23 research outputs found
Transaction Propagation on Permissionless Blockchains: Incentive and Routing Mechanisms
Existing permissionless blockchain solutions rely on peer-to-peer propagation
mechanisms, where nodes in a network transfer transaction they received to
their neighbors. Unfortunately, there is no explicit incentive for such
transaction propagation. Therefore, existing propagation mechanisms will not be
sustainable in a fully decentralized blockchain with rational nodes. In this
work, we formally define the problem of incentivizing nodes for transaction
propagation. We propose an incentive mechanism where each node involved in the
propagation of a transaction receives a share of the transaction fee. We also
show that our proposal is Sybil-proof. Furthermore, we combine the incentive
mechanism with smart routing to reduce the communication and storage costs at
the same time. The proposed routing mechanism reduces the redundant transaction
propagation from the size of the network to a factor of average shortest path
length. The routing mechanism is built upon a specific type of consensus
protocol where the round leader who creates the transaction block is known in
advance. Note that our routing mechanism is a generic one and can be adopted
independently from the incentive mechanism.Comment: 2018 Crypto Valley Conference on Blockchain Technolog
Game Theoretic Analysis of Tree Based Referrals for Crowd Sensing Social Systems with Passive Rewards
Participatory crowd sensing social systems rely on the participation of large
number of individuals. Since humans are strategic by nature, effective
incentive mechanisms are needed to encourage participation. A popular mechanism
to recruit individuals is through referrals and passive incentives such as
geometric incentive mechanisms used by the winning team in the 2009 DARPA
Network Challenge and in multi level marketing schemes. The effect of such
recruitment schemes on the effort put in by recruited strategic individuals is
not clear. This paper attempts to fill this gap. Given a referral tree and the
direct and passive reward mechanism, we formulate a network game where agents
compete for finishing crowd sensing tasks. We characterize the Nash equilibrium
efforts put in by the agents and derive closed form expressions for the same.
We discover free riding behavior among nodes who obtain large passive rewards.
This work has implications on designing effective recruitment mechanisms for
crowd sourced tasks. For example, usage of geometric incentive mechanisms to
recruit large number of individuals may not result in proportionate effort
because of free riding.Comment: 6 pages, 3 figures. Presented in Social Networking Workshop at
International Conference on Communication Systems and Networks (COMSNETS),
Bangalore, India, January 201
An Axiomatic Approach to Routing
Information delivery in a network of agents is a key issue for large, complex
systems that need to do so in a predictable, efficient manner. The delivery of
information in such multi-agent systems is typically implemented through
routing protocols that determine how information flows through the network.
Different routing protocols exist each with its own benefits, but it is
generally unclear which properties can be successfully combined within a given
algorithm. We approach this problem from the axiomatic point of view, i.e., we
try to establish what are the properties we would seek to see in such a system,
and examine the different properties which uniquely define common routing
algorithms used today.
We examine several desirable properties, such as robustness, which ensures
adding nodes and edges does not change the routing in a radical, unpredictable
ways; and properties that depend on the operating environment, such as an
"economic model", where nodes choose their paths based on the cost they are
charged to pass information to the next node. We proceed to fully characterize
minimal spanning tree, shortest path, and weakest link routing algorithms,
showing a tight set of axioms for each.Comment: In Proceedings TARK 2015, arXiv:1606.0729