• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.11
• /
• pp.5322-5342
• /
• 2016

In vehicle-to-vehicle (V2V) networks, where selfishness degrades node activity, countermeasures for collaboration enforcement must be provided to enable application of a sage and efficient network environment. Because vehicular networks feature both high mobility and various topologies, selfish behavior judgment and establishment of a stable routing protocol become intensely challenging. In this paper, a two-phase-based generous cooperative routing protocol (called GEC) is presented for V2V networks to provide resistance to selfishness. To detect selfish behaving vehicles, a packet forwarding watchdog and an average connection rate based on the multipath weight method are used, where evidence is gathered from different watchdogs. Then, multihop relay decisions are made using a generous cooperative algorithm based on game theory. Finally, through buffering of the multiple end-to-end paths and judicious choice of optimal cooperative routes, route maintenance phase is capable of dealing with congestion and rapidly exchanging traffic. Specifically, it is proved that the GEC is theoretically subgame perfect. Simulation results show that for V2V networks with inherently selfish nodes, the proposed method isolates uncooperative vehicles and is capable of accommodating both the mobility and congestion circumstances by facilitating information dissemination and reducing end-to-end delay.

• Journal of the military operations research society of Korea
• /
• v.27 no.1
• /
• pp.114-127
• /
• 2001

To support cooperative work in various scenes by computer systems, functions of sharing personalization, observation and control are needed. Especially, in advanced cooperative work, people have various position so that sometimes one person needs to restrict behavior of another. In this paper, we describe representation of access restriction of hypermedia system, which is framework of CSCW(Computer-Supported Cooperative Works) system we developed and propose a way to go on working cooperatively with people, restricting others.

• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.35S no.3
• /
• pp.102-109
• /
• 1998

In this paper, we propose a method of cooperative control based on immune system in distributed autonomous robotic system(DARS). Immune system is living body's self-protection and self-maintenance system. Thus these features can be applied to decision making of optimal swarm behavior in dynamically changing environment.For the purpose of applying immune system to DARS, a robot is regarded as a B lymphocyte(B cell), each environmental condition as an antigen, and a behavior strategy as an antibody respectively. The executing process of proposed method is as follows. When the environmental codintion changes, a robot select an appropriate beharior stategy. And its behavior stategy is stimulated and suppressed by other robot using communiation. Finally much stimulated strategy is adopted as a swarm behavior strategy. This control scheme is based on clonal selection and idotopic network hypothesis. And it is used for decision making of optimal swarm stragegy.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.16 no.1
• /
• pp.72-78
• /
• 2006

It is essential for robot to have the sensing and communication abilities in the swarm robot system. In general, as the number of robot goes on increasing, the limitation of communication capacity and information overflow occur in global communication system. Therefore a local communication is more effective than global one. In this paper, we propose the novel method for determining the optimal communication radius through the analyzing of the information propagation based on local communication. And we also propose a method of cooperative strategies and group behavior of swarm robot based on artificial immune system.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.16 no.2
• /
• pp.131-137
• /
• 2006

In swarm robot systems, each robot must behaves by itself according to the its states and environments, and if necessary, must cooperates with other robots in order to carry out a given task. Therefore it is essential that each robot has both learning and evolution ability to adapt the dynamic environments. In this paper, the new learning and evolution method based on reinforcement learning having delayed reward ability and distributed genetic algorithms is proposed for behavior learning and evolution of collective autonomous mobile robots. Reinforcement learning having delayed reward is still useful even though when there is no immediate reward. And by distributed genetic algorithm exchanging the chromosome acquired under different environments by communication each robot can improve its behavior ability. Specially, in order to improve the performance of evolution, selective crossover using the characteristic of reinforcement learning is adopted in this paper. we verify the effectiveness of the proposed method by applying it to cooperative search problem.

• Journal of Institute of Control, Robotics and Systems
• /
• v.6 no.12
• /
• pp.1079-1085
• /
• 2000

In this paper, we propose a method of cooperative control (T-cell modeling) and selection of group behavior strategy (B-cell modeling) based on immune system in distributed autonomous robotic system (DARS). An immune system is the living bodys self-protection and self-maintenance system. these features can be applied to decision making of the optimal swarm behavior in a dynamically changing environment. For applying immune system to DARS, a robot is regarded as a B-cell, each environmental condition as an antigen, a behavior strategy as an antibody, and control parameter as a T-cell, respectively. When the environmental condition (antigen) changes, a robot selects an appropriate behavior strategy (antibody). And its behavior strategy is stimulated and suppressed by other robots using communication (immune network). Finally, much stimulated strategy is adopted as a swarm behavior strategy. This control scheme is based on clonal selection and immune network hypothesis, and it is used for decision making of the optimal swarm strategy. Adaptation ability of the robot is enhanced by adding T-cell model as a control parameter in dynamic environments.

• Journal of Communications and Networks
• /
• v.13 no.3
• /
• pp.266-276
• /
• 2011

In wireless networks, it is well-known that intermediate nodes can be used as cooperative relays to reduce the transmission energy required to reliably deliver a message to an intended destination. When the network is under a central authority, energy allocations and cooperative pairings can be assigned to optimize the overall energy efficiency of the network. In networks with autonomous selfish nodes, however, nodes may not be willing to expend energy to relay messages for others. This problem has been previously addressed through the development of extrinsic incentive mechanisms, e.g., virtual currency, or the insertion of altruistic nodes in the network to enforce cooperative behavior. This paper considers the problem of how selfish nodes can decide on an efficient energy allocation and endogenously form cooperative partnerships in wireless networks without extrinsic incentive mechanisms or altruistic nodes. Using tools from both cooperative and non-cooperative game theory, the three main contributions of this paper are (i) the development of Pareto-efficient cooperative energy allocations that can be agreed upon by selfish nodes, based on axiomatic bargaining techniques, (ii) the development of necessary and sufficient conditions under which "natural" cooperation is possible in systems with fading and non-fading channels without extrinsic incentive mechanisms or altruistic nodes, and (iii) the development of techniques to endogenously form cooperative partnerships without central control. Numerical results with orthogonal amplify-and-forward cooperation are also provided to quantify the energy efficiency of a wireless network with sources selfishly allocating transmission/relaying energy and endogenously forming cooperative partnerships with respect to a network with centrally optimized energy allocations and pairing assignments.

• Journal of Ubiquitous Convergence Technology
• /
• v.1 no.1
• /
• pp.53-59
• /
• 2007

This paper proposes a new group mobility model for wireless communication. The mobility model considers the psychological and sociological behavior of each node and the perception of other nodes for describing interactions among a set of nodes. The model assumes no permanent membership of a group, capable of capturing natural behaviors as fork and join. It emulates a cooperative movement pattern observed in mobile ad hoc networks of military operation and campus, in which a set of mobile stations accomplish a cooperative motion affected by the individual behavior as well as a group behavior. The model also employs a physic model to avoid a sudden stopping and a sharping turning.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.17 no.5
• /
• pp.591-597
• /
• 2007

In swarm robot systems, each robot must behaves by itself according to the its states and environments, and if necessary, must cooperates with other robots in order to carry out a given task. Therefore it is essential that each robot has both learning and evolution ability to adapt the dynamic environments. In this paper, the new polygon based Q-learning algorithm and distributed genetic algorithms are proposed for behavior learning and evolution of collective autonomous mobile robots. And by distributed genetic algorithm exchanging the chromosome acquired under different environments by communication each robot can improve its behavior ability Specially, in order to improve the performance of evolution, selective crossover using the characteristic of reinforcement learning is adopted in this paper. we verify the effectiveness of the proposed method by applying it to cooperative search problem.

• The Mathematical Education
• /
• v.53 no.3
• /
• pp.399-412
• /
• 2014

The purpose of this article is to suggest using cooperative microteaching in pre-service mathematics teachers education based on their perceptions of it after actual application case. The background of this study is that cooperative learning came into the highlight as a good method to cultivate teachers' competencies for creativity and character education as well as students' creativity and character in the mathematics classroom. 20 pre-service mathematics teachers participated in their cooperative microteaching and 16 of them responded to the survey. The collected data showed that the merits of cooperative microteaching are to ease the burden of preparing for class, to discuss how to teach mathematics, to debate what lesson is better, to receive valuable feedback form their peer, and so on. Also, it provided them with the chance for self-improvement in that they kept to make up for the week points in their teaching behavior. Meanwhile, they wanted longer time to experience their teaching and their own lesson.