In this paper, we study linear static Stac kelberg problems with multiple leaders-followers in which each decision maker wi thin his group may or may not cooperate. An exact penalty function method is dev eloped. The ...In this paper, we study linear static Stac kelberg problems with multiple leaders-followers in which each decision maker wi thin his group may or may not cooperate. An exact penalty function method is dev eloped. The duality gaps of the followers’ problems are appended to the leaders’ objective function with a penalty. The structure leads to the decomposition of the composite problem into a series of linear programmings leading to an efficie nt algorithm. We prove that local optimality is reached for an exact penalty fun ction and illustrate the method with three examples. The model in this paper ext ends the stackelberg leader-follower model.展开更多
In rechargeable wireless sensor networks, a sensor cannot be always benefi cial to conserve energy when a network can harvest excessive energy from the environment due to its energy replenished continually and limited...In rechargeable wireless sensor networks, a sensor cannot be always benefi cial to conserve energy when a network can harvest excessive energy from the environment due to its energy replenished continually and limited energy storage capacity. Therefore, surplus energy of a node can be utilized for strengthening packet delivery efficiency and improving data collection rate. In this work, we propose an algorithm to compute an upper data generation rate that maximizes it as an optimization problem for a network with multiple sinks, which is formulated as a linear programming problem. Subsequently, a dual problem by introducing Lagrange multipliers is constructed, and subgradient algorithms are used to solve it in a distributed manner. The resulting algorithms are guaranteed to converge to an optimal data generation rate, which are illustrated by an example in which an optimum data generation rate is computed for a network of randomly distributed nodes. Through extensive simulation and experiments, we demonstrate our algorithm is efficient to maximize data collection rate in rechargeable wireless sensor networks.展开更多
文摘In this paper, we study linear static Stac kelberg problems with multiple leaders-followers in which each decision maker wi thin his group may or may not cooperate. An exact penalty function method is dev eloped. The duality gaps of the followers’ problems are appended to the leaders’ objective function with a penalty. The structure leads to the decomposition of the composite problem into a series of linear programmings leading to an efficie nt algorithm. We prove that local optimality is reached for an exact penalty fun ction and illustrate the method with three examples. The model in this paper ext ends the stackelberg leader-follower model.
基金supported by The Natural Science Foundation of Jiangsu Province of China(Grant No.BK20141474)funded by China Postdoctoral Science Foundation(Grant No.2015M571639)+3 种基金three Projects Funded by The Jiangsu Planned Projects for Postdoctoral Research Funds(Grant No.1402018C)The Key Laboratory of Computer Network and Information Integration(Southeast University)Ministry of Education(Grant No.K93-9-2015-09C)The Priority Academic Program Development(PAPD)of Jiangsu Higher Education Institutions
文摘In rechargeable wireless sensor networks, a sensor cannot be always benefi cial to conserve energy when a network can harvest excessive energy from the environment due to its energy replenished continually and limited energy storage capacity. Therefore, surplus energy of a node can be utilized for strengthening packet delivery efficiency and improving data collection rate. In this work, we propose an algorithm to compute an upper data generation rate that maximizes it as an optimization problem for a network with multiple sinks, which is formulated as a linear programming problem. Subsequently, a dual problem by introducing Lagrange multipliers is constructed, and subgradient algorithms are used to solve it in a distributed manner. The resulting algorithms are guaranteed to converge to an optimal data generation rate, which are illustrated by an example in which an optimum data generation rate is computed for a network of randomly distributed nodes. Through extensive simulation and experiments, we demonstrate our algorithm is efficient to maximize data collection rate in rechargeable wireless sensor networks.
