The Internet of Things

Stolpe, Marco

doi:10.1145/2980765.2980768

Cited by 66 publications

(13 citation statements)

References 75 publications

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“…By IoT data analysis, it could further support proactive behavior and planning, and new business opportunities may also be created. 19 Obviously, followee recommendation can be seemed as a social behavior that may create new business opportunities. Note that followee recommendation can be naturally modeled as a link prediction problem 18,20 from the viewpoint of social network dynamics.…”

Section: Related Workmentioning

confidence: 99%

Section: Iot Data Analysismentioning

confidence: 99%

“…There are also some works 19,25,26 that give detailed reviews about the IoT data analysis. Stolpe 19 presents the opportunities and challenges of research on communication-efficient decentralized analysis algorithms. Ahsan and Bais 25 review the role of big data in IoT through discussion of its protocols and architectural structure and present methods for testing these protocols and security considerations.…”

Section: Iot Data Analysismentioning

confidence: 99%

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FRFB: Top‐k Followee Recommendation by exploring the Following Behaviors in social networks

Xue

et al. 2018

Concurrency and Computation

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Summary As social networks such as micro‐blogging sites rapidly grow, deciding whom to follow (followee recommendation) becomes a significantly important problem. Most existing works exclusively rely on two traditional factors: the proximity between two users in the network topology or the similarity of the user‐generated contents in the social network, disregarding the effect of users' following behaviors. The challenge of how to effectively combine these two factors remains largely open. Moreover, most research studies simply sort the scores to find top‐k users, which is time‐consuming, especially for large‐scale networks. In this paper, we propose the idea that “predict users' following behaviors by following behaviors themselves.” We consider a user's following to others as a normal process of dynamic and coherent behavior, and we model the potential propagation of the users' following behaviors. Furthermore, based on our previous research on top‐k selection problem, we propose an effective top‐k followee recommendation algorithm, called FRFB. FRFB has low complexity and high scalability and, moreover, good adaptability to real‐life dynamic social networks. We conduct extensive experiments, with two real social network data sets (Wiki and Twitter), which show that FRFB outperforms the well‐known topology‐based followee recommendation algorithms.

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Section: Related Workmentioning

confidence: 99%

Section: Iot Data Analysismentioning

confidence: 99%

Section: Iot Data Analysismentioning

confidence: 99%

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FRFB: Top‐k Followee Recommendation by exploring the Following Behaviors in social networks

Xue

et al. 2018

Concurrency and Computation

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“…Smart organization is concerned with uniting the Internet of ings, WSN, and today's Internet to form a controlled, intelligent, and scientific coordination through real-time, effective, and scientific management, deepening resource utilization and productivity and providing a more socially valuable coordination effect [2]. Logistics technology involves various fields and covers logistics processes such as management, inventory, and distribution; on the other hand, the vigorous development of Internet of ings technology and the coordinated management of intelligent complete systems have produced a good soil for the traditional logistics technology to grow [3]. Today, where people's basic living needs have been met, people are focusing more on improving the quality of life [4].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Coordination Distribution of the Whole Supply Chain Based on the Internet of Things

Cui

2021

Complexity

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In this paper, through the intelligent research of the whole process of logistics and distribution with the Internet of Things supply chain, we study how to improve the development of the cold chain, reduce the loss in circulation, improve the social and economic benefits, and carry out intelligent information collection, monitoring, management, and information tracing of the whole cold chain. This paper analyzes and empirically studies the impact of key technologies of the Internet of Things in cold chain coordination from the perspective of building an intelligent cold chain coordination system with the Internet of Things technology. This paper analyzes the current situation of cold chain logistics and the impact that the application of IoT technology will have, explains that IoT technology can improve the intelligence level of the cold chain, and then introduces the application of intelligent cold chain logistics under IoT orientation, combining the process of cold chain logistics with the three-layer architecture of IoT technology. By extracting the key technologies of IoT perception layer, network layer, and intelligence layer, the intelligent cold chain coordination system based on IoT technology is constructed, and then, the correctness of the system is verified, to have some reference and evaluation for the cold chain construction. The system was then verified to have some reference and guidance significance for the construction and evaluation of the cold chain. The results of this paper are more accurate and more efficient.

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“…Several challenges have been identified in implementing mobile service computing in hybrid environments, for instance, security, communication, efficiency, and context-awareness [1]. Particularly, for an ample variety of IoT-based applications, interaction support and data analysis in real time is required, since they usually involve monitoring, autonomous decision-making, and delivery of early notifications that represent time-constrained activities [2,3,4,5]. If the system does not react on time, its effectiveness is under risk.…”

Section: Introductionmentioning

confidence: 99%

A Role-Based Software Architecture to Support Mobile Service Computing in IoT Scenarios

Finochietto

Eggly

Santos

et al. 2019

Sensors

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The interaction among components of an IoT-based system usually requires using low latency or real time for message delivery, depending on the application needs and the quality of the communication links among the components. Moreover, in some cases, this interaction should consider the use of communication links with poor or uncertain Quality of Service (QoS). Research efforts in communication support for IoT scenarios have overlooked the challenge of providing real-time interaction support in unstable links, making these systems use dedicated networks that are expensive and usually limited in terms of physical coverage and robustness. This paper presents an alternative to address such a communication challenge, through the use of a model that allows soft real-time interaction among components of an IoT-based system. The behavior of the proposed model was validated using state machine theory, opening an opportunity to explore a whole new branch of smart distributed solutions and to extend the state-of-the-art and the-state-of-the-practice in this particular IoT study scenario.

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The Internet of Things

Cited by 66 publications

References 75 publications

FRFB: Top‐k Followee Recommendation by exploring the Following Behaviors in social networks

FRFB: Top‐k Followee Recommendation by exploring the Following Behaviors in social networks

Intelligent Coordination Distribution of the Whole Supply Chain Based on the Internet of Things

A Role-Based Software Architecture to Support Mobile Service Computing in IoT Scenarios

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