Optimal Mechanism Design with Flexible Consumers and Costly Supply

The problem of designing a profit-maximizing, Bayesian incentive compatible and individually rational mechanism with flexible consumers and costly heterogeneous supply is considered. In our setup, each consumer is associated with a flexibility set that describes the subset of goods the consumer is equally interested in. Each consumer wants to consume one good from its flexibility set. The flexibility set of a consumer and the utility it gets from consuming a good from its flexibility set are its private information. We adopt the flexibility model of [1] and focus on the case of nested flexibility sets -- each consumer's flexibility set can be one of k nested sets. Examples of settings with this inherent nested structure are provided. On the supply side, we assume that the seller has an initial stock of free supply but it can purchase more goods for each of the nested sets at fixed exogenous prices. We characterize the allocation and purchase rules for a profit-maximizing, Bayesian incentive compatible and individually rational mechanism as the solution to an integer program. The optimal payment function is pinned down by the optimal allocation rule in the form of an integral equation. We show that the nestedness of flexibility sets can be exploited to obtain a simple description of the optimal allocations, purchases and payments in terms of thresholds that can be computed through a straightforward iterative procedure.Comment: 8 pages. arXiv admin note: text overlap with arXiv:1607.0252

An Online Auction Mechanism for Dynamic Virtual Cluster Provisioning in Geo-Distributed Clouds

postprin

E-COMMERCE IN AGRICULTURE: DEVELOPMENT, STRATEGY, AND MARKET IMPLICATIONS

Marketing,

Auction-Based Efficient Online Incentive Mechanism Designs in Wireless Networks

Recently, wide use of mobile devices and applications, such as YouTube and Twitter, has facilitated every aspect of our daily lives. Meanwhile, it has also posed great challenges to enable resource-demanding users to successfully access networks. Thus, in order to enlarge network capacity and fully make use of vacant resources, new communication architectures emerge, such as D2D communications, edge computing, and crowdsourcing, all of which ask for involvement of end mobile users in assisting transmission, computation, or network management. However, end mobile users are not always willing to actively provide such sharing services if no reimbursements are provided as they need to consume their own computation and communication resources. Besides, since mobile users are not always stationary, they can opt-in and opt-out the network for their own convenience. Thus, an important practical characteristic of wireless networks, i.e., the mobility of mobile users cannot be ignored, which means that the demands of mobile users span over a period of time. As one of promising solutions, the online incentive mechanism design has been introduced in wireless networks in order to motivate the participation of more mobile users under a dynamic environment. In this thesis, with the analyses of each stakeholder's economic payoffs in wireless networks, the auction-based online incentive mechanisms are proposed to achieve resource allocations, participant selections, and payment determinations in two wireless networks, i.e., Crowdsensing and mobile edge computing. In particular, i) an online incentive mechanism is designed to guarantee Quality of Information of each arriving task in mobile crowdsensing networks, followed by an enhanced online strategy which could further improves the competitive ratio; ii) an online incentive mechanism jointly considering communication and computation resource allocations in collaborative edge computing networks is proposed based on the primal-dual theory; iii) to deal with the nonlinear issue in edge computing networks, an nonlinear online incentive mechanism under energy budget constraints of mobile users is designed based on the Maximal-in-Distributional Range framework; and iv) inspired by the recent development of deep learning techniques, a deep incentive mechanism with the budget balance of each mobile user is proposed to maximize the net revenue of service providers by leveraging the multi-task machine learning model. Both theoretical analyses and numerical results demonstrate the effectiveness of the designed mechanisms

White Space Network Management: Spectrum Quanti cation, Spectrum Allocation and Network Design

Philosophiae Doctor - PhD (Computer Science)The unused spectrum in the television broadcasting frequency bands (so-called TV white spaces) can alleviate the spectrum crunch, and have potential to provide broadband connection to rural areas of countries in the developing world. Current research on TV white spaces focuses on how to detect them accurately, and how they can be shared or allocated to secondary devices. Therefore, the focus of this research is three-fold: to investigate a novel distributed framework, which does not use propagation models in detecting TV white spaces, and suitable for use in countries of the developing world; to investigate a suitable spectrum sharing mechanism for short-time leasing of the TV white spaces to secondary devices; and extend the research to investigate the design of a TV white space-ware network in TV white space frequencies