Time-Aware Service Ranking Prediction in the Internet of Things Environment

Huang, Yuze; Huang, Jiwei; Cheng, Bo; He, Simai; Chen, Junliang

doi:10.3390/s17050974

Cited by 17 publications

(5 citation statements)

References 40 publications

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“…G. White et al [31] propose a matrix factorization-based collaborative filtering framework, where they execute paths to dynamic adaptation via QoS prediction for time awareness. Huang et al [32] propose a time-aware service ranking prediction in IoT, which generates the global ranking of IoT objects from the collection of partial rankings for the recommendation. Urbieta et al [33] give a time-aware object recommendation supporting dynamic reasoning, which applies an abstract service model to represent objects and user tasks via fusing their profiles and the temporal factor.…”

Section: Machine Learning-based Service Recommendation In Iotmentioning

confidence: 99%

Cyber-Physical-Social Model for Service Recommendation in the Internet of Things

Gao

Liu

2022

Mathematical Problems in Engineering

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The services in the Internet of Things (IoT) are the key components to realize the value of IoT. The entity-oriented services are discovered from data. However, a large number of heterogeneous data and entities in IoT increase the difficulty of service development. For this, we propose a cyber-physical-social model to recommend services in IoT. The model consists of four layers: in the physical layer, the individual behavior pattern is defined. The system layer is responsible for handling interaction data to solve the heterogeneous data problem. The cyber layer is the agent layer, where we use the defined agents to establish service logic, shielding the entity heterogeneous problem. In the social layer, we explore the behavior similarity between individual users, achieving entity interaction in different scenes. In experiments, we obtain the data from 5 scenes, and the data is used for 6 experiments. In terms of accuracy and response time, our model has outstanding advantages compared with the previous methods.

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Section: Machine Learning-based Service Recommendation In Iotmentioning

confidence: 99%

Cyber-Physical-Social Model for Service Recommendation in the Internet of Things

Gao

Liu

2022

Mathematical Problems in Engineering

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“…Service oriented computing (SOA) has been widely studied in service composition [ 25 , 26 ], service selection [ 27 , 28 ], service recommendation [ 29 ], and service discovery [ 30 ]. These studies have been proved valuable in many areas, such as distributed computing, cloud computing, Internet of Things, and networked physical systems.…”

Section: Related Workmentioning

confidence: 99%

A Personalized QoS Prediction Approach for CPS Service Recommendation Based on Reputation and Location-Aware Collaborative Filtering

Kuang

Huang

et al. 2018

Sensors

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With the rapid development of cyber-physical systems (CPS), building cyber-physical systems with high quality of service (QoS) has become an urgent requirement in both academia and industry. During the procedure of building Cyber-physical systems, it has been found that a large number of functionally equivalent services exist, so it becomes an urgent task to recommend suitable services from the large number of services available in CPS. However, since it is time-consuming, and even impractical, for a single user to invoke all of the services in CPS to experience their QoS, a robust QoS prediction method is needed to predict unknown QoS values. A commonly used method in QoS prediction is collaborative filtering, however, it is hard to deal with the data sparsity and cold start problem, and meanwhile most of the existing methods ignore the data credibility issue. Thence, in order to solve both of these challenging problems, in this paper, we design a framework of QoS prediction for CPS services, and propose a personalized QoS prediction approach based on reputation and location-aware collaborative filtering. Our approach first calculates the reputation of users by using the Dirichlet probability distribution, so as to identify untrusted users and process their unreliable data, and then it digs out the geographic neighborhood in three levels to improve the similarity calculation of users and services. Finally, the data from geographical neighbors of users and services are fused to predict the unknown QoS values. The experiments using real datasets show that our proposed approach outperforms other existing methods in terms of accuracy, efficiency, and robustness.

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“…In recent years, there have been some studies involving specific perspectives of service changes. For example, Zhong et al [10] presented a method for perceiving changes in the invocation relations between APIs and mashups in a web service ecosystem for more accurate service recommendation; Huang et al [11] gave an approach for sensing QoS fluctuations in IoT environments to rank available services; Liu et al [12] proposed a method for predicting the service ecosystems' evolutionary trends by tracking the changes of service communities; and Hao et al [11] analyzed the changes of invocation relations between Apps by parsing source codes of these Apps. The most immediate work is by Zheng et al [13], [14], which introduced the distributed infrastructure to access Web services periodically and evaluate their QoS so that the fluctuations of QoS could be perceived timely.…”

Section: Introductionmentioning

confidence: 99%

External Service Sensing (ESS): Research Framework, Challenges and Opportunities

Wang,

Liu,

et al. 2021

Preprint

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The flourish of web-based services gave birth to the research area services computing, a rapidly-expanding academic community since nearly 20 years ago. Consensus has been reached on a set of representative research problems in services computing, such as service selection, service composition, service recommendation, and service quality prediction. An obvious fact is that most services keep constant changes to timely adapt to changes of external business/technical environment and changes of internal development strategies. However, traditional services computing research does not consider such changes sufficiently. Many works regard services as static entities; this leads to the situation that some proposed models/algorithms do not work in real world. Sensing various types of service changes is of great significance to the practicability and rationality of services computing research. In this paper, a new research problem External Service Sensing (ESS) is defined to cope with various changes in services, and a research framework of ESS is presented to elaborate the scope and boundary of ESS. This framework is composed of four orthogonal dimensions: sensing objects, sensing contents, sensing channels, and sensing techniques. Each concrete ESS problem is defined by combining different values in these dimensions, and existing research work related to service changes can be well adapted to this framework. Real-world case studies demonstrate the soundness of ESS and its framework. Finally, some challenges and opportunities in ESS research are listed for researchers in the services computing community. To the best of our knowledge, this is the first time to systematically define service change-related research as a standard services computing problem, and thus broadening the research scope of services computing.

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Time-Aware Service Ranking Prediction in the Internet of Things Environment

Cited by 17 publications

References 40 publications

Cyber-Physical-Social Model for Service Recommendation in the Internet of Things

Cyber-Physical-Social Model for Service Recommendation in the Internet of Things

A Personalized QoS Prediction Approach for CPS Service Recommendation Based on Reputation and Location-Aware Collaborative Filtering

External Service Sensing (ESS): Research Framework, Challenges and Opportunities

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