Evaluating Auction Mechanisms for the Preservation of Cost-Aware Digital Objects under Constrained Digital Preservation Budgets

Digital preservation is a field of research focused on designing strategies for maintaining digital objects accessible for general use in the coming years. Out of the many approaches to digital preservation, the present research article is a continuation work of a previously published article containing a proposal for a novel object-centered paradigm to address the digital preservation problem where digital objects share part of the responsibility for self-preservation. In the new framework, the behavior of digital objects is modeled to find the best preservation strategy. The results presented in the current article add a new economic constraint to the object behavior. Now, differently from the previous paper, migrations, copies and updates are not free to use, but subject to budget limitations to ensure the economic sustainability of the whole preservation system, forcing the now-called cost-aware digital objects for efficient management of available budget. The presented approach compares two auction-based mechanisms, a multi-unit auction and a combinatorial auction, with a simple direct purchase strategy as possible efficient behaviors for budget management. TiM, a simulated environment for running distributed digital ecosystems, is used to perform the experiments. The simulated results map the relation between the studied purchase models with the sustained quality level of digital objects, as a measure of its accessibility, together with its budget management capabilities. About the results, the best performance corresponds to the combinatorial auction model. The results are a good approach to deal with the digital preservation problem from a sustainable point of view and open the door to future implementations with other purchase strategies.

Verifying Privacy Preserving Combinatorial Auctions

<p>Suppose you are competing in an online sealed bid auction for some goods. How do you know the auction result can be trusted? The auction site could be performing actions that support its own commercial interests by blocking certain bidders or even reporting incorrect winning prices. This problem is magnified when the auctioneer is an unknown party and the auctions are for high value items. The incentive for the auctioneer to cheat can be high as they could stand to make a significant profit by inflating winning prices or by being paid by a certain bidder to announce them the winner. Verification of auction results provides confidence in the auction result by making it computationally infeasible for an auction participant to cheat and not get caught. This thesis examines the construction of verifiable privacy preserving combinatorial auction protocols. Two verifiable privacy preserving combinatorial auction protocols are produced by extending existing auction protocols.</p>

Verifying Privacy Preserving Combinatorial Auctions

<p>Suppose you are competing in an online sealed bid auction for some goods. How do you know the auction result can be trusted? The auction site could be performing actions that support its own commercial interests by blocking certain bidders or even reporting incorrect winning prices. This problem is magnified when the auctioneer is an unknown party and the auctions are for high value items. The incentive for the auctioneer to cheat can be high as they could stand to make a significant profit by inflating winning prices or by being paid by a certain bidder to announce them the winner. Verification of auction results provides confidence in the auction result by making it computationally infeasible for an auction participant to cheat and not get caught. This thesis examines the construction of verifiable privacy preserving combinatorial auction protocols. Two verifiable privacy preserving combinatorial auction protocols are produced by extending existing auction protocols.</p>

An Incentive-Compatible Combinatorial Auction Design for Charging Network Scheduling of Battery Electric Vehicles

Charging network scheduling for battery electric vehicles is a challenging research issue on deciding where and when to activate users’ charging under the constraints imposed by their time availability and energy demands, as well as the limited available capacities provided by the charging stations. Moreover, users’ strategic behaviors and untruthful revelation on their real preferences on charging schedules pose additional challenges to efficiently coordinate their charging in a market setting, where users are reasonably modelled as self-interested agents who strive to maximize their own utilities rather than the system-wide efficiency. To tackle these challenges, we propose an incentive-compatible combinatorial auction for charging network scheduling in a decentralized environment. In such a structured framework, users can bid for their preferred destination and charging time at different stations, and the scheduling specific problem solving structure is also embedded into the winner determination model to coordinate the charging at multiple stations. The objective is to maximize the social welfare across all users which is represented by their total values of scheduled finishing time. The Vickrey–Clarke–Groves payment rule is adopted to incentivize users to truthfully disclose their true preferences as a weakly dominant strategy. Moreover, the proposed auction is proved to be individually rational and weakly budget balanced through an extensive game-theoretical analysis. We also present a case study to demonstrate its applicability to real-world charging reservation scenarios using the charging network data from Manhattan, New York City.

Incentive Based Load Shedding Management in a Microgrid Using Combinatorial Auction with IoT Infrastructure

This paper presents a novel incentive-based load shedding management scheme within a microgrid environment equipped with the required IoT infrastructure. The proposed mechanism works on the principles of reverse combinatorial auction. We consider a region of multiple consumers who are willing to curtail their load in the peak hours in order to gain some incentives later. Using the properties of combinatorial auctions, the participants can bid in packages or combinations in order to maximize their and overall social welfare of the system. The winner determination problem of the proposed combinatorial auction, determined using particle swarm optimization algorithm and hybrid genetic algorithm, is also presented in this paper. The performance evaluation and stability test of the proposed scheme are simulated using MATLAB and presented in this paper. The results indicate that combinatorial auctions are an excellent choice for load shedding management where a maximum of 50 users participate.