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  • Open Access

    ARTICLE

    A New Task Scheduling Scheme Based on Genetic Algorithm for Edge Computing

    Zhang Nan1, Li Wenjing1,*, Liu Zhu1, Li Zhi1, Liu Yumin1, Nurun Nahar2

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 843-854, 2022, DOI:10.32604/cmc.2022.017504 - 03 November 2021

    Abstract With the continuous evolution of smart grid and global energy interconnection technology, amount of intelligent terminals have been connected to power grid, which can be used for providing resource services as edge nodes. Traditional cloud computing can be used to provide storage services and task computing services in the power grid, but it faces challenges such as resource bottlenecks, time delays, and limited network bandwidth resources. Edge computing is an effective supplement for cloud computing, because it can provide users with local computing services with lower latency. However, because the resources in a single edge… More >

  • Open Access

    ARTICLE

    Low Cost Autonomous Learning and Advising Smart Home Automation System

    Daniel Chioran*, Honoriu Valean

    Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1939-1952, 2022, DOI:10.32604/iasc.2022.020649 - 09 October 2021

    Abstract In today’s world, more than ever before, we are fascinated and drawn towards smart autonomous devices that make our lives safer and more comfortable. Devices that aid in reducing our energy consumption are also highly appreciated but often quite expensive to buy. This context is favorable for developing an autonomous smart home automation system (SHAS) with energy-saving potential and low price, making it widely accessible. This paper presents the design and prototype implementation of such a low-cost micro-controller based autonomous SHAS that learns the resident’s work schedule and integrates a wide array of sensors and… More >

  • Open Access

    ARTICLE

    An Energy Aware Algorithm for Edge Task Offloading

    Ao Xiong1, Meng Chen1,*, Shaoyong Guo1, Yongjie Li2, Yujing Zhao2, Qinghai Ou3, Chuan Liu4, Siwen Xu5, Xiangang Liu6

    Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1641-1654, 2022, DOI:10.32604/iasc.2022.018881 - 09 October 2021

    Abstract To solve the problem of energy consumption optimization of edge servers in the process of edge task unloading, we propose a task unloading algorithm based on reinforcement learning in this paper. The algorithm observes and analyzes the current environment state, selects the deployment location of edge tasks according to current states, and realizes the edge task unloading oriented to energy consumption optimization. To achieve the above goals, we first construct a network energy consumption model including servers’ energy consumption and link transmission energy consumption, which improves the accuracy of network energy consumption evaluation. Because of More >

  • Open Access

    ARTICLE

    Generating Synthetic Data to Reduce Prediction Error of Energy Consumption

    Debapriya Hazra, Wafa Shafqat, Yung-Cheol Byun*

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3151-3167, 2022, DOI:10.32604/cmc.2022.020143 - 27 September 2021

    Abstract Renewable and nonrenewable energy sources are widely incorporated for solar and wind energy that produces electricity without increasing carbon dioxide emissions. Energy industries worldwide are trying hard to predict future energy consumption that could eliminate over or under contracting energy resources and unnecessary financing. Machine learning techniques for predicting energy are the trending solution to overcome the challenges faced by energy companies. The basic need for machine learning algorithms to be trained for accurate prediction requires a considerable amount of data. Another critical factor is balancing the data for enhanced prediction. Data Augmentation is a… More >

  • Open Access

    ARTICLE

    Cross-Layer Hidden Markov Analysis for Intrusion Detection

    K. Venkatachalam1, P. Prabu2, B. Saravana Balaji3, Byeong-Gwon Kang4, Yunyoung Nam4,*, Mohamed Abouhawwash5,6

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3685-3700, 2022, DOI:10.32604/cmc.2022.019502 - 27 September 2021

    Abstract Ad hoc mobile cloud computing networks are affected by various issues, like delay, energy consumption, flexibility, infrastructure, network lifetime, security, stability, data transition, and link accomplishment. Given the issues above, route failure is prevalent in ad hoc mobile cloud computing networks, which increases energy consumption and delay and reduces stability. These issues may affect several interconnected nodes in an ad hoc mobile cloud computing network. To address these weaknesses, which raise many concerns about privacy and security, this study formulated clustering-based storage and search optimization approaches using cross-layer analysis. The proposed approaches were formed by cross-layer analysis based… More >

  • Open Access

    ARTICLE

    Type II Fuzzy Logic Based Cluster Head Selection for Wireless Sensor Network

    J. Jean Justus1,*, M. Thirunavukkarasan2, K. Dhayalini3, G. Visalaxi4, Adel Khelifi5, Mohamed Elhoseny6,7

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 801-816, 2022, DOI:10.32604/cmc.2022.019122 - 07 September 2021

    Abstract Wireless Sensor Network (WSN) forms an essential part of IoT. It is embedded in the target environment to observe the physical parameters based on the type of application. Sensor nodes in WSN are constrained by different features such as memory, bandwidth, energy, and its processing capabilities. In WSN, data transmission process consumes the maximum amount of energy than sensing and processing of the sensors. So, diverse clustering and data aggregation techniques are designed to achieve excellent energy efficiency in WSN. In this view, the current research article presents a novel Type II Fuzzy Logic-based Cluster… More >

  • Open Access

    ARTICLE

    A Virtual Machine Placement Strategy Based on Virtual Machine Selection and Integration

    Denghui Zhang1,*, Guocai Yin2

    Journal on Internet of Things, Vol.3, No.4, pp. 149-157, 2021, DOI:10.32604/jiot.2021.016936 - 30 December 2021

    Abstract Cloud data centers face the largest energy consumption. In order to save energy consumption in cloud data centers, cloud service providers adopt a virtual machine migration strategy. In this paper, we propose an efficient virtual machine placement strategy (VMP-SI) based on virtual machine selection and integration. Our proposed VMP-SI strategy divides the migration process into three phases: physical host state detection, virtual machine selection and virtual machine placement. The local regression robust (LRR) algorithm and minimum migration time (MMT) policy are individual used in the first and section phase, respectively. Then we design a virtual More >

  • Open Access

    ARTICLE

    Adaptive Error Curve Learning Ensemble Model for Improving Energy Consumption Forecasting

    Prince Waqas Khan, Yung-Cheol Byun*

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1893-1913, 2021, DOI:10.32604/cmc.2021.018523 - 21 July 2021

    Abstract Despite the advancement within the last decades in the field of smart grids, energy consumption forecasting utilizing the metrological features is still challenging. This paper proposes a genetic algorithm-based adaptive error curve learning ensemble (GA-ECLE) model. The proposed technique copes with the stochastic variations of improving energy consumption forecasting using a machine learning-based ensembled approach. A modified ensemble model based on a utilizing error of model as a feature is used to improve the forecast accuracy. This approach combines three models, namely CatBoost (CB), Gradient Boost (GB), and Multilayer Perceptron (MLP). The ensembled CB-GB-MLP model’s… More >

  • Open Access

    ARTICLE

    The Relationship between Urbanization and Domestic Energy Consumption: An Empirical Study of Shandong Province, China

    Doudou Liu1,*, Liang Qiao2,3, Feng Zhang4, Xueliang Yuan2

    Energy Engineering, Vol.118, No.5, pp. 1395-1409, 2021, DOI:10.32604/EE.2021.014697 - 16 July 2021

    Abstract The rapid development of urbanization has led to a rapid increase in total energy consumption. The proportion of domestic energy consumption to total energy consumption has gradually increased and has become the major driving force for energy consumption. With the pressure from urbanization and domestic energy consumption, it is necessary to study the impact of urbanization on domestic energy consumption of the regional level and to explore the function paths of these two factors. The findings are helpful to realize sustainable development based on the actual situation analysis, horizontal survey data and statistical yearbook panel… More >

  • Open Access

    ARTICLE

    LOA-RPL: Novel Energy-Efficient Routing Protocol for the Internet of Things Using Lion Optimization Algorithm to Maximize Network Lifetime

    Sankar Sennan1, Somula Ramasubbareddy2, Anand Nayyar3,4, Yunyoung Nam5,*, Mohamed Abouhawwash6,7

    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 351-371, 2021, DOI:10.32604/cmc.2021.017360 - 04 June 2021

    Abstract Energy conservation is a significant task in the Internet of Things (IoT) because IoT involves highly resource-constrained devices. Clustering is an effective technique for saving energy by reducing duplicate data. In a clustering protocol, the selection of a cluster head (CH) plays a key role in prolonging the lifetime of a network. However, most cluster-based protocols, including routing protocols for low-power and lossy networks (RPLs), have used fuzzy logic and probabilistic approaches to select the CH node. Consequently, early battery depletion is produced near the sink. To overcome this issue, a lion optimization algorithm (LOA)… More >

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