Incentive Mechanism of Selfish Nodes Based on Energy Optimization and Game Theory
With the proliferation of mobile intelligent terminals, opportunistic networks have attracted widespread attention as a complementary technology to multi-network convergence. Different from traditional wireless networks, message delivery in opportunistic networks does not rely on a fixed infrastructure, but rather storing messages in a cache and utilizing the movement and encounters of nodes to relay messages. However, in practical application scenarios, nodes have limited storage space and energy and will easily exhibit selfishness. An increase in the number of selfish nodes will drastically degrade the performance of the network. To solve the problem of significant network performance degradation when the number of selfish nodes is high, this paper proposes an Incentive mechanism of Selfish nodes based on Energy optimization and Game Theory (ISEGT). The mechanism abstracts the process of forwarding messages by nodes into a bargaining game process, and selectively forwards messages based on nodes' remaining energy and other circumstances. The experimental results show that the ISEGT mechanism can motivate selfish nodes to actively participate in message forwarding, which improves the success rate of message delivery and the survival rate of nodes, and optimizes the overall performance of the network.
Authors
- Winston K.G. Seah (ORCID: https://orcid.org/0000-0001-7147-5167)
- Gang Xu (ORCID: https://orcid.org/0000-0001-5731-5036)
- Feng Zhang (ORCID: https://orcid.org/0000-0002-1117-8715)
- Yuan Cheng (ORCID: https://orcid.org/0000-0002-5511-4414)
- X. Dong
Institutions
- Shanxi University (CN)
- Inner Mongolia University (CN)
- Victoria University of Wellington (NZ)
Publication Details
- Published
- 2024-10-06
- DOI
- https://doi.org/10.1109/smc54092.2024.10831195
- Primary Topic
- Complex Systems and Time Series Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China