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Authors: Tang, Long , Chetty, Madhu , Lim, Suryani
Date: 2011
Type: Text , Conference paper
Relation: 18th International Conference on Neural Information Processing, ICONIP 2011; Shanghai; China; 13th to 17th November 2011; published in Neural Information Processing, (Lecture Notes in Computer Science series) Vol. 7062 (1) p.36-47
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Description: Carbon trading scheme is being established around the world as an instrument in reducing global GHG emission. Being an emerging market, there only a few simple simulation studies related to carbon trading that have been reported. In this paper, we propose a novel carbon trading simulator capable of modeling traits of human traders in carbon markets. The model is driven by the concept of Nash equilibrium within an agent based modeling paradigm. The model is capable of implementing crucial issues such as carbon emissions, Marginal Abatement Cost Curve (MAC), and complex human trading behaviour. Experiments carried out provide insights into interaction between traders’ behaviour and how the interaction affects profitability
Description: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011, Vol.7064 (3)

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Internet of vehicles for e-health applications : a potential game for optimal network capacity

Authors: Lin, Din , Tang, Yu , Labeau, Fabrice , Yao, Yuanzhe , Imran, Muhammad , Vasilakos, Athanasios
Date: 2017
Type: Text , Journal article
Relation: IEEE Systems Journal Vol. 11, no. 3 (2017), p. 1888-1896
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Description: Wireless technologies are pervasive to support ubiquitous healthcare applications. However, a critical issue of using wireless communications under a healthcare scenario rests at the electromagnetic interference (EMI) caused by RF transmission, and a high level of EMI may lead to a critical malfunction of medical sensors. In view of EMI on medical sensors, we propose a power control algorithm under a noncooperative game theoretic framework to schedule data transmission. Our objective is to ensure that the noncooperative game of power control can achieve a network-level objective - the optimal network capacity, although the wireless users are selfish and only interested in optimizing their own channel capacity. To obtain this objective, we show that our proposed noncooperative game is a potential game and propose the best-response-dynamics algorithm which can ensure that the game strategy of each user is induced to the optimal solution to the problem of network-level optimal capacity. Numerical results illustrate that the proposed algorithm can achieve an enhancement of 8% of network performance than the existing algorithm against the variations of mobile hospital environments. © 2007-2012 IEEE.

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