Wireless Sensor Networks(WSNs),as a crucial component of the Internet of Things(IoT),are widely used in environmental monitoring,industrial control,and security surveillance.However,WSNs still face challenges such as ...Wireless Sensor Networks(WSNs),as a crucial component of the Internet of Things(IoT),are widely used in environmental monitoring,industrial control,and security surveillance.However,WSNs still face challenges such as inaccurate node clustering,low energy efficiency,and shortened network lifespan in practical deployments,which significantly limit their large-scale application.To address these issues,this paper proposes an Adaptive Chaotic Ant Colony Optimization algorithm(AC-ACO),aiming to optimize the energy utilization and system lifespan of WSNs.AC-ACO combines the path-planning capability of Ant Colony Optimization(ACO)with the dynamic characteristics of chaotic mapping and introduces an adaptive mechanism to enhance the algorithm’s flexibility and adaptability.By dynamically adjusting the pheromone evaporation factor and heuristic weights,efficient node clustering is achieved.Additionally,a chaotic mapping initialization strategy is employed to enhance population diversity and avoid premature convergence.To validate the algorithm’s performance,this paper compares AC-ACO with clustering methods such as Low-Energy Adaptive Clustering Hierarchy(LEACH),ACO,Particle Swarm Optimization(PSO),and Genetic Algorithm(GA).Simulation results demonstrate that AC-ACO outperforms the compared algorithms in key metrics such as energy consumption optimization,network lifetime extension,and communication delay reduction,providing an efficient solution for improving energy efficiency and ensuring long-term stable operation of wireless sensor networks.展开更多
Managing massive data flows effectively and resolving spectrum shortages are two challenges that smart grid communication networks(SGCN)must overcome.To address these problems,we provide a combined optimization approa...Managing massive data flows effectively and resolving spectrum shortages are two challenges that smart grid communication networks(SGCN)must overcome.To address these problems,we provide a combined optimization approach that makes use of cognitive radio(CR)and non-orthogonal multiple access(NOMA)technologies.Our work focuses on using user pairing(UP)and power allocation(PA)techniques to maximize energy efficiency(EE)in SGCN,particularly within neighbourhood area networks(NANs).We develop a joint optimization problem that takes into account the real-world limitations of a CR-NOMA setting.This problem is NP-hard,nonlinear,and nonconvex by nature.To address the computational complexity of the problem,we use the block coordinate descent(BCD)method,which breaks the problem into UP and PA subproblems.Initially,we proposed the zebra-optimization user pairing(ZOUP)algorithm to tackle the UP problem,which outperforms both orthogonal multiple access(OMA)and non-optimized NOMA(UPWO)by 78.8%and13.6%,respectively,at a SNR of 15 dB.Based on the ZOUP pairs,we subsequently proposed the PA approach,i.e.,ZOUPPA,which significantly outperforms UPWO and ZOUP by 53.2%and 25.4%,respectively,at an SNR of 15 dB.A detailed analysis of key parameters,including varying SNRs,power allocation constants,path loss exponents,user density,channel availability,and coverage radius,underscores the superiority of our approach.By facilitating the effective use of communication resources in SGCN,our research opens the door to more intelligent and energy-efficient grid systems.Our work tackles important issues in SGCN and lays the groundwork for future developments in smart grid communication technologies by combining modern optimization approaches with CR-NOMA.展开更多
Internet of things networks often suffer from early node failures and short lifespan due to energy limits.Traditional routing methods are not enough.This work proposes a new hybrid algorithm called ACOGA.It combines A...Internet of things networks often suffer from early node failures and short lifespan due to energy limits.Traditional routing methods are not enough.This work proposes a new hybrid algorithm called ACOGA.It combines Ant Colony Optimization(ACO)and the Greedy Algorithm(GA).ACO finds smart paths while Greedy makes quick decisions.This improves energy use and performance.ACOGA outperforms Hybrid Energy-Efficient(HEE)and Adaptive Lossless Data Compression(ALDC)algorithms.After 500 rounds,only 5%of ACOGA’s nodes are dead,compared to 15%for HEE and 20%for ALDC.The network using ACOGA runs for 1200 rounds before the first nodes fail.HEE lasts 900 rounds and ALDC only 850.ACOGA saves at least 15%more energy by better distributing the load.It also achieves a 98%packet delivery rate.The method works well in mixed IoT networks like Smart Water Management Systems(SWMS).These systems have different power levels and communication ranges.The simulation of proposed model has been done in MATLAB simulator.The results show that that the proposed model outperform then the existing models.展开更多
Energy efficiency(EE)is a critical design when taking into account circuit power consumption(CPC)in fifth-generation cellular networks.These problems arise because of the increasing number of antennas in massive multi...Energy efficiency(EE)is a critical design when taking into account circuit power consumption(CPC)in fifth-generation cellular networks.These problems arise because of the increasing number of antennas in massive multiple-input multiple-output(MIMO)systems,attributable to inter-cell interference for channel state information.Apart from that,a higher number of radio frequency(RF)chains at the base station and active users consume more power due to the processing activities in digital-to-analogue converters and power amplifiers.Therefore,antenna selection,user selection,optimal transmission power,and pilot reuse power are important aspects in improving energy efficiency in massive MIMO systems.This work aims to investigate joint antenna selection,optimal transmit power and joint user selection based on deriving the closed-form of the maximal EE,with complete knowledge of large-scale fading with maximum ratio transmission.It also accounts for channel estimation and eliminating pilot contamination as antennas M→∞.This formulates the optimization problem of joint optimal antenna selection,transmits power allocation and joint user selection to mitigate inter-cellinterference in downlink multi-cell massive MIMO systems under minimized reuse of pilot sequences based on a novel iterative low-complexity algorithm(LCA)for Newton’s methods and Lagrange multipliers.To analyze the precise power consumption,a novel power consumption scheme is proposed for each individual antenna,based on the transmit power amplifier and CPC.Simulation results demonstrate that the maximal EE was achieved using the iterative LCA based on reasonable maximum transmit power,in the case the noise power is less than the received power pilot.The maximum EE was achieved with the desired maximum transmit power threshold by minimizing pilot reuse,in the case the transmit power allocationρd=40 dBm,and the optimal EE=71.232 Mb/j.展开更多
Nowadays,energy consumption which closely contacts with environmental impacts of manufacturing processes has been highly commented as a new productivity criterion.However,little attention has paid to the development o...Nowadays,energy consumption which closely contacts with environmental impacts of manufacturing processes has been highly commented as a new productivity criterion.However,little attention has paid to the development of process planning methods that take energy consumption into account.An energy-efficient process planning model that incorporates manufacturing time and energy consumption is proposed.For solving the problem,an improved genetic algorithm method is employed to explore the optimal solution.Finally,a case study for process planning is given.The experimental result generates interesting effort,and therefore allows improving the energy efficiency of manufacturing processes in process planning.展开更多
Due to the wide range of applications,Wireless Sensor Networks(WSN)are increased in day to day life and becomes popular.WSN has marked its importance in both practical and research domains.Energy is the most significa...Due to the wide range of applications,Wireless Sensor Networks(WSN)are increased in day to day life and becomes popular.WSN has marked its importance in both practical and research domains.Energy is the most significant resource,the important challenge in WSN is to extend its lifetime.The energy reduction is a key to extend the network’s lifetime.Clustering of sensor nodes is one of the well-known and proved methods for achieving scalable and energy conserving WSN.In this paper,an energy efficient protocol is proposed using metaheuristic Echo location-based BAT algorithm(ECHO-BAT).ECHO-BAT works in two stages.First Stage clusters the sensor nodes and identifies tentativeCluster Head(CH)along with the entropy value using BAT algorithm.The second stage aims to find the nodes if any,with high residual energy within each cluster.CHs will be replaced by the member node with high residual energy with an objective to choose the CH with high energy to prolong the network’s lifetime.The performance of the proposed work is compared with Low-Energy Adaptive Clustering Hierarchy(LEACH),Power-Efficient Zoning Clustering Algorithm(PEZCA)and Chaotic Firefly Algorithm CH(CFACH)in terms of lifetime of network,death of first nodes,death of 125th node,death of the last node,network throughput and execution time.Simulation results show that ECHO-BAT outperforms the other methods in all the considered measures.The overall delivery ratio has also significantly optimized and improved by approximately 8%,proving the proposed approach to be an energy efficient WSN.展开更多
Our living environments are being gradually occupied with an abundant number of digital objects that have networking and computing capabilities. After these devices are plugged into a network, they initially advertise...Our living environments are being gradually occupied with an abundant number of digital objects that have networking and computing capabilities. After these devices are plugged into a network, they initially advertise their presence and capabilities in the form of services so that they can be discovered and, if desired, exploited by the user or other networked devices. With the increasing number of these devices attached to the network, the complexity to configure and control them increases, which may lead to major processing and communication overhead. Hence, the devices are no longer expected to just act as primitive stand-alone appliances that only provide the facilities and services to the user they are designed for, but also offer complex services that emerge from unique combinations of devices. This creates the necessity for these devices to be equipped with some sort of intelligence and self-awareness to enable them to be self-configuring and self-programming. However, with this ＂smart evolution＂, the cognitive load to configure and control such spaces becomes immense. One way to relieve this load is by employing artificial intelligence （AI） techniques to create an intelligent ＂presence＂ where the system will be able to recognize the users and autonomously program the environment to be energy efficient and responsive to the user＇s needs and behaviours. These AI mechanisms should be embedded in the user＇s environments and should operate in a non-intrusive manner. This paper will show how computational intelligence （CI）, which is an emerging domain of AI, could be employed and embedded in our living spaces to help such environments to be more energy efficient, intelligent, adaptive and convenient to the users.展开更多
This paper mainly studied the problem of energy conserving in wireless sensor networks for target tracking in defensing combats. Firstly, the structures of wireless sensor nodes and networks were illustrated;Secondly,...This paper mainly studied the problem of energy conserving in wireless sensor networks for target tracking in defensing combats. Firstly, the structures of wireless sensor nodes and networks were illustrated;Secondly, the analysis of existing energy consuming in the sensing layer and its calculation method were provided to build the energy conserving objective function;What’s more, the other two indicators in target tracking, including target detection probability and tracking accuracy, were combined to be regarded as the constraints of the energy conserving objective function. Fourthly, the three energy conserving approaches, containing optimizing the management scheme, prolonging the time interval between two adjacent observations, and transmitting the observations selectively, were introduced;In addition, the improved lion algorithm combined with the Logistic chaos sequence was proposed to obtain sensor management schemes. Finally, simulations had been made to prove the effectiveness of the proposed methods and algorithm.展开更多
One of the major constraints of wireless sensor networks is limited energy available to sensor nodes because of the small size of the batteries they use as source of power. Clustering is one of the routing techniques ...One of the major constraints of wireless sensor networks is limited energy available to sensor nodes because of the small size of the batteries they use as source of power. Clustering is one of the routing techniques that have been using to minimize sensor nodes’ energy consumption during operation. In this paper, A Novel Clustering Algorithm for Energy Efficiency in Wireless Sensor Networks (ANCAEE) has been proposed. The algorithm achieves good performance in terms of minimizing energy consumption during data transmission and energy consumptions are distributed uniformly among all nodes. ANCAEE uses a new method of clusters formation and election of cluster heads. The algorithm ensures that a node transmits its data to the cluster head with a single hop transmission and cluster heads forward their data to the base station with multi-hop transmissions. Simulation results show that our approach consumes less energy and effectively extends network utilization.展开更多
The scheduling problem of distributed permutation flow shop with limited buffer aiming at production efficiency measures has attracted widespread attention due to its closer alignment with real manufacturing environme...The scheduling problem of distributed permutation flow shop with limited buffer aiming at production efficiency measures has attracted widespread attention due to its closer alignment with real manufacturing environments.However,the energy efficiency metric is often ignored.The Energy-Efficient scheduling of Distributed Permutation Flow Shop Problem with Limited Buffer(EEDPFSP-LB)with the objectives of Makespan(C_(max))and Total Energy Consumption(TEC)is studied,and a Cooperative Fruit fly Optimization Algorithm(CFOA)is proposed in this paper.First,the critical path of EEDPFSP-LB is identified,and energy-efficient operation is applied to non-critical paths to reduce the system’s energy consumption.Second,five acceptance criteria for multi-objective optimization are introduced to enhance the diversity of the population.Third,to select a superior next-generation population,a new congestion calculation method is introduced to resolve the issue of indeterminate positional relationships among non-dominated solutions with identical crowding distances at the same dominance level.Finally,CFOA is extensively tested and compared with state-of-the-art algorithms across 360 instances,demonstrating CFOA’s strong competitiveness in solving EEDPFSP-LB.展开更多
The increasing demand for infotainment applications necessitates efficient bandwidth and energy resource allocation.Sixth-Generation(6G)networks,utilizing Cognitive Radio(CR)technology within CR Network(CRN),can enhan...The increasing demand for infotainment applications necessitates efficient bandwidth and energy resource allocation.Sixth-Generation(6G)networks,utilizing Cognitive Radio(CR)technology within CR Network(CRN),can enhance spectrum utilization by accessing unused spectrum when licensed Primary Mobile Equipment(PME)is inactive or served by a Primary Base Station(PrBS).Secondary Mobile Equipment(SME)accesses this spectrum through a Secondary Base Station(SrBS)using opportunistic access,i.e.,spectrum sensing.Hybrid Multiple Access(HMA),combining Orthogonal Multiple Access(OMA)and Non-Orthogonal Multiple Access(NOMA),can enhance Energy Efficiency(EE).Additionally,SME Clustering(SMEC)reduces inter-cluster interference,enhancing EE further.Despite these advancements,the integration of CR technology,HMA,and SMEC in CRN for better bandwidth utilization and EE remains unexplored.This paper introduces a new CRassisted SMEC-based Downlink HMA(CR-SMEC-DHMA)method for 6G CRN,aimed at jointly optimizing SME admission,SME association,sum rate,and EE subject to imperfect sensing,collision,and Quality of Service(QoS).A novel optimization problem,formulated as a non-linear fractional programming problem,is solved using the Charnes-Cooper Transformation(CCT)to convert into a concave optimization problem,and an ε-optimal Outer Approximation Algorithm(OAA)is employed to solve the concave optimization problem.Simulations demonstrate the effectiveness of the proposed CR-SMEC-DHMA,surpassing the performance of current OMAenabled CRN,NOMA-enabled CRN,SMEC-OMA enabled CRN,and SMEC-NOMA enabled CRN methods,with ε-optimal results obtained at ε=10^(−3),while satisfying Performance Measures(PMs)including SME admission in SMEC,SME association with SrBS,SME-channel opportunistic allocation through spectrum sensing,sum rate and overall EE within the 6G CRN.展开更多
For an energy-efficient induction machine, the life-cycle cost (LCC) usually is the most important index to the consumer. With this target, the optimization design of a motor is a complex nonlinear problem with constr...For an energy-efficient induction machine, the life-cycle cost (LCC) usually is the most important index to the consumer. With this target, the optimization design of a motor is a complex nonlinear problem with constraints. To solve the problem, the authors introduce a united random algorithm. At first, the problem is divided into two parts, the optimal rotor slots and the optimization of other dimensions. Before optimizing the rotor slots with genetic algorithm ( GA), the second part is solved with TABU algorithm to simplify the problem. The numerical results showed that this method is better than the method using a traditional algorithm.展开更多
In order to address environmental pollution and resource depletion caused by traditional power generation,this paper proposes an adaptive iterative dynamic-balance optimization algorithm that integrates the Improved D...In order to address environmental pollution and resource depletion caused by traditional power generation,this paper proposes an adaptive iterative dynamic-balance optimization algorithm that integrates the Improved Dung Beetle Optimizer(IDBO)with VariationalMode Decomposition(VMD).The IDBO-VMD method is designed to enhance the accuracy and efficiency of wind-speed time-series decomposition and to effectively smooth photovoltaic power fluctuations.This study innovatively improves the traditional variational mode decomposition(VMD)algorithm,and significantly improves the accuracy and adaptive ability of signal decomposition by IDBO selfoptimization of key parameters K and a.On this basis,Fourier transform technology is used to define the boundary point between high frequency and low frequency signals,and a targeted energy distribution strategy is proposed:high frequency fluctuations are allocated to supercapacitors to quickly respond to transient power fluctuations;Lowfrequency components are distributed to lead-carbon batteries,optimizing long-term energy storage and scheduling efficiency.This strategy effectively improves the response speed and stability of the energy storage system.The experimental results demonstrate that the IDBO-VMD algorithm markedly outperforms traditional methods in both decomposition accuracy and computational efficiency.Specifically,it effectively reduces the charge–discharge frequency of the battery,prolongs battery life,and optimizes the operating ranges of the state-of-charge(SOC)for both leadcarbon batteries and supercapacitors.In addition,the energy management strategy based on the algorithm not only improves the overall energy utilization efficiency of the system,but also shows excellent performance in the dynamic management and intelligent scheduling of renewable energy generation.展开更多
In recent years,Blockchain Technology has become a paradigm shift,providing Transparent,Secure,and Decentralized platforms for diverse applications,ranging from Cryptocurrency to supply chain management.Nevertheless,t...In recent years,Blockchain Technology has become a paradigm shift,providing Transparent,Secure,and Decentralized platforms for diverse applications,ranging from Cryptocurrency to supply chain management.Nevertheless,the optimization of blockchain networks remains a critical challenge due to persistent issues such as latency,scalability,and energy consumption.This study proposes an innovative approach to Blockchain network optimization,drawing inspiration from principles of biological evolution and natural selection through evolutionary algorithms.Specifically,we explore the application of genetic algorithms,particle swarm optimization,and related evolutionary techniques to enhance the performance of blockchain networks.The proposed methodologies aim to optimize consensus mechanisms,improve transaction throughput,and reduce resource consumption.Through extensive simulations and real-world experiments,our findings demonstrate significant improvements in network efficiency,scalability,and stability.This research offers a thorough analysis of existing optimization techniques,introduces novel strategies,and assesses their efficacy based on empirical outputs.展开更多
Most of the current deployment schemes for Wireless Sensor Networks (WSNs) do not take the network coverage and connectivity features into account, as well as the energy consumption. This paper introduces topology con...Most of the current deployment schemes for Wireless Sensor Networks (WSNs) do not take the network coverage and connectivity features into account, as well as the energy consumption. This paper introduces topology control into the optimization deployment scheme, establishes the mathe-matical model with the minimum sum of the sensing radius of each sensors, and uses the genetic al-gorithm to solve the model to get the optimal coverage solution. In the optimal coverage deployment, the communication and channel allocation are further studied. Then the energy consumption model of the coverage scheme is built to analyze the performance of the scheme. Finally, the scheme is simulated through the network simulator NS-2. The results show the scheme can not only save 36% energy av-eragely, but also achieve 99.8% coverage rate under the condition of 45 sensors being deployed after 80 iterations. Besides, the scheme can reduce the five times interference among channels.展开更多
The minimum energy per bit(EPB)as the energy efficiency(EE)metric in an automatic retransmission request(ARQ)based multi-hop system is analyzed under power and throughput constraints.Two ARQ protocols including type-I...The minimum energy per bit(EPB)as the energy efficiency(EE)metric in an automatic retransmission request(ARQ)based multi-hop system is analyzed under power and throughput constraints.Two ARQ protocols including type-I(ARQ-I)and repetition redundancy(ARQ-RR)are considered and expressions for the optimal power allocation(PA)are obtained.Using the obtained optimal powers,the EE-throughput tradeoff(EETT)is analyzed and the EETT closed-form expressions for both ARQ protocols and in arbitrary average channel gain values are obtained.It is shown that how different throughput requirements,especially the high levels,affect the EE performance.Additionally,asymptotic analysis is made in the feasible high throughput values and lower and upper EETT bounds are derived for ARQ-I protocol.To evaluate the EE a distributed PA scenario,as a benchmark,is presented and the energy savinggain obtained from the optimal PA in comparison with the distributed PA for ARQ-I and ARQ-RR protocols is discussed in different throughput values and node locations.展开更多
The static var compensator (SVC) is a cost-effective device in flexible AC transmission system (FACTS) family.We introduce an improved artificial hummingbird algorithm (IAHA) for optimal allocation of SVCs in distribu...The static var compensator (SVC) is a cost-effective device in flexible AC transmission system (FACTS) family.We introduce an improved artificial hummingbird algorithm (IAHA) for optimal allocation of SVCs in distribution networks to maximize energy efficiency.Three loading levels (low,medium,and high) per day are investigated.The proposed IAHA is evaluated on the IEEE 33-bus distribution network (DN) and 69-bus DN.The proposed IAHA demonstrates notable improvements in cost savings and voltage profile compared with the conventional artificial hummingbird algorithm (AHA).In addition,it enhances energy savings across various loading conditions and outperforms the conventional AHA in both best and average performance metrics.Although raising the compensation limit initially increases cost savings,the benefits decrease beyond a threshold,highlighting the importance of balancing the compensation levels for maximum efficiency.展开更多
We propose a novel cluster based distributed routing algorithm in a generalized form for heterogeneous wireless sensor networks. Heterogeneity with respect to number/types of communication interfaces, their data rates...We propose a novel cluster based distributed routing algorithm in a generalized form for heterogeneous wireless sensor networks. Heterogeneity with respect to number/types of communication interfaces, their data rates and that with respect to energy dissipation model have been exploited for energy and throughput efficiency. The algorithm makes routing assignment optimized for throughput and energy and has a complexity of N/K*logN+k2logk approximately, where N is the number of nodes and k is the number of kcluster heads. Performance experiments confirm the effectiveness of throughput and energy optimizations. The importance of choosing an optimal cluster radius has been shown. The energy consumption in the network scales up well with respect to the network size.展开更多
The generation of electricity,considering environmental and eco-nomic factors is one of the most important challenges of recent years.In this article,a thermoelectric generator(TEG)is proposed to use the thermal energ...The generation of electricity,considering environmental and eco-nomic factors is one of the most important challenges of recent years.In this article,a thermoelectric generator(TEG)is proposed to use the thermal energy of an electric water heater(EWH)to generate electricity independently.To improve the energy conversion efficiency of the TEG,a fuzzy logic con-troller(FLC)-based perturb&observe(P&O)type maximum power point tracking(MPPT)control algorithm is used in this study.An EWH is one of the major electricity consuming household appliances which causes a higher electricity price for consumers.Also,a significant amount of thermal energy generated by EWH is wasted every day,especially during the winter season.In recent years,TEGs have been widely developed to convert surplus or unused thermal energy into usable electricity.In this context,the proposed model is designed to use the thermal energy stored in the EWH to generate electricity.In addition,the generated electricity can be easily stored in a battery storage system to supply electricity to various household appliances with low-power-consumption.The proposed MPPT control algorithm helps the system to quickly reach the optimal point corresponding to the maximum power output and maintains the system operating point at the maximum power output level.To validate the usefulness of the proposed scheme,a study model was developed in the MATLAB Simulink environment and its performance was investigated by simulation under steady state and transient conditions.The results of the study confirmed that the system is capable of generating adequate power from the available thermal energy of EWH.It was also found that the output power and efficiency of the system can be improved by maintaining a higher temperature difference at the input terminals of the TEG.Moreover,the real-time temperature data of Abha city in Saudi Arabia is considered to analyze the feasibility of the proposed system for practical implementation.展开更多
The mining industry consumes an enormous amount of energy globally,the main part of which is conservable.Diesel is a key source of energy in mining operations,and mine locomotives have significant diesel consumption.T...The mining industry consumes an enormous amount of energy globally,the main part of which is conservable.Diesel is a key source of energy in mining operations,and mine locomotives have significant diesel consumption.Train speed has been recognized as the primary parameter affecting locomotive fuel consumption.In this study,an artificial intelligence(AI)look-forward control is developed as an online method for energy-efficiency improvement in mine-railway operation.An AI controller will modify the desired train-speed profile by accounting for the grade resistance and speed limits of the route ahead.Travel-time increment is applied as an improvement constraint.Recent models for mine-train-movement simulation have estimated locomotive fuel burn using an indirect index.An AI-developed algorithm for mine-train-movement simulation can correctly predict locomotive diesel consumption based on the considered values of the transfer parameters in this paper.This algorithm finds the mine-locomotive subsystems,and satisfies the practical diesel-consumption data specified in the locomotive’s manufacturer catalog.The model developed in this study has two main sections designed to estimate locomotive fuel consumption in different situations by using an artificial neural network(ANN),and an optimization section that applies a genetic algorithm(GA)to optimize train speed for the purpose of minimizing locomotive diesel consumption.The AI model proposed in this paper is learned and validated using real datasets collected from a mine-railway route in Western Australia.The simulation of a mine train with a commonly used locomotive in Australia GeneralMotors SD40-2(GM SD40-2)on a local railway track illustrates a significant reduction in diesel consumption along with a satisfactory travel-time increment.The simulation results also demonstrate that the AI look-forward controller has faster calculations than control systems based that use dynamic programming.展开更多
基金funded by the Natural Science Foundation of Xinjiang Uygur Autonomous Region:No.22D01B148Bidding Topics for the Center for Integration of Education and Production and Development of New Business in 2024:No.2024-KYJD05+1 种基金Basic Scientific Research Business Fee Project of Colleges and Universities in Autonomous Region:No.XJEDU2025P126Xinjiang College of Science&Technology School-level Scientific Research Fund Project:No.2024-KYTD01.
文摘Wireless Sensor Networks(WSNs),as a crucial component of the Internet of Things(IoT),are widely used in environmental monitoring,industrial control,and security surveillance.However,WSNs still face challenges such as inaccurate node clustering,low energy efficiency,and shortened network lifespan in practical deployments,which significantly limit their large-scale application.To address these issues,this paper proposes an Adaptive Chaotic Ant Colony Optimization algorithm(AC-ACO),aiming to optimize the energy utilization and system lifespan of WSNs.AC-ACO combines the path-planning capability of Ant Colony Optimization(ACO)with the dynamic characteristics of chaotic mapping and introduces an adaptive mechanism to enhance the algorithm’s flexibility and adaptability.By dynamically adjusting the pheromone evaporation factor and heuristic weights,efficient node clustering is achieved.Additionally,a chaotic mapping initialization strategy is employed to enhance population diversity and avoid premature convergence.To validate the algorithm’s performance,this paper compares AC-ACO with clustering methods such as Low-Energy Adaptive Clustering Hierarchy(LEACH),ACO,Particle Swarm Optimization(PSO),and Genetic Algorithm(GA).Simulation results demonstrate that AC-ACO outperforms the compared algorithms in key metrics such as energy consumption optimization,network lifetime extension,and communication delay reduction,providing an efficient solution for improving energy efficiency and ensuring long-term stable operation of wireless sensor networks.
文摘Managing massive data flows effectively and resolving spectrum shortages are two challenges that smart grid communication networks(SGCN)must overcome.To address these problems,we provide a combined optimization approach that makes use of cognitive radio(CR)and non-orthogonal multiple access(NOMA)technologies.Our work focuses on using user pairing(UP)and power allocation(PA)techniques to maximize energy efficiency(EE)in SGCN,particularly within neighbourhood area networks(NANs).We develop a joint optimization problem that takes into account the real-world limitations of a CR-NOMA setting.This problem is NP-hard,nonlinear,and nonconvex by nature.To address the computational complexity of the problem,we use the block coordinate descent(BCD)method,which breaks the problem into UP and PA subproblems.Initially,we proposed the zebra-optimization user pairing(ZOUP)algorithm to tackle the UP problem,which outperforms both orthogonal multiple access(OMA)and non-optimized NOMA(UPWO)by 78.8%and13.6%,respectively,at a SNR of 15 dB.Based on the ZOUP pairs,we subsequently proposed the PA approach,i.e.,ZOUPPA,which significantly outperforms UPWO and ZOUP by 53.2%and 25.4%,respectively,at an SNR of 15 dB.A detailed analysis of key parameters,including varying SNRs,power allocation constants,path loss exponents,user density,channel availability,and coverage radius,underscores the superiority of our approach.By facilitating the effective use of communication resources in SGCN,our research opens the door to more intelligent and energy-efficient grid systems.Our work tackles important issues in SGCN and lays the groundwork for future developments in smart grid communication technologies by combining modern optimization approaches with CR-NOMA.
文摘Internet of things networks often suffer from early node failures and short lifespan due to energy limits.Traditional routing methods are not enough.This work proposes a new hybrid algorithm called ACOGA.It combines Ant Colony Optimization(ACO)and the Greedy Algorithm(GA).ACO finds smart paths while Greedy makes quick decisions.This improves energy use and performance.ACOGA outperforms Hybrid Energy-Efficient(HEE)and Adaptive Lossless Data Compression(ALDC)algorithms.After 500 rounds,only 5%of ACOGA’s nodes are dead,compared to 15%for HEE and 20%for ALDC.The network using ACOGA runs for 1200 rounds before the first nodes fail.HEE lasts 900 rounds and ALDC only 850.ACOGA saves at least 15%more energy by better distributing the load.It also achieves a 98%packet delivery rate.The method works well in mixed IoT networks like Smart Water Management Systems(SWMS).These systems have different power levels and communication ranges.The simulation of proposed model has been done in MATLAB simulator.The results show that that the proposed model outperform then the existing models.
基金support under the Multi-Disciplinary Research(MDR)Grant(H470)the Ministry of Higher Education Malaysia under the Fundamental Research Grant Scheme(FRGS/1/2019/TK04/UTHM/02/8).
文摘Energy efficiency(EE)is a critical design when taking into account circuit power consumption(CPC)in fifth-generation cellular networks.These problems arise because of the increasing number of antennas in massive multiple-input multiple-output(MIMO)systems,attributable to inter-cell interference for channel state information.Apart from that,a higher number of radio frequency(RF)chains at the base station and active users consume more power due to the processing activities in digital-to-analogue converters and power amplifiers.Therefore,antenna selection,user selection,optimal transmission power,and pilot reuse power are important aspects in improving energy efficiency in massive MIMO systems.This work aims to investigate joint antenna selection,optimal transmit power and joint user selection based on deriving the closed-form of the maximal EE,with complete knowledge of large-scale fading with maximum ratio transmission.It also accounts for channel estimation and eliminating pilot contamination as antennas M→∞.This formulates the optimization problem of joint optimal antenna selection,transmits power allocation and joint user selection to mitigate inter-cellinterference in downlink multi-cell massive MIMO systems under minimized reuse of pilot sequences based on a novel iterative low-complexity algorithm(LCA)for Newton’s methods and Lagrange multipliers.To analyze the precise power consumption,a novel power consumption scheme is proposed for each individual antenna,based on the transmit power amplifier and CPC.Simulation results demonstrate that the maximal EE was achieved using the iterative LCA based on reasonable maximum transmit power,in the case the noise power is less than the received power pilot.The maximum EE was achieved with the desired maximum transmit power threshold by minimizing pilot reuse,in the case the transmit power allocationρd=40 dBm,and the optimal EE=71.232 Mb/j.
基金supported by a Marie Curie International Research Staff Exchange Scheme Fellowship within the 7th European Community Framework Programme(No.294931)the National Science Foundation of China (No.51175262)+1 种基金Jiangsu Province Science Foundation for Excellent Youths(No.BK2012032)Jiangsu Province Industry-Academy-Research Grant(No.BY201220116)
文摘Nowadays,energy consumption which closely contacts with environmental impacts of manufacturing processes has been highly commented as a new productivity criterion.However,little attention has paid to the development of process planning methods that take energy consumption into account.An energy-efficient process planning model that incorporates manufacturing time and energy consumption is proposed.For solving the problem,an improved genetic algorithm method is employed to explore the optimal solution.Finally,a case study for process planning is given.The experimental result generates interesting effort,and therefore allows improving the energy efficiency of manufacturing processes in process planning.
基金This work was supported by Taif University Researchers Supporting Program(project number:TURSP-2020/195),Taif University,Saudi Arabia.
文摘Due to the wide range of applications,Wireless Sensor Networks(WSN)are increased in day to day life and becomes popular.WSN has marked its importance in both practical and research domains.Energy is the most significant resource,the important challenge in WSN is to extend its lifetime.The energy reduction is a key to extend the network’s lifetime.Clustering of sensor nodes is one of the well-known and proved methods for achieving scalable and energy conserving WSN.In this paper,an energy efficient protocol is proposed using metaheuristic Echo location-based BAT algorithm(ECHO-BAT).ECHO-BAT works in two stages.First Stage clusters the sensor nodes and identifies tentativeCluster Head(CH)along with the entropy value using BAT algorithm.The second stage aims to find the nodes if any,with high residual energy within each cluster.CHs will be replaced by the member node with high residual energy with an objective to choose the CH with high energy to prolong the network’s lifetime.The performance of the proposed work is compared with Low-Energy Adaptive Clustering Hierarchy(LEACH),Power-Efficient Zoning Clustering Algorithm(PEZCA)and Chaotic Firefly Algorithm CH(CFACH)in terms of lifetime of network,death of first nodes,death of 125th node,death of the last node,network throughput and execution time.Simulation results show that ECHO-BAT outperforms the other methods in all the considered measures.The overall delivery ratio has also significantly optimized and improved by approximately 8%,proving the proposed approach to be an energy efficient WSN.
文摘Our living environments are being gradually occupied with an abundant number of digital objects that have networking and computing capabilities. After these devices are plugged into a network, they initially advertise their presence and capabilities in the form of services so that they can be discovered and, if desired, exploited by the user or other networked devices. With the increasing number of these devices attached to the network, the complexity to configure and control them increases, which may lead to major processing and communication overhead. Hence, the devices are no longer expected to just act as primitive stand-alone appliances that only provide the facilities and services to the user they are designed for, but also offer complex services that emerge from unique combinations of devices. This creates the necessity for these devices to be equipped with some sort of intelligence and self-awareness to enable them to be self-configuring and self-programming. However, with this ＂smart evolution＂, the cognitive load to configure and control such spaces becomes immense. One way to relieve this load is by employing artificial intelligence （AI） techniques to create an intelligent ＂presence＂ where the system will be able to recognize the users and autonomously program the environment to be energy efficient and responsive to the user＇s needs and behaviours. These AI mechanisms should be embedded in the user＇s environments and should operate in a non-intrusive manner. This paper will show how computational intelligence （CI）, which is an emerging domain of AI, could be employed and embedded in our living spaces to help such environments to be more energy efficient, intelligent, adaptive and convenient to the users.
基金funded by (Defense Pre-Research Fund Project of China), grant number 012015012600A2203NSFC (Natural Science Foundation of China), grant number 61573374。
文摘This paper mainly studied the problem of energy conserving in wireless sensor networks for target tracking in defensing combats. Firstly, the structures of wireless sensor nodes and networks were illustrated;Secondly, the analysis of existing energy consuming in the sensing layer and its calculation method were provided to build the energy conserving objective function;What’s more, the other two indicators in target tracking, including target detection probability and tracking accuracy, were combined to be regarded as the constraints of the energy conserving objective function. Fourthly, the three energy conserving approaches, containing optimizing the management scheme, prolonging the time interval between two adjacent observations, and transmitting the observations selectively, were introduced;In addition, the improved lion algorithm combined with the Logistic chaos sequence was proposed to obtain sensor management schemes. Finally, simulations had been made to prove the effectiveness of the proposed methods and algorithm.
文摘One of the major constraints of wireless sensor networks is limited energy available to sensor nodes because of the small size of the batteries they use as source of power. Clustering is one of the routing techniques that have been using to minimize sensor nodes’ energy consumption during operation. In this paper, A Novel Clustering Algorithm for Energy Efficiency in Wireless Sensor Networks (ANCAEE) has been proposed. The algorithm achieves good performance in terms of minimizing energy consumption during data transmission and energy consumptions are distributed uniformly among all nodes. ANCAEE uses a new method of clusters formation and election of cluster heads. The algorithm ensures that a node transmits its data to the cluster head with a single hop transmission and cluster heads forward their data to the base station with multi-hop transmissions. Simulation results show that our approach consumes less energy and effectively extends network utilization.
基金supported by the National Key Research and Development Program of China(No.2023YFC3011100)the National Natural Science Foundation of China(No.62373146)+3 种基金the Natural Science Foundation of Hunan Province(No.2022JJ30265)the Young Talent of Lifting Engineering for Science and Technology in Hunan Province(No.2022TJ-Q03)the Outstanding Youth Project of Education Department of Hunan Province(No.22B0476)the Key Project of Education Department of Hunan Province of China(No.23A0382).
文摘The scheduling problem of distributed permutation flow shop with limited buffer aiming at production efficiency measures has attracted widespread attention due to its closer alignment with real manufacturing environments.However,the energy efficiency metric is often ignored.The Energy-Efficient scheduling of Distributed Permutation Flow Shop Problem with Limited Buffer(EEDPFSP-LB)with the objectives of Makespan(C_(max))and Total Energy Consumption(TEC)is studied,and a Cooperative Fruit fly Optimization Algorithm(CFOA)is proposed in this paper.First,the critical path of EEDPFSP-LB is identified,and energy-efficient operation is applied to non-critical paths to reduce the system’s energy consumption.Second,five acceptance criteria for multi-objective optimization are introduced to enhance the diversity of the population.Third,to select a superior next-generation population,a new congestion calculation method is introduced to resolve the issue of indeterminate positional relationships among non-dominated solutions with identical crowding distances at the same dominance level.Finally,CFOA is extensively tested and compared with state-of-the-art algorithms across 360 instances,demonstrating CFOA’s strong competitiveness in solving EEDPFSP-LB.
文摘The increasing demand for infotainment applications necessitates efficient bandwidth and energy resource allocation.Sixth-Generation(6G)networks,utilizing Cognitive Radio(CR)technology within CR Network(CRN),can enhance spectrum utilization by accessing unused spectrum when licensed Primary Mobile Equipment(PME)is inactive or served by a Primary Base Station(PrBS).Secondary Mobile Equipment(SME)accesses this spectrum through a Secondary Base Station(SrBS)using opportunistic access,i.e.,spectrum sensing.Hybrid Multiple Access(HMA),combining Orthogonal Multiple Access(OMA)and Non-Orthogonal Multiple Access(NOMA),can enhance Energy Efficiency(EE).Additionally,SME Clustering(SMEC)reduces inter-cluster interference,enhancing EE further.Despite these advancements,the integration of CR technology,HMA,and SMEC in CRN for better bandwidth utilization and EE remains unexplored.This paper introduces a new CRassisted SMEC-based Downlink HMA(CR-SMEC-DHMA)method for 6G CRN,aimed at jointly optimizing SME admission,SME association,sum rate,and EE subject to imperfect sensing,collision,and Quality of Service(QoS).A novel optimization problem,formulated as a non-linear fractional programming problem,is solved using the Charnes-Cooper Transformation(CCT)to convert into a concave optimization problem,and an ε-optimal Outer Approximation Algorithm(OAA)is employed to solve the concave optimization problem.Simulations demonstrate the effectiveness of the proposed CR-SMEC-DHMA,surpassing the performance of current OMAenabled CRN,NOMA-enabled CRN,SMEC-OMA enabled CRN,and SMEC-NOMA enabled CRN methods,with ε-optimal results obtained at ε=10^(−3),while satisfying Performance Measures(PMs)including SME admission in SMEC,SME association with SrBS,SME-channel opportunistic allocation through spectrum sensing,sum rate and overall EE within the 6G CRN.
文摘For an energy-efficient induction machine, the life-cycle cost (LCC) usually is the most important index to the consumer. With this target, the optimization design of a motor is a complex nonlinear problem with constraints. To solve the problem, the authors introduce a united random algorithm. At first, the problem is divided into two parts, the optimal rotor slots and the optimization of other dimensions. Before optimizing the rotor slots with genetic algorithm ( GA), the second part is solved with TABU algorithm to simplify the problem. The numerical results showed that this method is better than the method using a traditional algorithm.
基金funded by the Institute of Smart Energy,Huaiyin Institute of Technology,under Grant No.HIT-ISE-2024-07.
文摘In order to address environmental pollution and resource depletion caused by traditional power generation,this paper proposes an adaptive iterative dynamic-balance optimization algorithm that integrates the Improved Dung Beetle Optimizer(IDBO)with VariationalMode Decomposition(VMD).The IDBO-VMD method is designed to enhance the accuracy and efficiency of wind-speed time-series decomposition and to effectively smooth photovoltaic power fluctuations.This study innovatively improves the traditional variational mode decomposition(VMD)algorithm,and significantly improves the accuracy and adaptive ability of signal decomposition by IDBO selfoptimization of key parameters K and a.On this basis,Fourier transform technology is used to define the boundary point between high frequency and low frequency signals,and a targeted energy distribution strategy is proposed:high frequency fluctuations are allocated to supercapacitors to quickly respond to transient power fluctuations;Lowfrequency components are distributed to lead-carbon batteries,optimizing long-term energy storage and scheduling efficiency.This strategy effectively improves the response speed and stability of the energy storage system.The experimental results demonstrate that the IDBO-VMD algorithm markedly outperforms traditional methods in both decomposition accuracy and computational efficiency.Specifically,it effectively reduces the charge–discharge frequency of the battery,prolongs battery life,and optimizes the operating ranges of the state-of-charge(SOC)for both leadcarbon batteries and supercapacitors.In addition,the energy management strategy based on the algorithm not only improves the overall energy utilization efficiency of the system,but also shows excellent performance in the dynamic management and intelligent scheduling of renewable energy generation.
文摘In recent years,Blockchain Technology has become a paradigm shift,providing Transparent,Secure,and Decentralized platforms for diverse applications,ranging from Cryptocurrency to supply chain management.Nevertheless,the optimization of blockchain networks remains a critical challenge due to persistent issues such as latency,scalability,and energy consumption.This study proposes an innovative approach to Blockchain network optimization,drawing inspiration from principles of biological evolution and natural selection through evolutionary algorithms.Specifically,we explore the application of genetic algorithms,particle swarm optimization,and related evolutionary techniques to enhance the performance of blockchain networks.The proposed methodologies aim to optimize consensus mechanisms,improve transaction throughput,and reduce resource consumption.Through extensive simulations and real-world experiments,our findings demonstrate significant improvements in network efficiency,scalability,and stability.This research offers a thorough analysis of existing optimization techniques,introduces novel strategies,and assesses their efficacy based on empirical outputs.
基金Supported by the National Natural Science Foundation of China (No. 60973139&60773041)the Natural Science Foundation of Jiangsu Province (BK2008451)+3 种基金Special Fund for Software Technology of Jiangsu Province, Jiangsu Provincial Research Scheme of Natural Science for Higher Education Institutions (08KJB520006)Postdoctoral Foundation (0801019C, 20090451240, 20090451241)Science & Technology Innovation Fund for Higher Education Institutions of Jiangsu Province (CX10B_198Z,CX09B_153Z)the Six Kinds of Top Talent of Jiangsu Province (2008118)
文摘Most of the current deployment schemes for Wireless Sensor Networks (WSNs) do not take the network coverage and connectivity features into account, as well as the energy consumption. This paper introduces topology control into the optimization deployment scheme, establishes the mathe-matical model with the minimum sum of the sensing radius of each sensors, and uses the genetic al-gorithm to solve the model to get the optimal coverage solution. In the optimal coverage deployment, the communication and channel allocation are further studied. Then the energy consumption model of the coverage scheme is built to analyze the performance of the scheme. Finally, the scheme is simulated through the network simulator NS-2. The results show the scheme can not only save 36% energy av-eragely, but also achieve 99.8% coverage rate under the condition of 45 sensors being deployed after 80 iterations. Besides, the scheme can reduce the five times interference among channels.
文摘The minimum energy per bit(EPB)as the energy efficiency(EE)metric in an automatic retransmission request(ARQ)based multi-hop system is analyzed under power and throughput constraints.Two ARQ protocols including type-I(ARQ-I)and repetition redundancy(ARQ-RR)are considered and expressions for the optimal power allocation(PA)are obtained.Using the obtained optimal powers,the EE-throughput tradeoff(EETT)is analyzed and the EETT closed-form expressions for both ARQ protocols and in arbitrary average channel gain values are obtained.It is shown that how different throughput requirements,especially the high levels,affect the EE performance.Additionally,asymptotic analysis is made in the feasible high throughput values and lower and upper EETT bounds are derived for ARQ-I protocol.To evaluate the EE a distributed PA scenario,as a benchmark,is presented and the energy savinggain obtained from the optimal PA in comparison with the distributed PA for ARQ-I and ARQ-RR protocols is discussed in different throughput values and node locations.
基金Prince Sattam bin Abdulaziz University for funding this research work through the project number PSAU/2024/01/31685.
文摘The static var compensator (SVC) is a cost-effective device in flexible AC transmission system (FACTS) family.We introduce an improved artificial hummingbird algorithm (IAHA) for optimal allocation of SVCs in distribution networks to maximize energy efficiency.Three loading levels (low,medium,and high) per day are investigated.The proposed IAHA is evaluated on the IEEE 33-bus distribution network (DN) and 69-bus DN.The proposed IAHA demonstrates notable improvements in cost savings and voltage profile compared with the conventional artificial hummingbird algorithm (AHA).In addition,it enhances energy savings across various loading conditions and outperforms the conventional AHA in both best and average performance metrics.Although raising the compensation limit initially increases cost savings,the benefits decrease beyond a threshold,highlighting the importance of balancing the compensation levels for maximum efficiency.
文摘We propose a novel cluster based distributed routing algorithm in a generalized form for heterogeneous wireless sensor networks. Heterogeneity with respect to number/types of communication interfaces, their data rates and that with respect to energy dissipation model have been exploited for energy and throughput efficiency. The algorithm makes routing assignment optimized for throughput and energy and has a complexity of N/K*logN+k2logk approximately, where N is the number of nodes and k is the number of kcluster heads. Performance experiments confirm the effectiveness of throughput and energy optimizations. The importance of choosing an optimal cluster radius has been shown. The energy consumption in the network scales up well with respect to the network size.
基金Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through the project number (IF2-PSAU/2022/01/22797).
文摘The generation of electricity,considering environmental and eco-nomic factors is one of the most important challenges of recent years.In this article,a thermoelectric generator(TEG)is proposed to use the thermal energy of an electric water heater(EWH)to generate electricity independently.To improve the energy conversion efficiency of the TEG,a fuzzy logic con-troller(FLC)-based perturb&observe(P&O)type maximum power point tracking(MPPT)control algorithm is used in this study.An EWH is one of the major electricity consuming household appliances which causes a higher electricity price for consumers.Also,a significant amount of thermal energy generated by EWH is wasted every day,especially during the winter season.In recent years,TEGs have been widely developed to convert surplus or unused thermal energy into usable electricity.In this context,the proposed model is designed to use the thermal energy stored in the EWH to generate electricity.In addition,the generated electricity can be easily stored in a battery storage system to supply electricity to various household appliances with low-power-consumption.The proposed MPPT control algorithm helps the system to quickly reach the optimal point corresponding to the maximum power output and maintains the system operating point at the maximum power output level.To validate the usefulness of the proposed scheme,a study model was developed in the MATLAB Simulink environment and its performance was investigated by simulation under steady state and transient conditions.The results of the study confirmed that the system is capable of generating adequate power from the available thermal energy of EWH.It was also found that the output power and efficiency of the system can be improved by maintaining a higher temperature difference at the input terminals of the TEG.Moreover,the real-time temperature data of Abha city in Saudi Arabia is considered to analyze the feasibility of the proposed system for practical implementation.
文摘The mining industry consumes an enormous amount of energy globally,the main part of which is conservable.Diesel is a key source of energy in mining operations,and mine locomotives have significant diesel consumption.Train speed has been recognized as the primary parameter affecting locomotive fuel consumption.In this study,an artificial intelligence(AI)look-forward control is developed as an online method for energy-efficiency improvement in mine-railway operation.An AI controller will modify the desired train-speed profile by accounting for the grade resistance and speed limits of the route ahead.Travel-time increment is applied as an improvement constraint.Recent models for mine-train-movement simulation have estimated locomotive fuel burn using an indirect index.An AI-developed algorithm for mine-train-movement simulation can correctly predict locomotive diesel consumption based on the considered values of the transfer parameters in this paper.This algorithm finds the mine-locomotive subsystems,and satisfies the practical diesel-consumption data specified in the locomotive’s manufacturer catalog.The model developed in this study has two main sections designed to estimate locomotive fuel consumption in different situations by using an artificial neural network(ANN),and an optimization section that applies a genetic algorithm(GA)to optimize train speed for the purpose of minimizing locomotive diesel consumption.The AI model proposed in this paper is learned and validated using real datasets collected from a mine-railway route in Western Australia.The simulation of a mine train with a commonly used locomotive in Australia GeneralMotors SD40-2(GM SD40-2)on a local railway track illustrates a significant reduction in diesel consumption along with a satisfactory travel-time increment.The simulation results also demonstrate that the AI look-forward controller has faster calculations than control systems based that use dynamic programming.
