is shaky at best. Therefore, the weight of the criteria is determined first. Then the rank of the alternatives is computed by the simultaneous evaluation of criteria and alternatives (SECA). Multi-criteria techniques like SECA may be used to objectively and accurately determine the weights of criteria. The best alternative is PVC3 followed by PVC1 then PVC2 then PVC4.

groups

Abedallah Z. Abualkishik mail -

Rasha Almajed mail

link https://doi.org/10.54216/IJWAC.040203

Volume & Issue

Vol. Volume 4 / Iss. Issue 2

Details open_in_new

American Journal of Business and Operations Research | 2022

Hybrid multi-criteria decision making model creation for bucket wheel excavator evaluation and selection

Residual tensions from manufacturing components and equipment assembly, functional job loads (fixed and dynamic loads), and the disrupted exploitation process all cause strains on bucket-wheel excavators in use (non-stationary dynamic loads). For the purpose of deciding which bucket wheel excavator (BWE) should participate in the rehabilitation and modernisation process, this study proposes a technique for assessing and rating BWEs. In this context, we use a multicriteria approache. The MCDM approach, including the Additive Ratio Assessment, is examined in this work (ARAS). The model, derived from MCDM procedures, are used to the task of assessing the primary metrics that define a BWE's performance. Each cluster of factors, together with their subparameters and potential values, will be subjected to the procedures. There are two sections to the model definitions. Using the ARAS technique, the first section identifies the parameters of most importance and defines their respective priority vectors. In the second section, options are analysed and ranked in accordance with the established criteria using a different set of techniques. The benefits were shown from two perspectives in the paper's findings. The first part is creating a framework that can be used to address other issues with the same structure. There's also the actual machine selection, which is based on a complicated examination of many different variables. In most cases, the model generalises well and may be re-used in future studies with comparable parameters.

groups

Abedallah Z. Abualkishik mail -

Rasha Almajed mail -

S. Ateeq Almutairi mail

link https://doi.org/10.54216/AJBOR.060106

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

Details open_in_new

International Journal of Wireless and Ad Hoc Communication | 2021

Multi-attribute decision-making method for prioritizing autonomous vehicles in real-time traffic management: towards active sustainable transport

In order to alleviate traffic congestion, traffic control systems are an important tool. These systems strive to boost the efficiency of road systems to optimize traffic flow on individual road segments. The benefits of real-time traffic control systems might be increased by integrating new telecommunication and autonomous car technology. There is six real-time traffic advancing sustainable development examined in this study: variable message signs and ramp meters, traffic diversion, and the integration of driverless vehicles into other traffic management systems, with four key criteria: economics, community and social, ecologic and traffic security, as well as 13 sub-criteria using MCDM. To do this, we offer unique additions to the WASPAS technique.

groups

Abedallah Z. Abualkishik mail -

Rasha Almajed mail -

William Thompson mail

link https://doi.org/10.54216/IJWAC.030204

Volume & Issue

Vol. Volume 3 / Iss. Issue 2

Details open_in_new

International Journal of Wireless and Ad Hoc Communication | 2022

Modeling of Leopard optimization based Node Localization Technique for Wireless Communication Networks

Wireless communication inculcates transfer of information not having any physical connection among two or more points. The significant operation of a sensor network becomes collecting and forwarding data to destiny. It is highly crucial to have an awareness regarding the place of collected data. This data is acquired by leveraging localization method in wireless sensor networks (WSNs). Localization is a way of determining the sensor nodes (SNs) location. Localization of SNs turns out to be an exciting research area, and several studies were performed till now. It is very favourable to model scalable, effectual, and low-cost localization systems for WSNs. This study develops a Leopard optimization based Node Localization Technique for Wireless Communication (LONLT-WC). The goal of the LONLT-WC model is to recognize the location of the nodes involved in the network. The LONLT-WC model involves the design of snow leopard optimization (SLO) algorithm, inspired from the characteristics of snow leopards.  The presented LONLT-WC approach computes the unidentified location of the nodes utilizing anchor nodes in the network with the accomplishment of least error rate. The experimental analysis of the LONLT-WC model involves a series of simulations and the results highlighted the betterment of the presented technique.

groups

Manal M. Nasir mail -

Salim M. Hebrisha mail

link https://doi.org/10.54216/IJWAC.040204

Volume & Issue

Vol. Volume 4 / Iss. Issue 2

Details open_in_new

American Journal of Business and Operations Research | 2022

Forecasting crude oil prices based on machine learning statistics methods and random sparse Bayesian learning

Oil price forecasting has received a great deal of interest from both professionals and scholars because of the unique characteristics of the oil price and its enormous impact on a wide range of economic sectors. In response to this problem, the authors set out to develop a strong model for accurately predicting the Brent crude oil price. We employed the Linear Regression and Random Forest models to examine the market interrelationships present in the oil price time series. Next, the models are given weights such that the experimental time series can be accurately predicted. These errors are quantified in terms of root mean squared errors (RMSE), average errors (MAE), and average percentage errors (MAPE). Results and forecast accuracy of the model as compared to the other model. To maximize their output and order levels and reduce the negative impact of potential shocks, countries that produce and import crude oil benefit greatly from accurate crude oil price forecasts.

groups

Irina V. Pustokhin mail -

Denis A. Pustokhin mail

link https://doi.org/10.54216/AJBOR.070202

Volume & Issue

Vol. Volume 7 / Iss. Issue 2

Details open_in_new

International Journal of Wireless and Ad Hoc Communication | 2022

Modified Flower Pollination Algorithm based Resource Management Model for Clustered IoT Network

Internet of Things (IoT) is a technological innovation that defined interaction and computation of latest period. The objects of Internet of Things would empower by embedded gadgets whose limited sources has to be managed effectively. IoT usually means a network of devices connected through wireless network and interacts through internet. Resource management, particularly energy management, becomes a serious problem while devising IoT gadgets. Numerous researchers stated that routing and clustering were energy effectual solutions for optimum resource management in IoT setting. This study introduces a Modified Flower Pollination Algorithm based Resource Management (MFPA-RMM) model for Clustered IoT Environment. The presented MFPA-RMM model majorly focuses on the clustering the IoT devices in such a way that the resources are proficiently managed. The MFPA-RMM model is derived based on the fuzzy c-means (FCM) with FPA. The FPA approach is called heuristic algorithm has benefits of global optimization and faster convergence, therefore it was incorporated to FCM system for resolving the advantages and disadvantages of FCM method termed FCM-FPA mechanism. The result analysis of the MFPA-RMM model reported the enhanced performance of the MFPA-RMM model over other well-known techniques like LEACH and TEEN.

groups

Tarek Gaber mail -

Chin-Shiuh Shieh mail -

Yuh-Chung Lin mail -

Fatma Masmoudi mail

link https://doi.org/10.54216/IJWAC.040205

Volume & Issue

Vol. Volume 4 / Iss. Issue 2

Details open_in_new