Recently,Multicore systems use Dynamic Voltage/Frequency Scaling(DV/FS)technology to allow the cores to operate with various voltage and/or frequencies than other cores to save power and enhance the performance.In thi...Recently,Multicore systems use Dynamic Voltage/Frequency Scaling(DV/FS)technology to allow the cores to operate with various voltage and/or frequencies than other cores to save power and enhance the performance.In this paper,an effective and reliable hybridmodel to reduce the energy and makespan in multicore systems is proposed.The proposed hybrid model enhances and integrates the greedy approach with dynamic programming to achieve optimal Voltage/Frequency(Vmin/F)levels.Then,the allocation process is applied based on the availableworkloads.The hybrid model consists of three stages.The first stage gets the optimum safe voltage while the second stage sets the level of energy efficiency,and finally,the third is the allocation stage.Experimental results on various benchmarks show that the proposed model can generate optimal solutions to save energy while minimizing the makespan penalty.Comparisons with other competitive algorithms show that the proposed model provides on average 48%improvements in energy-saving and achieves an 18%reduction in computation time while ensuring a high degree of system reliability.展开更多
Residential energy-efficiency measures, besides energy savings, provide opportunities for improvement of thermal comfort, air quality, lighting quality, and operation. However, all these benefits sometimes are not eno...Residential energy-efficiency measures, besides energy savings, provide opportunities for improvement of thermal comfort, air quality, lighting quality, and operation. However, all these benefits sometimes are not enough to convince a homeowner to pay the incremental cost associated with the energy-efficiency measure. The objective of this work is to develop a methodology for the economic evaluation of residential energy-efficiency measures that can simplify the economic analysis for the homeowner while taking into consideration all factors associated with the purchase, ownership, and selling of the house with the energy-efficiency measure. The methodology accounts for direct and indirect economic parameters associated to an energy-efficiency measure;direct parameters such as the mortgage interest and fuel price escalation rate, and indirect parameters such as savings account interest and marginal income tax rate. The methodology also considers different cases based on the service life of the energy-efficiency measure and loss of efficiency through a derating factor. To estimate the market value, the methodology uses the future energy cost savings instead of the cost of the EEM. Results from the methodology offer to homeowner annual net savings and net assets. The annual net savings gives the homeowner a measure of the annual positive cash flow that can be obtained from an energy-efficiency project;but more important, the net assets offer a measure of the added net wealth. To simplify and increase the use of the methodology by homeowners, the methodology has been implemented in an Excel tool that can be downloaded from the TxAIRE’s website.展开更多
This paper investigates the tradeoff between energy-efficiency capacity and spectrum sensing under hybrid spectrum sharing model, where the spectrum sharing method is based on sensing results of secondary user （SU）....This paper investigates the tradeoff between energy-efficiency capacity and spectrum sensing under hybrid spectrum sharing model, where the spectrum sharing method is based on sensing results of secondary user （SU）. The metric ＇bits per joule＇, which captures the effect of energy overhead in spectrum sensing, is adopted to evaluate energy-efficiency capacity. We first formulize the tradeoff between energy-efficiency capacity and spectrum sensing as an optimization problem with mixture constraint of sensing time and detection threshold. Under some certain condition on the domain of detection threshold, i.e. in which we can＇t improve energy-efficiency capacity through increasing the detection probability, the original optimization problem can be reduced to a new unconstrained one, which only relates to sensing time. Then the existence and uniqueness of optimal sensing time to achieve maximum energy-efficiency capacity are discussed and a low-complexity algorithm is proposed to obtain the optimal solution. Finally, numerical simulation is performed to verify the theoretical analysis results. The simulation results indicate that hybrid spectrum sharing is remarkably beneficial to energy-efficient transmission in cognitive radio networks （CRN）. And the proposed algorithm can quickly converge to the optimal solution.展开更多
Financial and environment considerations present new trends in wireless network known as green communication. As one of the most promising network architectures, the device-to-device （D2D） communication should take ...Financial and environment considerations present new trends in wireless network known as green communication. As one of the most promising network architectures, the device-to-device （D2D） communication should take seriously account to the energy-efficiency. Most of the existing work in the area of D2D communication only focus on the direct communication, however, the direct link D2D communication has to be limited in practice because of long distance, poor propagation medium and cellular interference, etc. A new energy-efficient multi-hop routing algorithm was investigated for multi-hop D2D system by jointly optimizing channel reusing and power allocation. Firstly, the energy-efficient multi-hop routing problem was formulated as a combinatorial optimization problem. Secondly, to obtain a desirable solution with reasonable computation cost, a heuristic multi-hop routing algorithm was presented to solve the formulated problem and to achieve a satisfactory energy-efficiency performance. Simulation shows the effectiveness of the proposed routing algorithm.展开更多
The energy-efficiency(EE)optimization problem was studied for resource allocation in an uplink single-cell network,in which multiple mobile users with different quality of service(QoS)requirements operate under a non-...The energy-efficiency(EE)optimization problem was studied for resource allocation in an uplink single-cell network,in which multiple mobile users with different quality of service(QoS)requirements operate under a non-orthogonal multiple access(NOMA)scheme.Firstly,a multi-user feasible power allocation region is derived as a multidimensional body that provides an efficient scheme to determine the feasibility of original channel and power assignment problem.Then,the size of feasible power allocation region was first introduced as utility function of the subchannel-user matching game in order to get high EE of the system and fairness among the users.Moreover,the power allocation optimization to the EE maximization is proved to be a monotonous decline function.The simulation results show that compared with the conventional schemes,the network connectivity of the proposed scheme is significantly enhanced and besides,for low rate massive connectivity networks,the proposed scheme obtains performance gains in the EE of the system.展开更多
An energy-efficient heuristic mechanism is presented to obtain the optimal solution for the coverage problem in sensor networks. The mechanism can ensure that all targets are fully covered corresponding to their level...An energy-efficient heuristic mechanism is presented to obtain the optimal solution for the coverage problem in sensor networks. The mechanism can ensure that all targets are fully covered corresponding to their levels of importance at minimum cost, and the ant colony optimization algorithm （ACO） is adopted to achieve the above metrics. Based on the novel design of heuristic factors, artificial ants can adaptively detect the energy status and coverage ability of sensor networks via local information. By introducing the evaluation function to global pheromone updating rule, the pheromone trail on the best solution is greatly enhanced, so that the convergence process of the algorithm is speed up. Finally, the optimal solution with a higher coverage- efficiency and a longer lifetime is obtained.展开更多
The convergence of Internet of things(IoT)and 5G holds immense potential for transforming industries by enabling real-time,massive-scale connectivity and automation.However,the growing number of devices connected to t...The convergence of Internet of things(IoT)and 5G holds immense potential for transforming industries by enabling real-time,massive-scale connectivity and automation.However,the growing number of devices connected to the IoT systems demands a communication network capable of handling vast amounts of data with minimal delay.These generated enormous complex,high-dimensional,high-volume,and high-speed data also brings challenges on its storage,transmission,processing,and energy cost,due to the limited computing capabilities,battery capacity,memory,and energy utilization of current IoT networks.In this paper,a seamless architecture by combining mobile and cloud computing is proposed.It can agilely bargain with 5G-IoT devices,sensor nodes,and mobile computing in a distributed manner,enabling minimized energy cost,high interoperability,and high scalability as well as overcoming the memory constraints.An artificial intelligence(AI)-powered green and energy-efficient architecture is then proposed for 5G-IoT systems and sustainable smart cities.The experimental results reveal that the proposed approach dramatically reduces the transmitted data volume and power consumption and yields superior results regarding interoperability,compression ratio,and energy saving.This is especially critical in enabling the deployment of 5G and even 6G wireless systems for smart cities.展开更多
Energy efficiency stands as an essential factor when implementing deep reinforcement learning(DRL)policies for robotic control systems.Standard algorithms,including Deep Deterministic Policy Gradient(DDPG),primarily o...Energy efficiency stands as an essential factor when implementing deep reinforcement learning(DRL)policies for robotic control systems.Standard algorithms,including Deep Deterministic Policy Gradient(DDPG),primarily optimize task rewards but at the cost of excessively high energy consumption,making them impractical for real-world robotic systems.To address this limitation,we propose Physics-Informed DDPG(PI-DDPG),which integrates physics-based energy penalties to develop energy-efficient yet high-performing control policies.The proposed method introduces adaptive physics-informed constraints through a dynamic weighting factor(λ),enabling policies that balance reward maximization with energy savings.Our motivation is to overcome the impracticality of rewardonly optimization by designing controllers that achieve competitive performance while substantially reducing energy consumption.PI-DDPG was evaluated in nine MuJoCo continuous control environments,where it demonstrated significant improvements in energy efficiency without compromising stability or performance.Experimental results confirm that PI-DDPG substantially reduces energy consumption compared to standard DDPG,while maintaining competitive task performance.For instance,energy costs decreased from 5542.98 to 3119.02 in HalfCheetah-v4 and from1909.13 to 1586.75 in Ant-v4,with stable performance in Hopper-v4(205.95 vs.130.82)and InvertedPendulum-v4(322.97 vs.311.29).Although DDPG sometimes yields higher rewards,such as in HalfCheetah-v4(5695.37 vs.4894.59),it requires significantly greater energy expenditure.These results highlight PI-DDPG as a promising energy-conscious alternative for robotic control.展开更多
Energy-efficient retrofitting(EER)of existing buildings has significant potential for addressing energy and environmental issues.However,the traditional market trading model is characterized by an inefficient dissemin...Energy-efficient retrofitting(EER)of existing buildings has significant potential for addressing energy and environmental issues.However,the traditional market trading model is characterized by an inefficient dissemination of critical information,which leads to insufficient incentives for market participants to trade.To solve these problems,this study constructs a three-party evolutionary game model with energy saving service companies(ESCO),homeowners,and trading information platforms as the main players,analyzes the interaction and evolution of the three parties'strategies under the scenario of government rewards and penalties,and explores the effects of the three parties'initial willingness and changes of model parameters on the evolution of their strategies.There are some findings as follows:first,the positive transactions of homeowners and ESCOs have less influence on the platform side;second,compared with homeowners,the government penalties have more obvious constraints on the platform side and ESCOs;third,government subsidies and EER revenues are the important factors influencing the speed of the evolution of three-party strategies,fourth,platform service compensation,the factors governing cost and benefit sharing are pivotal in determining the alignment of strategic choices among the three parties involved.Based on the research conclusions.This study offers theoretical guidance for the advancement of platform-based market transactions for EER.展开更多
In order to improve the energy efficiency(EE)in cognitive radio(CR),this paper investigates the joint design of cooperative spectrum sensing time and the power control optimization problem for the secondary user syste...In order to improve the energy efficiency(EE)in cognitive radio(CR),this paper investigates the joint design of cooperative spectrum sensing time and the power control optimization problem for the secondary user systems to achieve the maximum energy efficiency in a cognitive network based on hybrid spectrum sharing,meanwhile considering the maximum transmit power,user quality of service(QoS)requirements,interference limitations,and primary user protection.The optimization of energy efficient sensing time and power allocation is formulated as a non-convex optimization problem.The Dinkelbach’s method is adopted to solve this problem and to transform the non-convex optimization problem in fractional form into an equivalent optimization problem in the form of subtraction.Then,an iterative power allocation algorithm is proposed to solve the optimization problem.The simulation results show the effectiveness of the proposed algorithms for energy-efficient resource allocation in the cognitive network.展开更多
Cloud computing infrastructure has been evolving as a cost-effective platform for providing computational resources in the form of high-performance computing as a service(HPCaaS)to users for executing HPC applications...Cloud computing infrastructure has been evolving as a cost-effective platform for providing computational resources in the form of high-performance computing as a service(HPCaaS)to users for executing HPC applications.However,the broader use of the Cloud services,the rapid increase in the size,and the capacity of Cloud data centers bring a remarkable rise in energy consumption leading to a significant rise in the system provider expenses and carbon emissions in the environment.Besides this,users have become more demanding in terms of Quality-of-service(QoS)expectations in terms of execution time,budget cost,utilization,and makespan.This situation calls for the design of task scheduling policy,which ensures efficient task sequencing and allocation of computing resources to tasks to meet the trade-off between QoS promises and service provider requirements.Moreover,the task scheduling in the Cloud is a prevalent NP-Hard problem.Motivated by these concerns,this paper introduces and implements a QoS-aware Energy-Efficient Scheduling policy called as CSPSO,for scheduling tasks in Cloud systems to reduce the energy consumption of cloud resources and minimize the makespan of workload.The proposed multi-objective CSPSO policy hybridizes the search qualities of two robust metaheuristics viz.cuckoo search(CS)and particle swarm optimization(PSO)to overcome the slow convergence and lack of diversity of standard CS algorithm.A fitness-aware resource allocation(FARA)heuristic was developed and used by the proposed policy to allocate resources to tasks efficiently.A velocity update mechanism for cuckoo individuals is designed and incorporated in the proposed CSPSO policy.Further,the proposed scheduling policy has been implemented in the CloudSim simulator and tested with real supercomputing workload traces.The comparative analysis validated that the proposed scheduling policy can produce efficient schedules with better performance over other well-known heuristics and meta-heuristics scheduling policies.展开更多
This paper solves an energy-efficient optimization problem of a fixed-wing unmanned aerial vehicle(UAV) assisted full-duplex mobile relaying in maritime communication environments.Taking the speed and the acceleration...This paper solves an energy-efficient optimization problem of a fixed-wing unmanned aerial vehicle(UAV) assisted full-duplex mobile relaying in maritime communication environments.Taking the speed and the acceleration of the UAV and the information-causality constraints into consideration,the energy-efficiency of the system under investigation is maximized by jointly optimizing the UAV’s trajectory and the individual transmit power levels of the source and the UAV relay nodes.The optimization problem is non-convex and thus cannot be solved directly.Therefore,it is decoupled into two subproblems.One sub-problem is for the transmit power control at the source and the UAV relay nodes,and the other aims at optimizing the UAV s flight trajectory.By using the Lagrangian dual and Dinkelbach methods,the two sub-problems are solved,leading to an iterative algorithm for the joint design of transmit power control and trajectory optimization.Computer simulations demonstrated that by conducting the proposed algorithm,the flight trajectory of the UAV and the individual transmit power levels of the nodes can be flexibly adjusted according to the system conditions,and the proposed algorithm can achieve signiflcantly higher energy efficiency as compared with the other benchmark schemes.展开更多
The Internet of Things (IoT) represents a radical shifting paradigm fortechnological innovations as it can play critical roles in cyberspace applications invarious sectors, such as security, monitoring, medical, and e...The Internet of Things (IoT) represents a radical shifting paradigm fortechnological innovations as it can play critical roles in cyberspace applications invarious sectors, such as security, monitoring, medical, and environmental sectors,and also in control and industrial applications. The IoT in E-medicine unleashedthe design space for new technologies to give instant treatment to patients whilealso monitoring and tracking health conditions. This research presents a systemlevel architecture approach for IoT energy efficiency and security. The proposedarchitecture includes functional components that provide privacy managementand system security. Components in the security function group provide securecommunications through Multi-Authority Ciphertext-Policy Attributes-BasedEncryption (MA-CPABE). Because MA-CPABE is assigned to unlimited devices,presuming that the devices are reliable, the user encodes data with AdvancedEncryption Standard (AES) and protects the ABE approach using the solutionsof symmetric key. The Johnson’s algorithm with a new computation measure isused to increase network lifetime since an individual sensor node with limitedenergy represents the inevitable constraints for the broad usage of wireless sensornetworks. The optimal route from a source to destination turns out as the cornerstone for longevity of network and its sustainability. To reduce the energy consumption of networks, the evaluation measures consider the node’s residualenergy, the number of neighbors, their distance, and the link dependability. Theexperiment results demonstrate that the proposed model increases network lifeby about 12.25% (27.73%) compared to Floyd–Warshall’s, Bellman–Ford’s,and Dijkstra’s algorithms, lowering consumption of energy by eliminating thenecessity for re-routing the message as a result of connection failure.展开更多
In this paper, we provide a comprehensive survey of key energy-efficient Medium Access Control (MAC) protocols for Wireless Body Area Networks (WBANs). At the outset, we outline the crucial attributes of a good MAC pr...In this paper, we provide a comprehensive survey of key energy-efficient Medium Access Control (MAC) protocols for Wireless Body Area Networks (WBANs). At the outset, we outline the crucial attributes of a good MAC protocol for WBAN. Several sources that contribute to the energy inefficiency of WBAN are identified, and features of the various MAC protocols qualitatively compared. Then, we further investigate some representative TDMA-based energy-efficient MAC protocols for WBAN by emphasizing their strengths and weaknesses. Finally, we conclude with a number of open research issues with regard to WBAN MAC layer.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 goal of this study is to determine specific guidelines for Iraqi architects to contribute to the design and composition of energy-efficient housing units within the limits of a normal budget, locally available mat...The goal of this study is to determine specific guidelines for Iraqi architects to contribute to the design and composition of energy-efficient housing units within the limits of a normal budget, locally available materials and technologies. These units can provide comfort despite the current energy situation in Iraq. The study is based on a computer simulation for a reference building in Baghdad, which has been selected according to the urban conditions, building legislations, housing market and statistics. The final results displayed the main recommendations and the possibility to achieve up to 50% energy reduction with a pay-back period not exceeding two years in some cases. There are some measures that have big energy saving potential. Yet, some of the measures may require big investment or have some bad environmental impacts. Some other good measures are already being implemented.展开更多
UAV cooperative control has been applied in many complex UAV communication networks. It remains challenging to develop UAV cooperative coverage and UAV energy-efficient communication technology. In this paper, we inve...UAV cooperative control has been applied in many complex UAV communication networks. It remains challenging to develop UAV cooperative coverage and UAV energy-efficient communication technology. In this paper, we investigate current works about UAV coverage problem and propose a multi-UAV coverage model based on energy-efficient communication. The proposed model is decomposed into two steps: coverage maximization and power control, both are proved to be exact potential games(EPG) and have Nash equilibrium(NE) points. Then the multi-UAV energy-efficient coverage deployment algorithm based on spatial adaptive play(MUECD-SAP) is adopted to perform coverage maximization and power control, which guarantees optimal energy-efficient coverage deployment. Finally, simulation results show the effectiveness of our proposed approach, and confirm the reliability of proposed model.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
Recently,backscatter communication(BC)has been introduced as a green paradigm for Internet of Things(IoT).Meanwhile,unmanned aerial vehicles(UAVs)can serve as aerial base stations(BSs)to enhance the performance of BC ...Recently,backscatter communication(BC)has been introduced as a green paradigm for Internet of Things(IoT).Meanwhile,unmanned aerial vehicles(UAVs)can serve as aerial base stations(BSs)to enhance the performance of BC system thanks to their high mobility and flexibility.In this paper,we investigate the problem of energy efficiency(EE)for an energy-limited backscatter communication(BC)network,where backscatter devices(BDs)on the ground harvest energy from the wireless signal of a flying rotary-wing quadrotor.Specifically,we first reformulate the EE optimization problem as a Markov decision process(MDP)and then propose a deep reinforcement learning(DRL)algorithm to design the UAV trajectory with the constraints of the BD scheduling,the power reflection coefficients,the transmission power,and the fairness among BDs.Simulation results show the proposed DRL algorithm achieves close-to-optimal performance and significant EE gains compared to the benchmark schemes.展开更多
In the coexisted world of 3G,4G,5G and many other specialized wireless communication systems,billions of connections could be existing for various information transmission types.Unluckily,data show that the increase o...In the coexisted world of 3G,4G,5G and many other specialized wireless communication systems,billions of connections could be existing for various information transmission types.Unluckily,data show that the increase of network capacity is heavily more than the increase of the network energy efficiency in recent years,which could lead to more energy consumption per transmitted bit in the future network.As basic units in mobile communication systems,microwave/RF components and modules play key roles展开更多
文摘Recently,Multicore systems use Dynamic Voltage/Frequency Scaling(DV/FS)technology to allow the cores to operate with various voltage and/or frequencies than other cores to save power and enhance the performance.In this paper,an effective and reliable hybridmodel to reduce the energy and makespan in multicore systems is proposed.The proposed hybrid model enhances and integrates the greedy approach with dynamic programming to achieve optimal Voltage/Frequency(Vmin/F)levels.Then,the allocation process is applied based on the availableworkloads.The hybrid model consists of three stages.The first stage gets the optimum safe voltage while the second stage sets the level of energy efficiency,and finally,the third is the allocation stage.Experimental results on various benchmarks show that the proposed model can generate optimal solutions to save energy while minimizing the makespan penalty.Comparisons with other competitive algorithms show that the proposed model provides on average 48%improvements in energy-saving and achieves an 18%reduction in computation time while ensuring a high degree of system reliability.
文摘Residential energy-efficiency measures, besides energy savings, provide opportunities for improvement of thermal comfort, air quality, lighting quality, and operation. However, all these benefits sometimes are not enough to convince a homeowner to pay the incremental cost associated with the energy-efficiency measure. The objective of this work is to develop a methodology for the economic evaluation of residential energy-efficiency measures that can simplify the economic analysis for the homeowner while taking into consideration all factors associated with the purchase, ownership, and selling of the house with the energy-efficiency measure. The methodology accounts for direct and indirect economic parameters associated to an energy-efficiency measure;direct parameters such as the mortgage interest and fuel price escalation rate, and indirect parameters such as savings account interest and marginal income tax rate. The methodology also considers different cases based on the service life of the energy-efficiency measure and loss of efficiency through a derating factor. To estimate the market value, the methodology uses the future energy cost savings instead of the cost of the EEM. Results from the methodology offer to homeowner annual net savings and net assets. The annual net savings gives the homeowner a measure of the annual positive cash flow that can be obtained from an energy-efficiency project;but more important, the net assets offer a measure of the added net wealth. To simplify and increase the use of the methodology by homeowners, the methodology has been implemented in an Excel tool that can be downloaded from the TxAIRE’s website.
基金supported by the National Basic Research Program of China (2009CB320401)the National Key Scientific and Technological Project of China (2012ZX03004005-002)+1 种基金the Fundamental Research Funds for the Central Universities BUPT2011RCZJ018Research Funds of Doctoral Program of Higher Education of China (20090005110003)
文摘This paper investigates the tradeoff between energy-efficiency capacity and spectrum sensing under hybrid spectrum sharing model, where the spectrum sharing method is based on sensing results of secondary user （SU）. The metric ＇bits per joule＇, which captures the effect of energy overhead in spectrum sensing, is adopted to evaluate energy-efficiency capacity. We first formulize the tradeoff between energy-efficiency capacity and spectrum sensing as an optimization problem with mixture constraint of sensing time and detection threshold. Under some certain condition on the domain of detection threshold, i.e. in which we can＇t improve energy-efficiency capacity through increasing the detection probability, the original optimization problem can be reduced to a new unconstrained one, which only relates to sensing time. Then the existence and uniqueness of optimal sensing time to achieve maximum energy-efficiency capacity are discussed and a low-complexity algorithm is proposed to obtain the optimal solution. Finally, numerical simulation is performed to verify the theoretical analysis results. The simulation results indicate that hybrid spectrum sharing is remarkably beneficial to energy-efficient transmission in cognitive radio networks （CRN）. And the proposed algorithm can quickly converge to the optimal solution.
基金supported by the National Natural Science Foundation of China (61271182)the National Natural Science Funds of China for Young Scholar (61302080)
文摘Financial and environment considerations present new trends in wireless network known as green communication. As one of the most promising network architectures, the device-to-device （D2D） communication should take seriously account to the energy-efficiency. Most of the existing work in the area of D2D communication only focus on the direct communication, however, the direct link D2D communication has to be limited in practice because of long distance, poor propagation medium and cellular interference, etc. A new energy-efficient multi-hop routing algorithm was investigated for multi-hop D2D system by jointly optimizing channel reusing and power allocation. Firstly, the energy-efficient multi-hop routing problem was formulated as a combinatorial optimization problem. Secondly, to obtain a desirable solution with reasonable computation cost, a heuristic multi-hop routing algorithm was presented to solve the formulated problem and to achieve a satisfactory energy-efficiency performance. Simulation shows the effectiveness of the proposed routing algorithm.
基金This work was supported by the National Natural Science Foundation of China(11705108).
文摘The energy-efficiency(EE)optimization problem was studied for resource allocation in an uplink single-cell network,in which multiple mobile users with different quality of service(QoS)requirements operate under a non-orthogonal multiple access(NOMA)scheme.Firstly,a multi-user feasible power allocation region is derived as a multidimensional body that provides an efficient scheme to determine the feasibility of original channel and power assignment problem.Then,the size of feasible power allocation region was first introduced as utility function of the subchannel-user matching game in order to get high EE of the system and fairness among the users.Moreover,the power allocation optimization to the EE maximization is proved to be a monotonous decline function.The simulation results show that compared with the conventional schemes,the network connectivity of the proposed scheme is significantly enhanced and besides,for low rate massive connectivity networks,the proposed scheme obtains performance gains in the EE of the system.
基金The Natural Science Foundation of Jiangsu Province(NoBK2005409)
文摘An energy-efficient heuristic mechanism is presented to obtain the optimal solution for the coverage problem in sensor networks. The mechanism can ensure that all targets are fully covered corresponding to their levels of importance at minimum cost, and the ant colony optimization algorithm （ACO） is adopted to achieve the above metrics. Based on the novel design of heuristic factors, artificial ants can adaptively detect the energy status and coverage ability of sensor networks via local information. By introducing the evaluation function to global pheromone updating rule, the pheromone trail on the best solution is greatly enhanced, so that the convergence process of the algorithm is speed up. Finally, the optimal solution with a higher coverage- efficiency and a longer lifetime is obtained.
文摘The convergence of Internet of things(IoT)and 5G holds immense potential for transforming industries by enabling real-time,massive-scale connectivity and automation.However,the growing number of devices connected to the IoT systems demands a communication network capable of handling vast amounts of data with minimal delay.These generated enormous complex,high-dimensional,high-volume,and high-speed data also brings challenges on its storage,transmission,processing,and energy cost,due to the limited computing capabilities,battery capacity,memory,and energy utilization of current IoT networks.In this paper,a seamless architecture by combining mobile and cloud computing is proposed.It can agilely bargain with 5G-IoT devices,sensor nodes,and mobile computing in a distributed manner,enabling minimized energy cost,high interoperability,and high scalability as well as overcoming the memory constraints.An artificial intelligence(AI)-powered green and energy-efficient architecture is then proposed for 5G-IoT systems and sustainable smart cities.The experimental results reveal that the proposed approach dramatically reduces the transmitted data volume and power consumption and yields superior results regarding interoperability,compression ratio,and energy saving.This is especially critical in enabling the deployment of 5G and even 6G wireless systems for smart cities.
文摘Energy efficiency stands as an essential factor when implementing deep reinforcement learning(DRL)policies for robotic control systems.Standard algorithms,including Deep Deterministic Policy Gradient(DDPG),primarily optimize task rewards but at the cost of excessively high energy consumption,making them impractical for real-world robotic systems.To address this limitation,we propose Physics-Informed DDPG(PI-DDPG),which integrates physics-based energy penalties to develop energy-efficient yet high-performing control policies.The proposed method introduces adaptive physics-informed constraints through a dynamic weighting factor(λ),enabling policies that balance reward maximization with energy savings.Our motivation is to overcome the impracticality of rewardonly optimization by designing controllers that achieve competitive performance while substantially reducing energy consumption.PI-DDPG was evaluated in nine MuJoCo continuous control environments,where it demonstrated significant improvements in energy efficiency without compromising stability or performance.Experimental results confirm that PI-DDPG substantially reduces energy consumption compared to standard DDPG,while maintaining competitive task performance.For instance,energy costs decreased from 5542.98 to 3119.02 in HalfCheetah-v4 and from1909.13 to 1586.75 in Ant-v4,with stable performance in Hopper-v4(205.95 vs.130.82)and InvertedPendulum-v4(322.97 vs.311.29).Although DDPG sometimes yields higher rewards,such as in HalfCheetah-v4(5695.37 vs.4894.59),it requires significantly greater energy expenditure.These results highlight PI-DDPG as a promising energy-conscious alternative for robotic control.
基金supported by the National Natural Science Foundation of China(Grant No.71872122)the Late-stage Subsidy Project of Humanities and Social Sciences of the Education Department of China(Grant No.20JHQ095).
文摘Energy-efficient retrofitting(EER)of existing buildings has significant potential for addressing energy and environmental issues.However,the traditional market trading model is characterized by an inefficient dissemination of critical information,which leads to insufficient incentives for market participants to trade.To solve these problems,this study constructs a three-party evolutionary game model with energy saving service companies(ESCO),homeowners,and trading information platforms as the main players,analyzes the interaction and evolution of the three parties'strategies under the scenario of government rewards and penalties,and explores the effects of the three parties'initial willingness and changes of model parameters on the evolution of their strategies.There are some findings as follows:first,the positive transactions of homeowners and ESCOs have less influence on the platform side;second,compared with homeowners,the government penalties have more obvious constraints on the platform side and ESCOs;third,government subsidies and EER revenues are the important factors influencing the speed of the evolution of three-party strategies,fourth,platform service compensation,the factors governing cost and benefit sharing are pivotal in determining the alignment of strategic choices among the three parties involved.Based on the research conclusions.This study offers theoretical guidance for the advancement of platform-based market transactions for EER.
基金supported in part by the National Natural Science Foundation of China for Young Scholars under Grant No.61701167Young Elite Backbone Teachers in Blue and Blue Project of Jiangsu Province, China
文摘In order to improve the energy efficiency(EE)in cognitive radio(CR),this paper investigates the joint design of cooperative spectrum sensing time and the power control optimization problem for the secondary user systems to achieve the maximum energy efficiency in a cognitive network based on hybrid spectrum sharing,meanwhile considering the maximum transmit power,user quality of service(QoS)requirements,interference limitations,and primary user protection.The optimization of energy efficient sensing time and power allocation is formulated as a non-convex optimization problem.The Dinkelbach’s method is adopted to solve this problem and to transform the non-convex optimization problem in fractional form into an equivalent optimization problem in the form of subtraction.Then,an iterative power allocation algorithm is proposed to solve the optimization problem.The simulation results show the effectiveness of the proposed algorithms for energy-efficient resource allocation in the cognitive network.
文摘Cloud computing infrastructure has been evolving as a cost-effective platform for providing computational resources in the form of high-performance computing as a service(HPCaaS)to users for executing HPC applications.However,the broader use of the Cloud services,the rapid increase in the size,and the capacity of Cloud data centers bring a remarkable rise in energy consumption leading to a significant rise in the system provider expenses and carbon emissions in the environment.Besides this,users have become more demanding in terms of Quality-of-service(QoS)expectations in terms of execution time,budget cost,utilization,and makespan.This situation calls for the design of task scheduling policy,which ensures efficient task sequencing and allocation of computing resources to tasks to meet the trade-off between QoS promises and service provider requirements.Moreover,the task scheduling in the Cloud is a prevalent NP-Hard problem.Motivated by these concerns,this paper introduces and implements a QoS-aware Energy-Efficient Scheduling policy called as CSPSO,for scheduling tasks in Cloud systems to reduce the energy consumption of cloud resources and minimize the makespan of workload.The proposed multi-objective CSPSO policy hybridizes the search qualities of two robust metaheuristics viz.cuckoo search(CS)and particle swarm optimization(PSO)to overcome the slow convergence and lack of diversity of standard CS algorithm.A fitness-aware resource allocation(FARA)heuristic was developed and used by the proposed policy to allocate resources to tasks efficiently.A velocity update mechanism for cuckoo individuals is designed and incorporated in the proposed CSPSO policy.Further,the proposed scheduling policy has been implemented in the CloudSim simulator and tested with real supercomputing workload traces.The comparative analysis validated that the proposed scheduling policy can produce efficient schedules with better performance over other well-known heuristics and meta-heuristics scheduling policies.
基金National Natural Science Foundation of China(No.61871241)Nantong Science and Technology Project(JC2019114,JC2021129).
文摘This paper solves an energy-efficient optimization problem of a fixed-wing unmanned aerial vehicle(UAV) assisted full-duplex mobile relaying in maritime communication environments.Taking the speed and the acceleration of the UAV and the information-causality constraints into consideration,the energy-efficiency of the system under investigation is maximized by jointly optimizing the UAV’s trajectory and the individual transmit power levels of the source and the UAV relay nodes.The optimization problem is non-convex and thus cannot be solved directly.Therefore,it is decoupled into two subproblems.One sub-problem is for the transmit power control at the source and the UAV relay nodes,and the other aims at optimizing the UAV s flight trajectory.By using the Lagrangian dual and Dinkelbach methods,the two sub-problems are solved,leading to an iterative algorithm for the joint design of transmit power control and trajectory optimization.Computer simulations demonstrated that by conducting the proposed algorithm,the flight trajectory of the UAV and the individual transmit power levels of the nodes can be flexibly adjusted according to the system conditions,and the proposed algorithm can achieve signiflcantly higher energy efficiency as compared with the other benchmark schemes.
文摘The Internet of Things (IoT) represents a radical shifting paradigm fortechnological innovations as it can play critical roles in cyberspace applications invarious sectors, such as security, monitoring, medical, and environmental sectors,and also in control and industrial applications. The IoT in E-medicine unleashedthe design space for new technologies to give instant treatment to patients whilealso monitoring and tracking health conditions. This research presents a systemlevel architecture approach for IoT energy efficiency and security. The proposedarchitecture includes functional components that provide privacy managementand system security. Components in the security function group provide securecommunications through Multi-Authority Ciphertext-Policy Attributes-BasedEncryption (MA-CPABE). Because MA-CPABE is assigned to unlimited devices,presuming that the devices are reliable, the user encodes data with AdvancedEncryption Standard (AES) and protects the ABE approach using the solutionsof symmetric key. The Johnson’s algorithm with a new computation measure isused to increase network lifetime since an individual sensor node with limitedenergy represents the inevitable constraints for the broad usage of wireless sensornetworks. The optimal route from a source to destination turns out as the cornerstone for longevity of network and its sustainability. To reduce the energy consumption of networks, the evaluation measures consider the node’s residualenergy, the number of neighbors, their distance, and the link dependability. Theexperiment results demonstrate that the proposed model increases network lifeby about 12.25% (27.73%) compared to Floyd–Warshall’s, Bellman–Ford’s,and Dijkstra’s algorithms, lowering consumption of energy by eliminating thenecessity for re-routing the message as a result of connection failure.
基金supported by the MKE (The Ministry of Knowledge Economy), Korea, under the ITRC (Information Technology Research Center)support program supervised by the NIPA(National IT Industry Promotion Agency)under Grant No.NIPA-2011-(C1090-1121-0002)
文摘In this paper, we provide a comprehensive survey of key energy-efficient Medium Access Control (MAC) protocols for Wireless Body Area Networks (WBANs). At the outset, we outline the crucial attributes of a good MAC protocol for WBAN. Several sources that contribute to the energy inefficiency of WBAN are identified, and features of the various MAC protocols qualitatively compared. Then, we further investigate some representative TDMA-based energy-efficient MAC protocols for WBAN by emphasizing their strengths and weaknesses. Finally, we conclude with a number of open research issues with regard to WBAN MAC layer.
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 goal of this study is to determine specific guidelines for Iraqi architects to contribute to the design and composition of energy-efficient housing units within the limits of a normal budget, locally available materials and technologies. These units can provide comfort despite the current energy situation in Iraq. The study is based on a computer simulation for a reference building in Baghdad, which has been selected according to the urban conditions, building legislations, housing market and statistics. The final results displayed the main recommendations and the possibility to achieve up to 50% energy reduction with a pay-back period not exceeding two years in some cases. There are some measures that have big energy saving potential. Yet, some of the measures may require big investment or have some bad environmental impacts. Some other good measures are already being implemented.
基金supported by the National Natural Science Foundation of China under Grant No. 61771488in part by the Natural Science Foundation for Distinguished Young Scholars of Jiangsu Province under Grant No. BK20160034+1 种基金 in part by the Open Research Foundation of Science and Technology on Communication Networks Laboratorythe Guang Xi Universities Key Laboratory Fund of Embedded Technology and Intelligent System (Guilin University of Technology)
文摘UAV cooperative control has been applied in many complex UAV communication networks. It remains challenging to develop UAV cooperative coverage and UAV energy-efficient communication technology. In this paper, we investigate current works about UAV coverage problem and propose a multi-UAV coverage model based on energy-efficient communication. The proposed model is decomposed into two steps: coverage maximization and power control, both are proved to be exact potential games(EPG) and have Nash equilibrium(NE) points. Then the multi-UAV energy-efficient coverage deployment algorithm based on spatial adaptive play(MUECD-SAP) is adopted to perform coverage maximization and power control, which guarantees optimal energy-efficient coverage deployment. Finally, simulation results show the effectiveness of our proposed approach, and confirm the reliability of proposed model.
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the National Natural Science Foundation of China 61661021,61971191,61902214,and 61871321,in part by the Beijing Natural Science Foundation under Grant L182018,in part by the National Science and Technology Major Project of the Ministry of Science and Technology of China under Grant 2016ZX03001014-006in part by the open project of Shanghai Institute of Microsystem and Information Technology(20190910)+1 种基金in part by the Key project of Natural Science Foundation of Jiangxi Province(20202ACBL202006)in part by the open project of Key Laboratory of Wireless Sensor Network&Communication,Shanghai Institute of Microsystem and Information Technology,Chinese Academy of Sciences,865 Changning Road,Shanghai 200050 China,and in part by the Tsinghua University Initiative Scientific Research Program 2019Z08QCX19.
文摘Recently,backscatter communication(BC)has been introduced as a green paradigm for Internet of Things(IoT).Meanwhile,unmanned aerial vehicles(UAVs)can serve as aerial base stations(BSs)to enhance the performance of BC system thanks to their high mobility and flexibility.In this paper,we investigate the problem of energy efficiency(EE)for an energy-limited backscatter communication(BC)network,where backscatter devices(BDs)on the ground harvest energy from the wireless signal of a flying rotary-wing quadrotor.Specifically,we first reformulate the EE optimization problem as a Markov decision process(MDP)and then propose a deep reinforcement learning(DRL)algorithm to design the UAV trajectory with the constraints of the BD scheduling,the power reflection coefficients,the transmission power,and the fairness among BDs.Simulation results show the proposed DRL algorithm achieves close-to-optimal performance and significant EE gains compared to the benchmark schemes.
文摘In the coexisted world of 3G,4G,5G and many other specialized wireless communication systems,billions of connections could be existing for various information transmission types.Unluckily,data show that the increase of network capacity is heavily more than the increase of the network energy efficiency in recent years,which could lead to more energy consumption per transmitted bit in the future network.As basic units in mobile communication systems,microwave/RF components and modules play key roles
