V. R. Balaji^1,*, T. Kalavathi², J. Vellingiri³, N. Rajkumar⁴, Venkat Prasad Padhy⁵

Computer Systems Science and Engineering, Vol.42, No.1, pp. 289-301, 2022, DOI:10.32604/csse.2022.020971 - 02 December 2021

Abstract Data offloading at the network with less time and reduced energy consumption are highly important for every technology. Smart applications process the data very quickly with less power consumption. As technology grows towards 5G communication architecture, identifying a solution for QoS in 5G through energy-efficient computing is important. In this proposed model, we perform data offloading at 5G using the fuzzification concept. Mobile IoT devices create tasks in the network and are offloaded in the cloud or mobile edge nodes based on energy consumption. Two base stations, small (SB) and macro (MB) stations, are initialized More >

Xiao Wu^1,*, Zhaoting Wang¹, Mei Dong², Longfei Dong¹, Quan Ge¹

FDMP-Fluid Dynamics & Materials Processing, Vol.18, No.1, pp. 145-158, 2022, DOI:10.32604/fdmp.2022.018227 - 10 November 2021

Abstract Liquefied natural gas (LNG) is an important energy source and occupies an important proportion in natural gas consumption, therefore, the selection of appropriate liquefaction processes and related optimization should be seen as subjects of great importance. Accordingly, in the present review, we provide a comparative and critical analysis of the current status of natural gas liquefaction technology through examination of the advantages and disadvantages associated with three natural gas liquefaction processes (namely, the cascade liquefaction cycle, the expander-based cycle and the mixed refrigerant cycle). It is shown that the energy consumption related to the cascade More >

Abolfazl Mehbodniya¹, Surbhi Bhatia², Arwa Mashat³, Mohanraj Elangovan^4,*, Sudhakar Sengan⁵

Computer Systems Science and Engineering, Vol.41, No.3, pp. 1071-1082, 2022, DOI:10.32604/csse.2022.021529 - 10 November 2021

Abstract Wireless Sensor Network (WSN) is an independent device that comprises a discrete collection of Sensor Nodes (SN) to sense environmental positions, device monitoring, and collection of information. Due to limited energy resources available at SN, the primary issue is to present an energy-efficient framework and conserve the energy while constructing a route path along with each sensor node. However, many energy-efficient techniques focused drastically on energy harvesting and reduced energy consumption but failed to support energy-efficient routing with minimal energy consumption in WSN. This paper presents an energy-efficient routing system called Energy-aware Proportional Fairness Multi-user… More >

Zhang Nan¹, Li Wenjing^1,*, 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 >

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 >

Ao Xiong¹, Meng Chen^1,*, 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 >

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 >

K. Venkatachalam¹, P. Prabu², B. Saravana Balaji³, Byeong-Gwon Kang⁴, Yunyoung Nam^4,*, Mohamed Abouhawwash^5,6

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 >

J. Jean Justus^1,*, M. Thirunavukkarasan², K. Dhayalini³, G. Visalaxi⁴, Adel Khelifi⁵, Mohamed Elhoseny^6,7

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 >

Denghui Zhang^1,*, 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 >