A trust-based P2P resource search method integrating with Q-learning for future Internet

Liu, Huanlin; Chen, Gao-xiang; Chen, Yong; Chen, Qianbin

doi:10.1007/s12083-014-0279-x

Cited by 6 publications

(3 citation statements)

References 22 publications

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“…Duplicate subgraphs are subgraphs that are frequently observed in path nodes on a graph and provide the most repetition with the least support while exploring the pattern of the graph with k using the greedy local search technique. In [33], in order to solve the flooding problem that causes problems such as heavy traffic in the network if the TTL parameters are not well defined, the Random-walk algorithm is used in which a node is randomly select one of its neighbors and then transmit its request message. The node neighbor also selects another neighbor at random and sends the message again.…”

Section: An Intelligent Fuzzy Search Methods Based On Clustering Topo...mentioning

confidence: 99%

Cost-Effective Resources for Computing Approximation Queries in Mobile Cloud Computing Infrastructure

Sangaiah,

Javadpour,

Pinto

et al. 2023

Sensors

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Answering a query through a peer-to-peer database presents one of the greatest challenges due to the high cost and time required to obtain a comprehensive response. Consequently, these systems were primarily designed to handle approximation queries. In our research, the primary objective was to develop an intelligent system capable of responding to approximate set-value inquiries. This paper explores the use of particle optimization to enhance the system’s intelligence. In contrast to previous studies, our proposed method avoids the use of sampling. Despite the utilization of the best sampling methods, there remains a possibility of error, making it difficult to guarantee accuracy. Nonetheless, achieving a certain degree of accuracy is crucial in handling approximate queries. Various factors influence the accuracy of sampling procedures. The results of our studies indicate that the suggested method has demonstrated improvements in terms of the number of queries issued, the number of peers examined, and its execution time, which is significantly faster than the flood approach. Answering queries poses one of the most arduous challenges in peer-to-peer databases, as obtaining a complete answer is both costly and time-consuming. Consequently, approximation queries have been adopted as a solution in these systems. Our research evaluated several methods, including flood algorithms, parallel diffusion algorithms, and ISM algorithms. When it comes to query transmission, the proposed method exhibits superior cost-effectiveness and execution times.

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Section: An Intelligent Fuzzy Search Methods Based On Clustering Topo...mentioning

confidence: 99%

Cost-Effective Resources for Computing Approximation Queries in Mobile Cloud Computing Infrastructure

Sangaiah,

Javadpour,

Pinto

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…The complexity and packet loss in the mesh‐based P2P network were mitigated. Liu et al proposed a novel P2P search method that integrates trust mechanism with the Q‐learning process to achieve effective and smooth sharing of the file. An efficient method for selecting the neighbor node was provided by improving the Q‐learning process.…”

Section: Related Workmentioning

confidence: 99%

PSO‐based optimal peer selection approach for highly secure and trusted P2P system

Nallakannu¹,

Thiagarajan²

2016

Security Comm Networks

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There is a significant growth of the peer-to-peer (P2P) systems over the last few years because of their high potential for sharing file among the peers. Selection of the optimal peer ensures high data transmission rate and reduced network cost. However, it is highly difficult to select the optimal peer because of the variation in the heterogeneous capacity and dynamic capacity of the peers. Efficient peer selection approach is highly required to overcome this difficulty. This paper proposes a particle swarm optimization (PSO)-based optimal peer selection approach for a highly secure and trusted P2P system, based on the bandwidth and trust level of the peers. File replication is performed to effectively tackle the overloading of the hot files. Replacement of the replicated file is performed, based on their demand rates. The success rate and bandwidth rate of the peer are computed. Bandwidth and trust-based PSO is applied to select the optimal peer. This improves the success rate for the peer transmission. After selecting the optimal peer, the replicated file is encrypted and transmitted to the selected peer. Finally, the replicated file is decrypted. Our proposed approach achieves minimum average query delay and ensures a highly secure and trusted environment for the P2P systems. The performance of the proposed approach is evaluated by comparing the proposed approach with the existing techniques. From the comparison results, it is clearly evident that the proposed approach achieves higher success rate and lower link stress, delay, communication overhead, finished time and network cost.

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“…In front of such massive amount of learning resources, inevitably, students might get lost in the sea of knowledge or drift from the main topic from time to time, so the requirement of students for learning resource retrieval is growing and changing [6][7][8][9][10][11][12][13][14]. When a student has just get started with a certain course or come into contact with the knowledge of a certain field, he or she does not have the ability to analyze the relevance between the professional knowledge and the learning resources, so it's impossible for the student to accurately describe the learning resources in need [15][16][17]. Out of these concerns, now world field scholars are paying more attention to the problems of how to derive the true retrieval intentions of students from the fuzzy keywords given by them, and how to solve the data sparseness, cold start and scalability problems of the learning resource retrieval systems.…”

Section: Introductionmentioning

confidence: 99%

A New Learning Resource Retrieval Method Based on Multi-knowledge Association Mining

Xiao¹,

Yang²,

Zhang³

2023

Int. J. Emerg. Technol. Learn.

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Ever since the human society has entered the era of big data, the quantity and type of digital learning resources on the Internet are increasing exponentially, and the requirement of students for learning resource retrieval is on the rise. However, the existing methods of learning resource retrieval generally overlook the overall knowledge systems that the students have already possessed, so it’s impossible for them to predict the students’ learning path or perform deviation adjustment. In view of these issues, this paper aims to study a new learning resource retrieval method based on multi-knowledge association mining. At first, the paper introduces the application of the Knowledge Graph Embedding (KGE) technology in learning resource retrieval, proposes the problem of learning resource retrieval, and points out the goal of learning resource retrieval. Then, a breadth-first soft-matching search algorithm is introduced to attain the multiple association paths between students and learning resources, and a retrieval module is constructed based on association path to further learn the association paths between students and learning resources within the framework of feature learning, and to predict the probability of interactions between students and learning resources. At last, this paper evaluates the association paths and uses experimental results to verify the validity of the proposed learning resource retrieval method.

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A trust-based P2P resource search method integrating with Q-learning for future Internet

Cited by 6 publications

References 22 publications

Cost-Effective Resources for Computing Approximation Queries in Mobile Cloud Computing Infrastructure

Cost-Effective Resources for Computing Approximation Queries in Mobile Cloud Computing Infrastructure

PSO‐based optimal peer selection approach for highly secure and trusted P2P system

A New Learning Resource Retrieval Method Based on Multi-knowledge Association Mining

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