Abstract
This paper introduces evolutionary game theory as an analysis tool to study the evolution of the cooperative behavior of nodes in the opportunistic network. At the same time, considering the spatial distribution of nodes in the opportunistic network and the corresponding cooperative interaction relationship, the evolutionary graph theory is further applied to study the cooperative behavior in the context of considering the network space. In the collaboration model of this article, the vertices in the evolutionary graph theory are used to represent the nodes in the network, and the edges in the graph represent that the node pairs are within the communication range of each other. The node will play a game with neighboring nodes according to the payout matrix. Through repeated games between nodes, the network reaches a relatively stable final state, and the distribution of nodes with different behaviors in the final state of the network is checked. By adjusting the network parameters, we can analyze the distribution and evolution of cooperation in the network, and study the stability of the network. We realize the credible collaborative evolutionary game model by building a comprehensive simulation platform and verify its effectiveness and scalability. We design and implement an evolutionary game model independent of routing protocols, so that the model can be quickly deployed in traditional routing protocols. Subsequently, simulations verified that the reputation model played a role in guiding nodes to actively participate in network collaboration during the collaborative evolution process. And the greater the strength of the reputation incentive mechanism, the better the effect of the evolutionary model. The evolution model is deployed in multiple routing protocols, verifying that the model can play a role in different routing protocols. In addition, the evolution model can also play a role in different network environments and has good scalability.
Highlights
At present, most sensor nodes in the Internet of Things have limited computing and storage resources
In order to enable evolutionary graph theory to be applied topological structure as a dynamic opportunistic network, it is necessary to ensure that the node strategy update frequency is much greater than the topological change rate
By constructing an evolutionary game model, nodes are encouraged to actively participate in network collaboration in a self-evolving manner, so that the network maintains a cooperative state for a long time
Summary
Most sensor nodes in the Internet of Things have limited computing and storage resources. With the development of technology and the widespread popularity of Internet of Things applications in next-generation embedded computing devices, the physical resources of each node will be further expanded [1], [2]. The conclusion is that to apply the classic evolutionary game theory to a network with a specific spatial structure is to add a partial payment correction term to the initial payment matrix. This correction term is related to the initial payment matrix, and to the nodes in the network.
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