Browsing by Author "Safianowska, Maria B."

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An Auction Based Smart Service Robot Implemented on a Fog Computing Node
(2017-01-01)
Safianowska, Maria B.
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Chang, Yi-Chieh Peter
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Wang, Te-Jen
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Huang, Chih-Wei
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Huang, ChingYao
Adopting AR/VR technology on smart retail services is gaining more momentum with the progress in indoor map scanning technology and the research on AI deep learning algorithms. In this paper we propose the use of a Fog computing node to generate an AR/VR view of the real store on a web page. The customers can then use the service robot to view the merchandise in the real store via the web and make purchases. Since the service robot is a precious resource on the AR/VR business model, we develop an auction method to optimize the customer satisfaction and the owner satisfaction in terms of customer waiting time and the average number of transactions that are assisted by the service robot respectively. We demonstrate that the auction method is a critical part in the AR/VR smart business services when the number of service robots is much less than the number of active customers from the web and that it performs better than the standard preemptive method.
Preventing market collapse in auctions for perishable Internet of Things resources
(2017-11-29)
Safianowska, Maria B.
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Gdowski, Robert
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Huang, ChingYao
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電子工程學系及電子研究所
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Department of Electronics Engineering and Institute of Electronics
We are entering the era of digital business moments where autonomous things will buy and sell services from each other in order to make people's life easier. They will also negotiate in order to achieve the best price for their services which will result in Internet of Things auctions. Such auctions will be combinatorial and recurrent. Recurrent auctions pose a problem of bidder drop which leads to market collapse. Moreover, in Internet of Things, bidders will employ different strategies according to their pattern of consuming resources. We describe two bidding strategies which may lead to market collapse or very low revenue: opportunistic and periodic bidding. We devise two algorithms: revenue maximizing auction and highest bid lock auction, analyze their performance under these two bidding strategies, and compare them to traditional combinatorial auction. We find out that the revenue maximizing auction prevents market collapse under opportunistic bidding while highest bid lock auction provides the highest revenue under both strategies.