The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Xuemin Shen - One of the best experts on this subject based on the ideXlab platform.
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Dynamic computation offloading for mobile cloud computing a stochastic game theoretic approach
IEEE Transactions on Mobile Computing, 2019Co-Authors: Jianchao Zheng, Yueming Cai, Xuemin ShenAbstract:Driven by the growing popularity of mobile applications, mobile cloud computing has been envisioned as a promising approach to enhance computation capability of mobile devices and reduce the energy consumptions. In this paper, we investigate the problem of multi-user computation offloading for mobile cloud computing under Dynamic Environment, wherein mobile users become active or inactive Dynamically, and the wireless channels for mobile users to offload computation vary randomly. As mobile users are self-interested and selfish in offloading computation tasks to the mobile cloud, we formulate the mobile users’ offloading decision process under Dynamic Environment as a stochastic game. We prove that the formulated stochastic game is equivalent to a weighted potential game which has at least one Nash Equilibrium (NE). We quantify the efficiency of the NE, and further propose a multi-agent stochastic learning algorithm to reach the NE with a guaranteed convergence rate (which is also analytically derived). Finally, we conduct simulations to validate the effectiveness of the proposed algorithm and evaluate its performance under Dynamic Environment.
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stochastic game theoretic spectrum access in distributed and Dynamic Environment
IEEE Transactions on Vehicular Technology, 2015Co-Authors: Jianchao Zheng, Yueming Cai, Xuemin ShenAbstract:In this paper, we investigate the problem of channel selection for interference mitigation in opportunistic spectrum access networks using a stochastic game-theoretic approach. The studied network is distributed and Dynamic , where each user only has its individual information, and no information exchange is available among users. Moreover, each user is considered to be Dynamically active due to its specific data service requirement. Specifically, a user randomly becomes active and then competes for the wireless channel to transmit for a random duration. To capture such Dynamic interactions among users, a Dynamic interference graph is defined, and based on this, the interference mitigation problem is formulated as a graphical stochastic game. It is proved to be an exact potential game, in which the existence of the Nash equilibrium (NE) is guaranteed. Then, the performance bounds of the NE are theoretically analyzed. Furthermore, we design a fully distributed and online algorithm based on stochastic learning for the interference-mitigation channel selection, which is proved to converge to the NE of the formulated game. Finally, we conduct simulations to validate the effectiveness of the proposed algorithm for interference mitigation and throughput improvement in the distributed and Dynamic Environment.
Jianchao Zheng - One of the best experts on this subject based on the ideXlab platform.
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Dynamic computation offloading for mobile cloud computing a stochastic game theoretic approach
IEEE Transactions on Mobile Computing, 2019Co-Authors: Jianchao Zheng, Yueming Cai, Xuemin ShenAbstract:Driven by the growing popularity of mobile applications, mobile cloud computing has been envisioned as a promising approach to enhance computation capability of mobile devices and reduce the energy consumptions. In this paper, we investigate the problem of multi-user computation offloading for mobile cloud computing under Dynamic Environment, wherein mobile users become active or inactive Dynamically, and the wireless channels for mobile users to offload computation vary randomly. As mobile users are self-interested and selfish in offloading computation tasks to the mobile cloud, we formulate the mobile users’ offloading decision process under Dynamic Environment as a stochastic game. We prove that the formulated stochastic game is equivalent to a weighted potential game which has at least one Nash Equilibrium (NE). We quantify the efficiency of the NE, and further propose a multi-agent stochastic learning algorithm to reach the NE with a guaranteed convergence rate (which is also analytically derived). Finally, we conduct simulations to validate the effectiveness of the proposed algorithm and evaluate its performance under Dynamic Environment.
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stochastic game theoretic spectrum access in distributed and Dynamic Environment
IEEE Transactions on Vehicular Technology, 2015Co-Authors: Jianchao Zheng, Yueming Cai, Xuemin ShenAbstract:In this paper, we investigate the problem of channel selection for interference mitigation in opportunistic spectrum access networks using a stochastic game-theoretic approach. The studied network is distributed and Dynamic , where each user only has its individual information, and no information exchange is available among users. Moreover, each user is considered to be Dynamically active due to its specific data service requirement. Specifically, a user randomly becomes active and then competes for the wireless channel to transmit for a random duration. To capture such Dynamic interactions among users, a Dynamic interference graph is defined, and based on this, the interference mitigation problem is formulated as a graphical stochastic game. It is proved to be an exact potential game, in which the existence of the Nash equilibrium (NE) is guaranteed. Then, the performance bounds of the NE are theoretically analyzed. Furthermore, we design a fully distributed and online algorithm based on stochastic learning for the interference-mitigation channel selection, which is proved to converge to the NE of the formulated game. Finally, we conduct simulations to validate the effectiveness of the proposed algorithm for interference mitigation and throughput improvement in the distributed and Dynamic Environment.
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distributed channel selection for interference mitigation in Dynamic Environment a game theoretic stochastic learning solution
IEEE Transactions on Vehicular Technology, 2014Co-Authors: Jianchao Zheng, Yuhua Xu, Alagan AnpalaganAbstract:In this paper, we investigate the problem of distributed chan- nel selection for interference mitigation in a canonical communication network. The channel is assumed time-varying, and the active user set is considered Dynamically variable due to the specific service requirement. This problem is formulated as an exact potential game, and the optimality property of the solution to this problem is first analyzed. Then, we design a low-complexity fully distributed no-regret learning algorithm for chan- nel adaptation in a Dynamic Environment, where each active player can independently and automatically update its action with no information exchange. The proposed algorithm is proven to converge to a set of correlated equilibria with a probability of 1. Finally, we conduct simula- tions to demonstrate that the proposed algorithm achieves near-optimal performance for interference mitigation in Dynamic Environments. Index Terms—Distributed channel allocation, Dynamic Environment, interference mitigation, no-regret learning, potential game.
Alagan Anpalagan - One of the best experts on this subject based on the ideXlab platform.
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distributed channel selection for interference mitigation in Dynamic Environment a game theoretic stochastic learning solution
IEEE Transactions on Vehicular Technology, 2014Co-Authors: Jianchao Zheng, Yuhua Xu, Alagan AnpalaganAbstract:In this paper, we investigate the problem of distributed chan- nel selection for interference mitigation in a canonical communication network. The channel is assumed time-varying, and the active user set is considered Dynamically variable due to the specific service requirement. This problem is formulated as an exact potential game, and the optimality property of the solution to this problem is first analyzed. Then, we design a low-complexity fully distributed no-regret learning algorithm for chan- nel adaptation in a Dynamic Environment, where each active player can independently and automatically update its action with no information exchange. The proposed algorithm is proven to converge to a set of correlated equilibria with a probability of 1. Finally, we conduct simula- tions to demonstrate that the proposed algorithm achieves near-optimal performance for interference mitigation in Dynamic Environments. Index Terms—Distributed channel allocation, Dynamic Environment, interference mitigation, no-regret learning, potential game.
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Opportunistic Spectrum Access in Unknown Dynamic Environment: A Game-Theoretic Stochastic Learning Solution
IEEE Transactions on Wireless Communications, 2012Co-Authors: Yuhua Xu, Jinlong Wang, Qihui Wu, Alagan AnpalaganAbstract:We investigate the problem of distributed channel selection using a game-theoretic stochastic learning solution in an opportunistic spectrum access (OSA) system where the channel availability statistics and the number of the secondary users are apriori unknown. We formulate the channel selection problem as a game which is proved to be an exact potential game. However, due to the lack of information about other users and the restriction that the spectrum is time-varying with unknown availability statistics, the task of achieving Nash equilibrium (NE) points of the game is challenging. Firstly, we propose a genie-aided algorithm to achieve the NE points under the assumption of perfect Environment knowledge. Based on this, we investigate the achievable performance of the game in terms of system throughput and fairness. Then, we propose a stochastic learning automata (SLA) based channel selection algorithm, with which the secondary users learn from their individual action-reward history and adjust their behaviors towards a NE point. The proposed learning algorithm neither requires information exchange, nor needs prior information about the channel availability statistics and the number of secondary users. Simulation results show that the SLA based learning algorithm achieves high system throughput with good fairness.
John R Harrald - One of the best experts on this subject based on the ideXlab platform.
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modeling risk in the Dynamic Environment of maritime transportation
Winter Simulation Conference, 2001Co-Authors: Jason R W Merrick, Rene J Van Dorp, Thomas A Mazzuchi, John R HarraldAbstract:The Washington State Ferries are one of the largest ferry systems in the world. Accidents involving Washington State Ferries are rare events. However, low probability, high consequence events lead to difficulties in the risk assessment process. Due to the infrequent occurrence of such accidents, large accident databases are not available for a standard statistical analysis of the contribution of perceived risk factors to accident risk. In the WSF Risk Assessment, a modeling approach that combined system simulation, expert judgement and available data was used to estimate the contribution of risk factors to accident risk. Simulation is necessary to capture the Dynamic Environment of changing risk factors, such as traffic interactions, visibility or wind conditions, and to evaluate future scenario's that are designed to alter this Dynamic behavior for the purposes of risk reduction or improved passenger service. This paper describes the simulation component of the model used in the Washington State Ferries Risk Assessment.
Yueming Cai - One of the best experts on this subject based on the ideXlab platform.
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Dynamic computation offloading for mobile cloud computing a stochastic game theoretic approach
IEEE Transactions on Mobile Computing, 2019Co-Authors: Jianchao Zheng, Yueming Cai, Xuemin ShenAbstract:Driven by the growing popularity of mobile applications, mobile cloud computing has been envisioned as a promising approach to enhance computation capability of mobile devices and reduce the energy consumptions. In this paper, we investigate the problem of multi-user computation offloading for mobile cloud computing under Dynamic Environment, wherein mobile users become active or inactive Dynamically, and the wireless channels for mobile users to offload computation vary randomly. As mobile users are self-interested and selfish in offloading computation tasks to the mobile cloud, we formulate the mobile users’ offloading decision process under Dynamic Environment as a stochastic game. We prove that the formulated stochastic game is equivalent to a weighted potential game which has at least one Nash Equilibrium (NE). We quantify the efficiency of the NE, and further propose a multi-agent stochastic learning algorithm to reach the NE with a guaranteed convergence rate (which is also analytically derived). Finally, we conduct simulations to validate the effectiveness of the proposed algorithm and evaluate its performance under Dynamic Environment.
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stochastic game theoretic spectrum access in distributed and Dynamic Environment
IEEE Transactions on Vehicular Technology, 2015Co-Authors: Jianchao Zheng, Yueming Cai, Xuemin ShenAbstract:In this paper, we investigate the problem of channel selection for interference mitigation in opportunistic spectrum access networks using a stochastic game-theoretic approach. The studied network is distributed and Dynamic , where each user only has its individual information, and no information exchange is available among users. Moreover, each user is considered to be Dynamically active due to its specific data service requirement. Specifically, a user randomly becomes active and then competes for the wireless channel to transmit for a random duration. To capture such Dynamic interactions among users, a Dynamic interference graph is defined, and based on this, the interference mitigation problem is formulated as a graphical stochastic game. It is proved to be an exact potential game, in which the existence of the Nash equilibrium (NE) is guaranteed. Then, the performance bounds of the NE are theoretically analyzed. Furthermore, we design a fully distributed and online algorithm based on stochastic learning for the interference-mitigation channel selection, which is proved to converge to the NE of the formulated game. Finally, we conduct simulations to validate the effectiveness of the proposed algorithm for interference mitigation and throughput improvement in the distributed and Dynamic Environment.
