Performance optimization method for Internet of Things scene based on mobile edge computing and blockchain collaborative enabling

Abstract

The invention discloses a method for dynamically deciding a block generation strategy and an MEC calculation unloading strategy by utilizing a deep reinforcement learning (DRL) algorithm in a block chain technology collaborative energization Internet of Things scene based on mobile edge calculation (MEC) and a DPoS consensus mechanism so as to realize expandability optimization. The invention discloses a method for realizing distributed storage of application data in an Internet of Things scene by utilizing a block chain technology based on a DPoS consensus mechanism, so as to obtain higher data storage robustness and security. The invention discloses an edge resource collaboration mechanism for enabling Internet of Things nodes to strategically unload computing tasks to an MEC server through a base station based on an MEC technology. A deep deterministic strategy gradient (DDPG) algorithm based on distributed training enables Internet of Things nodes to adaptively and dynamically decide an optimal block generation strategy and an MEC calculation unloading strategy so as to jointly improve the expandability of the whole system.

Description

technical field [0001] The present invention relates to the performance optimization of the emerging Internet of Things architecture jointly empowered by Mobile Edge Computing (MEC) and blockchain technology. Distributed safe storage, high-efficiency processing and excellent system scalability for massive data belong to the field of wireless communication technology. Background technique [0002] As an important component of emerging information technology, the Internet of Things (IoT) has attracted high and wide attention in various fields such as manufacturing, logistics, and aviation. With the technological advancement of wireless communication and sensor networks, more and more smart objects are gradually involved in the IoT scene, and the emerging IoT architecture supports capturing a large amount of raw data in smart objects. Do targeted processing and analysis to support network management decisions, new IoT applications, and network data interaction. Therefore, how...

AI Technical Summary

Problems solved by technology

However, the traditional mathematical model-based optimization scheme has a high complexity of solving and dynamic optimization modeling is difficult, which is not enough to efficiently deal with the complex, high-dimensional and dynamic data characteristics in the Internet of Things architecture. At the same time, how to dynamically determine the optimal calculation of the MEC system Offloading strategies are also more challenging

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