Mobile edge computing （MEC） is a novel technique that can reduce mobiles＇ com- putational burden by tasks offioading, which emerges as a promising paradigm to provide computing capabilities in close proximity to mo...Mobile edge computing （MEC） is a novel technique that can reduce mobiles＇ com- putational burden by tasks offioading, which emerges as a promising paradigm to provide computing capabilities in close proximity to mobile users. In this paper, we will study the scenario where multiple mobiles upload tasks to a MEC server in a sing cell, and allocating the limited server resources and wireless chan- nels between mobiles becomes a challenge. We formulate the optimization problem for the energy saved on mobiles with the tasks being dividable, and utilize a greedy choice to solve the problem. A Select Maximum Saved Energy First （SMSEF） algorithm is proposed to realize the solving process. We examined the saved energy at different number of nodes and channels, and the results show that the proposed scheme can effectively help mobiles to save energy in the MEC system.展开更多
Selecting appropriate resources for running a job efficiently is one of the common objectives in a computational grid. Resource scheduling should consider the specific characteristics of the application, and decide th...Selecting appropriate resources for running a job efficiently is one of the common objectives in a computational grid. Resource scheduling should consider the specific characteristics of the application, and decide the metrics to be used accordingly. This paper presents a distributed resource scheduling framework mainly consisting of a job scheduler and a local scheduler. In order to meet the requirements of different applications, we adopt HGSA, a Heuristic-based Greedy Scheduling Algorithm, to schedule jobs in the grid, where the heuristic knowledge is the metric weights of the computing resources and the metric workload impact factors. The metric weight is used to control the effect of the metric on the application. For different applications, only metric weights and the metric workload impact factors need to be changed, while the scheduling algorithm remains the same. Experimental results are presented to demonstrate the adaptability of the HGSA.展开更多
Internet of Things (IoT) as an important and ubiquitous service paradigm is one of the most important issues in IoT applications to provide terminal users with effective and efficient services based on service communi...Internet of Things (IoT) as an important and ubiquitous service paradigm is one of the most important issues in IoT applications to provide terminal users with effective and efficient services based on service community. This paper presents a semantic-based similarity algorithm to build the IoT service community. Firstly, the algorithm reflects that the nodes of IoT contain a wealth of semantic information and makes them to build into the concept tree. Then tap the similarity of the semantic information based on the concept tree. Finally, we achieve the optimization of the service community through greedy algorithm and control the size of the service community by adjusting the threshold. Simulation results show the effectiveness and feasibility of this algorithm.展开更多
In order to reduce the disturbance on an authorizing user and lower the competition between cognitive users, assure the normal communication of a cognitive radio system, reliability theory is applied to describe if a ...In order to reduce the disturbance on an authorizing user and lower the competition between cognitive users, assure the normal communication of a cognitive radio system, reliability theory is applied to describe if a channel can be used by a cognitive user or not and the probability that the channel is continually used for a period. Three aspects including space, time domain and frequency domain are united for the research on the distribution of frequency spectrum. The simulation result shows that, in the space domain, time domain, frequency domain algorithm, the transmitted data volume and the total throughput of the system are superior to those in greedy algorithm and time domain—frequency domain algorithm, the novel algorithm is helpful to reduce the disturbance caused by a cognitive user to an authorizing user and lower the competition between cognitive users, this simulation result shows that the proposed algorithm is effective.展开更多
In this paper,a local-learning algorithm for multi-agent is presented based on the fact that individual agent performs local perception and local interaction under group environment.As for in-dividual-learning,agent a...In this paper,a local-learning algorithm for multi-agent is presented based on the fact that individual agent performs local perception and local interaction under group environment.As for in-dividual-learning,agent adopts greedy strategy to maximize its reward when interacting with envi-ronment.In group-learning,local interaction takes place between each two agents.A local-learning algorithm to choose and modify agents' actions is proposed to improve the traditional Q-learning algorithm,respectively in the situations of zero-sum games and general-sum games with unique equi-librium or multi-equilibrium.And this local-learning algorithm is proved to be convergent and the computation complexity is lower than the Nash-Q.Additionally,through grid-game test,it is indicated that by using this local-learning algorithm,the local behaviors of agents can spread to globe.展开更多
The greedy algorithm is a strong local searching algorithm. The genetic algorithm is generally applied to the global optimization problems. In this paper, we combine the greedy idea and the genetic algorithm to propos...The greedy algorithm is a strong local searching algorithm. The genetic algorithm is generally applied to the global optimization problems. In this paper, we combine the greedy idea and the genetic algorithm to propose the greedy genetic algorithm which incorporates the global exploring ability of the genetic algorithm and the local convergent ability of the greedy algorithm. Experimental results show that greedy genetic algorithm gives much better results than the classical genetic algorithm.展开更多
A connected and undirected graph model of active distribution networks with considering the function of interconnecting switches is constructed in this paper.Based on this model,the island partition problem of active ...A connected and undirected graph model of active distribution networks with considering the function of interconnecting switches is constructed in this paper.Based on this model,the island partition problem of active distribution networks can be described as a 1-neighbour knapsack problem.An effective heuristic algorithm named prospective greedy algorithm is then proposed to solve this problem.Case studies on PG&E 69-bus network show the validity of the proposed model and algorithm.展开更多
Real earthquake accelerograms are used in structural dynamic analysis.The criteria for record database establishment,record screening,selection principle,scaling adjustment,and sampling size are discussed in the prese...Real earthquake accelerograms are used in structural dynamic analysis.The criteria for record database establishment,record screening,selection principle,scaling adjustment,and sampling size are discussed in the present paper.An optimized selection method using coupled genetic and greedy algorithms is proposed,and energy input characteristics for the records which are selected only through matching acceleration design spectrum are analyzed.Using the proposed method,the mean and target spectra can reach a good agreement within a period range of interest.The dispersion among the response spectra of the different records is small,and the tolerance of the spectrum value corresponding to each period in the important period scope can be controlled more strictly than the specified limit.If the structural cumulative damage or structures with energy-dissipation components are considered,the records selected using the method that only match the acceleration design spectrum are not applicable.In this case,the selection method that considers the energy input demand is recommended.展开更多
Recently, crowdsourcing platforms have attracted a number of citizens to perform a variety of location- specific tasks. However, most existing approaches consider the arrangement of a set of tasks for a set of crowd w...Recently, crowdsourcing platforms have attracted a number of citizens to perform a variety of location- specific tasks. However, most existing approaches consider the arrangement of a set of tasks for a set of crowd workers, while few consider crowd workers arriving in a dynamic manner. Therefore, how to arrange suitable location-specific tasks to a set of crowd workers such that the crowd workers obtain maximum satisfaction when arriving sequentially represents a challenge. To address the limitation of existing approaches, we first identify a more general and useful model that considers not only the arrangement of a set of tasks to a set of crowd workers, but also all the dynamic arrivals of all crowd workers. Then, we present an effective crowd-task model which is applied to offiine and online settings, respectively. To solve the problem in an offiine setting, we first observe the characteristics of task planning （CTP） and devise a CTP algorithm to solve the problem. We also propose an effective greedy method and integrated simulated annealing （ISA） techniques to improve the algorithm performance. To solve the problem in an online setting, we develop a greedy algorithm for task planning. Finally, we verify the effectiveness and efficiency of the proposed solutions through extensive experiments using real and synthetic datasets.展开更多
基金supported by NSFC(No. 61571055)fund of SKL of MMW (No. K201815)Important National Science & Technology Specific Projects(2017ZX03001028)
文摘Mobile edge computing （MEC） is a novel technique that can reduce mobiles＇ com- putational burden by tasks offioading, which emerges as a promising paradigm to provide computing capabilities in close proximity to mobile users. In this paper, we will study the scenario where multiple mobiles upload tasks to a MEC server in a sing cell, and allocating the limited server resources and wireless chan- nels between mobiles becomes a challenge. We formulate the optimization problem for the energy saved on mobiles with the tasks being dividable, and utilize a greedy choice to solve the problem. A Select Maximum Saved Energy First （SMSEF） algorithm is proposed to realize the solving process. We examined the saved energy at different number of nodes and channels, and the results show that the proposed scheme can effectively help mobiles to save energy in the MEC system.
基金Project supported by the National Natural Science Foundation of China (No. 60225009), and the National Science Fund for Distin-guished Young Scholars, China
文摘Selecting appropriate resources for running a job efficiently is one of the common objectives in a computational grid. Resource scheduling should consider the specific characteristics of the application, and decide the metrics to be used accordingly. This paper presents a distributed resource scheduling framework mainly consisting of a job scheduler and a local scheduler. In order to meet the requirements of different applications, we adopt HGSA, a Heuristic-based Greedy Scheduling Algorithm, to schedule jobs in the grid, where the heuristic knowledge is the metric weights of the computing resources and the metric workload impact factors. The metric weight is used to control the effect of the metric on the application. For different applications, only metric weights and the metric workload impact factors need to be changed, while the scheduling algorithm remains the same. Experimental results are presented to demonstrate the adaptability of the HGSA.
基金Supported by the China Postdoctoral Science Foundation(No. 20100480701)the Ministry of Education of Humanities and Social Sciences Youth Fund Project(11YJC880119)
文摘Internet of Things (IoT) as an important and ubiquitous service paradigm is one of the most important issues in IoT applications to provide terminal users with effective and efficient services based on service community. This paper presents a semantic-based similarity algorithm to build the IoT service community. Firstly, the algorithm reflects that the nodes of IoT contain a wealth of semantic information and makes them to build into the concept tree. Then tap the similarity of the semantic information based on the concept tree. Finally, we achieve the optimization of the service community through greedy algorithm and control the size of the service community by adjusting the threshold. Simulation results show the effectiveness and feasibility of this algorithm.
基金supported by Natural Science Foundation of Heilongjiang Province of China(No.F2015017)
文摘In order to reduce the disturbance on an authorizing user and lower the competition between cognitive users, assure the normal communication of a cognitive radio system, reliability theory is applied to describe if a channel can be used by a cognitive user or not and the probability that the channel is continually used for a period. Three aspects including space, time domain and frequency domain are united for the research on the distribution of frequency spectrum. The simulation result shows that, in the space domain, time domain, frequency domain algorithm, the transmitted data volume and the total throughput of the system are superior to those in greedy algorithm and time domain—frequency domain algorithm, the novel algorithm is helpful to reduce the disturbance caused by a cognitive user to an authorizing user and lower the competition between cognitive users, this simulation result shows that the proposed algorithm is effective.
文摘In this paper,a local-learning algorithm for multi-agent is presented based on the fact that individual agent performs local perception and local interaction under group environment.As for in-dividual-learning,agent adopts greedy strategy to maximize its reward when interacting with envi-ronment.In group-learning,local interaction takes place between each two agents.A local-learning algorithm to choose and modify agents' actions is proposed to improve the traditional Q-learning algorithm,respectively in the situations of zero-sum games and general-sum games with unique equi-librium or multi-equilibrium.And this local-learning algorithm is proved to be convergent and the computation complexity is lower than the Nash-Q.Additionally,through grid-game test,it is indicated that by using this local-learning algorithm,the local behaviors of agents can spread to globe.
文摘The greedy algorithm is a strong local searching algorithm. The genetic algorithm is generally applied to the global optimization problems. In this paper, we combine the greedy idea and the genetic algorithm to propose the greedy genetic algorithm which incorporates the global exploring ability of the genetic algorithm and the local convergent ability of the greedy algorithm. Experimental results show that greedy genetic algorithm gives much better results than the classical genetic algorithm.
文摘A connected and undirected graph model of active distribution networks with considering the function of interconnecting switches is constructed in this paper.Based on this model,the island partition problem of active distribution networks can be described as a 1-neighbour knapsack problem.An effective heuristic algorithm named prospective greedy algorithm is then proposed to solve this problem.Case studies on PG&E 69-bus network show the validity of the proposed model and algorithm.
基金supported by the National Natural Science Foundation of China (Grant No. 90715016)
文摘Real earthquake accelerograms are used in structural dynamic analysis.The criteria for record database establishment,record screening,selection principle,scaling adjustment,and sampling size are discussed in the present paper.An optimized selection method using coupled genetic and greedy algorithms is proposed,and energy input characteristics for the records which are selected only through matching acceleration design spectrum are analyzed.Using the proposed method,the mean and target spectra can reach a good agreement within a period range of interest.The dispersion among the response spectra of the different records is small,and the tolerance of the spectrum value corresponding to each period in the important period scope can be controlled more strictly than the specified limit.If the structural cumulative damage or structures with energy-dissipation components are considered,the records selected using the method that only match the acceleration design spectrum are not applicable.In this case,the selection method that considers the energy input demand is recommended.
基金supported by the National High-Tech R&D Program(863) of China(No.2014AA015203)
文摘Recently, crowdsourcing platforms have attracted a number of citizens to perform a variety of location- specific tasks. However, most existing approaches consider the arrangement of a set of tasks for a set of crowd workers, while few consider crowd workers arriving in a dynamic manner. Therefore, how to arrange suitable location-specific tasks to a set of crowd workers such that the crowd workers obtain maximum satisfaction when arriving sequentially represents a challenge. To address the limitation of existing approaches, we first identify a more general and useful model that considers not only the arrangement of a set of tasks to a set of crowd workers, but also all the dynamic arrivals of all crowd workers. Then, we present an effective crowd-task model which is applied to offiine and online settings, respectively. To solve the problem in an offiine setting, we first observe the characteristics of task planning （CTP） and devise a CTP algorithm to solve the problem. We also propose an effective greedy method and integrated simulated annealing （ISA） techniques to improve the algorithm performance. To solve the problem in an online setting, we develop a greedy algorithm for task planning. Finally, we verify the effectiveness and efficiency of the proposed solutions through extensive experiments using real and synthetic datasets.
