Revenue maximizing auction for perishable IoT resources

Safianowska, Maria Barbara; Gdowski, Robert; Huang, Chao‐Wei

doi:10.1109/ictc.2016.7763510

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…The higher the ratio of bidders to resources, the more chances to achieve stability as the high demand may warrant enough competition for bidders to stay in the auction, which we have shown in our previous work. 17 In periodic bidding strategy shown in Figure 4(b), RMA fails to generate any significant revenue. TCA and HBLA show faster revenue increase with demand than in case of opportunistic bidding.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Lower bidders will drop out of auction when they realize they have no chance of winning, while higher bidders will lower their bids realizing they have less competition. In our previous work, 17 we have shown that even with scarce resources, the competition may not be enough for the prices to stabilize, causing market collapse. Below we will examine the effect of different bidding strategies on auctioneer's revenue in a recurrent TCA.…”

Section: Auctioneer and Winner Determinationmentioning

confidence: 95%

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Preventing market collapse in auctions for perishable Internet of Things resources

Safianowska

Gdowski

Huang

2017

International Journal of Distributed Sensor Networks

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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.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Auctioneer and Winner Determinationmentioning

confidence: 95%

Preventing market collapse in auctions for perishable Internet of Things resources

Safianowska

Gdowski

Huang

2017

International Journal of Distributed Sensor Networks

Self Cite

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“…This includes an analysis of IoT marketplace incentives, service patterns, information timeliness and social impacts. Two bidding algorithms to support IoT resource trading are introduced in [17]. The first algorithm aims to maximise the provider's revenue while the second is to lock the highest bid in the recurrent auction.…”

Section: B Iot Trading Mechanismsmentioning

confidence: 99%

AMACoT: A Marketplace Architecture for Trading Cloud of Things Resources

Alrawahi

Lee

Lotfi

2020

IEEE Internet Things J.

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Cloud of Things (CoT) is increasingly viewed as a paradigm that can satisfy the diverse requirements of emerging IoT applications. The potential of CoT is not yet realised due to challenges in sharing and reusing IoT physical resources across multiple applications. Existing approaches provide smallscale and hardware-dependent shared access to IoT resources. This paper considers using market mechanisms to commoditise CoT resources as the approach to enable shared access to CoT resources and to improve their reusability. In order to achieve this, the requirements for trading CoT resources are discussed to conceptualise the proposed approach. A generic description model for CoT resource is introduced to quantify the value of CoT resources. In this paper, a marketplace architecture for trading CoT resources referred to as AMACoT is proposed. By formulating the trading of CoT resources as an optimisation problem, the proposed approach is experimentally validated. The evaluation measures the system performance and verifies the optimisation problem using three evolutionary algorithms. The evaluation of the optimisation algorithms demonstrates the optimality of trading CoT resources solutions in terms of resource cost, resource utilisation, provider lock-in and provider profit.

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