Multilayer Internet-of-Things Middleware Based on Knowledge Graph

Xie, Cheng; Yu, Bin; Zeng, Zuoying; Yang, Yun; Liu, Qing

doi:10.1109/jiot.2020.3019707

Cited by 41 publications

(19 citation statements)

References 23 publications

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“…It can solve the problem of information interaction between classified, heterogeneous, and distributed devices. Xie et al [40] believed that the communication gap and heterogeneous access hinder the effective application of existing IOT systems. They proposed a multi-layer IOT framework to bridge the gap between IOT devices with different communication protocols.…”

Section: Literature Analysismentioning

confidence: 99%

“…It also belongs to the field of knowledge discovery. In terms of the knowledge graphs, the studies [40,45] have shown that, by generating various types of knowledge graphs, people can better analyze data and understand user needs. The above literature also involves two other aspects; for example, Gómez-Berbís et al [41] have proposed SEDIT, based on knowledge atlas technology.…”

Section: Literature Analysismentioning

confidence: 99%

“…It is compatible with databases and different data structures, and can effectively manage IOT data. Xie et al [40] used an IOT knowledge graph to manage all IOT devices in a system, which solved the communication gap and heterogeneous access problems in the system, as well as facilitating interactions between the operator and the system.…”

Section: Literature Analysismentioning

confidence: 99%

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The Architecture of Mass Customization-Social Internet of Things System: Current Research Profile

Dou

Sun

Wu³

et al. 2021

IJGI

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In the era of big data, mass customization (MC) systems are faced with the complexities associated with information explosion and management control. Thus, it has become necessary to integrate the mass customization system and Social Internet of Things, in order to effectively connecting customers with enterprises. We should not only allow customers to participate in MC production throughout the whole process, but also allow enterprises to control all links throughout the whole information system. To gain a better understanding, this paper first describes the architecture of the proposed system from organizational and technological perspectives. Then, based on the nature of the Social Internet of Things, the main technological application of the mass customization–Social Internet of Things (MC–SIOT) system is introduced in detail. On this basis, the key problems faced by the mass customization–Social Internet of Things system are listed. Our findings are as follows: (1) MC–SIOT can realize convenient information queries and clearly understand the user’s intentions; (2) the system can predict the changing relationships among different technical fields and help enterprise R&D personnel to find technical knowledge; and (3) it can interconnect deep learning technology and digital twin technology to better maintain the operational state of the system. However, there exist some challenges relating to data management, knowledge discovery, and human–computer interaction, such as data quality management, few data samples, a lack of dynamic learning, labor consumption, and task scheduling. Therefore, we put forward possible improvements to be assessed, as well as privacy issues and emotional interactions to be further discussed, in future research. Finally, we illustrate the behavior and evolutionary mechanism of this system, both qualitatively and quantitatively. This provides some idea of how to address the current issues pertaining to mass customization systems.

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Section: Literature Analysismentioning

confidence: 99%

Section: Literature Analysismentioning

confidence: 99%

Section: Literature Analysismentioning

confidence: 99%

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The Architecture of Mass Customization-Social Internet of Things System: Current Research Profile

Dou

Sun

Wu³

et al. 2021

IJGI

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“…A middleware seeks to effectively characterize the complications of IoT systems along with their physical component, hence letting the developers concentrate on solving the problems that did not result in any system failures [37]. Bearing in mind, the majority of those complications are generally reasoned by or associated with computational and/or communication matters.…”

Section: Middleware Layermentioning

confidence: 99%

Internet of Things, Preliminaries and Foundations

Abdel‐Basset

Moustafa

Hawash

et al. 2021

Deep Learning Techniques for IoT Security and Privacy

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This chapter mainly presents a detailed discussion of the IoT technologies and dependent systems with the main objectives of emphasizing the main attributes of IoT systems that might possibly threaten the security of the system. Firstly, the definition and of the IoT system and the detailed description of its architecture are presented along with a taxonomy for dividing its architecture into layers with different complementary roles. Secondly, the concepts of cloud computing, fog computing, and edge computing are discussed and compared in view of IoT systems. Finally, the learned lessons are summarized and pointed out in the last section of this chapter.

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“…In the meantime, time-sensitivity has been one of most important metric for network performance, and triggered a great deal of research interests in applications related with UAV. Therefore, in this paper, collaborative multi-task computation and caching offloading for UAV-enabled MEC networks is investigated to minimize the total energy consumption of GNs, 1 where the trajectory along with communication and computing resource allocation at UAV is optimized, and task offloading decision at GNs is determined, to satisfy the QoE requirement of time-sensitive tasks of GNs. The main contributions of this paper are summarized as follows:…”

mentioning

confidence: 99%

Collaborative offloading for UAV-enabled time-sensitive MEC networks

Zhao

et al. 2021

J Wireless Com Network

127

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Recently, unmanned aerial vehicle (UAV) acts as the aerial mobile edge computing (MEC) node to help the battery-limited Internet of Things (IoT) devices relieve burdens from computation and data collection, and prolong the lifetime of operating. However, IoT devices can ONLY ask UAV for either computing or caching help, and collaborative offloading services of UAV are rarely mentioned in the literature. Moreover, IoT device has multiple mutually independent tasks, which make collaborative offloading policy design even more challenging. Therefore, we investigate a UAV-enabled MEC networks with the consideration of multiple tasks either for computing or caching. Taking the quality of experience (QoE) requirement of time-sensitive tasks into consideration, we aim to minimize the total energy consumption of IoT devices by jointly optimizing trajectory, communication and computing resource allocation at UAV, and task offloading decision at IoT devices. Since this problem has highly non-convex objective function and constraints, we first decompose the original problem into three subproblems named as trajectory optimization ($$\mathbf {P}_{\mathbf {T}}$$ P T ), resource allocation at UAV ($$\mathbf {P}_{\mathbf {R}}$$ P R ) and offloading decisions at IoT devices ($$\mathbf {P}_{\mathbf {O}}$$ P O ) and then propose an iterative algorithm based on block coordinate descent method to cope with them in a sequence. Numerical results demonstrate that collaborative offloading can effectively reduce IoT devices’ energy consumption while meeting different kinds of offloading services, and satisfy the QoE requirement of time-sensitive tasks at IoT devices.

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Multilayer Internet-of-Things Middleware Based on Knowledge Graph

Cited by 41 publications

References 23 publications

The Architecture of Mass Customization-Social Internet of Things System: Current Research Profile

The Architecture of Mass Customization-Social Internet of Things System: Current Research Profile

Internet of Things, Preliminaries and Foundations

Collaborative offloading for UAV-enabled time-sensitive MEC networks

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