Energy Optimization Techniques Research Articles

Properties of La0.7Ca0.3MnO3 under extreme tensile strain

The complex phase diagram of manganites with simultaneously active spin, charge, orbital, and lattice degrees of freedom continues providing surprises. In a recent groundbreaking experiment, membranes of the perovskite manganite La$_{0.7}$Ca$_{0.3}$MnO$_3$ (LCMO) deposited on a flexible polymer layer were strained up to 8\% [S.~S. Hong {\it et al.}, Science {\bf 368}, 71 (2020)], much more than achieved by regular strain induced by a rigid substrate. By increasing this strain a metal-insulator transition was reported. Here we reproduce the results of the experiments using Monte Carlo simulations of the two-orbital double-exchange model including Jahn-Teller distortions, at hole density $x=1/3$. The full phase diagram with varying temperature and Jahn-Teller coupling $\lambda$ is presented. When the bandwidth $W$ of mobile electrons is reduced, thus when the effective Jahn-Teller coupling $\lambda/W$ is increased, a metal-insulator transition is found in our simulations, between a ferromagnetic metallic state with uniform charge distribution and an insulator with diagonal charge stripes that retains its ferromagnetic character. In between the hole-rich diagonals, staggered orbital order occurs. We also report resistivity and magnetization measurements alongside with spin correlations and charge structure factors. Our overall conclusions are in agreement with the recent experimental and density functional theory results by Hong {\it et al.}, and we confirm much earlier ground state predictions of striped ferromagnetic order using energy optimization techniques by T. Hotta {\it et al.}, Phys. Rev. Lett. {\bf 86}, 4922 (2001). The experimental observation of one of the states predicted by theory suggests that diagonal stripes could be achieved at other hole densities as well, such as $x=1/4$, if LCMO membranes with that hole doping were subject to similar strains.

Energy Optimization Technique in Wireless Network for Internet of Things

Energy Optimization Technique in Wireless Network for Internet of Things

Scheduling Based Energy Optimization Technique

Scheduling Based Energy Optimization Technique

Smartphone processor architecture, operations, and functions: current state-of-the-art and future outlook: energy performance trade-off

Balancing energy–performance trade-offs for smartphone processor operations is undergoing intense research considering the challenges with the evolving technology of mobile computing. However, to guarantee energy-efficient processor operation, layout, and architecture, it is necessary to identify and integrate optimization techniques and parameters influencing energy–performance trade-off in various processor activity domains. Existing literature on energy optimization in smartphones focuses primarily on individual sub-domains such as OS, GPU, and cloud offloading methods. It reflects multiple smartphone processor activities domains as being the most discussed but less integrated. Through this study, we intend to provide the current state-of-the-art energy optimization techniques for smartphone processor operations. It also classifies multiple energy-draining processor operations along with their thorough discussion of methodologies and popular optimization techniques. The study models smartphone processor sub-components highlighting conventional techniques and performance parameters among its varied domains affecting the device’s energy performance along with significant energy drain minimization without any serious performance degradation. The study analyzes these approaches in the context of applicability, performance, and expected future demands along with revealing limitations of those approaches and open research issues prevailing in the available literature. Finally, we conclude our study by summarizing the current state of the art for smartphone processor activities power consumption.

A Survey of Hierarchical Energy Optimization for Mobile Edge Computing

With the development of wireless technology, various emerging mobile applications are attracting significant attention and drastically changing our daily lives. Applications such as augmented reality and object recognition demand stringent delay and powerful processing capability, which exerts enormous pressure on mobile devices with limited resources and energy. In this article, a survey of techniques for mobile device energy optimization is presented in a hierarchy of device design and operation, computation offloading, wireless data transmission, and cloud execution of offloaded computation. Energy management strategies for mobile devices from hardware and software aspects are first discussed, followed by energy-efficient computation offloading frameworks for mobile applications that trade application response time for device energy consumption. Then, techniques for efficient wireless data communication to reduce transmission energy are summarized. Finally, the execution mechanisms of application components or tasks in various clouds are further described to provide energy-saving opportunities for mobile devices. We classify the research works based on key characteristics of devices and applications to emphasize their similarities and differences. We hope that this survey will give insights to researchers into energy management mechanisms on mobile devices, and emphasize the crucial importance of optimizing device energy consumption for more research efforts in this area.

Deep learning techniques for energy forecasting and condition monitoring in the manufacturing sector

The industrial and building sector demands the largest proportion of global energy, therefore adopting energy efficiency related strategies, optimization techniques and management is an important step towards global energy reduction. The use of machine learning techniques in energy forecasting is gaining popularity due to their ability to solve complex non-linear problems, however this is predominately seen in the residential and commercial sector. This study proposes and compares the use of two deep neural networks, feed forward and recurrent, to forecast manufacturing facility energy consumption and workshop conditions based on production schedules, climatic conditions, thermal properties of the facility building, along with building behaviour and use. The feed forward model was able to predict building energy, workshop air temperatures and humidity to an accuracy of 92.4%, 99.5% and 64.8% respectively when the model was provided with a new dataset, with the recurrent model predicting these variables to accuracies of 96.82%, 99.40% and 57.60%. The neural networks were trained with data obtained from the simulation of a medium sized manufacturing facility in the UK. Coupling simulation techniques with machine learning algorithms allows for a low cost, non-intrusive methodology of predicting and optimising building energy consumption in the manufacturing sector. Furthermore, the use of neural networks provided forecasted building energy profiles for the identification of spikes in energy consumption; an undesirable and considerable cost in the manufacturing sector, as well as the predication of manufacturing environmental conditions for condition monitoring of condition sensitive production environments.

Nanoemulsions: A review on low energy formulation methods, characterization, applications and optimization technique

Nanoemulsions are emulsions with droplet size in the order of 100 nm and are kinetically stabilized dispersions formulated by combination and stabilization of two immiscible phases using a surfactant. They exhibit useful properties due to small droplet size leading to high surface area per unit volume, higher stability, optically transparent appearance, flexible fluidity and increased bioavailability of lipophilic components. Recently, interdisciplinary applications of nanoemulsions in consumer products, i.e. pharmaceuticals, pesticides, cosmetics, food, paint and environmental applications have attracted interest in its research. Various authors have focussed on preparing nanoemulsions through different methods, including high-energy and low-energy. High energy methods mainly includes microfluidization, high pressure homogenization and ultrasonication whereas, low energy methods comprise of phase transition temperature, phase inversion composition, spontaneous emulsification, micro emulsion dilution and recently developed approach such as D phase emulsification (DPE). In this review article, we address the growing usefulness of low energy method due to ease in its scale-up, less consumption of energy and increased stability in formulation of nanoemulsions. It also includes review on characterization techniques, applications of nanoemulsions and optimization studies for industrial scale up.

Multiobjective Optimal Scheduling Framework for HVAC Devices in Energy-Efficient Buildings

The worldwide energy consumption has been growing in aggregate at a tremendous rate, and a majority of the same is due to heating ventilation air conditioning (HVAC) loads in urban buildings. With the help of the recent advances in energy management and optimization techniques, the operations and functioning of these devices can now be managed and controlled efficiently for an improved energy consumption scenario and thereby reducing cost. In this article, we propose a multiobjective optimal scheduling framework based on Johnson's elementary circuit finding algorithm for controlling HVAC devices, specifically for buildings that require continuous thermal comfort maintenance. Two primary objectives addressed in this article are: minimizing power fluctuation and maximizing thermal comfortability of the users. We use standard comfortability indices to quantify thermal comfortability. To reduce the computation time, we also propose two intelligent improvement schemes that prune the exponential search space of Johnson's algorithm. Furthermore, a new greedy scheduling algorithm has been proposed to obtain near-optimal solutions efficiently. All the proposed approaches have been studied in a simulated environment depicting a real-world scenario to evaluate their efficiency and effectiveness for practical implementations, including a comparative analysis with Karp's minimum mean cycle algorithm in this problem setup.

Electrification and Automation in Maritime Applications: Employing AI Techniques for Energy Optimization and Efficiency

The region of v?stra g?taland is an area in Western Sweden that has nearly 1.6 million inhabitants. In recent years, the number of trips made by the public transportation system has increased considerably, and everything indicates that it will continue to rise substantially in the coming years. Today, the region faces challenges in managing this expected increase in demand. A description of how public transportation is developing in V?stra G?taland is given in the Transport Provision Program; it estimates that the number of journeys made by public transportation will double, a goal that is sought at both the national and local levels. The number of public transportation journeys made in the country would then be roughly 400 million by 2025. In response to this, capacity is expected to increase by 70% and the travel time is projected to decrease by 20-25%. Along with these developments, efforts are being made to transition to a more environmentally friendly means of transportation through the use of alternative fuels, electrification, and an increased level of automated systems (with the additional objective of reducing the number of accidents).

A Comparative Analysis of Energy-Efficient Protocols for WBAN on Heterogeneous Transceivers

Abstract Wide use of wireless sensors in medical services has given rise to another field called wireless body area networks (WBANs). WBANs consist of networks of wearable or implanted sensors that continuously capture and measure the vital parameters of the body. These sensor nodes have limited resources in terms of processing power, battery, and memory. Thus, the use of power has to be controlled for prolonging the network lifetime of WBAN systems. For this purpose, various energy-efficient protocols and their respective energy optimization techniques were developed to optimize power consumption, which consequently results in high energy efficiency. This article explores a power optimization model of three low-power routing protocols, namely, mobility-supporting adaptive threshold based thermal-aware energy-efficient multi-hop protocol (M-ATTEMPT), MBSTAR, and distance aware relaying energy-efficient (DARE), for WBAN. These protocols are implemented on three different test beds, and their conserved energy is compared for a varying number of sensors. Test beds were equipped with an ARM7 processor with different transceivers, namely, Bluetooth, Zigbee, and Wi-Fi. These protocols were implemented with varying transceivers taken for local case studies. Results are analyzed for the uniqueness of each protocol and their potential to support quick development and deployment of low-power WBAN systems.

Analysis of Energy Consumption and Optimization Techniques for Writing Energy-Efficient Code

The unprecedented growth of connected devices, together with the remarkable convergence of a wide variety of technologies, have led to an exponential increase in the services that the internet of things (IoT) can offer, all aimed at improving quality of life. Consequently, in order to meet the numerous challenges this produces, the IoT has become a major subject of research. One of these challenges is the reduction of energy consumption given the significant limitations of some devices. In addition, although the search for energy efficiency was initially focused on hardware, it has become a concern for software developers too. In fact, it has become an intense area of research with the principal objective of analyzing and optimizing the energy consumption of software systems. This research analyzes the energy saving that can be achieved when using a broad set of techniques for writing energy-efficient code for Raspberry Pi devices. It also demonstrates that programmers can save more energy if they apply the proposed techniques manually than when relying on other automatic optimization options offered by the GNU compiler collection (GCC). Thus, it is important that programmers are aware of the significant impact these techniques can have on an application’s energy consumption.

Energy Optimization for Underwater Sensor Network using Nature Inspired Technique

There is lots of energy optimization technique is used in underwater sensor network but in this paper, nature-inspired technique, called Elephant Herding Optimization (EHO), and is proposed for solving optimization tasks. The EHO method is inspired by the herding behavior of the elephant group. In nature, the elephants having a place with various factions live respectively under the initiative of female authority, and the male elephants will leave their family bunch when they grow up. These two practices can be displayed into two after administrators: group refreshing administrator and isolating administrator. In EHO, the elephants in every faction are refreshed by its present position and female authority through group refreshing administrator. It is found from the outcomes that the EHO based vitality streamlining approach shows the outcomes compelling than established methodology as far as numerous parameters. In this work, the advancement dimension of the vitality to over 11% is accomplished utilizing Elephant Herd Optimization that is likewise utilized in numerous other areas for designing enhancement.

MOFPL: Multi-objective fractional particle lion algorithm for the energy aware routing in the WSN

Wireless Sensor Networks (WSN) has wider applications in the fields of the healthcare, military, and the weather monitoring. The efficient design of the WSN requires better energy optimization techniques, since the nodes in the WSN are battery operated. The traditional energy aware routing mechanisms neglect the delay, and the traffic rate of the WSN while improving the energy constraints in the node. This work overcomes these challenges by introducing a multi-objective energy aware routing protocol. This paper proposes the multi-objective fitness function based on the energy, delay, traffic rate, distance, and the cluster density. The energy-aware routing is done based on the proposed Multi-objective fractional particle lion algorithm (MOFPL). The proposed MOFPL algorithm finds the optimal cluster head from various cluster head nodes in the WSN. Then the optimal routing path is established based on the proposed multi-objective function. The proposed MOFPL algorithm has 5, 8, 10 alive nodes at the iteration round of 2000 for the WSN with 50, 75, and 100 nodes, respectively. Also, the proposed MOFPL algorithm has achieved higher normalized network energy of 0.05877 and 0.06022 for the WSN with 50 and 100 nodes, respectively.

An Efficient Energy Management in Office Using Bio-Inspired Energy Optimization Algorithms

Energy is one of the valuable resources in this biosphere. However, with the rapid increase of the population and increasing dependency on the daily use of energy due to smart technologies and the Internet of Things (IoT), the existing resources are becoming scarce. Therefore, to have an optimum usage of the existing energy resources on the consumer side, new techniques and algorithms are being discovered and used in the energy optimization process in the smart grid (SG). In SG, because of the possibility of bi-directional power flow and communication between the utility and consumers, an active and optimized energy scheduling technique is essential, which minimizes the end-user electricity bill, reduces the peak-to-average power ratio (PAR) and reduces the frequency of interruptions. Because of the varying nature of the power consumption patterns of consumers, optimized scheduling of energy consumption is a challenging task. For the maximum benefit of both the utility and consumers, to decide whether to store, buy or sale extra energy, such active environmental features must also be taken into consideration. This paper presents two bio-inspired energy optimization techniques; the grasshopper optimization algorithm (GOA) and bacterial foraging algorithm (BFA), for power scheduling in a single office. It is clear from the simulation results that the consumer electricity bill can be reduced by more than 34.69% and 37.47%, while PAR has a reduction of 56.20% and 20.87% with GOA and BFA scheduling, respectively, as compared to unscheduled energy consumption with the day-ahead pricing (DAP) scheme.

AMI-Based Energy Management for Islanded AC/DC Microgrids Utilizing Energy Conservation and Optimization

This paper presents a new approach for quasi real-time and adaptive energy management for islanded ac/dc microgrids using advanced metering infrastructure (AMI) data. This paper presents a sophisticated multi-objective algorithm by employing AMI-based energy conservation and optimization techniques, tailored for different operational scenarios. This paper argues that optimal operation can be achieved by defining new concepts, such as operation value factor that can inherently reduce system operational costs. Moreover, the results of this paper demonstrate how utilizing accurate load models can improve ac/dc microgrid efficiency. To test the performance and the applicability of the proposed solution, a modified 33-node islanded ac/dc microgrid is used as a case study in the presence of various types of ac/dc generating units and loads.

Multiparametric Monitoring in Equatorian Tomato Greenhouses (II): Energy Consumption Dynamics.

Tomato greenhouses are a crucial element in the Equadorian economy. Wireless sensor networks (WSNs) have received much attention in recent years in specialized applications such as precision farming. The energy consumption in WSNs is relevant nowadays for their adequate operation, and attention is being paid to analyzing the affecting factors, energy optimization techniques working on the network hardware or software, and characterizing the consumption in the nodes (especially in the ZigBee standard). However, limited information exists on the analysis of the consumption dynamics in each node, across different network technologies and communication topologies, or on the incidence of data transmission speed. The present study aims to provide a detailed analysis of the dynamics of the energy consumption for tomato greenhouse monitoring in Ecuador, in three types of WSNs, namely, ZigBee with star topology, ZigBee with mesh topology (referred to here as DigiMesh), and WiFi with access point topology. The networks were installed and maintained in operation with a line of sight between nodes and a 2-m length, whereas the energy consumption measurements of each node were acquired and stored in the laboratory. Each experiment was repeated ten times, and consumption measurements were taken every ten milliseconds at a rate of fifty thousand samples for each realization. The dynamics were scrutinized by analyzing the recorded time series using stochastic-process analysis methods, including amplitude probability functions and temporal autocorrelation, as well as bootstrap resampling techniques and representations of various embodiments with the so-called M-mode plots. Our results show that the energy consumption of each network strongly depends on the type of sensors installed in the nodes and on the network topology. Specifically, the CO sensor has the highest power consumption because its chemical composition requires preheating to start logging measurements. The ZigBee network is more efficient in energy saving independently of the transmission rate, since the communication modules have lower average consumption in data transmission, in contrast to the DigiMesh network, whose consumption is high due to its topology. Results also show that the average energy consumption in WiFi networks is the highest, given that the coordinator node is a Meshlium™ router with larger energy demand. The transmission duration in the ZigBee network is lower than in the other two networks. In conclusion, the ZigBee network with star topology is the most energy-suitable one when designing wireless monitoring systems in greenhouses. The proposed methodology for consumption dynamics analysis in tomato greenhouse WSNs can be applied to other scenarios where the practical choice of an energy-efficient network is necessary due to energy constrains in the sensor and coordinator nodes.

A novel DEA-OR algorithm for route failure recovery in dense wireless sensor networks

Wireless sensor nodes generally have less memory and low battery life. Due to this constraint, a strong algorithm is needed which can reduce the energy consumption. Communication in wireless sensor network (WSN) depends on active number of neighboring nodes and battery power of the operating node. Factors like neighbor availability, link stability, energy and route failures directly influence network performance, for which optimization is vital. Traditional optimization techniques advise solutions that consume higher number of iterations without considering post network metrics like link stability and path cost. To address the issues of the existing energy optimization techniques, we put forward an innovative distance and energy aware optimized routing (DEA-OR) algorithm for WSNs. Considering distance as the base factor, DEA-OR algorithm gives solution for energy efficient transmission and route failure recovery. The process is of two stages: weight based neighbor selection for routing and cost-confined greedy method for route failure recovery. In this process, backtracking process of source is prevented through which overhead in neighbor selection is minimized. Our proposed algorithm minimizes energy consumption, delay and overhead thereby improvising throughput and network lifetime.

New combined method for low energy consumption in Wireless Sensor Network applications

Over the past few years the applications for Wireless Sensor Networks (WSNs) have grown at an ever-increasing rate. However, the evolution of those networks has been reduced by the energy scarcity. It retards the development of the WSN performances required while exploring new applications and improving the WSN potential. Besides, in order to design energy-efficient solutions, it is important to take into account the power dissipation due to noncompliance with time constraints. As a result, we will provide a model of power management that will be simulated and validated by the STORM Simulator (Simulation TOol for Real time Multiprocessor scheduling). However, unlike traditional WSN energy management systems, our power manager reduces the energy consumption through a dual approach: a global and dynamic approach using the analysis of the behavior of the network and a local one applied at the node level. We have relied on energy optimization techniques to yield extensive lifetime for every node battery and mainly both Dynamic Power Management and Dynamic Voltage and Frequency Scaling, which are appropriate for the WSN. This model will be based on a global Earliest Deadline First scheduling policy. Besides, we aim to extend the STORM simulation tool to include those power management techniques.

Lightweight Block Ciphers for IoT: Energy Optimization and Survivability Techniques

With extraordinary growth in the Internet of Things (IoT), the amount of data exchanged between IoT devices is growing at an unprecedented scale. Most of the IoT devices are low-resource devices handling sensitive and confidential data. Conventional encryption methods are inappropriate for low-resource devices. Lightweight block ciphers are used to encrypt data on such devices, as it balances security requirements and energy consumption. The objective of this paper is to explore opportunities to improve performance and optimize energy consumption for cipher designs targeted for low-resource IoT devices. This paper also presents an energy management algorithm to improve IoT survivability against Denial-of-service attacks in the form of battery exhaustion. We developed a simple and effective model for lightweight cipher performance metrics. Model results were compared and validated with published application-specific integrated circuit (ASIC) and field-programmable gate array (FPGA) designs. Using the model, we explored opportunities for performance enhancement in future cipher designs. Our analysis indicates that the optimum energy is achieved when block size is between 48-bit and 96-bit. Also, increasing size of overhead logic from one round to two rounds increases encryption energy-per-bit by 3.4%. Further, the optimum energy is attained when the number of algorithm rounds is 16 or less. Optimum throughput is achieved by implementations with large block sizes and large number of implemented rounds. Next, we present a novel algorithm to manage cipher energy consumption. The algorithm allows low-resource IoT devices to encrypt critical messages during low-energy mode while balancing throughput, energy per bit, and device activity.

MultiDroid: An Energy Optimization Technique for Multi-Window Operations on OLED Smartphones

The inbuilt multi-window feature released with Android Nougat has enabled simultaneous working and visualization of multiple applications on mobile display screen. However, multi-window operations result in unnecessary energy drain due to increased multitasking, CPU load, and multithread processing. Considering linear dependence between power consumed and the displayed colors, we present the design and realization of MultiDroid , a novel display power reduction technique during multi-window operations for the OLED screens. MultiDroid employs dynamic local dimming based on the context switching between the application pairs displayed on the screen. The display optimization works based on the user interaction with the mobile screen, where dynamic changes on the display screens are implemented for the non-critical application window. MultiDroid architecture is validated through power modeling and correctness verification. Furthermore, a survey on multi-window usage has been conducted reflecting the application usage pattern. The performance of MultiDroid is evaluated on preferred application pairs on multi-window framework obtained from the survey. Modeling and comparative analysis of the energy profiles for the devised test cases on optimized and default multi-window screens reflect around 10% to 25% reduction in the overall device power consumption per hour with a negligible performance degradation. After experimental validation, we present user acceptance and feasibility of MultiDroid based on the feedback from 50 participants.