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
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
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
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
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
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
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
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
In wireless sensor networks(WSNs),nodes are usually powered by batteries.Since the energy consumption directly impacts the network lifespan,energy saving is a vital issue in WSNs,especially in the designing phase of c...In wireless sensor networks(WSNs),nodes are usually powered by batteries.Since the energy consumption directly impacts the network lifespan,energy saving is a vital issue in WSNs,especially in the designing phase of cryptographic algorithms.As a complementary mechanism,reputation has been applied to WSNs.Different from most reputation schemes that were based on beta distribution,negative multinomial distribution was deduced and its feasibility in the reputation modeling was proved.Through comparison tests with beta distribution based reputation in terms of the update computation,results show that the proposed method in this research is more energy-efficient for the reputation update and thus can better prolong the lifespan of WSNs.展开更多
For the past few decades,the Internet of Things(IoT)has been one of the main pillars wielding significant impact on various advanced industrial applications,including smart energy,smart manufacturing,and others.These ...For the past few decades,the Internet of Things(IoT)has been one of the main pillars wielding significant impact on various advanced industrial applications,including smart energy,smart manufacturing,and others.These applications are related to industrial plants,automation,and e-healthcare fields.IoT applications have several issues related to developing,planning,and managing the system.Therefore,IoT is transforming into G-IoT(Green Internet of Things),which realizes energy efficiency.It provides high power efficiency,enhances communication and networking.Nonetheless,this paradigm did not resolve all smart applications’challenges in edge infrastructure,such as communication bandwidth,centralization,security,and privacy.In this paper,we propose the OTS Scheme based Secure Architecture for Energy-Efficient IoT in Edge Infrastructure to resolve these challenges.An OTS-based Blockchain-enabled distributed network is used at the fog layer for security and privacy.We evaluated our proposed architecture’s performance quantitatively as well as security and privacy.We conducted a comparative analysis with existing studies with different measures,including computing cost time and communication cost.As a result of the evaluation,our proposed architecture showed better performance.展开更多
Hotel buildings are currently among the largest energy consumers in the world.Heating,ventilation,and air conditioning are the most energy-intensive building systems,accounting for more than half of total energy consu...Hotel buildings are currently among the largest energy consumers in the world.Heating,ventilation,and air conditioning are the most energy-intensive building systems,accounting for more than half of total energy consumption.An energy audit is used to predict the weak points of a building’s energy use system.Various factors influence building energy consumption,which can be modified to achieve more energy-efficient strategies.In this study,an existing hotel building in Central Taiwan is evaluated by simulating several scenarios using energy modeling over a year.Energy modeling is conducted by using Autodesk Revit 2025.It was discovered from the results that arranging the lighting schedule based on the ASHRAE Standard 90.1 could save up to 8.22%of energy consumption.And then the results also revealed that changing the glazing of the building into double-layer lowemissivity glass could reduce energy consumption by 14.58%.While the energy consumption of the building could also be decreased to 7.20%by changing the building orientation to the north.Meanwhile,moving the building location to Northern Taiwan could also minimize the energy consumption of the building by 3.23%.The results revealed that the double layer offers better thermal insulation,and low-emissivity glass can lower energy consumption,electricity costs,and CO_(2)emissions by up to 15.27%annually.While adjusting orientation and location can enhance energy performance,this approach is impractical for existing buildings,but this could be considered for designing new buildings.The results showed the relevancy of energy performance to CO_(2)emission production and electricity expenses.展开更多
This paper presents a co-time co-frequency fullduplex(CCFD)massive multiple-input multiple-output(MIMO)system to meet high spectrum efficiency requirements for beyond the fifth-generation(5G)and the forthcoming the si...This paper presents a co-time co-frequency fullduplex(CCFD)massive multiple-input multiple-output(MIMO)system to meet high spectrum efficiency requirements for beyond the fifth-generation(5G)and the forthcoming the sixth-generation(6G)networks.To achieve equilibrium of energy consumption,system resource utilization,and overall transmission capacity,an energy-efficient resource management strategy concerning power allocation and antenna selection is designed.A continuous quantum-inspired termite colony optimization(CQTCO)algorithm is proposed as a solution to the resource management considering the communication reliability while promoting energy conservation for the CCFD massive MIMO system.The effectiveness of CQTCO compared with other algorithms is evaluated through simulations.The results reveal that the proposed resource management scheme under CQTCO can obtain a superior performance in different communication scenarios,which can be considered as an eco-friendly solution for promoting reliable and efficient communication in future wireless networks.展开更多
In order to resolve the relay selection problem in wireless mobile relay networks （WMRNs）, a novel balanced energy-efficient mobile relay selection scheme is proposed in this paper. Compared with traditional counter...In order to resolve the relay selection problem in wireless mobile relay networks （WMRNs）, a novel balanced energy-efficient mobile relay selection scheme is proposed in this paper. Compared with traditional counter-based algorithm, distance and energy consumption are considered from network respect to provide a better network lifetime performance in the proposed scheme. Also, it performs well when nodes move freely at high speed. A random assessment delay （RAD） mechanism is added to avoid collisions and improve transmission efficiency. Simulation results reveal that, the proposed scheme has advantages in prolonging network lifetime, balancing energy compared with existing counter-based scheme. consumption and reducing the total energy consumption展开更多
High computational energy-efficiency and rapid real-timeresponse are the major concerns for applications of artificial intelligencein low-power mobile and Internet of Things deviceswith limited storage capacity. Due t...High computational energy-efficiency and rapid real-timeresponse are the major concerns for applications of artificial intelligencein low-power mobile and Internet of Things deviceswith limited storage capacity. Due to the outstanding superiorityof less memory requirement, low computation overheadand negligible accuracy degradation, deep neural networkswith binary/ternary weights (BTNNs) have been widely adoptedto replace traditional full-precision neural networks.展开更多
How to energy-efficiently maintain the topology of wireless sensor networks(WSNs) is still a difficult problem because of their numerous nodes,highly dynamic nature,varied application scenarios and limited resources.A...How to energy-efficiently maintain the topology of wireless sensor networks(WSNs) is still a difficult problem because of their numerous nodes,highly dynamic nature,varied application scenarios and limited resources.An energy-efficient multi-mode clusters maintenance(M2CM) method is proposed based on localized and event-driven mechanism in this work,which is different from the conventional clusters maintenance model with always periodically re-clustered among the whole network style based on time-trigger for hierarchical WSNs.M2 CM can meet such demands of clusters maintenance as adaptive local maintenance for the damaged clusters according to its changes in time and space field.,the triggers of M2 CM include such events as nodes' residual energy being under the threshold,the load imbalance of cluster head,joining in or exiting from any cluster for new node or disable one,etc.Based on neighboring relationship of the damaged clusters,one can start a single cluster(inner-cluster) maintenance or clusters(inter-cluster) maintenance program to meet diverse demands in the topology management of hierarchical WSNs.The experiment results based on NS2 simulation show that the proposed method can significantly save energy used in maintaining a damaged network,effectively narrow down the influenced area of clusters maintenance,and increase transmitted data and prolong lifetime of network compared to the traditional schemes.展开更多
In the past few years, energy-efficient technologies get more and more scientific and technological interest as the energy bottleneck stops the ＂Moore＇s law＂ according to the reports of International Roadmap for Se...In the past few years, energy-efficient technologies get more and more scientific and technological interest as the energy bottleneck stops the ＂Moore＇s law＂ according to the reports of International Roadmap for Semiconductors （ITRS）. Moreover, people start to take care seriously of the environmental impact due to the Dower dissioation.展开更多
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.
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.
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.
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
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.
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National Natural Science Foundations of China (No.61073177,60905037)
文摘In wireless sensor networks(WSNs),nodes are usually powered by batteries.Since the energy consumption directly impacts the network lifespan,energy saving is a vital issue in WSNs,especially in the designing phase of cryptographic algorithms.As a complementary mechanism,reputation has been applied to WSNs.Different from most reputation schemes that were based on beta distribution,negative multinomial distribution was deduced and its feasibility in the reputation modeling was proved.Through comparison tests with beta distribution based reputation in terms of the update computation,results show that the proposed method in this research is more energy-efficient for the reputation update and thus can better prolong the lifespan of WSNs.
基金the Advanced Research Project funded by SeoulTech(Seoul National University of Science and Technology).
文摘For the past few decades,the Internet of Things(IoT)has been one of the main pillars wielding significant impact on various advanced industrial applications,including smart energy,smart manufacturing,and others.These applications are related to industrial plants,automation,and e-healthcare fields.IoT applications have several issues related to developing,planning,and managing the system.Therefore,IoT is transforming into G-IoT(Green Internet of Things),which realizes energy efficiency.It provides high power efficiency,enhances communication and networking.Nonetheless,this paradigm did not resolve all smart applications’challenges in edge infrastructure,such as communication bandwidth,centralization,security,and privacy.In this paper,we propose the OTS Scheme based Secure Architecture for Energy-Efficient IoT in Edge Infrastructure to resolve these challenges.An OTS-based Blockchain-enabled distributed network is used at the fog layer for security and privacy.We evaluated our proposed architecture’s performance quantitatively as well as security and privacy.We conducted a comparative analysis with existing studies with different measures,including computing cost time and communication cost.As a result of the evaluation,our proposed architecture showed better performance.
基金support by the National Science and Technology Council under grant no.NSTC 112-2221-E-167-017-MY3.
文摘Hotel buildings are currently among the largest energy consumers in the world.Heating,ventilation,and air conditioning are the most energy-intensive building systems,accounting for more than half of total energy consumption.An energy audit is used to predict the weak points of a building’s energy use system.Various factors influence building energy consumption,which can be modified to achieve more energy-efficient strategies.In this study,an existing hotel building in Central Taiwan is evaluated by simulating several scenarios using energy modeling over a year.Energy modeling is conducted by using Autodesk Revit 2025.It was discovered from the results that arranging the lighting schedule based on the ASHRAE Standard 90.1 could save up to 8.22%of energy consumption.And then the results also revealed that changing the glazing of the building into double-layer lowemissivity glass could reduce energy consumption by 14.58%.While the energy consumption of the building could also be decreased to 7.20%by changing the building orientation to the north.Meanwhile,moving the building location to Northern Taiwan could also minimize the energy consumption of the building by 3.23%.The results revealed that the double layer offers better thermal insulation,and low-emissivity glass can lower energy consumption,electricity costs,and CO_(2)emissions by up to 15.27%annually.While adjusting orientation and location can enhance energy performance,this approach is impractical for existing buildings,but this could be considered for designing new buildings.The results showed the relevancy of energy performance to CO_(2)emission production and electricity expenses.
基金supported by the Ph.D.Student Research and Innovation Fund of the Fundamental Research Funds for the Central Universities(3072020GIP0803)Heilongjiang Province Key Laboratory Fund of High Accuracy Satellite Navigation and Marine Application Laboratory(HKL-2020-Y01)+2 种基金the National Natural Science Foundation of China(61571149)the Initiation Fund for Postdoctoral Research in Heilongjiang Province(LBH-Q19098)the Key Laboratory of Advanced Marine Communication and Information Technology,Ministry of Industry and Information Technology。
文摘This paper presents a co-time co-frequency fullduplex(CCFD)massive multiple-input multiple-output(MIMO)system to meet high spectrum efficiency requirements for beyond the fifth-generation(5G)and the forthcoming the sixth-generation(6G)networks.To achieve equilibrium of energy consumption,system resource utilization,and overall transmission capacity,an energy-efficient resource management strategy concerning power allocation and antenna selection is designed.A continuous quantum-inspired termite colony optimization(CQTCO)algorithm is proposed as a solution to the resource management considering the communication reliability while promoting energy conservation for the CCFD massive MIMO system.The effectiveness of CQTCO compared with other algorithms is evaluated through simulations.The results reveal that the proposed resource management scheme under CQTCO can obtain a superior performance in different communication scenarios,which can be considered as an eco-friendly solution for promoting reliable and efficient communication in future wireless networks.
基金Supported by the National High Technology Research and Development Programme of China （No. 2007AA01Z221, 2009AA01Z246） and the National Natural Science Foundation of China （No. 60832009）.
文摘In order to resolve the relay selection problem in wireless mobile relay networks （WMRNs）, a novel balanced energy-efficient mobile relay selection scheme is proposed in this paper. Compared with traditional counter-based algorithm, distance and energy consumption are considered from network respect to provide a better network lifetime performance in the proposed scheme. Also, it performs well when nodes move freely at high speed. A random assessment delay （RAD） mechanism is added to avoid collisions and improve transmission efficiency. Simulation results reveal that, the proposed scheme has advantages in prolonging network lifetime, balancing energy compared with existing counter-based scheme. consumption and reducing the total energy consumption
文摘High computational energy-efficiency and rapid real-timeresponse are the major concerns for applications of artificial intelligencein low-power mobile and Internet of Things deviceswith limited storage capacity. Due to the outstanding superiorityof less memory requirement, low computation overheadand negligible accuracy degradation, deep neural networkswith binary/ternary weights (BTNNs) have been widely adoptedto replace traditional full-precision neural networks.
基金supported by the National Natural Science Foundation of China(Grant No.61170219)the Joint Research Foundation of the Ministry of Education of the People’s Republic of China and China Mobile(Grant No.MCM20150202)the Science and Technology Project Affiliated to Chongqing Education Commission(KJ1602201)
文摘How to energy-efficiently maintain the topology of wireless sensor networks(WSNs) is still a difficult problem because of their numerous nodes,highly dynamic nature,varied application scenarios and limited resources.An energy-efficient multi-mode clusters maintenance(M2CM) method is proposed based on localized and event-driven mechanism in this work,which is different from the conventional clusters maintenance model with always periodically re-clustered among the whole network style based on time-trigger for hierarchical WSNs.M2 CM can meet such demands of clusters maintenance as adaptive local maintenance for the damaged clusters according to its changes in time and space field.,the triggers of M2 CM include such events as nodes' residual energy being under the threshold,the load imbalance of cluster head,joining in or exiting from any cluster for new node or disable one,etc.Based on neighboring relationship of the damaged clusters,one can start a single cluster(inner-cluster) maintenance or clusters(inter-cluster) maintenance program to meet diverse demands in the topology management of hierarchical WSNs.The experiment results based on NS2 simulation show that the proposed method can significantly save energy used in maintaining a damaged network,effectively narrow down the influenced area of clusters maintenance,and increase transmitted data and prolong lifetime of network compared to the traditional schemes.
文摘In the past few years, energy-efficient technologies get more and more scientific and technological interest as the energy bottleneck stops the ＂Moore＇s law＂ according to the reports of International Roadmap for Semiconductors （ITRS）. Moreover, people start to take care seriously of the environmental impact due to the Dower dissioation.
