Energy-efficient Resource Research Articles

Energy Efficient Resource Allocation for eHealth Monitoring Wireless Body Area Networks With Backscatter Communication

Electronic Health (eHealth) monitoring systems with wireless body area networks (WBANs) have recently emerged as promising solutions to provide sustainable and high-quality health services. In this paper, we propose an optimization framework to maximize the energy efficiency (EE) of a WBAN assisted by backscatter communication (BackCom) and energy harvesting technologies, subject to quality-of-service and power budget constraints. More specifically, the optimization problem jointly optimizes the transmit power of the aggregator, transmission time, and backscatter time of the WBAN consisting of energy-constrained sensor nodes (SNs) which have the ability to harvest energy from the signals transmitted by the aggregator. A generalized gamma distribution is adopted to characterize the channel propagation characteristics of patients under different arbitrary body movements and their corresponding transmission requirements during daily life activities. It is shown that the formulated EE optimization problem is a quasi-concave nonlinear fractional program, and it is transformed to an equivalent parametric problem by using the Dinkelbach algorithm to obtain the solution. We exploit the structure of the optimization problem and propose a low-complexity iterative-based suboptimal heuristic with performance fairly close to the optimized solution. Simulation results demonstrate the effectiveness of the proposed schemes in maximizing EE of the WBAN, whereas the comparisons with the related work from the literature reaffirm the superiority of the proposed algorithms.

Nonlinear and nonparametric causal relationship between financial inclusion and sustainable environment in South Asia.

This study analyzes the causal connection between financial inclusion and carbon emission in selected South Asian countries through a quantile technique-based linear Granger and non-parametric causality test. The analysis of the studycovers quarterly data from 1980Q1 to 2019Q4. However, the linear Granger causality assessment outcome does not indicate any causal relationship between financial inclusion and carbon emission. In contrast, results from non-parametric assessment reveal a non-linear connection between the variables. The non-parametric test results of the South Asian countries exhibit that financial inclusion leads to carbon emission, which instigates the deterioration of the environment, except for Bhutan. Subsequently, creating awareness by promoting renewable energy resources is essential while investing in fuel-efficient technology to reduce the dependence on fossil fuels. The results of this study provide significant information to the governments and policymakers in emerging countries to improve financial literacy among people to reduce the risk of global warming by encouraging investment in energy-efficient resources.

Multi-factor nature inspired SLA-aware energy efficient resource management for cloud environments

Multi-factor nature inspired SLA-aware energy efficient resource management for cloud environments

Digital twin intelligent system for industrial internet of things-based big data management and analysis in cloud environments

This work surveys and illustrates multiple open challenges in the field of industrial Internet of Things (IoT)-based big data management and analysis in cloud environments. Challenges arising from the fields of machine learning in cloud infrastructures, artificial intelligence techniques for big data analytics in cloud environments, and federated learning cloud systems are elucidated. Additionally, reinforcement learning, which is a novel technique that allows large cloud-based data centers, to allocate more energy-efficient resources is examined. Moreover, we propose an architecture that attempts to combine the features offered by several cloud providers to achieve an energy-efficient industrial IoT-based big data management framework (EEIBDM) established outside of every user in the cloud. IoT data can be integrated with techniques such as reinforcement and federated learning to achieve a digital twin scenario for the virtual representation of industrial IoT-based big data of machines and room temperatures. Furthermore, we propose an algorithm for determining the energy consumption of the infrastructure by evaluating the EEIBDM framework. Finally, future directions for the expansion of this research are discussed.

Energy Efficiency Maximization for Hybrid TDMA-NOMA System With Opportunistic Time Assignment

In this paper, we consider an energy efficient resource allocation technique for a hybrid time division multiple access (TDMA) - non-orthogonal multiple access (NOMA) system. In such a hybrid system, the available time for transmission is divided into several sub-time slots, and a sub-time slot is allocated to serve a group of users (i.e., cluster). Furthermore, signals for the users in each cluster are transmitted based on the NOMA approach. With NOMA, multiple users can be served simultaneously through utilizing power domain multiplexing at transmitter and successive interference cancellation (SIC) at receiver. In this paper, to maximize the energy efficiency (EE), we jointly allocate both the available time slots and the available transmit power in the hybrid TDMA-NOMA system. In particular, we formulate an EE maximization (EE-Max) problem aiming to maximize the overall EE of the system with a per-user minimum rate and transmit power constraints. However, this joint optimization problem is non-convex in nature, and thus, cannot be solved directly. Therefore, we develop an iterative algorithm by approximating the original problem into a convex one with sequential convex approximation (SCA) and a novel second-order cone (SOC) approach. Simulation results demonstrate that the performance of the proposed hybrid TDMA-NOMA system with joint resource allocation outperforms the system with equal time allocation in terms of the overall EE. Simulation results further confirm that the proposed iterative approaches with SCA and SOC techniques converge within a few number of iterations while yielding the solution to the original non-convex problem.

The Role of Plant Foods in the Evolution and Dispersal of Early Humans: A Perspec- tive from Across the Wallace Line

In recent years, new evidence for the early use of plant foods has challenged the stereotype of the meat-eating Paleolithic. Whilst often making up the smaller component of the diet, plant foods are key to hominin diets, carbohydrates especially providing an efficient energy resource. This paper reviews the current evidence for the role of plant foods in the evolution and dispersal of early modern humans and our closest ancestors, with a focus on new evidence for early diet from Island Southeast Asia, Australia and New Guinea. It demonstrates the importance of plant foods and their processing, to the dietary flexibility and adaptive capacity of our species.

PRESENT DEVELOPMENT OF ANODE MATERIALS IN LITHIUM-ION BATTERIES: A REVIEW

Recently the world is suffering from insufficiency of natural energy resources in comparison to higher energy consumption, lack of environmental health (Climate change, Air-pollution, plastic waste and so on) and the sudden enhanced usage of- portable electronics, electric vehicles, HEV on daily bases- motivates scientist to invent new, efficient, clean and low-cost alternative energy resources such as- Lithium Ion Battery. On account of high capacity, fast charge-discharge rates, light weight, long lifetime, high stability Lithium-ion batteries are very promising energy storage devices. Their great electrochemical performance makes them commercially viable. These rechargeable batteries have proved themselves as a rising star and contain a lot more potential to fulfil the future energy requirements. This paper will highlight a review on Li-ion Batteries from their birth till now, the growth and increments on their fabrication techniques and represents advantages of carbonic anodes. It also gives us an idea about why carbon-based nanostructures (graphite, graphene, CNTs, CQDs, and GQDs) and their composites are the best choice of anode materials for Li-ion Batteries. We hope to present a brief knowledge about different carbon-based anode with comprehensive study of different anodic materials which were already available in the literature.

Energy-Efficient Mobile Agent Protocol for Secure IoT Sustainable Applications

The Internet of Things (IoT) and sensor technologies are combined with various communication networks in smart appliances and perform a significant role. Connected devices sense, analyze, and send environmental data, as well as support applications’ connections. Mobile agents can be explored to provide sensing intelligence with IoT-based systems. Many strategies have been proposed to address the issue of energy efficiency while maintaining the sensor load at a low cost. However, advancements are still desired. Furthermore, without fully trustworthy relationships, sensitive data are at risk, and the solution must provide privacy protection against unexpected events. With the development of two algorithms, this study proposes a mobile agent-based efficient energy resource management solution and also protects IoT appliances. Firstly, the software agents perform a decision using past and present precepts, and by exploring rule-based conditions, it offers an energy-efficient recommended system. Second, data from IoT appliances are securely evaluated on edge interfaces before being transferred to end-centers for verification. Simulations-based tests are conducted and verified the significance of the proposed protocol against other studies in terms of network metrics.

Energy Efficient Resource Allocation for IRS Assisted CoMP Systems

A novel intelligent reconfigurable surface (IRS) assisted coordinated multi-point (CoMP) system is proposed. Our objective is to maximize the energy efficiency (EE) of this system by jointly optimizing base station (BS) clustering, user association, sub-carrier assignment, power allocation, and optimal design of the IRS, while satisfying the users’ quality of service requirements. Considering the amplitude and phase shift characteristics, both ideal and non-ideal IRS are investigated. The formulated problem is proved to be NP-hard. By analyzing its structure, we decouple it into the power allocation sub-problem, the BS clustering, UE association, and sub-carrier assignment sub-problem, and the reflection coefficients design sub-problem. For the power allocation sub-problem, we invoke the fractional programming to find the optimal solution. For the reflection coefficients design sub-problem of ideal IRS, the optimal solution is derived with the Lagrangian dual method. Whereas quantization-based method is employed to find the discrete phase shifts for non-ideal IRS. We finally propose a genetic algorithm (GA) to represent the potential solutions of sub-carrier assignment, and combine the other two optimization algorithms as fitness estimator in GA. Numerical results validate the feasibility, fast convergence, and the flexibility of the proposed algorithm. It shows that the proposed scheme outperform the system without IRS and that with a random initialized IRS.

국내 에너지효율투자 활성화를 위한 에너지효율향상 의무화제도(EERS) 제도개선 방안

탄소중립 시대에서 에너지효율은 단순히 연료절약을 통한 경제적 편익 제공에 한정되지 않고 가장 효과적인 기후변화 대응수단으로 평가받는다. 본 연구에서는 에너지효율 향상을 위한 제도의 일환인 에너지효율향상 의무화제도(Energy Efficiency Resource Standards, EERS)의 국내 현황에 대해 분석하여 문제점을 파악하고, 해당 제도가 성공적으로 정착해 에너지효율투자 환경을 조성할 수 있도록 제도개선 방안을 제시한다. 국내 EERS 제도는 전력 및 열, 가스의 세 가지 에너지원으로 이루어져 있으며 에너지원별 목표와 의무당사자, 이행수단이 다르다. 또한, 각 에너지는 국내 시장현황이 다르고, 에너지원을 담당하는 EERS 의무당사자의 시장입지가 달라 의무당사자별 에너지효율 사업의 경제성과 효율투자 유인발생이 다르다. EERS 제도가 에너지효율투자 유인을 발생시키려면 비용보전 방안을 갖추고, 페널티 및 인센티브 제도를 활용해야 하나 현 제도설계 하에서는 활용이 어렵다. 에너지원 간의 전환 허용, 잠재량 분석을 통한 목표수준 재설정, 목표형태 조정을 통해 EERS 제도가 개선될 수 있을 것이다.

Design of cultural emperor penguin optimizer for energy-efficient resource scheduling in green cloud computing environment.

In recent times, energy related issues have become challenging with the increasing size of data centers. Energy related issues problems are becoming more and more serious with the growing size of data centers. Green cloud computing (GCC) becomes a recent computing platform which aimed to handle energy utilization in cloud data centers. Load balancing is generally employed to optimize resource usage, throughput, and delay. Aiming at the reduction of energy utilization at the data centers of GCC, this paper designs an energy efficient resource scheduling using Cultural emperor penguin optimizer (CEPO) algorithm, called EERS-CEPO in GCC environment. The proposed model is aimed to distribute work load amongst several data centers or other resources and thereby avoiding overload of individual resources. The CEPO algorithm is designed based on the fusion of cultural algorithm (CA) and emperor penguin optimizer (EPO), which boosts the exploitation capabilities of EPO algorithm using the CA, shows the novelty of the work. The EERS-CEPO algorithm has derived a fitness function to optimally schedule the resources in data centers, minimize the operational and maintenance cost of the GCC, and thereby decrease the energy utilization and heat generation. To ensure the improvised performance of the EERS-CEPO algorithm, a wide range of experiments is performed and the experimental outcomes highlighted the better performance over the recent state of art techniques.

5G Hybrid System Design and Energy Efficient Resource Allocation Deployment

5G communication provide a promising platform for new, innovative and diverse enhanced mobile broadband (eMBB) and massive device connectivity applications, such as streaming media, machine vision and Internet of Things (IoT), real-time and dynamic data processing, intensive computation. However, 5G multimedia devices deployment relies on the coverage of base stations, which is inefficient and costly in wide-area coverage and physical penetration. In this paper, a 5G and wide-area Ad Hoc network fusion architecture is proposed to flexibly provide scalable 5G and extensible low-power devices interconnection liberated from geographical restriction, which consists of a low-power wide-area network and an edge processing gateway. Moreover, the intelligent edge gateway near a specific base station can support real-time ultra-high-definition video streams access and achieve traffic optimization by compressing, intelligent identification and preprocessing of the video streams to alleviate traffic congestion. The coverage capacity efficiency of wide-area Ad Hoc networks is restricted by the "funnel effect" in multihop cascading, and adaptive resource allocation strategies will present a promising approach to realize energy-efficient deployment. A non-convex optimization problem is formulated to maximize the energy-efficient deployment of Ad Hoc network. Then, a coordination and optimization strategy of internal resource allocation in deployed multihop nodes based on Lagrange relaxation algorithm was presented to solve the optimization problem. The actual system deployment and real measurement proved that the system function is running normally and stably. The experimental simulation test results show that the proposed 5G wide-area Ad Hoc network can effectively make up for the adaptive streaming needs of 5G coverage blind spots. Compared with static resource allocation, the proposed resource allocation and deployment scheme reduces energy consumption by 42.31%.

Evolution of hydrate habit and formation properties evolution during hydrate phase transition in fractured-porous medium

Natural gas hydrate, as an efficient and clean energy resource, are naturally distributed in porous and fractured-porous medium. With the most recent development of advanced micro-scale imaging techniques, hydrate habits evolution, hydrate occurrences, and pore structure evolution as well as seepage characteristics during hydrate phase transition in porous hydrate-bearing sediments have been studied extensively at pore scale. However, there are few studies on gas hydrates in fractured-porous sediment. In this work, xenon hydrate phase transition experiment by excess-gas method is carried out in a fractured sandstone core with in-situ micro computed tomography (micro-CT) scanning to explore the evolution of hydrate habits and physical parameters of the host sediment. The results indicate that hydrate-bearing sediment is a dynamic equilibrium system as hydrate synthesis and decomposition occur simultaneously at each moment of hydrate phase transition induced by pressure change. The hydrate occurrences in fractured hydrate reservoirs include contiguous-sheet, clustered and isolated, which are slightly different from that of porous hydrate formation; and the contiguous-sheet hydrate is the occurrence that dominantly determines the seepage characteristics of fractured hydrate-bearing sediments. In addition, the logic diagram for hydrate growth paths in fractured-porous medium is presented for the first time. These findings are significant for detailed understanding of pore-scale hydrate distribution throughout phase transition process and provide theoretical basis for precise modeling of permeability in host sediments.

A Brief Overview of Energy Efficiency Resources in Emerging Wireless Communication Systems

It is crucial to design new communication technologies to surmount the setbacks in RF communication systems. A suitable energy-efficiency scheme helps evade needless energy consumption in wireless communication. Appropriate choice of the most suitable energy-efficiency scheme aids in selecting the most energy-efficient equipment to minimize the expense of energy towards decreasing individual network element energy consumption without affecting their unique features. This review presents the energy efficiency challenges in wireless communication by employing different technologies. The emergence of visible light communication (VLC) provides an energy-efficient wireless communication system despite the various challenges inherent in its adoption that limit its physical realization. This work seeks to harness the potential of the transmission capabilities of VLC while providing an insight into novel practical implementation techniques. The work also addresses the energy consumption problem of low-active components and idle period of active components of base stations by using sleep modes for their systematic turning off and on. The high cost of power supply and the environmental emission of gases from base stations are also addressed by integrating a renewable energy resource into the conventional standalone diesel generators. Overall, the work provides an overview of information necessary for foundational research in energy-efficient resources applied to emerging wireless communication systems.

Energy-Efficient Computational Offloading for Secure NOMA-Enabled Mobile Edge Computing Networks

Computational offloading and nonorthogonal multiple access (NOMA) are two promising technologies for alleviating the problems of limited battery capacity, insufficient computational capability, and massive deployment of terminal equipment in the Internet of Things (IoT) era. However, offloading data may be threatened by malicious eavesdroppers, which leads to more energy consumptions to avoid being eavesdropped. In this work, we study the energy-efficient way of computational offloading under the condition of certain security requirement in a secure NOMA-enabled mobile-edge computing (MEC) networks, where K end users are intended to offload their data to the N -antenna access point (AP) through the same resource block under the threat of an eavesdropper. We first address energy-efficient local resource allocation by minimizing sum-energy consumption of end users, subject to CPU frequencies, offloading bits, secrecy offloading rate, and transmit power. We then optimize the local resources to obtain the minimum computation latency of task for each end user, with the constraint of certain energy budget. The solutions to the above two optimization problems are given and demonstrated numerically by a 3-user scenario.

Battery Storage at the Secondary Distribution Electricity Grid by Investigating End-Users Load Demand Measurements

Energy storage systems is expected to be utilized to cover the increased electrification of energy demands and to alleviate the electrical energy production from intermittent energy sources such as solar and wind. Aggregated and distributed battery energy storage systems may improve electricity grids operability and security by providing smart energy management options and efficient resources allocation. In this paper, battery storage at the secondary distribution level is explored. The investigation is based on the end-user energy demand behavior. As such, the electrical energy consumption patterns are measured and analyzed in a residential area. Measurements were collected and analyzed in order to record the customers’ behaviors aiming to reveal their differences and similarities. Following this, aggregated and distributed battery energy storage systems are computed based on the features of the measured electrical power consumption patterns aiming to estimate the factors that could potentially incentivize the installation of a battery system either as aggregated at the low voltage transformer side or as distributed system at the load side. The parameters that affect the economic viability of the system are qualitatively evaluated with regard to the profitability of the system.

Nexuses Between Energy Efficiency, Renewable Energy Consumption, Foreign Direct Investment, Energy Consumption, Global Trade, Logistics and Manufacturing Industries of Emerging Economies: In the Era of COVID-19 Pandemic

This study aims to find the nexuses among energy efficiency, renewable energy consumption, foreign direct investment, logistics industry, manufacturing industry and global trade during the COVID-19 pandemic and their impact on global supply chains in exporting nations of the world. The data for this study has been extracted from the World Development Indicators and Statista 2021 for 13 years ranging from 2007-to 2020 for nine top exporting countries. The fixed effect panel estimation technique was implied to examine and analyze the data. The results of our study revealed that highly risky diseases significantly impact supply chain operations globally. Global supply chains, logistics and manufacturing industries significantly influence global trade operations. Our results implicate that the overall international trade and logistics can be enhanced by improving the manufacturing and logistics industries by coping with the risk of pandemic diseases. Moreover, by utilizing cost-effective, renewable and efficient energy resources companies address sustainability issues of global trade and operations. By exerting further attention to the proficiency of the levies approval process, competence and quality of logistics services, and ease of assembling competitively priced shipments, the governments can significantly enhance the export from the logistics industry. Also, increasing manufacturing and agricultural value-added healthier consequences might be acquired in global supply chain operations from the manufacturing industry.

Energy-Aware Optimization of Zero-Energy Device Networks

In this work, the optimal resource allocation for a zero-energy device network with quality of service requirements is investigated, aiming to minimize the average energy consumption. Under these considerations and accounting for the circuit power consumption of the devices’ hardware, the employment of slotted ALOHA for the uplink data transmission is examined. The derived non-convex optimization problem is transformed into an equivalent convex problem in order to be optimally solved in polynomial time. The solutions of the optimization problem are then given in closed form. Finally, simulation results demonstrate the optimal resource allocation strategy of the zero-energy device network. Notably, taking into account the circuit power consumption leads to a more energy efficient resource allocation scheme.

Look-ahead energy efficient VM allocation approach for data centers

Energy efficiency is an important issue for reducing environmental dissipation. Energy efficient resource provisioning in cloud environments is a challenging problem because of its dynamic nature and varied application workload characteristics. In the literature, live migration of virtual machines (VMs) among servers is commonly proposed to reduce energy consumption and to optimize resource usage, although it comes with essential drawbacks, such as migration cost and performance degradation. Energy efficient provisioning is addressed at the data center level in this research. A novel efficient resource management algorithm for virtualized data centers that optimizes the number of servers to meet the requirements of dynamic workloads without migration is proposed in this paper. The proposed approach, named Look-ahead Energy Efficient VM Allocation (LAA), contains a Holt Winters-based prediction module. Energy efficiency and performance are inversely proportional. The energy-performance trade-off relies on periodic comparisons of the predicted and active numbers of servers. To evaluate the proposed algorithm, experiments are conducted with real-world workload traces from Google Cluster. LAA is compared with the best approach provided by CloudSim based on VM migration called Local Regression-Minimum Migration Time (LR-MMT). The experimental results show that the proposed algorithm leads to a consumption reduction of up to 45% to complete one workload compared with the LR-MMT.

Combing Effects of Economic Development and Globalization Towards Energy Efficiency and Environmental Degradation: Fresh Analysis From Energy Efficient Resources

How much environmental pollution can be reduced by the efficient use of financial, natural, and energy resources in the current globalization. Thus, this study provides empirical evidence in support of the theoretical argument by investigating the impact of financial development, environmental assets, globalization, coal, natural gas, and sustainable carbon emissions in 32 developed countries from 1990 to 2018. Ecological degradation (estimated by carbon dioxide emissions) experienced a structural shift that was considerably more pronounced in 2000–2011 than in 1991–1998. A broad variety of econometric methodologies (such as the Chow test, Cross-country regression, and the Generalized Method of Moments (GMM)) were applied. As a consequence, environmental deterioration is strongly linked to economic development and urbanization, according to the findings. These nations’ ecological footprints are favorably influenced by financial development, environmental assets, and non-renewable energy, whereas globalization and sustainable sources have a negative impact. Environmental degradation may be slowed by combining globalization’s impact on financial growth with the conservation of natural resources such as renewable energy sources. In order to improve their economic and ecological resource frameworks, these nations will need to increase their use of solar and other renewable energy.