Energy-Efficient Smart Routing Based on Link Correlation Mining for Wireless Edge Computing in IoT

Zhou, Xiaokang; Yang, Xiang; Ma, Jun; Wang, Kevin I‐Kai

doi:10.1109/jiot.2021.3077937

Cited by 145 publications

(54 citation statements)

References 28 publications

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“…The recommendation system can reach the demand of a personalized selection of users, help e-commerce enterprises to better serve users, improve competitiveness and achieve profitability. With the increasing demand for music information and the access and utilization of music information and the rapid development of the music industry, domestic and foreign scholars have actively explored the issues related to music information [ 6 ]. Their research mainly involves the concept, characteristics, and functions of music information, music information demand and behaviour, music information retrieval, library music information resource construction and service, digital music intellectual property rights, the music industry, and other aspects.…”

Section: Introductionmentioning

confidence: 99%

Variational Fuzzy Neural Network Algorithm for Music Intelligence Marketing Strategy Optimization

Sun

2022

Computational Intelligence and Neuroscience

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In this paper, we use a variational fuzzy neural network algorithm to conduct an in-depth analysis and research on the optimization of music intelligent marketing strategy. The music recommendation system proposed in this paper includes a user modelling module, audio feature extraction module, and recommendation algorithm module. The basic idea of the recommendation algorithm is as follows: firstly, the historical behavioural information of music users is collected, and the user preference model is constructed by using the method of matrix decomposition of the hidden semantic model; then, the audio resources in the system are preprocessed and the spectrum map that can represent the music features is extracted; the similarity between the user’s preferred features and the music potential features are calculated to generate recommendations for the target user. The user-music dataset for model training and testing is constructed in-house, and the network model structure used for system experiments is designed based on a typical convolutional neural network model, while the model training tuning parameters are compared and selected. Finally, the model is trained and tested in this paper, and the system is evaluated in terms of both prediction rating accuracy and recommendation list generation accuracy using root mean square error, accuracy, recall, and F1 value as recommendation quality evaluation metrics. The experimental results show that the recommendation algorithm in this paper has certain feasibility and effectiveness. Compared with other traditional music recommendation algorithms, this paper makes full use of the powerful advantage of deep neural networks to automatically extract features and obtain higher-level music feature representations from the audio content, while incorporating the historical behavioural information of user interactions with music, which can effectively alleviate the problems such as cold start in recommendation systems.

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Section: Introductionmentioning

confidence: 99%

Variational Fuzzy Neural Network Algorithm for Music Intelligence Marketing Strategy Optimization

Sun

2022

Computational Intelligence and Neuroscience

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“…However, with the expansion of the IoT system, service demand is increasing (e.g., response time, intelligent analysis, data storage, and privacy protection). With the popularity of 5G technology, this demand further increases [11,12], a trusted computer or cluster of computers deployed at the edge of a network with rich service resources to provide computing and data storage services to nearby mobile devices. In 2011, Bonomi et al proposed the concept of fog computing [13], which introduces a fog computing layer between the device and the cloud.…”

Section: Related Workmentioning

confidence: 99%

A Data-Secured Intelligent IoT System for Agricultural Environment Monitoring

Zhou

Xiao

Lü

et al. 2022

Wireless Communications and Mobile Computing

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Collecting environmental information of crop growth and dynamically adjusting agricultural production has been proved an effective way to improve the total agricultural yield. Agricultural IoT technology, which integrates the information sensing equipment, communication network, and information processing systems, can support such an intelligent manner in the agricultural environment. Traditional agricultural IoT could meet the service demand of small-scale agricultural production scenarios to a certain extent. However, the emerging application scenario of the agricultural environment is becoming more and more complicated, and the data nodes of the underlying access to IoT backend system are increasing in large number, while the upper-layer applications are requiring high quality of data service. Hence, the traditional architecture-based (i.e., centralised cloud computing) IoT systems suffer from the problems such as small network coverage, data security issue, and limited power supply time while attempting to provide high-quality services at the edge of the network. Emerging edge computing offers the opportunity to solve these issues. This paper builds an intelligent IoT system for agricultural environment monitoring by integrating edge computing and artificial intelligence. We conducted an experiment to validate the proposed system considering the reliability and usability. The experimental results prove the system’s reliability (e.g., data packet loss rate is less than 0.1%). The proposed system achieves the concurrency of 500TPS and the average response time of 300 ms, which meet the practical requirements in agricultural environment monitoring.

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“…To deal with such increasing privacy and security concerns, modern IoT or distributed systems need to be able to detect and prevent network intrusions in a more intelligent way. Many research efforts have been devoted to develop machine learning or deep learning-based approaches for network intrusion detection system (NIDS), in order to prevent any deviation or misuse in IoT systems and infrastructures [5]- [7]. Although NIDS has been well exploited in detecting malicious network activities, one of the main vulnerabilities of existing NIDS is the lack of ability to detect unknown types of network intrusion, due to the limited or imbalanced intrusion data during the model training process [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Adversarial Attacks Against Graph-Neural-Network-Based IoT Network Intrusion Detection System

Zhou

Liang

Liu

et al. 2022

IEEE Internet Things J.

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197

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From 2012 to 2015, he was a Research Associate with the Faculty of Human Sciences, Waseda University. He has been working as a Visiting Researcher with the RIKEN Center for Advanced Intelligence Project, RIKEN, Tokyo, since 2017. He has been engaged in interdisciplinary research works in the fields of computer science and engineering, information systems, and social and human informatics. His recent research interests include ubiquitous computing, big data, machine learning, behavior and cognitive informatics, cyber-physical-social systems, cyber intelligence, and security.Dr. Zhou is a member of IEEE CS, and ACM, USA, IPSJ, and JSAI, Japan, and CCF, China.

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Energy-Efficient Smart Routing Based on Link Correlation Mining for Wireless Edge Computing in IoT

Cited by 145 publications

References 28 publications

Variational Fuzzy Neural Network Algorithm for Music Intelligence Marketing Strategy Optimization

Variational Fuzzy Neural Network Algorithm for Music Intelligence Marketing Strategy Optimization

A Data-Secured Intelligent IoT System for Agricultural Environment Monitoring

Hierarchical Adversarial Attacks Against Graph-Neural-Network-Based IoT Network Intrusion Detection System

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