An efficient multidimensional fusion algorithm for IoT data based on partitioning

Zhou, Jing; Hu, Liang; Wang, Rui; Lu, Huimin; Zhao, Kuo

doi:10.1109/tst.2013.6574675

Cited by 53 publications

(19 citation statements)

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“…As a first future work, we aim at investigating how to derive these rules. At this purpose, we plan to exploit existing data fusion schemes proposed for IoT domain [35,36], where different methodologies have been designed (e.g., probability based, artificial intelligence based) aiming at combining data from multiple sensors to produce more accurate, more complete, and more dependable information that could not be possible to achieve through a single sensor. These methods could be deployed in our framework to model the data flow, and thus derivation paths.…”

Section: Resultsmentioning

confidence: 99%

Decentralizing privacy enforcement for Internet of Things smart objects

2018

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Internet of Things (IoT) is now evolving into a loosely coupled, decentralized system of cooperating smart objects, where highspeed data processing, analytics and shorter response times are becoming more necessary than ever. Such decentralization has a great impact on the way personal information generated and consumed by smart objects should be protected, because, without centralized data management, it is more difficult to control how data are combined and used by smart objects. To cope with this issue, in this paper, we propose a framework where users of smart objects can specify their privacy preferences. Compliance check of user individual privacy preferences is performed directly by smart objects. Moreover, acknowledging that embedding the enforcement mechanism into smart objects implies some overhead, we have extensively tested the proposed framework on different scenarios, and the obtained results show the feasibility of our approach.

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Section: Resultsmentioning

confidence: 99%

Decentralizing privacy enforcement for Internet of Things smart objects

2018

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“…In order to increase the intelligence and scalability of architecture and alleviate the burden on users, we design a structure that contains the upper and lower levels of service. Metadata format vary greatly since which are grounded in heterogeneous sensor sources [27]. To circumvent this obstacle, we express various types of data into XML format and set up rules to make operations on the metadata.…”

Section: The Iot Middleware Designmentioning

confidence: 99%

A Data Processing Middleware Based on SOA for the Internet of Things

Wang

Zhou

et al. 2015

Journal of Sensors

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The Internet of Things (IoT) emphasizes on connecting every object around us by leveraging a variety of wireless communication technologies. Heterogeneous data fusion is widely considered to be a promising and urgent challenge in the data processing of the IoT. In this study, we first discuss the development of the concept of the IoT and give a detailed description of the architecture of the IoT. And then we design a middleware platform based on service-oriented architecture (SOA) for integration of multisource heterogeneous information. New research angle regarding flexible heterogeneous information fusion architecture for the IoT is the theme of this paper. Experiments using environmental monitoring sensor data derived from indoor environment are performed for system validation. Through the theoretical analysis and experimental verification, the data processing middleware architecture represents better adaptation to multisensor and multistream application scenarios in the IoT, which improves heterogeneous data utilization value. The data processing middleware based on SOA for the IoT establishes a solid foundation of integration and interaction for diverse networks data among heterogeneous systems in the future, which simplifies the complexity of integration process and improves reusability of components in the system.

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“…Liu [34] et al, studies the integration of RFID and wireless sensor networks to expend their overall functionality and capacity. Zhou [35] et al propose a efficient multidimensional fusion algorithm on IoT data.…”

Section: Related Workmentioning

confidence: 99%

Shelving Interference and Joint Identification in Large-Scale RFID Systems

Yang

Han

et al. 2015

IEEE Trans. Parallel Distrib. Syst.

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Prior work on anti-collision for Radio Frequency IDentification (RFID) systems usually schedule adjacent readers to exclusively interrogate tags for avoiding reader collisions. Although such a pattern can effectively deal with collisions, the lack of readers' collaboration wastes numerous time on the scheduling process and dramatically degrades the throughput of identification. Even worse, the tags within the overlapped interrogation regions of adjacent readers (termed as contentious tags), even if the number of such tags is very small, introduce a significant delay to the identification process. In this paper, we propose a new strategy for collision resolution. First, we shelve the collisions and identify the tags that do not involve reader collisions. Second, we perform a joint identification, in which adjacent readers collaboratively identify the contentious tags. In particular, we find that neighboring readers can cause a new type of tag collision, cross-tag-collision, which may impede the joint identification. We propose a protocol stack, named Season, to undertake the tasks in two phases and solve the cross-tag-collision. We conduct extensive simulations and preliminary implementation to demonstrate the efficiency of our scheme. The results show that our scheme can achieve above 6× improvement on the identification throughput in a large-scale dense reader environment.

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An efficient multidimensional fusion algorithm for IoT data based on partitioning

Cited by 53 publications

References 0 publications

Decentralizing privacy enforcement for Internet of Things smart objects

Decentralizing privacy enforcement for Internet of Things smart objects

A Data Processing Middleware Based on SOA for the Internet of Things

Shelving Interference and Joint Identification in Large-Scale RFID Systems

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