In every network,delay and energy are crucial for communication and network life.In wireless sensor networks,many tiny nodes create networks with high energy consumption and compute routes for better communication.Wir...In every network,delay and energy are crucial for communication and network life.In wireless sensor networks,many tiny nodes create networks with high energy consumption and compute routes for better communication.Wireless Sensor Networks(WSN)is a very complex scenario to compute minimal delay with data aggregation and energy efficiency.In this research,we compute minimal delay and energy efficiency for improving the quality of service of any WSN.The proposed work is based on energy and distance parameters as taken dependent variables with data aggregation.Data aggregation performs on different models,namely Hybrid-Low Energy Adaptive Clustering Hierarchy(H-LEACH),Low Energy Adaptive Clustering Hierarchy(LEACH),and Multi-Aggregator-based Multi-Cast(MAMC).The main contribution of this research is to a reduction in delay and optimized energy solution,a novel hybrid model design in this research that ensures the quality of service in WSN.This model includes a whale optimization technique that involves heterogeneous functions and performs optimization to reach optimized results.For cluster head selection,Stable Election Protocol(SEP)protocol is used and Power-Efficient Gathering in Sensor Information Systems(PEGASIS)is used for driven-path in routing.Simulation results evaluate that H-LEACH provides minimal delay and energy consumption by sensor nodes.In the comparison of existing theories and our proposed method,HLEACH is providing energy and delay reduction and improvement in quality of service.MATLAB 2019 is used for simulation work.展开更多
The accelerated development of wireless network technology has resulted in the emergence of Wireless Body Area Network(WBAN),which is a technology commonly used in the medical field.WBAN consists of tiny sensor nodes ...The accelerated development of wireless network technology has resulted in the emergence of Wireless Body Area Network(WBAN),which is a technology commonly used in the medical field.WBAN consists of tiny sensor nodes that interconnect with each other and set in the human body to collect and transmit the patient data to the physician,to monitor the patients remotely.These nodes typically have limited battery energy that led to a shortage of network lifetime.Therefore,energy efficiency is considered one of the most demanding challenges in routing design for WBAN.Many proposed routing mechanisms inWBAN did not cover the source node energy and energy harvesting techniques.Therefore,this study proposes an Efficient Energy Aware Routing(EEAR)mechanism.This paper constructs a path cost function that considers three parameters:residual energy,number of hops to the sink,and the distance between the nodes.Besides,data aggregationwith filtration and hybrid energy harvesting technique are used to extend the network lifetime,reduce the network traffic load,andmaintain the source node energy.Extensive simulations using MATLAB have been performed to evaluate the performance of the proposed mechanism.EEAR is contrasted with the two latest schemes,called Priority-based Congestion-avoidance Routing Protocol(PCRP)and Energy Efficient Routing Protocol(EERP).The results show the significant performance of theEEARmechanism in terms of network lifetime,residual energy,network stability,and throughput.展开更多
In cloud environment,an efficient resource management establishes the allocation of computational resources of cloud service providers to the requests of users for meeting the user’s demands.The proficient resource m...In cloud environment,an efficient resource management establishes the allocation of computational resources of cloud service providers to the requests of users for meeting the user’s demands.The proficient resource management and work allocation determines the accomplishment of the cloud infrastructure.However,it is very difficult to persuade the objectives of the Cloud Service Providers(CSPs)and end users in an impulsive cloud domain with random changes of workloads,huge resource availability and complicated service policies to handle them,With that note,this paper attempts to present an Efficient Energy-Aware Resource Management Model(EEARMM)that works in a decentralized manner.Moreover,the model involves in reducing the number of migrations by definite workload management for efficient resource utilization.That is,it makes an effort to reduce the amount of physical devices utilized for load balancing with certain resource and energy consumption management of every machine.The Estimation Model Algorithm(EMA)is given for determining the virtual machine migration.Further,VM-Selection Algorithm(SA)is also provided for choosing the appropriate VM to migrate for resource management.By the incorporation of these algorithms,overloading of VM instances can be avoided and energy efficiency can be improved considerably.The performance evaluation and comparative analysis,based on the dynamic workloads in different factors provides evidence to the efficiency,feasibility and scalability of the proposed model in cloud domain with high rate of resources and workload management.展开更多
To solve the problem of energy efficiency of modern enterprise it is necessary to reduce energy consumption.One of the possible ways is proposed in this research.A multi-level hierarchical system for energy efficiency...To solve the problem of energy efficiency of modern enterprise it is necessary to reduce energy consumption.One of the possible ways is proposed in this research.A multi-level hierarchical system for energy efficiency management of the enterprise is designed,it is based on the modular principle providing rapid modernization.The novelty of the work is the development of new and improvement of the existing methods and models,in particular:1)models for dynamic analysis of IT tools for data acquisition and processing(DAAP)in multilevel energy management systems,which are based on Petri nets;2)method of synthesis of DAAP tools in energy efficiency management information systems(EEMIS)of the enterprise which provides a reduction in hardware and time costs from 10%to 40%;3)method of conflict-free data exchange determining the minimum memory speed for the synthesis of realtime exchanges,it reduces the cost and energy consumption;4)method of calculating the signal of postsynaptic excitation of neural elements decreases the processing time of technological data two or more times.The proposed methods,models and tools have been tested while implementing the EEMIS of the intelligent mini-greenhouse,as a result,energy efficiency has increased by 12%-25%(depending on season and peculiarities of growing plants).展开更多
The seamless integration of intelligent Internet of Things devices with conventional wireless sensor networks has revolutionized data communication for different applications,such as remote health monitoring,industria...The seamless integration of intelligent Internet of Things devices with conventional wireless sensor networks has revolutionized data communication for different applications,such as remote health monitoring,industrial monitoring,transportation,and smart agriculture.Efficient and reliable data routing is one of the major challenges in the Internet of Things network due to the heterogeneity of nodes.This paper presents a traffic-aware,cluster-based,and energy-efficient routing protocol that employs traffic-aware and cluster-based techniques to improve the data delivery in such networks.The proposed protocol divides the network into clusters where optimal cluster heads are selected among super and normal nodes based on their residual energies.The protocol considers multi-criteria attributes,i.e.,energy,traffic load,and distance parameters to select the next hop for data delivery towards the base station.The performance of the proposed protocol is evaluated through the network simulator NS3.40.For different traffic rates,number of nodes,and different packet sizes,the proposed protocol outperformed LoRaWAN in terms of end-to-end packet delivery ratio,energy consumption,end-to-end delay,and network lifetime.For 100 nodes,the proposed protocol achieved a 13%improvement in packet delivery ratio,10 ms improvement in delay,and 10 mJ improvement in average energy consumption over LoRaWAN.展开更多
As the demands of massive connections and vast coverage rapidly grow in the next wireless communication networks, rate splitting multiple access(RSMA) is considered to be the new promising access scheme since it can p...As the demands of massive connections and vast coverage rapidly grow in the next wireless communication networks, rate splitting multiple access(RSMA) is considered to be the new promising access scheme since it can provide higher efficiency with limited spectrum resources. In this paper, combining spectrum splitting with rate splitting, we propose to allocate resources with traffic offloading in hybrid satellite terrestrial networks. A novel deep reinforcement learning method is adopted to solve this challenging non-convex problem. However, the neverending learning process could prohibit its practical implementation. Therefore, we introduce the switch mechanism to avoid unnecessary learning. Additionally, the QoS constraint in the scheme can rule out unsuccessful transmission. The simulation results validates the energy efficiency performance and the convergence speed of the proposed algorithm.展开更多
Wireless Body Area Network(WBAN)is a cutting-edge technology that is being used in healthcare applications to monitor critical events in the human body.WBAN is a collection of in-body and on-body sensors that monitor ...Wireless Body Area Network(WBAN)is a cutting-edge technology that is being used in healthcare applications to monitor critical events in the human body.WBAN is a collection of in-body and on-body sensors that monitor human physical parameters such as temperature,blood pressure,pulse rate,oxygen level,body motion,and so on.They sense the data and communicate it to the Body Area Network(BAN)Coordinator.The main challenge for the WBAN is energy consumption.These issues can be addressed by implementing an effective Medium Access Control(MAC)protocol that reduces energy consumption and increases network lifetime.The purpose of the study is to minimize the energy consumption and minimize the delay using IEEE 802.15.4 standard.In our proposed work,if any critical events have occurred the proposed work is to classify and prioritize the data.We gave priority to the highly critical data to get the Guarantee Tine Slots(GTS)in IEEE 802.15.4 standard superframe to achieve greater energy efficiency.The proposed MAC provides higher data rates for critical data based on the history and current condition and also provides the best reliable service to high critical data and critical data by predicting node similarity.As an outcome,we proposed a MAC protocol for Variable Data Rates(MVDR).When compared to existing MAC protocols,the MVDR performed very well with low energy intake,less interruption,and an enhanced packet-sharing ratio.展开更多
There is a huge amount of energy savings potential in public building sector that has yet to be realized.By prioritizing energy efficiency in its own buildings and thus promoting the development of required knowledge ...There is a huge amount of energy savings potential in public building sector that has yet to be realized.By prioritizing energy efficiency in its own buildings and thus promoting the development of required knowledge in terms of new technology and construction methods,the public sector will lead the way in efforts to increase the rate of renovations.The low-cost insulation strategies and a comparison of cost with existing insulation materials has been described in this study.We have repeatedly faced energy crises and will continue to do so in the future if appropriate action is not taken in a timely manner.Properly implementing energy-saving initiatives in for achieving thermal comfort in buildings as well as reducing the energy costs would undoubtedly inspire the residential sector,resulting in significant reductions in energy usage.Simulations were carried out to study insulation layers on various building components like exterior walls,floor and roofs,generating different scenarios for a building as a base model,which were then compared and analysed to verify the literature used to develop the cases.The proposed recommendations,which have been validated,are certain to increase building energy efficiency,achieve thermal comfort in low cost than what is currently being used.展开更多
With the flexible deployment and high mobility of Unmanned Aerial Vehicles(UAVs)in an open environment,they have generated con-siderable attention in military and civil applications intending to enable ubiquitous conn...With the flexible deployment and high mobility of Unmanned Aerial Vehicles(UAVs)in an open environment,they have generated con-siderable attention in military and civil applications intending to enable ubiquitous connectivity and foster agile communications.The difficulty stems from features other than mobile ad-hoc network(MANET),namely aerial mobility in three-dimensional space and often changing topology.In the UAV network,a single node serves as a forwarding,transmitting,and receiving node at the same time.Typically,the communication path is multi-hop,and routing significantly affects the network’s performance.A lot of effort should be invested in performance analysis for selecting the optimum routing system.With this motivation,this study modelled a new Coati Optimization Algorithm-based Energy-Efficient Routing Process for Unmanned Aerial Vehicle Communication(COAER-UAVC)technique.The presented COAER-UAVC technique establishes effective routes for communication between the UAVs.It is primarily based on the coati characteristics in nature:if attacking and hunting iguanas and escaping from predators.Besides,the presented COAER-UAVC technique concentrates on the design of fitness functions to minimize energy utilization and communication delay.A varied group of simulations was performed to depict the optimum performance of the COAER-UAVC system.The experimental results verified that the COAER-UAVC technique had assured improved performance over other approaches.展开更多
As the main link of ground engineering,crude oil gathering and transportation systems require huge energy consumption and complex structures.It is necessary to establish an energy efficiency evaluation system for crud...As the main link of ground engineering,crude oil gathering and transportation systems require huge energy consumption and complex structures.It is necessary to establish an energy efficiency evaluation system for crude oil gathering and transportation systems and identify the energy efficiency gaps.In this paper,the energy efficiency evaluation system of the crude oil gathering and transportation system in an oilfield in western China is established.Combined with the big data analysis method,the GA-BP neural network is used to establish the energy efficiency index prediction model for crude oil gathering and transportation systems.The comprehensive energy consumption,gas consumption,power consumption,energy utilization rate,heat utilization rate,and power utilization rate of crude oil gathering and transportation systems are predicted.Considering the efficiency and unit consumption index of the crude oil gathering and transportation system,the energy efficiency evaluation system of the crude oil gathering and transportation system is established based on a game theory combined weighting method and TOPSIS evaluation method,and the subjective weight is determined by the triangular fuzzy analytic hierarchy process.The entropy weight method determines the objective weight,and the combined weight of game theory combines subjectivity with objectivity to comprehensively evaluate the comprehensive energy efficiency of crude oil gathering and transportation systems and their subsystems.Finally,the weak links in energy utilization are identified,and energy conservation and consumption reduction are improved.The above research provides technical support for the green,efficient and intelligent development of crude oil gathering and transportation systems.展开更多
Virtual machine(VM)consolidation is an effective way to improve resource utilization and reduce energy consumption in cloud data centers.Most existing studies have considered VM consolidation as a bin-packing problem,...Virtual machine(VM)consolidation is an effective way to improve resource utilization and reduce energy consumption in cloud data centers.Most existing studies have considered VM consolidation as a bin-packing problem,but the current schemes commonly ignore the long-term relationship between VMs and hosts.In addition,there is a lack of long-term consideration for resource optimization in the VM consolidation,which results in unnecessary VM migration and increased energy consumption.To address these limitations,a VM consolidation method based on multi-step prediction and affinity-aware technique for energy-efficient cloud data centers(MPaAF-VMC)is proposed.The proposed method uses an improved linear regression prediction algorithm to predict the next-moment resource utilization of hosts and VMs,and obtains the stage demand of resources in the future period through multi-step prediction,which is realized by iterative prediction.Then,based on the multi-step prediction,an affinity model between the VM and host is designed using the first-order correlation coefficient and Euclidean distance.During the VM consolidation,the affinity value is used to select the migration VM and placement host.The proposed method is compared with the existing consolidation algorithms on the PlanetLab and Google cluster real workload data using the CloudSim simulation platform.Experimental results show that the proposed method can achieve significant improvement in reducing energy consumption,VM migration costs,and service level agreement(SLA)violations.展开更多
The 3D Underwater Sensor Network(USNs)has become the most optimistic medium for tracking and monitoring underwater environment.Energy and collision are two most critical factors in USNs for both sparse and dense regio...The 3D Underwater Sensor Network(USNs)has become the most optimistic medium for tracking and monitoring underwater environment.Energy and collision are two most critical factors in USNs for both sparse and dense regions.Due to harsh ocean environment,it is a challenge to design a reliable energy efficient with collision free protocol.Diversity in link qualities may cause collision and frequent communication lead to energy loss;that effects the network performance.To overcome these challenges a novel protocol Forwarder Selection Energy Efficient Routing(FSE2R)is proposed.Our proposal’s key idea is based on computation of node distance from the sink,Residual Energy(RE)of each node and Signal to Interference Noise Ratio(SINR).The node distance from sink and RE is computed for reliable forwarder node selection and SINR is used for analysis of collision.The novel proposal compares with existing protocols like H2AB,DEEP,and E2LR to achieve Quality of Service(QoS)in terms of through-put,packet delivery ratio and energy consumption.The comparative analysis shows that FSE2R gives on an average 30%less energy consumption,24.62%better PDR and 48.31%less end-to-end delay compared to other protocols.展开更多
The developments of multi-core systems(MCS)have considerably improved the existing technologies in thefield of computer architecture.The MCS comprises several processors that are heterogeneous for resource capacities,...The developments of multi-core systems(MCS)have considerably improved the existing technologies in thefield of computer architecture.The MCS comprises several processors that are heterogeneous for resource capacities,working environments,topologies,and so on.The existing multi-core technology unlocks additional research opportunities for energy minimization by the use of effective task scheduling.At the same time,the task scheduling process is yet to be explored in the multi-core systems.This paper presents a new hybrid genetic algorithm(GA)with a krill herd(KH)based energy-efficient scheduling techni-que for multi-core systems(GAKH-SMCS).The goal of the GAKH-SMCS tech-nique is to derive scheduling tasks in such a way to achieve faster completion time and minimum energy dissipation.The GAKH-SMCS model involves a multi-objectivefitness function using four parameters such as makespan,processor utilization,speedup,and energy consumption to schedule tasks proficiently.The performance of the GAKH-SMCS model has been validated against two datasets namely random dataset and benchmark dataset.The experimental outcome ensured the effectiveness of the GAKH-SMCS model interms of makespan,pro-cessor utilization,speedup,and energy consumption.The overall simulation results depicted that the presented GAKH-SMCS model achieves energy effi-ciency by optimal task scheduling process in MCS.展开更多
Trusted Execution Environment(TEE)is an important part of the security architecture of modern mobile devices,but its secure interaction process brings extra computing burden to mobile devices.This paper takes open por...Trusted Execution Environment(TEE)is an important part of the security architecture of modern mobile devices,but its secure interaction process brings extra computing burden to mobile devices.This paper takes open portable trusted execution environment(OP-TEE)as the research object and deploys it to Raspberry Pi 3B,designs and implements a benchmark for OP-TEE,and analyzes its program characteristics.Furthermore,the application execution time,energy consumption and energy-delay product(EDP)are taken as the optimization objectives,and the central processing unit(CPU)frequency scheduling strategy of mobile devices is dynamically adjusted according to the characteristics of different applications through the combined model.The experimental result shows that compared with the default strategy,the scheduling method proposed in this paper saves 21.18%on average with the Line Regression-Decision Tree scheduling model with the shortest delay as the optimization objective.The Decision Tree-Support Vector Regression(SVR)scheduling model,which takes the lowest energy consumption as the optimization goal,saves 22%energy on average.The Decision Tree-K-Nearest Neighbor(KNN)scheduling model with the lowest EDP as the optimization objective optimizes about 33.9%on average.展开更多
For achieving Energy-Efficiency in wireless sensor networks(WSNs),different schemes have been proposed which focuses only on reducing the energy consumption.A shortest path determines for the Base Station(BS),but faul...For achieving Energy-Efficiency in wireless sensor networks(WSNs),different schemes have been proposed which focuses only on reducing the energy consumption.A shortest path determines for the Base Station(BS),but fault tolerance and energy balancing gives equal importance for improving the network lifetime.For saving energy in WSNs,clustering is considered as one of the effective methods for Wireless Sensor Networks.Because of the excessive overload,more energy consumed by cluster heads(CHs)in a cluster based WSN to receive and aggregate the information from member sensor nodes and it leads to failure.For increasing the WSNs’lifetime,the CHs selection has played a key role in energy consumption for sensor nodes.An Energy Efficient Unequal Fault Tolerant Clustering Approach(EEUFTC)is proposed for reducing the energy utilization through the intelligent methods like Particle Swarm Optimization(PSO).In this approach,an optimal Master Cluster Head(MCH)-Master data Aggregator(MDA),selection method is proposed which uses the fitness values and they evaluate based on the PSO for two optimal nodes in each cluster to act as Master Data Aggregator(MDA),and Master Cluster Head.The data from the cluster members collected by the chosen MCH exclusively and the MDA is used for collected data reception from MCH transmits to the BS.Thus,the MCH overhead reduces.During the heavy communication of data,overhead controls using the scheduling of Energy-Efficient Time Division Multiple Access(EE-TDMA).To describe the proposed method superiority based on various performance metrics,simulation and results are compared to the existing methods.展开更多
Unmanned aerial vehicles(UAVs) are advantageous for data collection in wireless sensor networks(WSNs) due to its low cost of use,flexible deployment,controllable mobility,etc. However,how to cope with the inherent iss...Unmanned aerial vehicles(UAVs) are advantageous for data collection in wireless sensor networks(WSNs) due to its low cost of use,flexible deployment,controllable mobility,etc. However,how to cope with the inherent issues of energy limitation and data security in the WSNs is challenging in such an application paradigm. To this end,based on the framework of physical layer security,an optimization problem for maximizing secrecy energy efficiency(EE) of data collection is formulated,which focuses on optimizing the UAV’s positions and the sensors’ transmit power. To overcome the difficulties in solving the optimization problem,the methods of fractional programming and successive convex approximation are then adopted to gradually transform the original problem into a series of tractable subproblems which are solved in an iterative manner. As shown in simulation results,by the joint designs in the spatial domain of UAV and the power domain of sensors,the proposed algorithm achieves a significant improvement of secrecy EE and rate.展开更多
The Hodgkin–Huxley model assumes independent ion channel activation,although mutual interactions are common in biological systems.This raises the problem why neurons would favor independent over cooperative channel a...The Hodgkin–Huxley model assumes independent ion channel activation,although mutual interactions are common in biological systems.This raises the problem why neurons would favor independent over cooperative channel activation.In this study,we evaluate how cooperative activation of sodium channels affects the neuron’s information processing and energy consumption.Simulations of the stochastic Hodgkin–Huxley model with cooperative activation of sodium channels show that,while cooperative activation enhances neuronal information processing capacity,it greatly increases the neuron’s energy consumption.As a result,cooperative activation of sodium channel degrades the energy efficiency for neuronal information processing.This discovery improves our understanding of the design principles for neural systems,and may provide insights into future designs of the neuromorphic computing devices as well as systematic understanding of pathological mechanisms for neural diseases.展开更多
This paper considers a high energy efficiency dynamic connected(HEDC)structure,which promotes the practicability and reduces the power consumption of hybrid precoding system by lowresolution phase shifters(PSs).Based ...This paper considers a high energy efficiency dynamic connected(HEDC)structure,which promotes the practicability and reduces the power consumption of hybrid precoding system by lowresolution phase shifters(PSs).Based on the proposed structure,a new hybrid precoding algorithm is presented to optimize the energy efficiency,namely,HP-HEDC algorithm.Firstly,via a new defined effective optimal precoding matrix,the problem of optimizing the analog switch precoding matrix is formulated as a sparse representation problem.Thus,the optimal analog switch precoding matrix can be readily obtained by the branch-and-bound method.Then,the digital precoding matrix optimization problem is modeled as a dictionary update problem and solved by the method of optimal direction(MOD).Finally,the diagonal entries of the analog PS precoding matrix are optimized by exhaustive search independently since PS and antenna is one-to-one.Simulation results show that the HEDC structure enjoys low power consumption and satisfactory spectral efficiency.The proposed algorithm presents at least 50%energy efficiency improvement compared with other algorithms when the PS resolution is set as 3-bit.展开更多
The application of Intelligent Internet of Things(IIoT)in constructing distribution station areas strongly supports platform transformation,upgrade,and intelligent integration.The sensing layer of IIoT comprises the e...The application of Intelligent Internet of Things(IIoT)in constructing distribution station areas strongly supports platform transformation,upgrade,and intelligent integration.The sensing layer of IIoT comprises the edge convergence layer and the end sensing layer,with the former using intelligent fusion terminals for real-time data collection and processing.However,the influx of multiple low-voltage in the smart grid raises higher demands for the performance,energy efficiency,and response speed of the substation fusion terminals.Simultaneously,it brings significant security risks to the entire distribution substation,posing a major challenge to the smart grid.In response to these challenges,a proposed dynamic and energy-efficient trust measurement scheme for smart grids aims to address these issues.The scheme begins by establishing a hierarchical trust measurement model,elucidating the trust relationships among smart IoT terminals.It then incorporates multidimensional measurement factors,encompassing static environmental factors,dynamic behaviors,and energy states.This comprehensive approach reduces the impact of subjective factors on trust measurements.Additionally,the scheme incorporates a detection process designed for identifying malicious low-voltage end sensing units,ensuring the prompt identification and elimination of any malicious terminals.This,in turn,enhances the security and reliability of the smart grid environment.The effectiveness of the proposed scheme in pinpointing malicious nodes has been demonstrated through simulation experiments.Notably,the scheme outperforms established trust metric models in terms of energy efficiency,showcasing its significant contribution to the field.展开更多
In recent advancements within wireless sensor networks(WSN),the deployment of unmanned aerial vehicles(UAVs)has emerged as a groundbreaking strategy for enhancing routing efficiency and overall network functionality.T...In recent advancements within wireless sensor networks(WSN),the deployment of unmanned aerial vehicles(UAVs)has emerged as a groundbreaking strategy for enhancing routing efficiency and overall network functionality.This research introduces a sophisticated framework,driven by computational intelligence,that merges clustering techniques with UAV mobility to refine routing strategies in WSNs.The proposed approach divides the sensor field into distinct sectors and implements a novel weighting system for the selection of cluster heads(CHs).This system is primarily aimed at reducing energy consumption through meticulously planned routing and path determination.Employing a greedy algorithm for inter-cluster dialogue,our framework orchestrates CHs into an efficient communication chain.Through comparative analysis,the proposed model demonstrates a marked improvement over traditional methods such as the cluster chain mobile agent routing(CCMAR)and the energy-efficient cluster-based dynamic algorithms(ECCRA).Specifically,it showcases an impressive 15%increase in energy conservation and a 20%reduction in data transmission time,highlighting its advanced performance.Furthermore,this paper investigates the impact of various network parameters on the efficiency and robustness of the WSN,emphasizing the vital role of sophisticated computational strategies in optimizing network operations.展开更多
基金The authors are thankful to the Deanship of Scientific Research at Najran University for funding this work under the Research Collaboration Funding program Grant Code NU/RC/SERC/11/7.
文摘In every network,delay and energy are crucial for communication and network life.In wireless sensor networks,many tiny nodes create networks with high energy consumption and compute routes for better communication.Wireless Sensor Networks(WSN)is a very complex scenario to compute minimal delay with data aggregation and energy efficiency.In this research,we compute minimal delay and energy efficiency for improving the quality of service of any WSN.The proposed work is based on energy and distance parameters as taken dependent variables with data aggregation.Data aggregation performs on different models,namely Hybrid-Low Energy Adaptive Clustering Hierarchy(H-LEACH),Low Energy Adaptive Clustering Hierarchy(LEACH),and Multi-Aggregator-based Multi-Cast(MAMC).The main contribution of this research is to a reduction in delay and optimized energy solution,a novel hybrid model design in this research that ensures the quality of service in WSN.This model includes a whale optimization technique that involves heterogeneous functions and performs optimization to reach optimized results.For cluster head selection,Stable Election Protocol(SEP)protocol is used and Power-Efficient Gathering in Sensor Information Systems(PEGASIS)is used for driven-path in routing.Simulation results evaluate that H-LEACH provides minimal delay and energy consumption by sensor nodes.In the comparison of existing theories and our proposed method,HLEACH is providing energy and delay reduction and improvement in quality of service.MATLAB 2019 is used for simulation work.
文摘The accelerated development of wireless network technology has resulted in the emergence of Wireless Body Area Network(WBAN),which is a technology commonly used in the medical field.WBAN consists of tiny sensor nodes that interconnect with each other and set in the human body to collect and transmit the patient data to the physician,to monitor the patients remotely.These nodes typically have limited battery energy that led to a shortage of network lifetime.Therefore,energy efficiency is considered one of the most demanding challenges in routing design for WBAN.Many proposed routing mechanisms inWBAN did not cover the source node energy and energy harvesting techniques.Therefore,this study proposes an Efficient Energy Aware Routing(EEAR)mechanism.This paper constructs a path cost function that considers three parameters:residual energy,number of hops to the sink,and the distance between the nodes.Besides,data aggregationwith filtration and hybrid energy harvesting technique are used to extend the network lifetime,reduce the network traffic load,andmaintain the source node energy.Extensive simulations using MATLAB have been performed to evaluate the performance of the proposed mechanism.EEAR is contrasted with the two latest schemes,called Priority-based Congestion-avoidance Routing Protocol(PCRP)and Energy Efficient Routing Protocol(EERP).The results show the significant performance of theEEARmechanism in terms of network lifetime,residual energy,network stability,and throughput.
文摘In cloud environment,an efficient resource management establishes the allocation of computational resources of cloud service providers to the requests of users for meeting the user’s demands.The proficient resource management and work allocation determines the accomplishment of the cloud infrastructure.However,it is very difficult to persuade the objectives of the Cloud Service Providers(CSPs)and end users in an impulsive cloud domain with random changes of workloads,huge resource availability and complicated service policies to handle them,With that note,this paper attempts to present an Efficient Energy-Aware Resource Management Model(EEARMM)that works in a decentralized manner.Moreover,the model involves in reducing the number of migrations by definite workload management for efficient resource utilization.That is,it makes an effort to reduce the amount of physical devices utilized for load balancing with certain resource and energy consumption management of every machine.The Estimation Model Algorithm(EMA)is given for determining the virtual machine migration.Further,VM-Selection Algorithm(SA)is also provided for choosing the appropriate VM to migrate for resource management.By the incorporation of these algorithms,overloading of VM instances can be avoided and energy efficiency can be improved considerably.The performance evaluation and comparative analysis,based on the dynamic workloads in different factors provides evidence to the efficiency,feasibility and scalability of the proposed model in cloud domain with high rate of resources and workload management.
文摘To solve the problem of energy efficiency of modern enterprise it is necessary to reduce energy consumption.One of the possible ways is proposed in this research.A multi-level hierarchical system for energy efficiency management of the enterprise is designed,it is based on the modular principle providing rapid modernization.The novelty of the work is the development of new and improvement of the existing methods and models,in particular:1)models for dynamic analysis of IT tools for data acquisition and processing(DAAP)in multilevel energy management systems,which are based on Petri nets;2)method of synthesis of DAAP tools in energy efficiency management information systems(EEMIS)of the enterprise which provides a reduction in hardware and time costs from 10%to 40%;3)method of conflict-free data exchange determining the minimum memory speed for the synthesis of realtime exchanges,it reduces the cost and energy consumption;4)method of calculating the signal of postsynaptic excitation of neural elements decreases the processing time of technological data two or more times.The proposed methods,models and tools have been tested while implementing the EEMIS of the intelligent mini-greenhouse,as a result,energy efficiency has increased by 12%-25%(depending on season and peculiarities of growing plants).
基金This work was supported by the Basic Science Research Program through the NationalResearch Foundation ofKorea(NRF)funded by the Ministry of Education under Grant RS-2023-00237300 and Korea Institute of Planning and Evaluation for Technology in Food,Agriculture and Forestry(IPET)through the Agriculture and Food Convergence Technologies Program for Research Manpower Development,funded by Ministry of Agriculture,Food and Rural Affairs(MAFRA)(Project No.RS-2024-00397026).
文摘The seamless integration of intelligent Internet of Things devices with conventional wireless sensor networks has revolutionized data communication for different applications,such as remote health monitoring,industrial monitoring,transportation,and smart agriculture.Efficient and reliable data routing is one of the major challenges in the Internet of Things network due to the heterogeneity of nodes.This paper presents a traffic-aware,cluster-based,and energy-efficient routing protocol that employs traffic-aware and cluster-based techniques to improve the data delivery in such networks.The proposed protocol divides the network into clusters where optimal cluster heads are selected among super and normal nodes based on their residual energies.The protocol considers multi-criteria attributes,i.e.,energy,traffic load,and distance parameters to select the next hop for data delivery towards the base station.The performance of the proposed protocol is evaluated through the network simulator NS3.40.For different traffic rates,number of nodes,and different packet sizes,the proposed protocol outperformed LoRaWAN in terms of end-to-end packet delivery ratio,energy consumption,end-to-end delay,and network lifetime.For 100 nodes,the proposed protocol achieved a 13%improvement in packet delivery ratio,10 ms improvement in delay,and 10 mJ improvement in average energy consumption over LoRaWAN.
文摘As the demands of massive connections and vast coverage rapidly grow in the next wireless communication networks, rate splitting multiple access(RSMA) is considered to be the new promising access scheme since it can provide higher efficiency with limited spectrum resources. In this paper, combining spectrum splitting with rate splitting, we propose to allocate resources with traffic offloading in hybrid satellite terrestrial networks. A novel deep reinforcement learning method is adopted to solve this challenging non-convex problem. However, the neverending learning process could prohibit its practical implementation. Therefore, we introduce the switch mechanism to avoid unnecessary learning. Additionally, the QoS constraint in the scheme can rule out unsuccessful transmission. The simulation results validates the energy efficiency performance and the convergence speed of the proposed algorithm.
文摘Wireless Body Area Network(WBAN)is a cutting-edge technology that is being used in healthcare applications to monitor critical events in the human body.WBAN is a collection of in-body and on-body sensors that monitor human physical parameters such as temperature,blood pressure,pulse rate,oxygen level,body motion,and so on.They sense the data and communicate it to the Body Area Network(BAN)Coordinator.The main challenge for the WBAN is energy consumption.These issues can be addressed by implementing an effective Medium Access Control(MAC)protocol that reduces energy consumption and increases network lifetime.The purpose of the study is to minimize the energy consumption and minimize the delay using IEEE 802.15.4 standard.In our proposed work,if any critical events have occurred the proposed work is to classify and prioritize the data.We gave priority to the highly critical data to get the Guarantee Tine Slots(GTS)in IEEE 802.15.4 standard superframe to achieve greater energy efficiency.The proposed MAC provides higher data rates for critical data based on the history and current condition and also provides the best reliable service to high critical data and critical data by predicting node similarity.As an outcome,we proposed a MAC protocol for Variable Data Rates(MVDR).When compared to existing MAC protocols,the MVDR performed very well with low energy intake,less interruption,and an enhanced packet-sharing ratio.
文摘There is a huge amount of energy savings potential in public building sector that has yet to be realized.By prioritizing energy efficiency in its own buildings and thus promoting the development of required knowledge in terms of new technology and construction methods,the public sector will lead the way in efforts to increase the rate of renovations.The low-cost insulation strategies and a comparison of cost with existing insulation materials has been described in this study.We have repeatedly faced energy crises and will continue to do so in the future if appropriate action is not taken in a timely manner.Properly implementing energy-saving initiatives in for achieving thermal comfort in buildings as well as reducing the energy costs would undoubtedly inspire the residential sector,resulting in significant reductions in energy usage.Simulations were carried out to study insulation layers on various building components like exterior walls,floor and roofs,generating different scenarios for a building as a base model,which were then compared and analysed to verify the literature used to develop the cases.The proposed recommendations,which have been validated,are certain to increase building energy efficiency,achieve thermal comfort in low cost than what is currently being used.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Large Groups Project under grant number(235/44)Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2023R114)+1 种基金Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4310373DSR71)This study is supported via funding from Prince Sattam bin Abdulaziz University project number(PSAU/2023/R/1444).
文摘With the flexible deployment and high mobility of Unmanned Aerial Vehicles(UAVs)in an open environment,they have generated con-siderable attention in military and civil applications intending to enable ubiquitous connectivity and foster agile communications.The difficulty stems from features other than mobile ad-hoc network(MANET),namely aerial mobility in three-dimensional space and often changing topology.In the UAV network,a single node serves as a forwarding,transmitting,and receiving node at the same time.Typically,the communication path is multi-hop,and routing significantly affects the network’s performance.A lot of effort should be invested in performance analysis for selecting the optimum routing system.With this motivation,this study modelled a new Coati Optimization Algorithm-based Energy-Efficient Routing Process for Unmanned Aerial Vehicle Communication(COAER-UAVC)technique.The presented COAER-UAVC technique establishes effective routes for communication between the UAVs.It is primarily based on the coati characteristics in nature:if attacking and hunting iguanas and escaping from predators.Besides,the presented COAER-UAVC technique concentrates on the design of fitness functions to minimize energy utilization and communication delay.A varied group of simulations was performed to depict the optimum performance of the COAER-UAVC system.The experimental results verified that the COAER-UAVC technique had assured improved performance over other approaches.
基金This work was financially supported by the National Natural Science Foundation of China(52074089 and 52104064)Natural Science Foundation of Heilongjiang Province of China(LH2019E019).
文摘As the main link of ground engineering,crude oil gathering and transportation systems require huge energy consumption and complex structures.It is necessary to establish an energy efficiency evaluation system for crude oil gathering and transportation systems and identify the energy efficiency gaps.In this paper,the energy efficiency evaluation system of the crude oil gathering and transportation system in an oilfield in western China is established.Combined with the big data analysis method,the GA-BP neural network is used to establish the energy efficiency index prediction model for crude oil gathering and transportation systems.The comprehensive energy consumption,gas consumption,power consumption,energy utilization rate,heat utilization rate,and power utilization rate of crude oil gathering and transportation systems are predicted.Considering the efficiency and unit consumption index of the crude oil gathering and transportation system,the energy efficiency evaluation system of the crude oil gathering and transportation system is established based on a game theory combined weighting method and TOPSIS evaluation method,and the subjective weight is determined by the triangular fuzzy analytic hierarchy process.The entropy weight method determines the objective weight,and the combined weight of game theory combines subjectivity with objectivity to comprehensively evaluate the comprehensive energy efficiency of crude oil gathering and transportation systems and their subsystems.Finally,the weak links in energy utilization are identified,and energy conservation and consumption reduction are improved.The above research provides technical support for the green,efficient and intelligent development of crude oil gathering and transportation systems.
基金supported by the National Natural Science Foundation of China(62172089,61972087,62172090).
文摘Virtual machine(VM)consolidation is an effective way to improve resource utilization and reduce energy consumption in cloud data centers.Most existing studies have considered VM consolidation as a bin-packing problem,but the current schemes commonly ignore the long-term relationship between VMs and hosts.In addition,there is a lack of long-term consideration for resource optimization in the VM consolidation,which results in unnecessary VM migration and increased energy consumption.To address these limitations,a VM consolidation method based on multi-step prediction and affinity-aware technique for energy-efficient cloud data centers(MPaAF-VMC)is proposed.The proposed method uses an improved linear regression prediction algorithm to predict the next-moment resource utilization of hosts and VMs,and obtains the stage demand of resources in the future period through multi-step prediction,which is realized by iterative prediction.Then,based on the multi-step prediction,an affinity model between the VM and host is designed using the first-order correlation coefficient and Euclidean distance.During the VM consolidation,the affinity value is used to select the migration VM and placement host.The proposed method is compared with the existing consolidation algorithms on the PlanetLab and Google cluster real workload data using the CloudSim simulation platform.Experimental results show that the proposed method can achieve significant improvement in reducing energy consumption,VM migration costs,and service level agreement(SLA)violations.
基金The authors would like to thank for the support from Taif University Researchers Supporting Project number(TURSP-2020/10),Taif University,Taif,Saudi Arabia.
文摘The 3D Underwater Sensor Network(USNs)has become the most optimistic medium for tracking and monitoring underwater environment.Energy and collision are two most critical factors in USNs for both sparse and dense regions.Due to harsh ocean environment,it is a challenge to design a reliable energy efficient with collision free protocol.Diversity in link qualities may cause collision and frequent communication lead to energy loss;that effects the network performance.To overcome these challenges a novel protocol Forwarder Selection Energy Efficient Routing(FSE2R)is proposed.Our proposal’s key idea is based on computation of node distance from the sink,Residual Energy(RE)of each node and Signal to Interference Noise Ratio(SINR).The node distance from sink and RE is computed for reliable forwarder node selection and SINR is used for analysis of collision.The novel proposal compares with existing protocols like H2AB,DEEP,and E2LR to achieve Quality of Service(QoS)in terms of through-put,packet delivery ratio and energy consumption.The comparative analysis shows that FSE2R gives on an average 30%less energy consumption,24.62%better PDR and 48.31%less end-to-end delay compared to other protocols.
基金supported by Taif University Researchers Supporting Program(Project Number:TURSP-2020/195)Taif University,Saudi Arabia.Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R203)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The developments of multi-core systems(MCS)have considerably improved the existing technologies in thefield of computer architecture.The MCS comprises several processors that are heterogeneous for resource capacities,working environments,topologies,and so on.The existing multi-core technology unlocks additional research opportunities for energy minimization by the use of effective task scheduling.At the same time,the task scheduling process is yet to be explored in the multi-core systems.This paper presents a new hybrid genetic algorithm(GA)with a krill herd(KH)based energy-efficient scheduling techni-que for multi-core systems(GAKH-SMCS).The goal of the GAKH-SMCS tech-nique is to derive scheduling tasks in such a way to achieve faster completion time and minimum energy dissipation.The GAKH-SMCS model involves a multi-objectivefitness function using four parameters such as makespan,processor utilization,speedup,and energy consumption to schedule tasks proficiently.The performance of the GAKH-SMCS model has been validated against two datasets namely random dataset and benchmark dataset.The experimental outcome ensured the effectiveness of the GAKH-SMCS model interms of makespan,pro-cessor utilization,speedup,and energy consumption.The overall simulation results depicted that the presented GAKH-SMCS model achieves energy effi-ciency by optimal task scheduling process in MCS.
基金funded by National Key Research and Development Program of China under Grant No.2019YFC1520904 from January 2020 to April 2023funded by Shaanxi Innovation Program under Grant 2023-CX-TD-04 January 2023 to December 2025.
文摘Trusted Execution Environment(TEE)is an important part of the security architecture of modern mobile devices,but its secure interaction process brings extra computing burden to mobile devices.This paper takes open portable trusted execution environment(OP-TEE)as the research object and deploys it to Raspberry Pi 3B,designs and implements a benchmark for OP-TEE,and analyzes its program characteristics.Furthermore,the application execution time,energy consumption and energy-delay product(EDP)are taken as the optimization objectives,and the central processing unit(CPU)frequency scheduling strategy of mobile devices is dynamically adjusted according to the characteristics of different applications through the combined model.The experimental result shows that compared with the default strategy,the scheduling method proposed in this paper saves 21.18%on average with the Line Regression-Decision Tree scheduling model with the shortest delay as the optimization objective.The Decision Tree-Support Vector Regression(SVR)scheduling model,which takes the lowest energy consumption as the optimization goal,saves 22%energy on average.The Decision Tree-K-Nearest Neighbor(KNN)scheduling model with the lowest EDP as the optimization objective optimizes about 33.9%on average.
基金The authors would like to thank for the support from Taif University Researchers Supporting Project number(TURSP-2020/239),Taif University,Taif,Saudi Arabia.
文摘For achieving Energy-Efficiency in wireless sensor networks(WSNs),different schemes have been proposed which focuses only on reducing the energy consumption.A shortest path determines for the Base Station(BS),but fault tolerance and energy balancing gives equal importance for improving the network lifetime.For saving energy in WSNs,clustering is considered as one of the effective methods for Wireless Sensor Networks.Because of the excessive overload,more energy consumed by cluster heads(CHs)in a cluster based WSN to receive and aggregate the information from member sensor nodes and it leads to failure.For increasing the WSNs’lifetime,the CHs selection has played a key role in energy consumption for sensor nodes.An Energy Efficient Unequal Fault Tolerant Clustering Approach(EEUFTC)is proposed for reducing the energy utilization through the intelligent methods like Particle Swarm Optimization(PSO).In this approach,an optimal Master Cluster Head(MCH)-Master data Aggregator(MDA),selection method is proposed which uses the fitness values and they evaluate based on the PSO for two optimal nodes in each cluster to act as Master Data Aggregator(MDA),and Master Cluster Head.The data from the cluster members collected by the chosen MCH exclusively and the MDA is used for collected data reception from MCH transmits to the BS.Thus,the MCH overhead reduces.During the heavy communication of data,overhead controls using the scheduling of Energy-Efficient Time Division Multiple Access(EE-TDMA).To describe the proposed method superiority based on various performance metrics,simulation and results are compared to the existing methods.
基金Supported by the National Natural Science Foundation of China(No.61871401).
文摘Unmanned aerial vehicles(UAVs) are advantageous for data collection in wireless sensor networks(WSNs) due to its low cost of use,flexible deployment,controllable mobility,etc. However,how to cope with the inherent issues of energy limitation and data security in the WSNs is challenging in such an application paradigm. To this end,based on the framework of physical layer security,an optimization problem for maximizing secrecy energy efficiency(EE) of data collection is formulated,which focuses on optimizing the UAV’s positions and the sensors’ transmit power. To overcome the difficulties in solving the optimization problem,the methods of fractional programming and successive convex approximation are then adopted to gradually transform the original problem into a series of tractable subproblems which are solved in an iterative manner. As shown in simulation results,by the joint designs in the spatial domain of UAV and the power domain of sensors,the proposed algorithm achieves a significant improvement of secrecy EE and rate.
基金supported by the Fundamental Research Funds for the Central Universities(Grant No.lzujbky-2021-62)the Shanghai Municipal Science and Technology Major Project(Grant No.2018SHZDZX01)Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence(LCNBI)and ZJLab,and the National Natural Science Foundation of China(Grant No.12247101).
文摘The Hodgkin–Huxley model assumes independent ion channel activation,although mutual interactions are common in biological systems.This raises the problem why neurons would favor independent over cooperative channel activation.In this study,we evaluate how cooperative activation of sodium channels affects the neuron’s information processing and energy consumption.Simulations of the stochastic Hodgkin–Huxley model with cooperative activation of sodium channels show that,while cooperative activation enhances neuronal information processing capacity,it greatly increases the neuron’s energy consumption.As a result,cooperative activation of sodium channel degrades the energy efficiency for neuronal information processing.This discovery improves our understanding of the design principles for neural systems,and may provide insights into future designs of the neuromorphic computing devices as well as systematic understanding of pathological mechanisms for neural diseases.
基金supported by the National Natural Science Foundation of China(Grant No.61971117)the Natural Science Foundation of Hebei Province(Grant No.F2020501007)the S&T Program of Hebei(No.22377717D)。
文摘This paper considers a high energy efficiency dynamic connected(HEDC)structure,which promotes the practicability and reduces the power consumption of hybrid precoding system by lowresolution phase shifters(PSs).Based on the proposed structure,a new hybrid precoding algorithm is presented to optimize the energy efficiency,namely,HP-HEDC algorithm.Firstly,via a new defined effective optimal precoding matrix,the problem of optimizing the analog switch precoding matrix is formulated as a sparse representation problem.Thus,the optimal analog switch precoding matrix can be readily obtained by the branch-and-bound method.Then,the digital precoding matrix optimization problem is modeled as a dictionary update problem and solved by the method of optimal direction(MOD).Finally,the diagonal entries of the analog PS precoding matrix are optimized by exhaustive search independently since PS and antenna is one-to-one.Simulation results show that the HEDC structure enjoys low power consumption and satisfactory spectral efficiency.The proposed algorithm presents at least 50%energy efficiency improvement compared with other algorithms when the PS resolution is set as 3-bit.
基金This project is partly funded by Science and Technology Project of State Grid Zhejiang Electric Power Co.,Ltd.“Research on active Security Defense Strategies for Distribution Internet of Things Based on Trustworthy,under Grant No.5211DS22000G”.
文摘The application of Intelligent Internet of Things(IIoT)in constructing distribution station areas strongly supports platform transformation,upgrade,and intelligent integration.The sensing layer of IIoT comprises the edge convergence layer and the end sensing layer,with the former using intelligent fusion terminals for real-time data collection and processing.However,the influx of multiple low-voltage in the smart grid raises higher demands for the performance,energy efficiency,and response speed of the substation fusion terminals.Simultaneously,it brings significant security risks to the entire distribution substation,posing a major challenge to the smart grid.In response to these challenges,a proposed dynamic and energy-efficient trust measurement scheme for smart grids aims to address these issues.The scheme begins by establishing a hierarchical trust measurement model,elucidating the trust relationships among smart IoT terminals.It then incorporates multidimensional measurement factors,encompassing static environmental factors,dynamic behaviors,and energy states.This comprehensive approach reduces the impact of subjective factors on trust measurements.Additionally,the scheme incorporates a detection process designed for identifying malicious low-voltage end sensing units,ensuring the prompt identification and elimination of any malicious terminals.This,in turn,enhances the security and reliability of the smart grid environment.The effectiveness of the proposed scheme in pinpointing malicious nodes has been demonstrated through simulation experiments.Notably,the scheme outperforms established trust metric models in terms of energy efficiency,showcasing its significant contribution to the field.
基金supported in part by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(NRF-2021R1A6A1A03039493)in part by the NRF Grant funded by the Korea government(MSIT)(NRF-2022R1A2C1004401).
文摘In recent advancements within wireless sensor networks(WSN),the deployment of unmanned aerial vehicles(UAVs)has emerged as a groundbreaking strategy for enhancing routing efficiency and overall network functionality.This research introduces a sophisticated framework,driven by computational intelligence,that merges clustering techniques with UAV mobility to refine routing strategies in WSNs.The proposed approach divides the sensor field into distinct sectors and implements a novel weighting system for the selection of cluster heads(CHs).This system is primarily aimed at reducing energy consumption through meticulously planned routing and path determination.Employing a greedy algorithm for inter-cluster dialogue,our framework orchestrates CHs into an efficient communication chain.Through comparative analysis,the proposed model demonstrates a marked improvement over traditional methods such as the cluster chain mobile agent routing(CCMAR)and the energy-efficient cluster-based dynamic algorithms(ECCRA).Specifically,it showcases an impressive 15%increase in energy conservation and a 20%reduction in data transmission time,highlighting its advanced performance.Furthermore,this paper investigates the impact of various network parameters on the efficiency and robustness of the WSN,emphasizing the vital role of sophisticated computational strategies in optimizing network operations.