• International journal of advanced smart convergence
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• v.10 no.3
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• pp.10-16
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• 2021

In this paper, we devise a Shapley-value-based covert channel in smartphones. More specifically, unlike ordinary use of Shapley value in cooperative game, we make use of a series of Shapley values, which are computed from sensor data collected from smartphones, in order to create a covert channel between encoding smartphone and decoding smartphone. To the best of our knowledge, we are the first to contrive covert channel based on Shapley values. We evaluate the encoding process of our proposed covert channel through simulation and present our evaluation results.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.533_534
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• 2009

본 논문에서는 수요반응(Demand Response) 프로그램 적용 시 각 소비자의 부하패턴 변화에 따른 계통운영자(Independent System Operator : ISO)의 총 발전비용 변화량을 Shapley Value를 이용하여 각 소비자가 총 발전비용 감소에 기여한 정도를 파악할 것이다. 또한 사례연구를 통하여 수요반응 프로그램에서의 각 부하패턴 변화 유형에 따른 총 발전비용 감소에 대한 기여도 및 Shapley Value 적용 전후의 결과를 비교 분석하였다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.669-673
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• 1998

This paper makes a study of the Shapley value in cooperative fuzzy games, games with fuzzy coalitions, which enable the representation of players' participation degree to each coalition. The Shapley value has so far been introduced only in an class of fuzzy games where a coalition value is not monotone with respect to each player's participation degree. We consider a more natural class of fuzzy games such that a coalition value is monotone with regard to each player's participation degree. The properties of fuzzy games in this class are investigated. Four axioms of Shapley functions are described and a Shapley function of a fuzzy fame in the class is given.

• Journal of information and communication convergence engineering
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• v.16 no.1
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• pp.38-42
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• 2018

In this paper, we consider measurement allocation problem in a spatially correlated sensor field. Our goal is to determine the probability of each sensor's being measured based on its contribution to the estimation reliability; it is desirable that a sensor improving the estimation reliability is measured more frequently. We consider a spatial correlation model of a sensor field reflecting transmission power limit, noise in measurement and transmission channel, and channel attenuation. Then the estimation reliability is defined distortion error between event source and its estimation at sink. Motivated by the correlation nature, we model the measurement allocation problem into a cooperative game, and then quantify each sensor's contribution using Shapley value. Against the intractability in the computation of exact Shapley value, we deploy a randomized method that enables to compute the approximate Shapley value within a reasonable time. Besides, we envisage a measurement scheduling achieving the balance between network lifetime and estimation reliability.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.4
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• pp.195-201
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• 2023

The degree of contribution each sensor makes towards the total information gathered by all sensors is not uniform in spatially correlated sensor fields. Considering bit allocation problem in such a spatially correlated sensor field, the number of bits to be allocated to each sensor should be proportional to the degree of contribution the sensor makes. In this paper, we deploy Shapley value, a representative solution concept in cooperative game theory, and utilize it in order to quantify the degree of contribution each sensor makes. Shapley value is a system that determines the contribution of an individual player when two or more players work in collaboration with each other. To this end, we cast the bit allocation problem into a cooperative game called bit allocation game where sensors are regarded as the players, and a payoff function is given in the criteria of mutual information. We show that the Shapley value fairly quantifies an individual sensor's contribution to the total payoff achieved by all sensors following its desirable properties. By numerical experiments, we confirm that sensor that needs more bits to cover its area has larger Shapley value in spatially correlated sensor fields.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.399-402
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• 2017

We consider an sensor partitioning problem for energy-efficient measurement scheduling in a spatially correlated sensor field where sensors are located randomly. We divide the whole sensors into subsets of k sensors in the way of letting each subset give similar amount of mutual information. Then it allows more prolonged life time of the sensor field than measuring the sensors that gives most information only. To this end, we compute the Shapley value of each sensor and compose the subsets so that each subset can have total Shapley value similar with the other subsets.

• IE interfaces
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• v.17 no.2
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• pp.142-148
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• 2004

The objective of bridge construction cost allocation is to distribute in a fair and rational manner the bridge construction costs among those vehicles using the bridge. In most bridge construction cost allocation studies, bridge construction costs are mainly distributed according to traffic load(gross vehicle weight), without any consideration of bridge capacity requirements(the number of lanes). In this paper, a bridge cost allocation method for considering both traffic capacity and traffic loads is developed. The proposed method is based on cooperative game theory, particularly two concepts known as the Aumann-Shapley (A-S) value and Shapley value. This method can help to analyze the impact of traffic capacity costs. By applying the proposed method to an example, traffic capacity cost is found to be high so that traffic capacity should be considered to allocate the bridge construction costs to vehicle classes in a more equitable manner.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.12
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• pp.2354-2358
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• 2009

Demand response (DR) can be used to improve the efficiency of electricity markets and increase the reliability of power systems. As more utilities attempt to reduce the purchasing costs by implementing DR programs strategically, there is an increasing need for studies of how to allocate the reduced purchasing costs among DR program participants. The rebates or incentives can be given to DR program participants in proportion to the participants' contributions to the reduced purchasing costs. This paper presents Shapley Value-based method to determine the DR program participants' contributions to the reduced purchasing costs. A numerical example is presented to validate the effectiveness of the proposed method.

• The KIPS Transactions:PartB
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• v.18B no.3
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• pp.165-172
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• 2011

In this paper, we propose Ant-Q learning Algorithm[1], which uses the habits of biological ants, to find a new way to solve Stable Marriage Problem(SMP)[3] presented by Gale-Shapley[2]. The issue of SMP is to find optimum matching for a stable marriage based on their preference lists (PL). The problem of Gale-Shapley algorithm is to get a stable matching for only male (or female). We propose other way to satisfy various requirements for SMP. ACS(Ant colony system) is an swarm intelligence method to find optimal solution by using phermone of ants. We try to improve ACS technique by adding Q learning[9] concept. This Ant-Q method can solve SMP problem for various requirements. The experiment results shows the proposed method is good for the problem.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.434-437
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• 2016

In this paper, we consider a measurement allocation problem for gathering reliable data from a spatially correlated sensor field. We allocate the probability of each sensor's being measured considering its marginal contribution in entire data gathering; higher measurement probability is given to a sensor that gives higher reilable data. First we establish a correlation model considering limit in each sensor's transmission power, noise in the process of measurement and transmission, and attenutations in wireless channel. Then we evaluate the reliability of gathered data by estimating distortion error in sink node. We model the measurement allocation problem in spatially correlated sensor field into a cooperative game, and quantifiy each sensor's marginal contribution using Shapley Value. Then, the probability of each sensor's being measured is given in proportion to the Shapley Value.