Knowledge Engineering on Internet of Things through Reinforcement Learning

Shafik, Wasswa; Akabr, Seyed

doi:10.5120/ijca2020919952

Cited by 10 publications

(11 citation statements)

References 35 publications

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“…Now the G-IoT needs some technological solutions to manage waste and use it for energy production. In using the G-IoT concept, many countries, e.g., China, are producing energy, and the most developed countries are using their waste for many other purposes as well, in addition to producing several resources as alternatives to natural resources [26][27][28][29][30][31][32][33][34][35]. is strategy has been realized through the adoption of G-IoT concepts and innovative technologies such as sensors, smart waste collection, and waste burning systems.…”

Section: Reduction Of Hazardous Gas Emissionsmentioning

confidence: 99%

Green Internet of Things and Big Data Application in Smart Cities Development

et al. 2021

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This study reveals that increases in the global population command an augmented demand for products and services that calls for more effective ways of using existing natural resources and materials. The recent development of information and communication technologies, which had a great impact on many areas, also had a damaging effect on the environment and human health. Therefore, societies are moving toward a greener future by reducing the consumption of nonrenewable materials, raw materials, and resources while at the same time decreasing energy pollution and consumption. Since information technology is considered a tool for solving ecological difficulties, the green Internet of things (G-IoT) is playing a vital role in creating a sustainable home. Extensive data analysis is required to obtain a valuable overview of the large and diverse data generated by the G-IoT. The gathered information will facilitate forecasting, decision-making, and other activities related to smart urban services and then contribute to the incessant development of G-IoT technology. Therefore, even if sustainable and smart cities become an actuality, the G-IoT approach and the knowledge gained through big data (BD) analysis will make cities more sustainable, safer, and smarter. The goal of this article is to combine innovation in technological development with the main focus on resource sharing in creating cities that improve the quality of life while reducing pollution and realizing more efficient use of the raw materials. In the practice of big data science, it is always of interest to provide the best description of the data under consideration. Recent studies have pointed out the applicability of the statistical distributions in modeling data in applied sciences. In this article, we introduce a new family of statistical models to provide the best description of the life span of the wireless sensors network’s data. Based on the proposed approach, a special submodel called new exponent power-Weibull distribution is studied in detail. The applicability of the proposed model is shown by analyzing the life span of the wireless sensors network’s data.

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Section: Reduction Of Hazardous Gas Emissionsmentioning

confidence: 99%

Green Internet of Things and Big Data Application in Smart Cities Development

et al. 2021

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“…In case D , the need to analyze performance raises represented in equation (7). is a portion of the workload of the node is at times partially in the fog and the rest within the computations of the cloud.…”

Section: Service Of End-usersmentioning

confidence: 99%

“…This leads to the constrain that 0 D D 1by the node , then processed one in the cloud is − 1. Therefore, the need to have D is to obtain and satisfy (7). * = /,0F…”

Section: Service Of End-usersmentioning

confidence: 99%

See 1 more Smart Citation

Internet of Things-Based Energy Efficiency Optimization Model in Fog Smart Cities

Shafik

Matinkhah

Sanda

et al. 2021

JOIV : Int. J. Inform. Visualization

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In recent years, the IoT) Internet of Things (IoT) allows devices to connect to the Internet that has become a promising research area mainly due to the constant emerging of the dynamic improvement of technologies and their associated challenges. In an approach to solve these challenges, fog computing came to play since it closely manages IoT connectivity. Fog-Enabled Smart Cities (IoT-ESC) portrays equitable energy consumption of a 7% reduction from 18.2% renewable energy contribution, which extends resource computation as a great advantage. The initialization of IoT-Enabled Smart Grids including (FESC) like fog nodes in fog computing, reduced workload in Terminal Nodes services (TNs) that are the sensors and actuators of the Internet of Things (IoT) set up. This paper proposes an integrated energy-efficiency model computation about the response time and delays service minimization delay in FESC. The FESC gives an impression of an auspicious computing model for location, time, and delay-sensitive applications supporting vertically -isolated, service delay, sensitive solicitations by providing abundant, ascendable, and scattered figuring stowage and system associativity. We first reviewed the persisting challenges in the proposed state-of-the models and based on them. We introduce a new model to address mainly energy efficiency about response time and the service delays in IoT-ESC. The iFogsim simulated results demonstrated that the proposed model minimized service delay and reduced energy consumption during computation. We employed IoT-ESC to decide autonomously or semi-autonomously whether the computation is to be made on Fog nodes or its transfer to the cloud.

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“…For the Chinese language, an interesting case is proposed by Cai et al [45] which is the individual periphery recognition due to the fact of the significance to accomplish precise entity mining of Chinas' electronic medical records. The Chinese advanced the archetypal that chains multilength entity recognitions without depending on any medicinal lexicons.…”

Section: Role Of DL In Improvingmentioning

confidence: 99%

A 5G Beam Selection Machine Learning Algorithm for Unmanned Aerial Vehicle Applications

Shafik

Matinkhah

Ahmad

2020

Wireless Communications and Mobile Computing

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The unmanned aerial vehicles (UAVs) emerged into a promising research trend within the recurrent year where current and future networks are to use enhanced connectivity in these digital immigrations in different fields like medical, communication, and search and rescue operations among others. The current technologies are using fixed base stations to operate onsite and off-site in the fixed position with its associated problems like poor connectivity. This open gate for the UAV technology is to be used as a mobile alternative to increase accessibility with fifth-generation (5G) connectivity that focuses on increased availability and connectivity. There has been less usage of wireless technologies in the medical field. This paper first presents a study on deep learning to medical field application in general and provides detailed steps that are involved in the multiarmed bandit (MAB) approach in solving the UAV biomedical engineering technology device and medical exploration to exploitation dilemma. The paper further presents a detailed description of the bandit network applicability to achieve close optimal performance and efficiency of medical engineered devices. The simulated results depicted that a multiarmed bandit problem approach can be applied in optimizing the performance of any medical networked device issue compared to the Thompson sampling, Bayesian algorithm, and ε-greedy algorithm. The results obtained further illustrated the optimized utilization of biomedical engineering technology systems achieving thus close optimal performance on the average period through deep learning of realistic medical situations.

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Knowledge Engineering on Internet of Things through Reinforcement Learning

Cited by 10 publications

References 35 publications

Green Internet of Things and Big Data Application in Smart Cities Development

Green Internet of Things and Big Data Application in Smart Cities Development

Internet of Things-Based Energy Efficiency Optimization Model in Fog Smart Cities

A 5G Beam Selection Machine Learning Algorithm for Unmanned Aerial Vehicle Applications

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