EIHDP Edge-Intelligent Hierarchical Dynamic Pricing Based on Cloud-Edge-Client Collaboration for IoT Systems

Abstract : Nowadays, IoT systems can better satisfy the service requirements of users with effectively utilizing edge computing resources. Designing an appropriate pricing scheme is critical for users to obtain the optimal computing resources at a reasonable price and for service providers to maximize profits. This problem is complicated with incomplete information. The state-of-the-art solutions focus on the pricing game between a single service provider and users, which ignores the competition among multiple edge service providers. For this challenge, we design an edge-intelligent hierarchical dynamic pricing mechanism based on cloud-edge-client collaboration. We describe an improved double-layer Stackelberg game model. Technically, we propose a novel pricing prediction algorithm based on double-label Radius K-nearest Neighbors, which reduces the number of invalid games to accelerate the game convergence. The experimental results show that our proposed mechanism effectively improves the quality of service for users and realizes the maximum benefit equilibrium for service providers, compared with the traditional pricing scheme. Our proposed mechanism is highly suitable for the IoT applications (e.g., intelligent agriculture or Internet of Vehicles), where there are multiple competing edge service providers for resource allocation.

 EXISTING SYSTEM :

 ? The Internet of Things (IoT) has pervaded our daily life by making things interconnected through the Internet smarter, distributed and more autonomous. ? In, the authors proposed a simple, robust, efficient, scalable, and heterogeneous multi-tenant database architecture for the ad-hoc cloud to allow various organizations to collaborate and create a common cloud platform without harming their existence or profitability. ? As one can see, the existing works focus only on the combination of fog and cloud platforms in term of the communication network (e.g., low latency) and management of F2C resources. ? As with any emerging technology, the promising advancements are also accompanied with numerous challenges that are not yet well summarized in the existing literature.

 DISADVANTAGE :

 ? In the followup research, because of the above problems, there can be a clearer research plan: combining the existing knowledge graph research results, in the process of knowledge extraction, the existing sample data information is transformed into a semantic model and a knowledge graph. ? Although the above literature has made certain research on edge computing and knowledge sharing, there are still some problems: (1) In the traditional cloud edge structure, the cloud carries more computing and control tasks. ? Once recorded, the order is issued directly to the manufacturing resource edge, generating the control flow related to the processing task. ? Once a similar action sample set Cl is found, the knowledge reuse impact is evaluated on qualified samples.

 PROPOSED SYSTEM :

 • Due to the utilization of a massive number of mobile devices in our daily life, the researchers have proposed building cloud infrastructure by integrating many mobile devices that could act simultaneously as customers and providers of services. • In, an IoT-Based computational framework for healthcare has been proposed to monitor human activities involving physical effort. • In this work, the quality of the service and the mobility of IoT devices or the fog nodes are, however, not discussed, since the proposed solution operates inside a single fog domain. • In, the authors have proposed a cloud-aware framework based on mobile edge features for designing elastic mobile applications.

 ADVANTAGE :

 ? The manufacturing production line’s efficiency is reflected in the multi-task collaborative production and dynamic production line reconfiguration. ? Intelligent manufacturing’s physical resources include equipment used in a series of intermediate processes such as product processing, packaging, and transportation. ? The attributes used include the equipment’s name, number, physical size, capabilities, and the processing operations that can be performed. ? The actions are defined for each manufacturing station, and the action primitives are used for processing. ? Each new processing task is decomposed and refined, and the original action primitive combination is used to reuse the original manufacturing resource knowledge.