As the demand for advanced computational systems capable of handling large data volumes rises,nano-electronic devices,such as memristors,are being developed for efficient data processing,especially in reservoir comput...As the demand for advanced computational systems capable of handling large data volumes rises,nano-electronic devices,such as memristors,are being developed for efficient data processing,especially in reservoir computing(RC).RC enables the processing of temporal information with minimal training costs,making it a promising approach for neuromorphic computing.However,current memristor devices of-ten suffer from limitations in dynamic conductance and temporal behavior,which affects their perfor-mance in these applications.In this study,we present a multilayered indium-tin-oxide(ITO)/ZnO/indium-gallium-zinc oxide(IGZO)/ZnO/ITO memristor fabricated via radiofrequency sputtering to explore its fil-amentary and nonfilamentary resistive switching(RS)characteristics.High-resolution transmission elec-tron microscopy confirmed the polycrystalline structure of the ZnO/IGZO/ZnO active layer.Dual-switching modes were demonstrated by controlling the current compliance(I_(CC)).In the filamentary mode,the memristor exhibited a large memory window(10^(3)),low-operating voltages(±2 V),excellent cycle-to-cycle stability,and multilevel switching with controlled reset-stop voltages,making it suitable for high-density memory applications.Nonfilamentary switching demonstrated stable on/off ratios above 10,en-durance up to 102 cycles,and retention suited for short-term memory.Key synaptic behaviors,such as paired-pulse facilitation(PPF),post-tetanic potentiation(PTP),and spike-rate dependent plasticity(SRDP)were successfully emulated by modulating pulse amplitude,width,and interval.Experience-dependent plasticity(EDP)was also demonstrated,further replicating biological synaptic functions.These tempo-ral properties were utilized to develop a 4-bit reservoir computing system with 16 distinct conductance states,enabling efficient information encoding.For image recognition tasks,convolutional neural net-work(CNN)simulations achieved a high accuracy of 98.45%after 25 training epochs,outperforming the accuracy achieved following artificial neural network(ANN)simulations(87.79%).These findings demon-strate that the multilayered memristor exhibits high performance in neuromorphic systems,particularly for complex pattern recognition tasks,such as digit and letter classification.展开更多
The unmanned aerial vehicle(UAV)-assisted mobile edge computing(MEC)has been deemed a promising solution for energy-constrained devices to run smart applications with computationintensive and latency-sensitive require...The unmanned aerial vehicle(UAV)-assisted mobile edge computing(MEC)has been deemed a promising solution for energy-constrained devices to run smart applications with computationintensive and latency-sensitive requirements,especially in some infrastructure-limited areas or some emergency scenarios.However,the multi-UAVassisted MEC network remains largely unexplored.In this paper,the dynamic trajectory optimization and computation offloading are studied in a multi-UAVassisted MEC system where multiple UAVs fly over a target area with different trajectories to serve ground users.By considering the dynamic channel condition and random task arrival and jointly optimizing UAVs'trajectories,user association,and subchannel assignment,the average long-term sum of the user energy consumption minimization problem is formulated.To address the problem involving both discrete and continuous variables,a hybrid decision deep reinforcement learning(DRL)-based intelligent energyefficient resource allocation and trajectory optimization algorithm is proposed,named HDRT algorithm,where deep Q network(DQN)and deep deterministic policy gradient(DDPG)are invoked to process discrete and continuous variables,respectively.Simulation results show that the proposed HDRT algorithm converges fast and outperforms other benchmarks in the aspect of user energy consumption and latency.展开更多
In recent years,fog computing has become an important environment for dealing with the Internet of Things.Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing.Task schedul...In recent years,fog computing has become an important environment for dealing with the Internet of Things.Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing.Task scheduling is crucial for efficiently handling IoT user requests,thereby improving system performance,cost,and energy consumption across nodes in cloud computing.With the large amount of data and user requests,achieving the optimal solution to the task scheduling problem is challenging,particularly in terms of cost and energy efficiency.In this paper,we develop novel strategies to save energy consumption across nodes in fog computing when users execute tasks through the least-cost paths.Task scheduling is developed using modified artificial ecosystem optimization(AEO),combined with negative swarm operators,Salp Swarm Algorithm(SSA),in order to competitively optimize their capabilities during the exploitation phase of the optimal search process.In addition,the proposed strategy,Enhancement Artificial Ecosystem Optimization Salp Swarm Algorithm(EAEOSSA),attempts to find the most suitable solution.The optimization that combines cost and energy for multi-objective task scheduling optimization problems.The backpack problem is also added to improve both cost and energy in the iFogSim implementation as well.A comparison was made between the proposed strategy and other strategies in terms of time,cost,energy,and productivity.Experimental results showed that the proposed strategy improved energy consumption,cost,and time over other algorithms.Simulation results demonstrate that the proposed algorithm increases the average cost,average energy consumption,and mean service time in most scenarios,with average reductions of up to 21.15%in cost and 25.8%in energy consumption.展开更多
The increasing popularity of quantum computing has resulted in a considerable rise in demand for cloud quantum computing usage in recent years.Nevertheless,the rapid surge in demand for cloud-based quantum computing r...The increasing popularity of quantum computing has resulted in a considerable rise in demand for cloud quantum computing usage in recent years.Nevertheless,the rapid surge in demand for cloud-based quantum computing resources has led to a scarcity.In order to meet the needs of an increasing number of researchers,it is imperative to facilitate efficient and flexible access to computing resources in a cloud environment.In this paper,we propose a novel quantum computing paradigm,Virtual QPU(VQPU),which addresses this issue and enhances quantum cloud throughput with guaranteed circuit fidelity.The proposal introduces three innovative concepts:(1)The integration of virtualization technology into the field of quantum computing to enhance quantum cloud throughput.(2)The introduction of an asynchronous execution of circuits methodology to improve quantum computing flexibility.(3)The development of a virtual QPU allocation scheme for quantum tasks in a cloud environment to improve circuit fidelity.The concepts have been validated through the utilization of a self-built simulated quantum cloud platform.展开更多
Organic electrochemical transistor(OECT)devices demonstrate great promising potential for reservoir computing(RC)systems,but their lack of tunable dynamic characteristics limits their application in multi-temporal sca...Organic electrochemical transistor(OECT)devices demonstrate great promising potential for reservoir computing(RC)systems,but their lack of tunable dynamic characteristics limits their application in multi-temporal scale tasks.In this study,we report an OECT-based neuromorphic device with tunable relaxation time(τ)by introducing an additional vertical back-gate electrode into a planar structure.The dual-gate design enablesτreconfiguration from 93 to 541 ms.The tunable relaxation behaviors can be attributed to the combined effects of planar-gate induced electrochemical doping and back-gateinduced electrostatic coupling,as verified by electrochemical impedance spectroscopy analysis.Furthermore,we used theτ-tunable OECT devices as physical reservoirs in the RC system for intelligent driving trajectory prediction,achieving a significant improvement in prediction accuracy from below 69%to 99%.The results demonstrate that theτ-tunable OECT shows a promising candidate for multi-temporal scale neuromorphic computing applications.展开更多
The cloud-fog computing paradigm has emerged as a novel hybrid computing model that integrates computational resources at both fog nodes and cloud servers to address the challenges posed by dynamic and heterogeneous c...The cloud-fog computing paradigm has emerged as a novel hybrid computing model that integrates computational resources at both fog nodes and cloud servers to address the challenges posed by dynamic and heterogeneous computing networks.Finding an optimal computational resource for task offloading and then executing efficiently is a critical issue to achieve a trade-off between energy consumption and transmission delay.In this network,the task processed at fog nodes reduces transmission delay.Still,it increases energy consumption,while routing tasks to the cloud server saves energy at the cost of higher communication delay.Moreover,the order in which offloaded tasks are executed affects the system’s efficiency.For instance,executing lower-priority tasks before higher-priority jobs can disturb the reliability and stability of the system.Therefore,an efficient strategy of optimal computation offloading and task scheduling is required for operational efficacy.In this paper,we introduced a multi-objective and enhanced version of Cheeta Optimizer(CO),namely(MoECO),to jointly optimize the computation offloading and task scheduling in cloud-fog networks to minimize two competing objectives,i.e.,energy consumption and communication delay.MoECO first assigns tasks to the optimal computational nodes and then the allocated tasks are scheduled for processing based on the task priority.The mathematical modelling of CO needs improvement in computation time and convergence speed.Therefore,MoECO is proposed to increase the search capability of agents by controlling the search strategy based on a leader’s location.The adaptive step length operator is adjusted to diversify the solution and thus improves the exploration phase,i.e.,global search strategy.Consequently,this prevents the algorithm from getting trapped in the local optimal solution.Moreover,the interaction factor during the exploitation phase is also adjusted based on the location of the prey instead of the adjacent Cheetah.This increases the exploitation capability of agents,i.e.,local search capability.Furthermore,MoECO employs a multi-objective Pareto-optimal front to simultaneously minimize designated objectives.Comprehensive simulations in MATLAB demonstrate that the proposed algorithm obtains multiple solutions via a Pareto-optimal front and achieves an efficient trade-off between optimization objectives compared to baseline methods.展开更多
As emerging two-dimensional(2D)materials,carbides and nitrides(MXenes)could be solid solutions or organized structures made up of multi-atomic layers.With remarkable and adjustable electrical,optical,mechanical,and el...As emerging two-dimensional(2D)materials,carbides and nitrides(MXenes)could be solid solutions or organized structures made up of multi-atomic layers.With remarkable and adjustable electrical,optical,mechanical,and electrochemical characteristics,MXenes have shown great potential in brain-inspired neuromorphic computing electronics,including neuromorphic gas sensors,pressure sensors and photodetectors.This paper provides a forward-looking review of the research progress regarding MXenes in the neuromorphic sensing domain and discussed the critical challenges that need to be resolved.Key bottlenecks such as insufficient long-term stability under environmental exposure,high costs,scalability limitations in large-scale production,and mechanical mismatch in wearable integration hinder their practical deployment.Furthermore,unresolved issues like interfacial compatibility in heterostructures and energy inefficiency in neu-romorphic signal conversion demand urgent attention.The review offers insights into future research directions enhance the fundamental understanding of MXene properties and promote further integration into neuromorphic computing applications through the convergence with various emerging technologies.展开更多
The advancement of flexible memristors has significantly promoted the development of wearable electronic for emerging neuromorphic computing applications.Inspired by in-memory computing architecture of human brain,fle...The advancement of flexible memristors has significantly promoted the development of wearable electronic for emerging neuromorphic computing applications.Inspired by in-memory computing architecture of human brain,flexible memristors exhibit great application potential in emulating artificial synapses for highefficiency and low power consumption neuromorphic computing.This paper provides comprehensive overview of flexible memristors from perspectives of development history,material system,device structure,mechanical deformation method,device performance analysis,stress simulation during deformation,and neuromorphic computing applications.The recent advances in flexible electronics are summarized,including single device,device array and integration.The challenges and future perspectives of flexible memristor for neuromorphic computing are discussed deeply,paving the way for constructing wearable smart electronics and applications in large-scale neuromorphic computing and high-order intelligent robotics.展开更多
High-entropy oxides(HEOs)have emerged as a promising class of memristive materials,characterized by entropy-stabilized crystal structures,multivalent cation coordination,and tunable defect landscapes.These intrinsic f...High-entropy oxides(HEOs)have emerged as a promising class of memristive materials,characterized by entropy-stabilized crystal structures,multivalent cation coordination,and tunable defect landscapes.These intrinsic features enable forming-free resistive switching,multilevel conductance modulation,and synaptic plasticity,making HEOs attractive for neuromorphic computing.This review outlines recent progress in HEO-based memristors across materials engineering,switching mechanisms,and synaptic emulation.Particular attention is given to vacancy migration,phase transitions,and valence-state dynamics—mechanisms that underlie the switching behaviors observed in both amorphous and crystalline systems.Their relevance to neuromorphic functions such as short-term plasticity and spike-timing-dependent learning is also examined.While encouraging results have been achieved at the device level,challenges remain in conductance precision,variability control,and scalable integration.Addressing these demands a concerted effort across materials design,interface optimization,and task-aware modeling.With such integration,HEO memristors offer a compelling pathway toward energy-efficient and adaptable brain-inspired electronics.展开更多
Neuromorphic devices have garnered significant attention as potential building blocks for energy-efficient hardware systems owing to their capacity to emulate the computational efficiency of the brain.In this regard,r...Neuromorphic devices have garnered significant attention as potential building blocks for energy-efficient hardware systems owing to their capacity to emulate the computational efficiency of the brain.In this regard,reservoir computing(RC)framework,which leverages straightforward training methods and efficient temporal signal processing,has emerged as a promising scheme.While various physical reservoir devices,including ferroelectric,optoelectronic,and memristor-based systems,have been demonstrated,many still face challenges related to compatibility with mainstream complementary metal oxide semiconductor(CMOS)integration processes.This study introduced a silicon-based schottky barrier metal-oxide-semiconductor field effect transistor(SB-MOSFET),which was fabricated under low thermal budget and compatible with back-end-of-line(BEOL).The device demonstrated short-term memory characteristics,facilitated by the modulation of schottky barriers and charge trapping.Utilizing these characteristics,a RC system for temporal data processing was constructed,and its performance was validated in a 5×4 digital classification task,achieving an accuracy exceeding 98%after 50 training epochs.Furthermore,the system successfully processed temporal signal in waveform classification and prediction tasks using time-division multiplexing.Overall,the SB-MOSFET's high compatibility with CMOS technology provides substantial advantages for large-scale integration,enabling the development of energy-efficient reservoir computing hardware.展开更多
Nowadays,advances in communication technology and cloud computing have spawned a variety of smart mobile devices,which will generate a great amount of computing-intensive businesses,and require corresponding resources...Nowadays,advances in communication technology and cloud computing have spawned a variety of smart mobile devices,which will generate a great amount of computing-intensive businesses,and require corresponding resources of computation and communication.Multiaccess edge computing(MEC)can offload computing-intensive tasks to the nearby edge servers,which alleviates the pressure of devices.Ultra-dense network(UDN)can provide effective spectrum resources by deploying a large number of micro base stations.Furthermore,network slicing can support various applications in different communication scenarios.Therefore,this paper integrates the ultra-dense network slicing and the MEC technology,and introduces a hybrid computing offloading strategy in order to satisfy various quality of service(QoS)of edge devices.In order to dynamically allocate limited resources,the above problem is formulated as multiagent distributed deep reinforcement learning(DRL),which will achieve low overhead computation offloading strategy and real-time resource allocation decisions.In this context,federated learning is added to train DRL agents in a distributed manner,where each agent is dedicated to exploring actions composed of offloading decisions and allocating resources,so as to jointly optimize system delay and energy consumption.Simulation results show that the proposed learning algorithm has better performance compared with other strategies in literature.展开更多
This study proposes a lightweight rice disease detection model optimized for edge computing environments.The goal is to enhance the You Only Look Once(YOLO)v5 architecture to achieve a balance between real-time diagno...This study proposes a lightweight rice disease detection model optimized for edge computing environments.The goal is to enhance the You Only Look Once(YOLO)v5 architecture to achieve a balance between real-time diagnostic performance and computational efficiency.To this end,a total of 3234 high-resolution images(2400×1080)were collected from three major rice diseases Rice Blast,Bacterial Blight,and Brown Spot—frequently found in actual rice cultivation fields.These images served as the training dataset.The proposed YOLOv5-V2 model removes the Focus layer from the original YOLOv5s and integrates ShuffleNet V2 into the backbone,thereby resulting in both model compression and improved inference speed.Additionally,YOLOv5-P,based on PP-PicoDet,was configured as a comparative model to quantitatively evaluate performance.Experimental results demonstrated that YOLOv5-V2 achieved excellent detection performance,with an mAP 0.5 of 89.6%,mAP 0.5–0.95 of 66.7%,precision of 91.3%,and recall of 85.6%,while maintaining a lightweight model size of 6.45 MB.In contrast,YOLOv5-P exhibited a smaller model size of 4.03 MB,but showed lower performance with an mAP 0.5 of 70.3%,mAP 0.5–0.95 of 35.2%,precision of 62.3%,and recall of 74.1%.This study lays a technical foundation for the implementation of smart agriculture and real-time disease diagnosis systems by proposing a model that satisfies both accuracy and lightweight requirements.展开更多
With the dawning of the Internet of Everything(IoE) era, more and more novel applications are being deployed. However, resource constrained devices cannot fulfill the resource-requirements of these applications. This ...With the dawning of the Internet of Everything(IoE) era, more and more novel applications are being deployed. However, resource constrained devices cannot fulfill the resource-requirements of these applications. This paper investigates the computation offloading problem of the coexistence and synergy between fog computing and cloud computing in IoE by jointly optimizing the offloading decisions, the allocation of computation resource and transmit power. Specifically, we propose an energy-efficient computation offloading and resource allocation(ECORA) scheme to minimize the system cost. The simulation results verify the proposed scheme can effectively decrease the system cost by up to 50% compared with the existing schemes, especially for the scenario that the computation resource of fog computing is relatively small or the number of devices increases.展开更多
Mobile Edge Computing(MEC)is promising to alleviate the computation and storage burdens for terminals in wireless networks.The huge energy consumption of MEC servers challenges the establishment of smart cities and th...Mobile Edge Computing(MEC)is promising to alleviate the computation and storage burdens for terminals in wireless networks.The huge energy consumption of MEC servers challenges the establishment of smart cities and their service time powered by rechargeable batteries.In addition,Orthogonal Multiple Access(OMA)technique cannot utilize limited spectrum resources fully and efficiently.Therefore,Non-Orthogonal Multiple Access(NOMA)-based energy-efficient task scheduling among MEC servers for delay-constraint mobile applications is important,especially in highly-dynamic vehicular edge computing networks.The various movement patterns of vehicles lead to unbalanced offloading requirements and different load pressure for MEC servers.Self-Imitation Learning(SIL)-based Deep Reinforcement Learning(DRL)has emerged as a promising machine learning technique to break through obstacles in various research fields,especially in time-varying networks.In this paper,we first introduce related MEC technologies in vehicular networks.Then,we propose an energy-efficient approach for task scheduling in vehicular edge computing networks based on DRL,with the purpose of both guaranteeing the task latency requirement for multiple users and minimizing total energy consumption of MEC servers.Numerical results demonstrate that the proposed algorithm outperforms other methods.展开更多
With the new promising technique of mobile edge computing (MEC) emerging, by utilizing the edge computing and cloud computing capabilities to realize the HTTP adaptive video streaming transmission in MEC-based 5G netw...With the new promising technique of mobile edge computing (MEC) emerging, by utilizing the edge computing and cloud computing capabilities to realize the HTTP adaptive video streaming transmission in MEC-based 5G networks has been widely studied. Although many works have been done, most of the existing works focus on the issues of network resource utilization or the quality of experience (QoE) promotion, while the energy efficiency is largely ignored. In this paper, different from previous works, in order to realize the energy efficiency for video transmission in MEC-enhanced 5G networks, we propose a joint caching and transcoding schedule strategy for HTTP adaptive video streaming transmission by taking the caching and transcoding into consideration. We formulate the problem of energy-efficient joint caching and transcoding as an integer programming problem to minimize the system energy consumption. Due to solving the optimization problem brings huge computation complexity, therefore, to make the optimization problem tractable, a heuristic algorithm based on simulated annealing algorithm is proposed to iteratively reach the global optimum solution with a lower complexity and higher accuracy. Finally, numerical simulation results are illustrated to demonstrated that our proposed scheme brings an excellent performance.展开更多
To efficiently complete a complex computation task,the complex task should be decomposed into subcomputation tasks that run parallel in edge computing.Wireless Sensor Network(WSN)is a typical application of parallel c...To efficiently complete a complex computation task,the complex task should be decomposed into subcomputation tasks that run parallel in edge computing.Wireless Sensor Network(WSN)is a typical application of parallel computation.To achieve highly reliable parallel computation for wireless sensor network,the network's lifetime needs to be extended.Therefore,a proper task allocation strategy is needed to reduce the energy consumption and balance the load of the network.This paper proposes a task model and a cluster-based WSN model in edge computing.In our model,different tasks require different types of resources and different sensors provide different types of resources,so our model is heterogeneous,which makes the model more practical.Then we propose a task allocation algorithm that combines the Genetic Algorithm(GA)and the Ant Colony Optimization(ACO)algorithm.The algorithm concentrates on energy conservation and load balancing so that the lifetime of the network can be extended.The experimental result shows the algorithm's effectiveness and advantages in energy conservation and load balancing.展开更多
Mobile Edge Computing (MEC) has been considered a promising solution that can address capacity and performance challenges in legacy systems such as Mobile Cloud Computing (MCC). In particular, such challenges include ...Mobile Edge Computing (MEC) has been considered a promising solution that can address capacity and performance challenges in legacy systems such as Mobile Cloud Computing (MCC). In particular, such challenges include intolerable delay, congestion in the core network, insufficient Quality of Experience (QoE), high cost of resource utility, such as energy and bandwidth. The aforementioned challenges originate from limited resources in mobile devices, the multi-hop connection between end-users and the cloud, high pressure from computation-intensive and delay-critical applications. Considering the limited resource setting at the MEC, improving the efficiency of task offloading in terms of both energy and delay in MEC applications is an important and urgent problem to be solved. In this paper, the key objective is to propose a task offloading scheme that minimizes the overall energy consumption along with satisfying capacity and delay requirements. Thus, we propose a MEC-assisted energy-efficient task offloading scheme that leverages the cooperative MEC framework. To achieve energy efficiency, we propose a novel hybrid approach established based on Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO) to solve the optimization problem. The proposed approach considers efficient resource allocation such as sub-carriers, power, and bandwidth for offloading to guarantee minimum energy consumption. The simulation results demonstrate that the proposed strategy is computational-efficient compared to benchmark methods. Moreover, it improves energy utilization, energy gain, response delay, and offloading utility.展开更多
Ubiquitous computing must incorporate a certain level of security.For the severely resource constrained applications,the energy-efficient and small size cryptography algorithm implementation is a critical problem.Hard...Ubiquitous computing must incorporate a certain level of security.For the severely resource constrained applications,the energy-efficient and small size cryptography algorithm implementation is a critical problem.Hardware implementations of the advanced encryption standard(AES)for authentication and encryption are presented.An energy consumption variable is derived to evaluate low-power design strategies for battery-powered devices.It proves that compact AES architectures fail to optimize the AES hardware energy,whereas reducing invalid switching activities and implementing power-optimized sub-modules are the reasonable methods.Implementations of different substitution box(S-Boxes)structures are presented with 0.25μm 1.8 V CMOS(complementary metal oxide semiconductor)standard cell library.The comparisons and trade-offs among area,security,and power are explored.The experimental results show that Galois field composite S-Boxes have smaller size and highest security but consume considerably more power,whereas decoder-switch-encoder S-Boxes have the best power characteristics with disadvantages in terms of size and security.The combination of these two type S-Boxes instead of homogeneous S-Boxes in AES circuit will lead to optimal schemes.The technique of latch-dividing data path is analyzed,and the quantitative simulation results demonstrate that this approach diminishes the glitches effectively at a very low hardware cost.展开更多
Mobile cloud computing（MCC） combines mobile Internet and cloud computing to improve the performance of mobile applications. However, MCC faces the problem of energy efficiency because of randomly varying channels. A...Mobile cloud computing（MCC） combines mobile Internet and cloud computing to improve the performance of mobile applications. However, MCC faces the problem of energy efficiency because of randomly varying channels. A scheduling algorithm is proposed by introducing the Lyapunov optimization, which can dynamically choose users to transmit data based on queue backlog and channel statistics. The Lyapunov analysis shows that the proposed scheduling algorithm can make a tradeoff between queue backlog and energy consumption in the channel-aware mobile cloud computing system. The simulation results verify the effectiveness of the proposed algorithm.展开更多
基金supported by the National R&D Pro-gram through the National Research Foundation of Korea(NRF)funded by the Ministry of Science and ICT(Nos.RS-2024-00356939 and RS-2024-00405691).
文摘As the demand for advanced computational systems capable of handling large data volumes rises,nano-electronic devices,such as memristors,are being developed for efficient data processing,especially in reservoir computing(RC).RC enables the processing of temporal information with minimal training costs,making it a promising approach for neuromorphic computing.However,current memristor devices of-ten suffer from limitations in dynamic conductance and temporal behavior,which affects their perfor-mance in these applications.In this study,we present a multilayered indium-tin-oxide(ITO)/ZnO/indium-gallium-zinc oxide(IGZO)/ZnO/ITO memristor fabricated via radiofrequency sputtering to explore its fil-amentary and nonfilamentary resistive switching(RS)characteristics.High-resolution transmission elec-tron microscopy confirmed the polycrystalline structure of the ZnO/IGZO/ZnO active layer.Dual-switching modes were demonstrated by controlling the current compliance(I_(CC)).In the filamentary mode,the memristor exhibited a large memory window(10^(3)),low-operating voltages(±2 V),excellent cycle-to-cycle stability,and multilevel switching with controlled reset-stop voltages,making it suitable for high-density memory applications.Nonfilamentary switching demonstrated stable on/off ratios above 10,en-durance up to 102 cycles,and retention suited for short-term memory.Key synaptic behaviors,such as paired-pulse facilitation(PPF),post-tetanic potentiation(PTP),and spike-rate dependent plasticity(SRDP)were successfully emulated by modulating pulse amplitude,width,and interval.Experience-dependent plasticity(EDP)was also demonstrated,further replicating biological synaptic functions.These tempo-ral properties were utilized to develop a 4-bit reservoir computing system with 16 distinct conductance states,enabling efficient information encoding.For image recognition tasks,convolutional neural net-work(CNN)simulations achieved a high accuracy of 98.45%after 25 training epochs,outperforming the accuracy achieved following artificial neural network(ANN)simulations(87.79%).These findings demon-strate that the multilayered memristor exhibits high performance in neuromorphic systems,particularly for complex pattern recognition tasks,such as digit and letter classification.
基金supported by National Natural Science Foundation of China(No.62471254)National Natural Science Foundation of China(No.92367302)。
文摘The unmanned aerial vehicle(UAV)-assisted mobile edge computing(MEC)has been deemed a promising solution for energy-constrained devices to run smart applications with computationintensive and latency-sensitive requirements,especially in some infrastructure-limited areas or some emergency scenarios.However,the multi-UAVassisted MEC network remains largely unexplored.In this paper,the dynamic trajectory optimization and computation offloading are studied in a multi-UAVassisted MEC system where multiple UAVs fly over a target area with different trajectories to serve ground users.By considering the dynamic channel condition and random task arrival and jointly optimizing UAVs'trajectories,user association,and subchannel assignment,the average long-term sum of the user energy consumption minimization problem is formulated.To address the problem involving both discrete and continuous variables,a hybrid decision deep reinforcement learning(DRL)-based intelligent energyefficient resource allocation and trajectory optimization algorithm is proposed,named HDRT algorithm,where deep Q network(DQN)and deep deterministic policy gradient(DDPG)are invoked to process discrete and continuous variables,respectively.Simulation results show that the proposed HDRT algorithm converges fast and outperforms other benchmarks in the aspect of user energy consumption and latency.
基金supported and funded by theDeanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2503).
文摘In recent years,fog computing has become an important environment for dealing with the Internet of Things.Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing.Task scheduling is crucial for efficiently handling IoT user requests,thereby improving system performance,cost,and energy consumption across nodes in cloud computing.With the large amount of data and user requests,achieving the optimal solution to the task scheduling problem is challenging,particularly in terms of cost and energy efficiency.In this paper,we develop novel strategies to save energy consumption across nodes in fog computing when users execute tasks through the least-cost paths.Task scheduling is developed using modified artificial ecosystem optimization(AEO),combined with negative swarm operators,Salp Swarm Algorithm(SSA),in order to competitively optimize their capabilities during the exploitation phase of the optimal search process.In addition,the proposed strategy,Enhancement Artificial Ecosystem Optimization Salp Swarm Algorithm(EAEOSSA),attempts to find the most suitable solution.The optimization that combines cost and energy for multi-objective task scheduling optimization problems.The backpack problem is also added to improve both cost and energy in the iFogSim implementation as well.A comparison was made between the proposed strategy and other strategies in terms of time,cost,energy,and productivity.Experimental results showed that the proposed strategy improved energy consumption,cost,and time over other algorithms.Simulation results demonstrate that the proposed algorithm increases the average cost,average energy consumption,and mean service time in most scenarios,with average reductions of up to 21.15%in cost and 25.8%in energy consumption.
文摘The increasing popularity of quantum computing has resulted in a considerable rise in demand for cloud quantum computing usage in recent years.Nevertheless,the rapid surge in demand for cloud-based quantum computing resources has led to a scarcity.In order to meet the needs of an increasing number of researchers,it is imperative to facilitate efficient and flexible access to computing resources in a cloud environment.In this paper,we propose a novel quantum computing paradigm,Virtual QPU(VQPU),which addresses this issue and enhances quantum cloud throughput with guaranteed circuit fidelity.The proposal introduces three innovative concepts:(1)The integration of virtualization technology into the field of quantum computing to enhance quantum cloud throughput.(2)The introduction of an asynchronous execution of circuits methodology to improve quantum computing flexibility.(3)The development of a virtual QPU allocation scheme for quantum tasks in a cloud environment to improve circuit fidelity.The concepts have been validated through the utilization of a self-built simulated quantum cloud platform.
基金supported by the National Key Research and Development Program of China under Grant 2022YFB3608300in part by the National Nature Science Foundation of China(NSFC)under Grants 62404050,U2341218,62574056,62204052。
文摘Organic electrochemical transistor(OECT)devices demonstrate great promising potential for reservoir computing(RC)systems,but their lack of tunable dynamic characteristics limits their application in multi-temporal scale tasks.In this study,we report an OECT-based neuromorphic device with tunable relaxation time(τ)by introducing an additional vertical back-gate electrode into a planar structure.The dual-gate design enablesτreconfiguration from 93 to 541 ms.The tunable relaxation behaviors can be attributed to the combined effects of planar-gate induced electrochemical doping and back-gateinduced electrostatic coupling,as verified by electrochemical impedance spectroscopy analysis.Furthermore,we used theτ-tunable OECT devices as physical reservoirs in the RC system for intelligent driving trajectory prediction,achieving a significant improvement in prediction accuracy from below 69%to 99%.The results demonstrate that theτ-tunable OECT shows a promising candidate for multi-temporal scale neuromorphic computing applications.
基金appreciation to the Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R384)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The cloud-fog computing paradigm has emerged as a novel hybrid computing model that integrates computational resources at both fog nodes and cloud servers to address the challenges posed by dynamic and heterogeneous computing networks.Finding an optimal computational resource for task offloading and then executing efficiently is a critical issue to achieve a trade-off between energy consumption and transmission delay.In this network,the task processed at fog nodes reduces transmission delay.Still,it increases energy consumption,while routing tasks to the cloud server saves energy at the cost of higher communication delay.Moreover,the order in which offloaded tasks are executed affects the system’s efficiency.For instance,executing lower-priority tasks before higher-priority jobs can disturb the reliability and stability of the system.Therefore,an efficient strategy of optimal computation offloading and task scheduling is required for operational efficacy.In this paper,we introduced a multi-objective and enhanced version of Cheeta Optimizer(CO),namely(MoECO),to jointly optimize the computation offloading and task scheduling in cloud-fog networks to minimize two competing objectives,i.e.,energy consumption and communication delay.MoECO first assigns tasks to the optimal computational nodes and then the allocated tasks are scheduled for processing based on the task priority.The mathematical modelling of CO needs improvement in computation time and convergence speed.Therefore,MoECO is proposed to increase the search capability of agents by controlling the search strategy based on a leader’s location.The adaptive step length operator is adjusted to diversify the solution and thus improves the exploration phase,i.e.,global search strategy.Consequently,this prevents the algorithm from getting trapped in the local optimal solution.Moreover,the interaction factor during the exploitation phase is also adjusted based on the location of the prey instead of the adjacent Cheetah.This increases the exploitation capability of agents,i.e.,local search capability.Furthermore,MoECO employs a multi-objective Pareto-optimal front to simultaneously minimize designated objectives.Comprehensive simulations in MATLAB demonstrate that the proposed algorithm obtains multiple solutions via a Pareto-optimal front and achieves an efficient trade-off between optimization objectives compared to baseline methods.
基金supported by the NSFC(12474071)Natural Science Foundation of Shandong Province(ZR2024YQ051,ZR2025QB50)+6 种基金Guangdong Basic and Applied Basic Research Foundation(2025A1515011191)the Shanghai Sailing Program(23YF1402200,23YF1402400)funded by Basic Research Program of Jiangsu(BK20240424)Open Research Fund of State Key Laboratory of Crystal Materials(KF2406)Taishan Scholar Foundation of Shandong Province(tsqn202408006,tsqn202507058)Young Talent of Lifting engineering for Science and Technology in Shandong,China(SDAST2024QTB002)the Qilu Young Scholar Program of Shandong University。
文摘As emerging two-dimensional(2D)materials,carbides and nitrides(MXenes)could be solid solutions or organized structures made up of multi-atomic layers.With remarkable and adjustable electrical,optical,mechanical,and electrochemical characteristics,MXenes have shown great potential in brain-inspired neuromorphic computing electronics,including neuromorphic gas sensors,pressure sensors and photodetectors.This paper provides a forward-looking review of the research progress regarding MXenes in the neuromorphic sensing domain and discussed the critical challenges that need to be resolved.Key bottlenecks such as insufficient long-term stability under environmental exposure,high costs,scalability limitations in large-scale production,and mechanical mismatch in wearable integration hinder their practical deployment.Furthermore,unresolved issues like interfacial compatibility in heterostructures and energy inefficiency in neu-romorphic signal conversion demand urgent attention.The review offers insights into future research directions enhance the fundamental understanding of MXene properties and promote further integration into neuromorphic computing applications through the convergence with various emerging technologies.
基金supported by the NSFC(12474071)Natural Science Foundation of Shandong Province(ZR2024YQ051)+5 种基金Open Research Fund of State Key Laboratory of Materials for Integrated Circuits(SKLJC-K2024-12)the Shanghai Sailing Program(23YF1402200,23YF1402400)Natural Science Foundation of Jiangsu Province(BK20240424)Taishan Scholar Foundation of Shandong Province(tsqn202408006)Young Talent of Lifting engineering for Science and Technology in Shandong,China(SDAST2024QTB002)the Qilu Young Scholar Program of Shandong University.
文摘The advancement of flexible memristors has significantly promoted the development of wearable electronic for emerging neuromorphic computing applications.Inspired by in-memory computing architecture of human brain,flexible memristors exhibit great application potential in emulating artificial synapses for highefficiency and low power consumption neuromorphic computing.This paper provides comprehensive overview of flexible memristors from perspectives of development history,material system,device structure,mechanical deformation method,device performance analysis,stress simulation during deformation,and neuromorphic computing applications.The recent advances in flexible electronics are summarized,including single device,device array and integration.The challenges and future perspectives of flexible memristor for neuromorphic computing are discussed deeply,paving the way for constructing wearable smart electronics and applications in large-scale neuromorphic computing and high-order intelligent robotics.
基金financially supported by the National Natural Science Foundation of China(Grant No.12172093)the Guangdong Basic and Applied Basic Research Foundation(Grant No.2021A1515012607)。
文摘High-entropy oxides(HEOs)have emerged as a promising class of memristive materials,characterized by entropy-stabilized crystal structures,multivalent cation coordination,and tunable defect landscapes.These intrinsic features enable forming-free resistive switching,multilevel conductance modulation,and synaptic plasticity,making HEOs attractive for neuromorphic computing.This review outlines recent progress in HEO-based memristors across materials engineering,switching mechanisms,and synaptic emulation.Particular attention is given to vacancy migration,phase transitions,and valence-state dynamics—mechanisms that underlie the switching behaviors observed in both amorphous and crystalline systems.Their relevance to neuromorphic functions such as short-term plasticity and spike-timing-dependent learning is also examined.While encouraging results have been achieved at the device level,challenges remain in conductance precision,variability control,and scalable integration.Addressing these demands a concerted effort across materials design,interface optimization,and task-aware modeling.With such integration,HEO memristors offer a compelling pathway toward energy-efficient and adaptable brain-inspired electronics.
基金supported in part by the Chinese Academy of Sciences(No.XDA0330302)NSFC program(No.22127901)。
文摘Neuromorphic devices have garnered significant attention as potential building blocks for energy-efficient hardware systems owing to their capacity to emulate the computational efficiency of the brain.In this regard,reservoir computing(RC)framework,which leverages straightforward training methods and efficient temporal signal processing,has emerged as a promising scheme.While various physical reservoir devices,including ferroelectric,optoelectronic,and memristor-based systems,have been demonstrated,many still face challenges related to compatibility with mainstream complementary metal oxide semiconductor(CMOS)integration processes.This study introduced a silicon-based schottky barrier metal-oxide-semiconductor field effect transistor(SB-MOSFET),which was fabricated under low thermal budget and compatible with back-end-of-line(BEOL).The device demonstrated short-term memory characteristics,facilitated by the modulation of schottky barriers and charge trapping.Utilizing these characteristics,a RC system for temporal data processing was constructed,and its performance was validated in a 5×4 digital classification task,achieving an accuracy exceeding 98%after 50 training epochs.Furthermore,the system successfully processed temporal signal in waveform classification and prediction tasks using time-division multiplexing.Overall,the SB-MOSFET's high compatibility with CMOS technology provides substantial advantages for large-scale integration,enabling the development of energy-efficient reservoir computing hardware.
文摘Nowadays,advances in communication technology and cloud computing have spawned a variety of smart mobile devices,which will generate a great amount of computing-intensive businesses,and require corresponding resources of computation and communication.Multiaccess edge computing(MEC)can offload computing-intensive tasks to the nearby edge servers,which alleviates the pressure of devices.Ultra-dense network(UDN)can provide effective spectrum resources by deploying a large number of micro base stations.Furthermore,network slicing can support various applications in different communication scenarios.Therefore,this paper integrates the ultra-dense network slicing and the MEC technology,and introduces a hybrid computing offloading strategy in order to satisfy various quality of service(QoS)of edge devices.In order to dynamically allocate limited resources,the above problem is formulated as multiagent distributed deep reinforcement learning(DRL),which will achieve low overhead computation offloading strategy and real-time resource allocation decisions.In this context,federated learning is added to train DRL agents in a distributed manner,where each agent is dedicated to exploring actions composed of offloading decisions and allocating resources,so as to jointly optimize system delay and energy consumption.Simulation results show that the proposed learning algorithm has better performance compared with other strategies in literature.
文摘This study proposes a lightweight rice disease detection model optimized for edge computing environments.The goal is to enhance the You Only Look Once(YOLO)v5 architecture to achieve a balance between real-time diagnostic performance and computational efficiency.To this end,a total of 3234 high-resolution images(2400×1080)were collected from three major rice diseases Rice Blast,Bacterial Blight,and Brown Spot—frequently found in actual rice cultivation fields.These images served as the training dataset.The proposed YOLOv5-V2 model removes the Focus layer from the original YOLOv5s and integrates ShuffleNet V2 into the backbone,thereby resulting in both model compression and improved inference speed.Additionally,YOLOv5-P,based on PP-PicoDet,was configured as a comparative model to quantitatively evaluate performance.Experimental results demonstrated that YOLOv5-V2 achieved excellent detection performance,with an mAP 0.5 of 89.6%,mAP 0.5–0.95 of 66.7%,precision of 91.3%,and recall of 85.6%,while maintaining a lightweight model size of 6.45 MB.In contrast,YOLOv5-P exhibited a smaller model size of 4.03 MB,but showed lower performance with an mAP 0.5 of 70.3%,mAP 0.5–0.95 of 35.2%,precision of 62.3%,and recall of 74.1%.This study lays a technical foundation for the implementation of smart agriculture and real-time disease diagnosis systems by proposing a model that satisfies both accuracy and lightweight requirements.
基金supported by the Fundamental Research Funds for the Central Universities (No. 2018YJS008)the National Natural Science Foundation of China (61471031, 61661021, 61531009)+4 种基金Beijing Natural Science Foundation (L182018)the Open Research Fund of National Mobile Communications Research Laboratory, Southeast University (No. 2017D14)the State Key Laboratory of Rail Traffi c Control and Safety (Contract No. RCS2017K009)Science and Technology Program of Jiangxi Province (20172BCB22016, 20171BBE50057)Shenzhen Science and Technology Program under Grant (No. JCYJ20170817110410346)
文摘With the dawning of the Internet of Everything(IoE) era, more and more novel applications are being deployed. However, resource constrained devices cannot fulfill the resource-requirements of these applications. This paper investigates the computation offloading problem of the coexistence and synergy between fog computing and cloud computing in IoE by jointly optimizing the offloading decisions, the allocation of computation resource and transmit power. Specifically, we propose an energy-efficient computation offloading and resource allocation(ECORA) scheme to minimize the system cost. The simulation results verify the proposed scheme can effectively decrease the system cost by up to 50% compared with the existing schemes, especially for the scenario that the computation resource of fog computing is relatively small or the number of devices increases.
基金supported in part by the National Natural Science Foundation of China under Grant 61971084 and Grant 62001073in part by the National Natural Science Foundation of Chongqing under Grant cstc2019jcyj-msxmX0208in part by the open research fund of National Mobile Communications Research Laboratory,Southeast University,under Grant 2020D05.
文摘Mobile Edge Computing(MEC)is promising to alleviate the computation and storage burdens for terminals in wireless networks.The huge energy consumption of MEC servers challenges the establishment of smart cities and their service time powered by rechargeable batteries.In addition,Orthogonal Multiple Access(OMA)technique cannot utilize limited spectrum resources fully and efficiently.Therefore,Non-Orthogonal Multiple Access(NOMA)-based energy-efficient task scheduling among MEC servers for delay-constraint mobile applications is important,especially in highly-dynamic vehicular edge computing networks.The various movement patterns of vehicles lead to unbalanced offloading requirements and different load pressure for MEC servers.Self-Imitation Learning(SIL)-based Deep Reinforcement Learning(DRL)has emerged as a promising machine learning technique to break through obstacles in various research fields,especially in time-varying networks.In this paper,we first introduce related MEC technologies in vehicular networks.Then,we propose an energy-efficient approach for task scheduling in vehicular edge computing networks based on DRL,with the purpose of both guaranteeing the task latency requirement for multiple users and minimizing total energy consumption of MEC servers.Numerical results demonstrate that the proposed algorithm outperforms other methods.
基金support by the Major National Science and Technology Projects (No. 2018ZX03001014-003)
文摘With the new promising technique of mobile edge computing (MEC) emerging, by utilizing the edge computing and cloud computing capabilities to realize the HTTP adaptive video streaming transmission in MEC-based 5G networks has been widely studied. Although many works have been done, most of the existing works focus on the issues of network resource utilization or the quality of experience (QoE) promotion, while the energy efficiency is largely ignored. In this paper, different from previous works, in order to realize the energy efficiency for video transmission in MEC-enhanced 5G networks, we propose a joint caching and transcoding schedule strategy for HTTP adaptive video streaming transmission by taking the caching and transcoding into consideration. We formulate the problem of energy-efficient joint caching and transcoding as an integer programming problem to minimize the system energy consumption. Due to solving the optimization problem brings huge computation complexity, therefore, to make the optimization problem tractable, a heuristic algorithm based on simulated annealing algorithm is proposed to iteratively reach the global optimum solution with a lower complexity and higher accuracy. Finally, numerical simulation results are illustrated to demonstrated that our proposed scheme brings an excellent performance.
基金supported by Postdoctoral Science Foundation of China(No.2021M702441)National Natural Science Foundation of China(No.61871283)。
文摘To efficiently complete a complex computation task,the complex task should be decomposed into subcomputation tasks that run parallel in edge computing.Wireless Sensor Network(WSN)is a typical application of parallel computation.To achieve highly reliable parallel computation for wireless sensor network,the network's lifetime needs to be extended.Therefore,a proper task allocation strategy is needed to reduce the energy consumption and balance the load of the network.This paper proposes a task model and a cluster-based WSN model in edge computing.In our model,different tasks require different types of resources and different sensors provide different types of resources,so our model is heterogeneous,which makes the model more practical.Then we propose a task allocation algorithm that combines the Genetic Algorithm(GA)and the Ant Colony Optimization(ACO)algorithm.The algorithm concentrates on energy conservation and load balancing so that the lifetime of the network can be extended.The experimental result shows the algorithm's effectiveness and advantages in energy conservation and load balancing.
基金supported by the Chinese Scholarship Council(CSC)under MOFCOM(No.2017MOC010907)any opinions,findings,and conclusions are those of the authors and do not necessarily reflect the views of the above agency.
文摘Mobile Edge Computing (MEC) has been considered a promising solution that can address capacity and performance challenges in legacy systems such as Mobile Cloud Computing (MCC). In particular, such challenges include intolerable delay, congestion in the core network, insufficient Quality of Experience (QoE), high cost of resource utility, such as energy and bandwidth. The aforementioned challenges originate from limited resources in mobile devices, the multi-hop connection between end-users and the cloud, high pressure from computation-intensive and delay-critical applications. Considering the limited resource setting at the MEC, improving the efficiency of task offloading in terms of both energy and delay in MEC applications is an important and urgent problem to be solved. In this paper, the key objective is to propose a task offloading scheme that minimizes the overall energy consumption along with satisfying capacity and delay requirements. Thus, we propose a MEC-assisted energy-efficient task offloading scheme that leverages the cooperative MEC framework. To achieve energy efficiency, we propose a novel hybrid approach established based on Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO) to solve the optimization problem. The proposed approach considers efficient resource allocation such as sub-carriers, power, and bandwidth for offloading to guarantee minimum energy consumption. The simulation results demonstrate that the proposed strategy is computational-efficient compared to benchmark methods. Moreover, it improves energy utilization, energy gain, response delay, and offloading utility.
基金the"863"High Technology Research and Development Program of China(2006AA01Z226)the Scientific Research Foundation of Huazhong University of Science and Technology(2006Z011B)the Program for New Century Excellent Talents in University(NCET-07-0328).
文摘Ubiquitous computing must incorporate a certain level of security.For the severely resource constrained applications,the energy-efficient and small size cryptography algorithm implementation is a critical problem.Hardware implementations of the advanced encryption standard(AES)for authentication and encryption are presented.An energy consumption variable is derived to evaluate low-power design strategies for battery-powered devices.It proves that compact AES architectures fail to optimize the AES hardware energy,whereas reducing invalid switching activities and implementing power-optimized sub-modules are the reasonable methods.Implementations of different substitution box(S-Boxes)structures are presented with 0.25μm 1.8 V CMOS(complementary metal oxide semiconductor)standard cell library.The comparisons and trade-offs among area,security,and power are explored.The experimental results show that Galois field composite S-Boxes have smaller size and highest security but consume considerably more power,whereas decoder-switch-encoder S-Boxes have the best power characteristics with disadvantages in terms of size and security.The combination of these two type S-Boxes instead of homogeneous S-Boxes in AES circuit will lead to optimal schemes.The technique of latch-dividing data path is analyzed,and the quantitative simulation results demonstrate that this approach diminishes the glitches effectively at a very low hardware cost.
基金supported by the National Natural Science Foundation of China(61173017)the National High Technology Research and Development Program(863 Program)(2014AA01A701)
文摘Mobile cloud computing（MCC） combines mobile Internet and cloud computing to improve the performance of mobile applications. However, MCC faces the problem of energy efficiency because of randomly varying channels. A scheduling algorithm is proposed by introducing the Lyapunov optimization, which can dynamically choose users to transmit data based on queue backlog and channel statistics. The Lyapunov analysis shows that the proposed scheduling algorithm can make a tradeoff between queue backlog and energy consumption in the channel-aware mobile cloud computing system. The simulation results verify the effectiveness of the proposed algorithm.
