EIHDP Edge-Intelligent Hierarchical Dynamic Pricing Based on Cloud-Edge-Client Collaboration for IoT Systems
ABSTARCT :
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 :
? In terms of ERD, this paper considers solving the problem of ERD from the perspective of reinforcement learning. First, the following problems are found through analysis of existing research: the potential meaning of the state sequence is ignored, the reward function is imperfect, and the performance of the RDM is poor.
? Existing models generally ignore the difference between the original tweets and their reply information, even though individual multitask joint learning models (such as Ma et al.) use two independent networks to process the original tweet and the reply information, but these two independent networks are often two networks with the same structure.
? For the existing multitask model, there is a problem that the optimal representation of the reply information cannot be obtained.
DISADVANTAGE :
? To solve this problem, we have adopted a method of station matching. When the product passes through the station, the product manufacturing information will be read.
? Once recorded, the order is issued directly to the manufacturing resource edge, generating the control flow related to the processing task.
? However, there are still some noteworthy issues in this study that need to be mentioned.
? Once a similar action sample set Cl is found, the knowledge reuse impact is evaluated on qualified samples. The action primitives with high similarity and good execution effect should be reused.
PROPOSED SYSTEM :
• In the rumor detection part, a dual-engine rumor detection model based on deep learning is proposed to realize the differential feature extraction of original tweets and their replies.
• Related researchers have proposed a multitask learning model for rumor detection and user stance detection.
• It can be seen that the model RDM2_RL2 proposed uses the least amount of information.
• It can be seen that the average reward value of the model is stable between 23 and 24 after 40 rounds of training, indicating that the DRQN-based control module proposed is effective.
• Therefore, the model proposed has good performance in the accuracy and timeliness of rumor detection.
ADVANTAGE :
? In particular, Robot2 chose a more efficient execution plan through comparing the two actions’ execution effect evaluations during the learning process.
? The manufacturing production line’s efficiency is reflected in the multi-task collaborative production and dynamic production line reconfiguration.
? A matching mechanism of knowledge search and reuse is proposed so that the processing experience of manufacturing resources can be reused.
? The knowledge that is frequently used can be directly obtained on the edge side.
? Intelligent manufacturing’s physical resources include equipment used in a series of intermediate processes such as product processing, packaging, and transportation.
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