Browsing by Author "Hung, Jason C."

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A Coalitional Graph Game Approach for Minimum Transmission Broadcast in IoT Networks
(2020-01-01)
Chan, Yu-Wei
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Chien, Feng-Tsun
;
Chang, Min-Kuan
;
Ho, Wei-Chun
;
Hung, Jason C.
In this work, we consider the broadcast mechanisms for both reliable links and unreliable links in the Internet of Things (IoT) networks. Focusing on the minimum transmission broadcast (MTB) problem, we propose an efficient algorithm, termed as the Connected Dominating Set Algorithm based on Coalitional Graph Game (CDSA-CGG), to find the minimum connected dominating set (MCDS). Distinct from related work in the literature which adopts a non-cooperative game approach and assumes the interaction among nodes is limited to neighboring nodes, we formulate the interaction and cooperation among nodes over reliable links and over unreliable links as a connected forwarding graph based on coalitional graph game. We prove that the forwarding graph constructed by CDSA-CGG is a Nash network. In addition, we also prove that the final resulting graph is pairwise stable. The simulation results show that the proposed coalition graph game-based algorithm performs well in terms of the number of transmissions, convergence rate, and lifetime of nodes.
Coalitional Power Control and Time Allocation for Multiuser Cognitive Networks
(2013-09-01)
Chan, Yu-Wei
;
Chien, Feng-Tsun
;
Park, James J.
;
Hung, Jason C.
;
Yen, Neil Y.
This paper considers the problem of resource allocation for a cooperative cognitive radio network with multiple primary users (PUs) and multiple secondary users (SUs), in which secondary users (SUs) relay traffics for primary users, in exchange for dedicated transmission time for their own transmission needs. However, since the PUs and SUs are rational, they aim at maximizing their own utility. Therefore, the problem of conflicting interests between them exists. We formulate the problem as a coalitional game to analyze the cooperation behavior between PUs and SUs. We propose to form the grand coalition by grouping all PUs and SUs in a set, and to use the core and the Shapley value solution concepts, respectively to analyze the stability and fairness of cooperative resource allocation. The optimal fraction of time allocation and power allocation for PUs and SUs are derived by an iterative updating algorithm, respectively. Simulation results show the benefits of our proposed approach as compared with other approaches.