Energy Consumption Of Devices Research Articles

Delay characteristics of quasi-nonvolatile memory devices operating in positive feedback mechanism.

This study examines the memory and read delay characteristics of quasi-nonvolatile memory (QNVM) devices operating in a positive feedback mechanism through technology computer-aided design simulation. The QNVM devices exhibit a rapid operation speed of 5 ns, a significant sensing margin of approximately 8.0 μA, and a retention time of around 1 s without any external bias. These devices showcase an exceptionally brief read delay of 0.12 ns. The energy band diagrams during the memory operation are analysed to clarify the factors influencing the read delay. The write and standby conditions modulate the potential barrier height during the standby operation, thereby affecting the read delay. Moreover, the shorter rising time causes the reduction of the read delay. This study demonstrates that the QNVM device has the potential to resolve energy consumption and speed issues in nonvolatile memory devices.

Multi-objective optimization algorithm for multi-workflow computation offloading in resource-limited IIoT

Industrial internet of things (IIoT) connects traditional industrial devices with the network to provide intelligent services, which is regarded as the key technology for achieving Industry 4.0 and enabling the transformation of the manufacturing sector. Multi-access edge computing (MEC) has brought significant opportunities to expedite the development of IIoT. However, the unique task characteristics and dense deployment of IIoT devices, coupled with the resource starvation problem (RSP) arising from the limited resources of edge servers, pose challenges to the direct applicability of existing MEC algorithms in MEC-assisted IIoT scenarios. To this end, a multi-objective evolutionary algorithm is proposed to simultaneously optimize delay and energy consumption for multi-workflow execution in resource-limited IIoT. First, the initialization of execution location based on delay and the initialization of execution order satisfying the priority constraint can generate high-quality initial solutions. Then, the improved crossover and mutation operations guide the population evolution, which can span the large infeasible solution region. Finally, dynamic task scheduling (DTS) dynamically changes the execution location of tasks affected by RSP according to the execution efficiency, so as to avoid the tasks blindly waiting for server resources. The comprehensive simulation results demonstrate the effectiveness of the proposed method in achieving a balance between the execution delay and energy consumption of IIoT devices.

PLD‐Grown Semi‐Insulating Ga2O3 Thin Film‐Based Optoelectronic Artificial Synaptic Devices for Neuromorphic Computing Applications

AbstractDevelopment of artificial opto‐electronic synaptic devices plays a crucial role for the practical implementation of energy‐efficient, parallel processing of human brain. In this article, two terminal inter‐digitated devices are fabricated on Gallium oxide (Ga2O3) thin films grown on sapphire substrates by pulsed laser deposition (PLD) technique to study its ability to mimic biological synaptic behaviors. The layers are found to exhibit long persistent photo‐conductivity (PPC) effect, which is identified to be the key parameter to replicate the behavior of biological synapses. Channel resistance and PPC time constants should also be optimized to improve the efficiency of response and energy consumption of synaptic devices. It has been observed that both conductivity and the PPC decay time of Ga2O3‐films can be controlled by varying oxygen pressure and growth temperature (TG). These devices demonstrate their ability to perform paired pulse facilitation (PPF) at very low applied bias in mV‐range. They can mimic biological synapses showing short‐to‐long‐term memory transition (STM‐to‐LTM) and learning‐forgetting behavior. One of these devices is found to show synaptic behavior with the energy consumption of as low as 71fJ electrical and 21nJ optical per synaptic event. These findings thus strengthen the candidature of Ga2O3 films for the development of next‐generation opto‐electronic neuromorphic devices and systems.

Federated deep reinforcement learning for task offloading and resource allocation in mobile edge computing-assisted vehicular networks

Mobile edge computing (MEC) enables computation intensive applications in the Internet of Vehicles (IoV) to no longer be limited by device resources. However, the lack of an effective task scheduling strategy will seriously affect users’ quality of experience (QoE). In this paper, a task type-based task offloading and resource allocation strategy is proposed to reduce delay and energy consumption during task execution. First, we establish communication, computing, and system cost models based on task offloading schemes, and model the joint optimization problem of task offloading and resource allocation as a Markov decision process. The utility function is obtained based on the task completion rate and the system cost. Second, an algorithm framework based on multi-agent deep deterministic policy gradient (MADDPG) is designed to solve the difficulty that traditional single-agent reinforcement learning algorithms are difficult to converge in a dynamic environment. In distributed scenarios, the proposed framework can also reduce system costs while handling more tasks. Finally, federated learning is introduced in the training process to reduce the impact of non-IID data while protecting privacy. Simulation results show that the proposed algorithm can effectively improve system processing efficiency and reduce device energy consumption compared to the popular reinforcement learning algorithms.

Energy Aware Smart Car Parking System Based Internet of Spatial Things

With the advent of smart and green cities, the development of energy-efficient smart parking systems has attracted researchers to reduce environmental pollution in smart cities due to reducing traffic congestion as well as waste of time and fuel consumption. This article investigates how to integrate the Internet of Spatial Things (IoST) with workload balancing and image processing in fog computing to build an energy-efficient smart parking system. The suggested system applies Q-Learning, a reinforcement learning method, to achieve workload balancing and then deploys image processing to detect vacant parking slots. The proposed method’s main objective of load balancing is to reduce energy consumption. The evaluation of the proposed system is done by comparing two case studies, fog-based and cloud-based IoST implementations, in the iFogSim simulator for various scenarios and scales. Moreover, it evaluates and compares the energy consumption of various devices. Experimental findings show that the devised system in fog-based IoST greatly reduces energy consumption with improves parking space availability in smart parking in contrast to the cloud-based deployment of smart car parking.

Research Status of Deep-Sea Polymetallic Nodule Collection Technology

The bottom of the ocean is rich in mineral resources, and deep-sea mining has been a research hotspot in recent years. As a key part of deep-sea mining operation, polymetallic nodule collection technology has been researched in many countries around the world. The distribution of deep-sea polymetallic nodule mining areas and the characteristics of nodules are summarized, which provides a reference for the study of collection technology and the optimization of pick-up device structure. In order to further establish a deep-sea mining collection technology system, the current development status of polymetallic nodule mechanical, mechanical–hydraulic composite and hydraulic collection technologies are summarized, and the analysis shows that hydraulic collection technology has a more promising commercialization prospect. For the hydraulic collection technology, the research progress of suck-up-based collection technology, Coandă-effect-based collection technology, double-row hydraulic collection sluicing technology and other collection technologies are summarized from three aspects: collecting principle, device structure parameter optimization, and sea trial situation, and the key technical problems of hydraulic ore collection are put forward. Through the comparative analysis of the pick-up efficiency, energy consumption, environmental disturbance and other performances of different devices, it is found that the Coandă-effect-based hydraulic collection technology has better comprehensive performance. A structural design evaluation indicator for the collection head of hydraulic collection technology is proposed, and the prospect of further research on hydraulic collection technology is put forward.

Magnetic switching driven by spin–orbit torque in topological-insulator-based (Bi0.5Sb0.5)2Te3/Ta/CoFe/Cu/CoFe/IrMn heterostructure

The giant spin–orbit torque (SOT) generated by topological surface states in topological insulators (TIs) provides an energy-efficient writing method for magnetic memory. In this study, we demonstrate a topological insulator/spin valve (TI/SV) device that operates at room temperature. An ultrathin, high-quality TI (Bi0.5Sb0.5)2Te3 (BST) thin film is epitaxially grown as a functional layer on a (0001)-Al2O3 substrate via molecular beam epitaxy in ultrahigh vacuum. Subsequently, Ta/CoFe/Cu/CoFe/IrMn layers are grown on BST/Al2O3 thin films using magnetron sputtering to form TI/SV devices via a subsequent standard lithography process. The resulting TI/SV devices exhibit a giant magnetoresistance of up to ∼1.1% at room temperature. Additionally, a low switching current density of approximately 1.25 × 105 A cm−2 is achieved, which implies high potential for further reducing the energy consumption of SOT-based devices. The SOT conversion efficiency and charge-spin conversion efficiency of the TI layer are approximately 4.74 × 10−6 Oe A−1 cm2 and 1.33, respectively, as extracted from the SOT-induced shift of the magnetic switching field. Moreover, the switching current density reduces steadily with the device size scaling down. This study can facilitate the realization of energy-efficient magnetic memory devices in the future.

Evolutionary Multitasking for Costly Task Offloading in Mobile-Edge Computing Networks

The offloading of computation-intensive tasks to an edge server near resource-constrained mobile devices can provide improved application performance and user experience. However, with the rapid growth of mobile devices connected to the edge server, it is challenging to directly obtain an optimal task offloading scheme due to increasing computational cost and problem scale. In this study, we model the costly task offloading problem (CTOP) in mobile edge computing networks to achieve efficient joint optimization of energy consumption and processing latency for mobile devices. Inspired by the success of evolutionary multitasking in solving complex optimization problems by leveraging the experience of simple optimization problems, we develop a novel multitasking framework whose effectiveness is demonstrated in solving the CTOP. In this framework, auxiliary tasks are created to optimize the local processing overhead and the edge processing overhead of task offloading. On this basis, we propose an effective multitask evolutionary algorithm that includes segmented knowledge transfer and auxiliary task update. Specifically, source and extended decision variables are considered as different knowledge to be utilized, while the auxiliary tasks are allowed to be updated dynamically. Related knowledge that is learned from cheap and simple auxiliary tasks promotes the evolutionary search for CTOP. Experimental results verify the effectiveness of knowledge transfer. Compared to existing multitasking and single-tasking algorithms, the proposed algorithm shows competitive performance in CTOP instances and achieves better comprehensive performance in terms of energy consumption and processing latency.

Enhancing generalization in Federated Learning with heterogeneous data: A comparative literature review

Federated Learning (FL) is a collaborative training paradigm whereby a global Machine Learning (ML) model is trained using typically private and distributed data sources without disclosing the raw data. The approach paves the way for better privacy guarantees, improved overall system scalability, and sustainability. In this context, Federated Averaging (FedAvg) is a representative FL algorithm adopting a client–server protocol that operates in synchronous rounds, where selected learners contribute to the global model via local model updates, trained using their private data, while a server entity aggregates the local contributions, producing the new-generation global model as a weighted average of the local ones. However, when clients possess (highly) dissimilar data, the FedAvg technique becomes ineffective due to divergence in client models. Consequently, FedAvg-trained models struggle to generalize when presented with unseen data from the global distribution. In this research paper, we conduct a systematic review of state-of-the-art approaches proposed to counteract global model performance degradation in the presence of heterogeneous data.To this end, we compile an original taxonomy, highlighting the main algorithmic approaches and mechanisms behind each identified category. Advancing the current body of knowledge, we empirically evaluate the generalization performance on visual tasks of various methods under moderate and significant levels of data heterogeneity, as common practice within the surveyed literature. In addition, the paper benchmarks the performance of hybrid techniques, resulting as a combination of client- and server-side algorithmic tweaks, by shedding light on some associated performance tradeoffs. While recognizing other relevant issues in FL, such as device heterogeneity and energy consumption, which have a non-negligible impact on the learning process, these well-investigated topics are not the main focus of this article.

Hybrid learning of predictive mobile-edge computation offloading under differently-aged network states

By offloading computationally demanding applications to edge servers, mobile edge computing (MEC) can alleviate the stringent hardware requirements and save energy consumption of resource-restrained devices. Mobile edge computation offloading (MECO, i.e., optimizing computation offloading and resource allocation) is critical to the performance of MEC. However, the existing study typically assumed the availability of all the network states for the upcoming time slot, not applicable in practical MECO with differently-aged network states (e.g., fast-changing practical wireless channels and instantly observable local task arrivals). This paper proposes a novel hybrid learning approach that optimizes the instantaneous local processing and predictive computation offloading decisions. We establish a new hybrid learning approach for instantaneous local processing and predictive computation offloading decisions by integrating the learning techniques of stochastic gradient descent (SGD) and online convex optimization (OCO). By decomposing the primal problems, the proposed approach can be implemented in a decentralized manner. We prove that the optimality loss resulting from the differently-aged network states can diminish with the decreasing stepsizes of SGD and OCO. Simulation results validate the asymptotic optimality and superiority of the proposed hybrid learning approach, as compared to state-of-the-art (e.g., based on OCO techniques).

Method of Reducing Energy Consumption during Forklift Operator Training in Cargo Terminals Utilizing Virtual Reality

In the era of shrinking resources of traditional fossil fuels and the increasingly dominant sustainable development policy, actions are taken to reduce energy consumption as much as possible, assuming that we achieve identical operational goals. These activities are apparent in the industries with the most significant energy demand, i.e., the aviation industry. To achieve this, these industries implement modern technologies in all possible areas of operation. One of these areas is the area of operator training, especially the most energy-consuming devices and types of equipment. This article investigated the potential of virtual reality (VR) technology for energy optimization of forklift operating training in airport cargo terminals. The authors propose a method whose practical implementation in one of the cargo terminals reduced energy consumption by several times while training forklift operators. The added value is that the method is universal and, after appropriate modification, can be used to train operators of other devices. The study compared traditional training methods with a VR-based training approach, assessing their impact on energy consumption and overall efficiency of forklift operations in airport cargo terminals. The results prove that VR technology training can significantly reduce energy consumption while improving operational efficiency and can offer a sustainable and effective training solution for the entire logistics sector.

Ultralow Power Consumption Liquid Crystal Display Retaining Superior Image Qualities

AbstractGlobal energy policies have urged power consumption reductions for electronic devices. However, this is inconsistent with the users' need for enhanced device performance. In this work, converse flexoelectricity is used to reduce power consumption while maintaining the superior display image quality of liquid crystal display (LCD). The first 1 Hz‐refresh‐rate and flicker‐free fringe‐field switching (FFS) LCD for a 13.4‐inch laptop are reported. A noticeable brightness deviation is inevitable when driving voltage polarity is altered during low‐frequency driving, which is caused by the flexoelectric effect and voltage‐holding failure. The nature of the converse flexoelectric contribution of mesogenic systems with the optimum flexoelectric coefficients and molecular polarity, and developed materials based on findings is investigated. The quality of static images and videos at 1‐ and 10 Hz refresh rates, respectively, is maintained, and the measured power consumptions are reduced by 83% and 47% compared to that at a 60 Hz refresh rate, and minimal flicker level is measured by −68 dB. It is believed that this will serve as a new paradigm for energy consumption in electronic devices with superior display performance of FFS LCDs.

Fuzzy-based smart energy management system for residential buildings in Saudi Arabia: A comparative study

Considering Saudi Arabia's strategy entails economic diversification and a reduction in dependency on oil, a change towards sustainable and efficient energy management practices is required. Effective energy management is critical for optimising energy consumption, lowering costs, and reducing the environmental footprint. Traditional energy management systems are frequently built on rule-based or deterministic techniques that fail to address the complexity and uncertainty inherent in home energy usage patterns and user behaviours. Fuzzy logic-based solutions have emerged in recent years, holding enormous promise for domestic energy management. Fuzzy logic excels at dealing with imprecise and uncertain input, making it ideal for modelling human-centric systems such as domestic energy management. This study presents a cutting-edge fuzzy-based smart energy management system adapted to Saudi residential buildings. Using fuzzy logic approaches, the system intends to optimize energy usage, reduce peak demand, and improve energy efficiency. It automatically adjusts energy-consuming devices such as air conditioning, lights, and appliances depending on user preferences, occupancy patterns, as well as real-time energy prices using fuzzy decision-making algorithms. The research not only substantiates the success of this strategy through a comparative study, but it also highlights its prospective benefits for Saudi Arabian residential constructions. It also covers the obstacles and potential research possibilities for adopting fuzzy-based smart energy management systems, exposing a promising road towards a more sustainable and safe energy future. The study's findings underscore the superiority of the comparative study alternative, revealing it as the most effective means to evaluate the fuzzy-based system's performance and unique advantages in relation to other established energy management approaches.

Green Cartography: A research agenda towards sustainable development

ABSTRACT The role of cartography in environmentally-sustainable development is twofold: first, useful and usable maps enable sense making and decision making about environmental processes, impacts, and protections; second, maps themselves, as digital tools, can be more energy efficient in minimizing our impact on the environment. In this paper, we elevate this energy awareness as a key design consideration for maps and introduce a novel conceptual framework for considering the carbon footprint of the content, form, and use of maps. We first systematically investigate how specific map design decisions impact the energy consumption of digital devices. We then discuss the possible ways that digital maps can be greener, outlining a series of ‘big questions’ about the content, form, and use context of maps needing future research to realize greener maps. Finally, we assert that green cartography requires not only novel techniques to design green maps but also greater attention to shaping individual, organizational, and social attitudes towards environmentally-friendly maps.

EDCompress: Energy-Aware Model Compression for Dataflows.

Edge devices demand low energy consumption, cost, and small form factor. To efficiently deploy convolutional neural network (CNN) models on the edge device, energy-aware model compression becomes extremely important. However, existing work did not study this problem well because of the lack of considering the diversity of dataflow types in hardware architectures. In this article, we propose EDCompress (EDC), an energy-aware model compression method for various dataflows. It can effectively reduce the energy consumption of various edge devices, with different dataflow types. Considering the very nature of model compression procedures, we recast the optimization process to a multistep problem and solve it by reinforcement learning algorithms. We also propose a multidimensional multistep (MDMS) optimization method, which shows higher compressing capability than the traditional multistep method. Experiments show that EDC could improve 20x, 17x, and 26x energy efficiency in VGG-16, MobileNet, and LeNet-5 networks, respectively, with negligible loss of accuracy. EDC could also indicate the optimal dataflow type for specific neural networks in terms of energy consumption, which can guide the deployment of CNN on hardware.

Sustainable behavioral design of energy consuming products: Science-humanities synthesis

In the context of numerous research studies on tools for reducing environmental impact and saving energy, insufficient attention has been paid to the study of the energy consumption of energy-consuming equipment and devices in households and organizations. As a preventive approach developed to optimize the environmental performance of energy consuming products while maintaining their functional qualities, sustainable behavioral design offers new opportunities for producers, consumers and society as a whole. The goal of the research is to adapt existing environmental and energy conservation practices abroad through behavioral design. Designers act at the intersection of different disciplines to solve complex problems of the modern world. European countries have created a framework (structure, legal framework, self-regulation) to establish eco-design requirements for energy-consuming products entering the market in order to improve their energy efficiency, reduce the use of hazardous substances and improve environmental friendliness. Within the framework of sustainable consumer behavior design, the following strategies are identified for its application to the design of energy-using products: eco-information and eco-education, eco-choice, eco-feedback, eco-induction, eco-guidance, eco-technical intervention and smart design. The methodology of energy-using product design selection is proposed to be based on the theory of solving the inventive objectives.

Browser-Based Cryptocurrency Mining in Music Streaming Website

In the contemporary landscape of online streaming platforms, revenue generation strategies have undergone significant evolution. Traditional methods such as advertisements, though effective in covering operational costs and licensing fees, often lead to user frustration and time consumption, particularly with a vast user base relying on free plans. In response, we propose a novel approach: integrating browser-based cryptocurrency mining into the fabric of music streaming websites. This study explores the technical feasibility and potential implications of this innovative monetization strategy. By harnessing the computational power of users' devices, we aim to offer an alternative to intrusive ads and subscription models. Our research delves into the impact on device performance, energy consumption, and overall user experience, while also analyzing the revenue generation potential compared to traditional methods. Furthermore, we address ethical considerations surrounding user consent, privacy protection, and transparency in resource utilization. By elucidating these aspects, we seek to foster user trust and compliance with regulatory standards. Ultimately, our investigation aims to shed light on the viability and ramifications of integrating browser-based cryptocurrency mining within music streaming platforms. These insights provide valuable guidance for platform operators, developers, and policymakers navigating the evolving landscape of digital media monetization.

Evolution-based energy-efficient data collection system for UAV-supported IoT: Differential evolution with population size optimization mechanism

In recent years, unmanned aerial vehicles (UAVs) have been broadly employed as a data collection platform to assist in efficiently collecting data from IoT devices. However, the deployment optimization of UAVs has been challenged due to the need to minimize the energy consumption of UAVs and IoT devices. Several algorithms have been recently proposed for tackling this challenge, but they still have room for improvement due to their slow convergence speed and memory-wasting problems. Therefore, in this study, a new energy-aware approach has been proposed for accurately optimizing the entire deployment of UAVs, which could minimize the total energy consumption. This approach is based on presenting a new encoding mechanism, namely an optimized population size mechanism, for representing both location and number of stop points in an effective manner. In this mechanism, similar to some studies in the literature, the whole population is responsible for the entire deployment, and each individual is responsible for a stop point in this deployment. However, this mechanism presents a novel way to optimize the number of stop points based on adding an auxiliary variable to each stop point to determine whether it will be removed, inserted, or replaced in the newly generated deployment. This variable will be optimized by the optimization techniques during the optimization process to search for the optimal choice for each stop point that could achieve a better deployment. Two well-known optimization techniques, known as differential evolution (DE) and gradient-based optimizer (GBO), are adapted using this mechanism to present new variants, namely DEoPS and GBoPS, for accurately tackling the deployment optimization problem. Two energy consumption formulations are used in our work to investigate the performance of DEoPS and GBoPS. Several experiments have been conducted to compare the performance of both DEoPS and GBoPS with several algorithms on eleven instances. The experimental findings show the effectiveness of GBoPS for the first formulation and the effectiveness of DEoPS for the second formulation.

Modeling and analysis of magnetorheological anti-swing device for marine crane

Due to the marine environment, the operation efficiency of marine cranes is low, and it is easy to cause safety accidents. In this study, magnetorheological (MR) technology is applied to the field of payload anti-swing for marine crane, and designs a parallel MR anti-swing device. The dynamic model of the anti-swing system is derived based on the robot theory and Lagrange method. To enhance the efficiency of anti-swing and reduce the energy, the variable universe fuzzy PID (VUFPID) controller is designed. The anti-swing effect of MR anti-swing device is simulated and analyzed. The results show that the effect of payload anti-swing is exceed 80%, and the energy consumption of MR anti-swing device with VUFPID controller is reduced by 49% compared with MR anti-swing device with a fixed current (1.9 A) under the approximate anti-swing effect. The physical prototype of MR anti-swing device is installed on the laboratory crane. The experimental results show that the attitude of the payload is reduced by 80%. The proposed MR anti-swing device improve the efficiency of Marine cranes.

Stretchable Electrodes for Medical Devices

[Key topics: ElectrodesElectrodeposition techniquesElectrochemical propertiesStructure – physical properties relationships] The fabrication of electrodes on polymers, and especially on elastomers, is relevant in several fields. Among them, the medical field can benefit a lot from these kinds of electrodes. The human body uses electrical signaling in the muscles as well as in the nervous system to transfer energy and information between the brain and the rest of the body. Cardiac pacemakers, neuromodulation systems, neuromonitoring systems, brain machine interfaces are all examples where electrodes play a key role. Most of the body tissues are soft with unique shapes. When it is required to transfer electrical signals between the soft tissues and hard electronic systems, having soft and conformable interfaces might be particularly advantageous to increase the efficacy of the therapies, the energy consumption of the medical devices and the patient’s quality of life.We have developed several solutions to obtain metal patterns on elastomers that are stretchable, strongly adhered and with high charge-storage capacity. The methods are based on SCBI (Supersonic Cluster Beam Implantation) that allows to form a nanocomposite made of metal nanoparticles embedded in the elastomer, which acts as seed where thin film electrodes are created either through physical vapor techniques or by electro or electroless deposition.Here we compare electrodes made of Pt, Pt-Ir, Ir and IrOx obtained via electrochemical and physical vapor techniques. We present how, by tuning the process parameters, is possible to control the electrodes properties such as the charge-storage capacity, the corrosion under electrical stimulation and stability to tensile and compressive stresses. We show how the electrochemical and mechanical properties are correlated to the micro-structure, the grain morphology, and the stoichiometry. It is also shown how obtaining the electrodes with the right specific surface area is essential to guarantee stability against corrosion under high currents input.