Proof-of-Search: Combining Blockchain Consensus Formation with Solving Optimization Problems
August 06, 2019 Β· Declared Dead Β· π IEEE Access
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Naoki Shibata
arXiv ID
1908.01915
Category
cs.DC: Distributed Computing
Citations
34
Venue
IEEE Access
Last Checked
6 months ago
Abstract
To address the large amount of energy wasted by blockchains, we propose a decentralized consensus protocol for blockchains in which the computation can be used to search for good approximate solutions to any optimization problem. Our protocol allows the wasted energy to be used for finding approximate solutions to problems submitted by any nodes~(called clients). Our protocol works in a similar way to proof-of-work, and it makes nodes evaluate a large number of solution candidates to add a new block to the chain. A client provides a search program that implements any search algorithm that finds a good solution by evaluating a large number of solution candidates. The node that finds the best approximate solution is rewarded by the client. Our analysis shows that the probability of a fork and the variance in the block time with our protocol are lower than those in proof-of-work.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Distributed Computing
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Reproducing GW150914: the first observation of gravitational waves from a binary black hole merger
R.I.P.
π»
Ghosted
MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
R.I.P.
π»
Ghosted
Adaptive Federated Learning in Resource Constrained Edge Computing Systems
R.I.P.
π»
Ghosted
Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing
R.I.P.
π»
Ghosted
iFogSim: A Toolkit for Modeling and Simulation of Resource Management Techniques in Internet of Things, Edge and Fog Computing Environments
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted