Abstract
With the development of wisdom network, this paper assumes that intelligent devices become more and more intelligent, which can easily collect and provide a variety of context awareness data. The research goal is to design a dynamic conflict resolution strategy for context-aware resource allocation. The limited availability of resources inevitably leads to conflicts. Considering the characteristics of wisdom network, the quality of service when solving conflicts, a mechanism is proposed to improve the quality of services and to solve the resources allocation conflicts. This paper constructs the optimal model of context-aware based on a differential game and optimizes the resource allocation of context-aware based on the priority of scenarios. Fog computing is used to provide enough computing resources for the control of resource allocation of context-aware. The Bellman dynamic programming is introduced to solve the feedback Nash equilibrium solution of the proposed differential game model, to obtain the optimal allocation of service resources and solve the effectiveness of resource allocation.
Highlights
The next-generation network is a combination of the network intelligence, ubiquitous network, Internet of Things, and so on, which can always be considered a wisdom network
Wisdom network is bound to become a synonym for the next-generation network, and context awareness is the feeler of wisdom network [6, 7]
This paper proposes a conflict resolution model of context awareness based on a differential game
Summary
The next-generation network is a combination of the network intelligence, ubiquitous network, Internet of Things, and so on, which can always be considered a wisdom network. In the wisdom network, there are different priority situations of different context-aware tasks, and there are conflicts in service resource allocation among all these tasks. As the resource for context awareness is limited in the researched network, fog computing is introduced as the main controller for the resource allocation, to achieve optimal resource allocation for different context-aware tasks. (1) Resource allocation of context-aware is researched based on the priority of scenarios. In the proposed game model, the system state is given by the dynamic variations of the available allocated service resources, and the objectives for all scenarios are to find the optimal resource allocation strategies to maximize the revenue function.
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