Wireless sensor networks (WSNs) are mainly characterized by their limited and non-replenishable energy supply. Hence, the energy efficiency of the infrastructure greatly affects the network lifetime. Clustering is one...Wireless sensor networks (WSNs) are mainly characterized by their limited and non-replenishable energy supply. Hence, the energy efficiency of the infrastructure greatly affects the network lifetime. Clustering is one of the methods that can expand the lifespan of the whole network by grouping the sensor nodes according to some criteria and choosing the appropriate cluster heads(CHs). The balanced load of the CHs has an important effect on the energy consumption balancing and lifespan of the whole network. Therefore, a new CHs election method is proposed using an adaptive discrete particle swarm optimization (ADPSO) algorithm with a fitness value function considering the load balancing and energy consumption. Simulation results not only demonstrate that the proposed algorithm can have better performance in load balancing than low-energy adaptive clustering hierarchy (LEACH), hybrid energy-efficient distributed clustering (HEED), and dynamic clustering algorithm with balanced load (DCBL), but also imply that the proposed algorithm can extend the network lifetime more.展开更多
Wireless Sensor Network(WSN)consists of a group of limited energy source sensors that are installed in a particular region to collect data from the environment.Designing the energy-efficient data collection methods in...Wireless Sensor Network(WSN)consists of a group of limited energy source sensors that are installed in a particular region to collect data from the environment.Designing the energy-efficient data collection methods in largescale wireless sensor networks is considered to be a difficult area in the research.Sensor node clustering is a popular approach for WSN.Moreover,the sensor nodes are grouped to form clusters in a cluster-based WSN environment.The battery performance of the sensor nodes is likewise constrained.As a result,the energy efficiency of WSNs is critical.In specific,the energy usage is influenced by the loads on the sensor node as well as it ranges from the Base Station(BS).Therefore,energy efficiency and load balancing are very essential in WSN.In the proposed method,a novel Grey Wolf Improved Particle Swarm Optimization with Tabu Search Techniques(GW-IPSO-TS)was used.The selection of Cluster Heads(CHs)and routing path of every CH from the base station is enhanced by the proposed method.It provides the best routing path and increases the lifetime and energy efficiency of the network.End-to-end delay and packet loss rate have also been improved.The proposed GW-IPSO-TS method enhances the evaluation of alive nodes,dead nodes,network survival index,convergence rate,and standard deviation of sensor nodes.Compared to the existing algorithms,the proposed method outperforms better and improves the lifetime of the network.展开更多
The tremendous advancement in distributed computing and Internet of Things(IoT)applications has resulted in the adoption of fog computing as today’s widely used framework complementing cloud computing.Thus,suitable a...The tremendous advancement in distributed computing and Internet of Things(IoT)applications has resulted in the adoption of fog computing as today’s widely used framework complementing cloud computing.Thus,suitable and effective applications could be performed to satisfy the applications’latency requirement.Resource allocation techniques are essential aspects of fog networks which prevent unbalanced load distribution.Effective resource management techniques can improve the quality of service metrics.Due to the limited and heterogeneous resources available within the fog infrastructure,the fog layer’s resources need to be optimised to efficiently manage and distribute them to different applications within the IoT net-work.There has been limited research on resource management strategies in fog networks in recent years,and a limited systematic review has been done to compile these studies.This article focuses on current developments in resource allocation strategies for fog-IoT networks.A systematic review of resource allocation techniques with the key objective of enhancing QoS is provided.Steps involved in conducting this systematic literature review include developing research goals,accessing studies,categorizing and critically analysing the studies.The resource management approaches engaged in this article are load balancing and task offloading techniques.For the load balancing approach,a brief survey of recent work done according to their sub-categories,including stochastic,probabilistic/statistic,graph theory and hybrid techniques is provided whereas for task offloading,the survey is performed according to the destination of task offloading.Efficient load balancing and task-offloading approaches contribute significantly to resource management,and tremendous effort has been put into this critical topic.Thus,this survey presents an overview of these extents and a comparative analysis.Finally,the study discusses ongoing research issues and potential future directions for developing effective management res展开更多
Research interest in sensor networks routing largely considers minimization of energy consumption as a major performance criterion to provide maximum sensors network lifetime. When considering energy conservation, rou...Research interest in sensor networks routing largely considers minimization of energy consumption as a major performance criterion to provide maximum sensors network lifetime. When considering energy conservation, routing protocols should also be designed to achieve fault tolerance in communications. Moreover, due to dynamic topology and random deployment, incorporating reliability into protocols for WSNs is very important. Hence, we propose an improved scalable clustering-based load balancing scheme (SCLB) in this paper. In SCLB scheme, scalability is achieved by dividing the network into overlapping multihop clusters each with its own cluster head node. Simulation results show that the proposed scheme achieves longer network lifetime with desirable reliability at the initial state compare with the existing multihop load balancing approach.展开更多
Purpose-Current industrial scenario is largely dependent on cloud computing paradigms.On-demand services provided by cloud data centre are paid as per use.Hence,it is very important to make use of the allocated resour...Purpose-Current industrial scenario is largely dependent on cloud computing paradigms.On-demand services provided by cloud data centre are paid as per use.Hence,it is very important to make use of the allocated resources to the maximum.The resource utilization is highly dependent on the allocation of resources to the incoming request.The allocation of requests is done with respect to the physical machines present in the datacenter.While allocating the tasks to these physical machines,it needs to be allocated in such a way that no physical machine is underutilized or over loaded.To make sure of this,optimal load balancing is very important.Design/methodology/approach-The paper proposes an algorithm which makes use of the fitness functions and duopoly game theory to allocate the tasks to the physical machines which can handle the resource requirement of the incoming tasks.The major focus of the proposed work is to optimize the load balancing in a datacenter.When optimization happens,none of the physical machine is neither overloaded nor under-utilized,hence resulting in efficient utilization of the resources.Findings-The performance of the proposed algorithm is compared with different existing load balancing algorithms such as round-robin load(RR)ant colony optimization(ACO),artificial bee colony(ABC)with respect to the selected parameters response time,virtual machine migrations,host shut down and energy consumption.All the four parameters gave a positive result when the algorithm is simulated.Originality/value-The contribution of this paper is towards the domain of cloud load balancing.The paper is proposing a novel approach to optimize the cloud load balancing process.The results obtained show that response time,virtual machine migrations,host shut down and energy consumption are reduced in comparison to few of the existing algorithms selected for the study.The proposed algorithm based on the duopoly function and fitness function brings in an optimized performance compared to the four algorithms analysed.展开更多
基金National Natural Science Foundations of China(No. 61103175,No. 11141005)Technology Innovation Platform Project of Fujian Province,China (No. 2009J1007)+1 种基金Key Project Development Foundation of Education Committee of Fujian Province,China (No.JA11011)Project Development Foundations of Fuzhou University,China (No. 2010-XQ-21,No. XRC-1037)
文摘Wireless sensor networks (WSNs) are mainly characterized by their limited and non-replenishable energy supply. Hence, the energy efficiency of the infrastructure greatly affects the network lifetime. Clustering is one of the methods that can expand the lifespan of the whole network by grouping the sensor nodes according to some criteria and choosing the appropriate cluster heads(CHs). The balanced load of the CHs has an important effect on the energy consumption balancing and lifespan of the whole network. Therefore, a new CHs election method is proposed using an adaptive discrete particle swarm optimization (ADPSO) algorithm with a fitness value function considering the load balancing and energy consumption. Simulation results not only demonstrate that the proposed algorithm can have better performance in load balancing than low-energy adaptive clustering hierarchy (LEACH), hybrid energy-efficient distributed clustering (HEED), and dynamic clustering algorithm with balanced load (DCBL), but also imply that the proposed algorithm can extend the network lifetime more.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Larg Groups project Under Grant Number(71/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R238)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:22UQU4340237DSR20.
文摘Wireless Sensor Network(WSN)consists of a group of limited energy source sensors that are installed in a particular region to collect data from the environment.Designing the energy-efficient data collection methods in largescale wireless sensor networks is considered to be a difficult area in the research.Sensor node clustering is a popular approach for WSN.Moreover,the sensor nodes are grouped to form clusters in a cluster-based WSN environment.The battery performance of the sensor nodes is likewise constrained.As a result,the energy efficiency of WSNs is critical.In specific,the energy usage is influenced by the loads on the sensor node as well as it ranges from the Base Station(BS).Therefore,energy efficiency and load balancing are very essential in WSN.In the proposed method,a novel Grey Wolf Improved Particle Swarm Optimization with Tabu Search Techniques(GW-IPSO-TS)was used.The selection of Cluster Heads(CHs)and routing path of every CH from the base station is enhanced by the proposed method.It provides the best routing path and increases the lifetime and energy efficiency of the network.End-to-end delay and packet loss rate have also been improved.The proposed GW-IPSO-TS method enhances the evaluation of alive nodes,dead nodes,network survival index,convergence rate,and standard deviation of sensor nodes.Compared to the existing algorithms,the proposed method outperforms better and improves the lifetime of the network.
基金The project was funded under Grant of the Fundamental Research Grant Scheme Malaysia Higher Education:FRGS/1/2019/ICT03/UITM/03/1.
文摘The tremendous advancement in distributed computing and Internet of Things(IoT)applications has resulted in the adoption of fog computing as today’s widely used framework complementing cloud computing.Thus,suitable and effective applications could be performed to satisfy the applications’latency requirement.Resource allocation techniques are essential aspects of fog networks which prevent unbalanced load distribution.Effective resource management techniques can improve the quality of service metrics.Due to the limited and heterogeneous resources available within the fog infrastructure,the fog layer’s resources need to be optimised to efficiently manage and distribute them to different applications within the IoT net-work.There has been limited research on resource management strategies in fog networks in recent years,and a limited systematic review has been done to compile these studies.This article focuses on current developments in resource allocation strategies for fog-IoT networks.A systematic review of resource allocation techniques with the key objective of enhancing QoS is provided.Steps involved in conducting this systematic literature review include developing research goals,accessing studies,categorizing and critically analysing the studies.The resource management approaches engaged in this article are load balancing and task offloading techniques.For the load balancing approach,a brief survey of recent work done according to their sub-categories,including stochastic,probabilistic/statistic,graph theory and hybrid techniques is provided whereas for task offloading,the survey is performed according to the destination of task offloading.Efficient load balancing and task-offloading approaches contribute significantly to resource management,and tremendous effort has been put into this critical topic.Thus,this survey presents an overview of these extents and a comparative analysis.Finally,the study discusses ongoing research issues and potential future directions for developing effective management res
文摘Research interest in sensor networks routing largely considers minimization of energy consumption as a major performance criterion to provide maximum sensors network lifetime. When considering energy conservation, routing protocols should also be designed to achieve fault tolerance in communications. Moreover, due to dynamic topology and random deployment, incorporating reliability into protocols for WSNs is very important. Hence, we propose an improved scalable clustering-based load balancing scheme (SCLB) in this paper. In SCLB scheme, scalability is achieved by dividing the network into overlapping multihop clusters each with its own cluster head node. Simulation results show that the proposed scheme achieves longer network lifetime with desirable reliability at the initial state compare with the existing multihop load balancing approach.
文摘Purpose-Current industrial scenario is largely dependent on cloud computing paradigms.On-demand services provided by cloud data centre are paid as per use.Hence,it is very important to make use of the allocated resources to the maximum.The resource utilization is highly dependent on the allocation of resources to the incoming request.The allocation of requests is done with respect to the physical machines present in the datacenter.While allocating the tasks to these physical machines,it needs to be allocated in such a way that no physical machine is underutilized or over loaded.To make sure of this,optimal load balancing is very important.Design/methodology/approach-The paper proposes an algorithm which makes use of the fitness functions and duopoly game theory to allocate the tasks to the physical machines which can handle the resource requirement of the incoming tasks.The major focus of the proposed work is to optimize the load balancing in a datacenter.When optimization happens,none of the physical machine is neither overloaded nor under-utilized,hence resulting in efficient utilization of the resources.Findings-The performance of the proposed algorithm is compared with different existing load balancing algorithms such as round-robin load(RR)ant colony optimization(ACO),artificial bee colony(ABC)with respect to the selected parameters response time,virtual machine migrations,host shut down and energy consumption.All the four parameters gave a positive result when the algorithm is simulated.Originality/value-The contribution of this paper is towards the domain of cloud load balancing.The paper is proposing a novel approach to optimize the cloud load balancing process.The results obtained show that response time,virtual machine migrations,host shut down and energy consumption are reduced in comparison to few of the existing algorithms selected for the study.The proposed algorithm based on the duopoly function and fitness function brings in an optimized performance compared to the four algorithms analysed.
