Implementation of Trusted Traceability Query Using Blockchain and Deep Reinforcement Learning in Resource Management

Table 2

Literature review.

Classification of proposed approaches	Year	Authors	Strengths	Gaps	Objectives	Field of application	Constraints
Trusted traceability query of blockchain	2021	Vikaliana et al. [4]	It can quickly summarize and sort out the literature	High complexity	Helps the system to carry out the literature review	Traceability of agricultural enterprise commodities	The limited scope of use
	2019	Chen et al. [5]	Three main areas of enterprise management, user query, and government supervision are designed to track information flow and system	High cost	The application value hypothesis of NFC technology in the agricultural product supply chain is proposed and verified	Improvement of the agricultural product supply chain	The dataset used is small
	2019	George et al. [6]	In addition to enhancing the traceability of food (products), the prototype can grade the quality of food consumed by human beings	There are few actual use scenarios	A restaurant prototype using blockchain and product identification to achieve more reliable food traceability is proposed	Restaurants	The system construction is complex
	2021	Yang et al. [7]	Traceability of product information in product supply chain	It needs to be used with the database	It improves the transparency and credibility of traceability information	Agriculture products	The limited scope of use
	2021	Liu et al. [8]	Exact distance query	Logistics, transportation, and product traceability	It solves the problems of data leakage and query leakage in data outsourcing	Products	The performance requirements of computing equipment are high
RM	2021	Munaye et al. [9]	The scheme has good results. In the evaluation task, the evaluation results converge quickly, which is suitable for heterogeneous IoT (IoT) networks with low complexity	The experimental object is relatively single	The resource use of IoT networks is optimized	Wireless networks	The performance requirements of computing equipment are high
	2020	Chen et al. [10]	Each scheduling slot makes decentralized optimal band allocation and packet scheduling decisions. The performance of the previous algorithm is greatly improved	The experiment is carried out only under ideal conditions	Radio RM	Wireless networks	High performance requirements for computing equipment
	2020	Yang et al. [11]	The priority experience replay and coordinated learning mechanism are adopted to enable distributed communication links, which improve the network performance and access success probability	The algorithm is tested only under ideal conditions	The radio block joint allocation and transmission power control strategy are optimized	Wireless networks	High requirements for the hardware equipment