Weizhi Ran, Sulemana Nantogma, Shangyan Zhang, Xu Yang
No abstract is available for this record.
Follow blockchain research across journals, conferences, and preprint repositories.
6 results · page 1 of 1
Weizhi Ran, Sulemana Nantogma, Shangyan Zhang, Xu Yang
No abstract is available for this record.
Akaki Mamageishvili, Christoph Schlegel, Benny Sudakov, Danning Sui
We study the amount of maximal extractable value (MEV) captured by validators, as a function of searcher competition, in blockchains with competitive block building markets such as Ethereum. We argue that the core is a suitable solution concept in this context that makes robust predictions that are independent of implementation details or specific mechanisms chosen. We characterize how much value validators extract in the core and quantify the surplus share of validators as a function of searcher competition. Searchers can obtain at most the marginal value increase of the winning block relative to the best block that can be built without their bundles. Dually this gives a lower bound on the value extracted by the validator. If arbitrages are easy to find and many searchers find similar bundles, the validator gets paid all value almost surely, while searchers can capture most value if there is little searcher competition per arbitrage. Moreover, mechanisms that implement core allocations in dominant strategies, for submodular values, there is a unique dominant-strategy incentive compatible core-selecting mechanism that gives each searcher exactly their marginal value contribution to the winning block. We extend our model to multiple concurrent proposers in which, under mild assumptions, the core is empty. We validate our theoretical prediction empirically with aggregate bundle data and find a significant positive relation between the number of submitted backruns for the same opportunity and the median value captured by the proposer from the opportunity.
Gary B. Gorton, Jeffery Zhang
No abstract is available for this record.
Xinghua Li, Yunwei Wang, Pandi Vijayakumar, Debiao He · 6 authors
A dynamic group key is required for secure communication in the Unmanned Aerial Vehicles Ad-Hoc Network (UAANET). However, due to the unreliable wireless channel and high-dynamic topology of UAANET, the situation that a node is missing certain group key broadcast messages occurs frequently. Existing group key distribution schemes cannot be directly applied to the UAANET, because of their poor security or real-time. Therefore, we present a mutual-healing group key distribution scheme based on the blockchain. Firstly, the Ground Control Station (GCS) builds a private blockchain where the group keys distributed by GCS are recorded. Meanwhile, through the blockchain, a dynamic list of UAANET membership certificates is also managed. According to different attack models, a basic mutual-healing protocol and an enhanced one are designed based on the Longest-Lost-Chain mechanism to recover the node's lost group keys with the aid of its neighbors. Security analysis and extensive experiments show that, compared with the existing mutual-healing schemes, our proposed solution can effectively resist various attacks with small overhead on time and storage.
Shigen Gao, Hairong Dong, Bin Ning, Xiuming Yao
No abstract is available for this record.
J.S. Baras, Xiaobo Tan, P. Hovareshti
Decentralized control methods are appealing in coordination of multiple vehicles due to their low demand for long-range communication and their robustness to single-point failures. In this paper we explore a decentralized approach to path generation for a group of vehicles in a battlefield scenario. The mission is to maneuver the vehicles to cover a target area while avoiding obstacles and threats during the maneuver. Each vehicle makes its moving decision by minimizing a potential function that encodes information about its neighbours, obstacles, threats and the target. Preliminary analysis of vehicle behaviors is conducted. Simulation has shown that this approach leads to interesting emergent behaviors, and the behaviors can be varied by adjusting the weighting coefficients of different potential function terms.