Toward Refined Nash Equilibria for the SET K-COVER Problem via a Memorial Mixed-Response Algorithm

Sun, Changhao; Wang, Xiaochu; Qiu, Huaxin; Sun, Wei; Zhou, Qingrui

doi:10.1109/tsmc.2021.3049580

Cited by 6 publications

(3 citation statements)

References 28 publications

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“…In [14], a time variant log-linear learning algorithm is proposed to solve the Set k-Cover problem in which the problem is formulated as a spatial potential game. In [15], a memorial mixed-response algorithm is proposed to solve the Set k-Cover problem. Each sensor node as a player updates its memory using a temporary action.…”

Section: Introductionmentioning

confidence: 99%

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Distributed Homology-based Algorithm for solving Set k-Cover problem in Heterogeneous Directional Sensor Networks

Varposhti

2023

Preprint

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Coverage is one of the fundamental problems in directional sensor networks (DSNs). This problem is more complicated when we deal with heterogeneous DSNs (HDSNs). Prolonging the network lifetime is another important problem in this area. The problem of finding k disjoint cover sets known as the Set k-Cover problem can solve both the coverage and lifetime issues. In this paper a distributed algorithm is proposed for the Set k-Cover problem in HDSNs and then the method is applied for target k-tracking problem. In the Set k-Cover problem, directional sensors are partitioned into k disjoint sets where each set covers the entire area, and in object k-tracking problem, the object must be tracked by at least k sensors. The proposed algorithms are based on the notion of homology in Algebraic Topology. We consider the Nerve complex corresponding to the HDSN and demonstrate how topological properties of the Nerve complex of the network can be used to formulate the Set k-Cover problem as an integer linear programming problem. Then, we propose a distributed algorithm based on the subgradient method for this problem. After that, we propose a distributed algorithm for object k-tracking based on the solution of the Set k-Cover problem. Finally, we evaluate the performance of the proposed algorithms by conducting simulation experiments.

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Section: Introductionmentioning

confidence: 99%

“…To solve the Set k-Cover problem in a distributed manner, some researchers have focused on game theoretic based solutions [14][15][16][17]. In [14], a time variant log-linear learning algorithm is proposed to solve the Set k-Cover problem in which the problem is formulated as a spatial potential game.…”

Section: Introductionmentioning

confidence: 99%

Distributed Homology-based Algorithm for solving Set k-Cover problem in Heterogeneous Directional Sensor Networks

Varposhti

2023

Preprint

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“…By using existing methods in the literature, Nash equilibrium solutions could be easily obtained in a distributed manner. However, because more than one Nash equilibrium exists, which differs sharply in terms of the system-level objective, higher quality solutions could be hardly guaranteed without the aid of a central authority, as pointed out in [21] and [22]. For this, the objective of this article is to present a distributed algorithm that provides better approximation for the MWVC problem, where each vertex makes decisions by relying on local information of its own and its immediate neighbors only.…”

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confidence: 99%

Better Approximation for Distributed Weighted Vertex Cover via Game-Theoretic Learning

Sun

Qiu

Sun

et al. 2022

IEEE Trans. Syst. Man Cybern, Syst.

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Toward better approximation for the minimumweighted vertex cover (MWVC) problem in multiagent systems, we present a distributed algorithm from the perspective of learning in games. For self-organized coordination and optimization, we see each vertex as a potential game player who makes decisions using local information of its own and the immediate neighbors. The resulting Nash equilibrium is classified into two categories, i.e., the inferior Nash equilibrium (INE) and the dominant Nash equilibrium (DNE). We show that the optimal solution must be a DNE. To achieve better approximation ratios, local rules of perturbation and weighted memory are designed, with the former destroying the stability of an INE and the latter facilitating the refinement of a DNE. By showing the existence of an improvement path from any INE to a DNE, we prove that when the memory length is larger than 1, our algorithm converges in finite time to DNEs, which could not be improved by exchanging the action of a selected node with all its unselected neighbors. Moreover, additional freedom for solution efficiency refinement is provided by increasing the memory length. Finally, intensive comparison experiments demonstrate the superiority of the presented methodology to the state of the art, both in solution efficiency and computation speed.

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Distributed homology-based algorithm for solving Set k-Cover problem in heterogeneous directional sensor networks

Varposhti

2023

J Supercomput

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Toward Refined Nash Equilibria for the SET K-COVER Problem via a Memorial Mixed-Response Algorithm

Cited by 6 publications

References 28 publications

Distributed Homology-based Algorithm for solving Set k-Cover problem in Heterogeneous Directional Sensor Networks

Distributed Homology-based Algorithm for solving Set k-Cover problem in Heterogeneous Directional Sensor Networks

Better Approximation for Distributed Weighted Vertex Cover via Game-Theoretic Learning

Distributed homology-based algorithm for solving Set k-Cover problem in heterogeneous directional sensor networks

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