A oneM2M-Based Query Engine for Internet of Things (IoT) Data Streams

Widya, Putu Wiramaswara; Yustiawan, Yoga; Kwon, Joonho

doi:10.3390/s18103253

Cited by 8 publications

(5 citation statements)

References 29 publications

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“…The fifth article in this group is entitled "A oneM2M-Based Query Engine for Internet of Things (IoT) Data Streams" [9], by Putu Wiramaswara Widya, Yoga Yustiawan and Joonho Kwon. This paper presents a query engine, called OMQ, based on the standard oneM2M (one machine-to-machine) for the interoperability of the architecture and protocols of IoT.…”

Section: Summary Of the Contributions In This Special Issuementioning

confidence: 99%

Algorithm and Distributed Computing for the Internet of Things

Gómez–Pulido

Silva

Hara

2020

Sensors

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The ongoing generalization of Internet of Things and its presence and application in multiple fields is generating a large amount of data that can be used to extract knowledge, among other purposes. In this context, algorithmic techniques and efficient computer systems provide an opportunity to successfully address efficient data processing and intelligent data analysis. As a result, multiple services can be improved, resources can be optimized and real-world problems of interest can be solved. This Special Issue on Algorithm and Distributed Computing for the Internet of Things gives the opportunity to know recent advances in the application of modern technologies hardware and software to the Internet of Things.

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Section: Summary Of the Contributions In This Special Issuementioning

confidence: 99%

Algorithm and Distributed Computing for the Internet of Things

Gómez–Pulido

Silva

Hara

2020

Sensors

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“…Thus, responding to a large number of queries in a very short time period is a challenge, especially at a rate not feasible to human users. In addition, providing efficient query processing and generating accurate and quick response to queries is critical [84]- [86]. For example, Wu et al [84] proposed a timeseries based framework using statistics-based techniques to carry out information aggregation of machine traffic in IoT systems, from which the aggregated search results provided insightful information for resource planning for IoT systems.…”

Section: ) Search Techniquesmentioning

confidence: 99%

Search Engine for the Internet of Things: Lessons From Web Search, Vision, and Opportunities

et al. 2019

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With the development of the Internet of Things (IoT), massive numbers of IoT devices (smart sensors, cameras, phones, and so on) have been deployed and utilized in various environments, supporting numerous smart-world applications. Those devices communicate with computing infrastructure servers over network infrastructures to send relevant collected data in order to link physical objects to the cyber world. As the number of IoT devices increases rapidly, the volume of collected data likewise increases prodigiously. Thus, how to search for and through specific IoT datasets among the enormous amount of data become critical issues for potential data consumers. Moreover, various IoT devices and applications establish IoT-based systems, also known as the smart-world systems or smart cyber-physical systems (CPS), such as smart grids, smart transportation, smart healthcare, smart cities, smart homes, and smart manufacturing systems, among others. However, the individual CPS are independently designed and deployed, such that they collect and analyze data independently, with no information sharing or interconnection, raising serious challenges in searching for valuable information. Thus, in order to efficiently and precisely utilize the IoT datasets, suitable search techniques designed for the IoT environments are fundamental. In this paper, we first summarize popular web search techniques and survey existing research on the search and analysis related to the IoT. We then outline the opportunities and challenges of the IoT search techniques. Furthermore, we propose a problem space for the IoT search techniques and provide a clear view of potential future research directions.INDEX TERMS Internet of Things, search engine, system architecture, challenges and research opportunities, data mining and analytics.

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“…Query engines for the IoT domain [24] provide results in real time and data are processed at the devices at network edge. Such edge-centric processing is important in real-time, mission-critical applications such as self-driving vehicles [7].…”

Section: Related Workmentioning

confidence: 99%

Knowledge-centric Analytics Queries Allocation in Edge Computing Environments

Sagkriotis

Kolomvatsos

Anagnostopoulos

et al. 2019

2019 IEEE Symposium on Computers and Communications (ISCC)

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The Internet of Things (IoT) involves a huge number of devices that collect data and deliver them to the Cloud. The processing of data at the Cloud is characterized by increased latency in providing responses to analytics queries defined by analysts or applications. Hence, Edge Computing (EC) comes into the scene to provide data processing close to the source. The collected data can be stored in edge devices and queries can be executed there to reduce latency. In this paper, we envision a case where entities located in the Cloud undertake the responsibility of receiving analytics queries and decide on the most appropriate edge nodes for queries execution. The decision is based on statistical signatures of the datasets of nodes and the statistical matching between statistics and analytics queries. Edge nodes regularly update their statistical signatures to support such decision process. Our performance evaluation shows the advantages and the shortcomings of our proposed schema in edge computing environments.

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A oneM2M-Based Query Engine for Internet of Things (IoT) Data Streams

Cited by 8 publications

References 29 publications

Algorithm and Distributed Computing for the Internet of Things

Algorithm and Distributed Computing for the Internet of Things

Search Engine for the Internet of Things: Lessons From Web Search, Vision, and Opportunities

Knowledge-centric Analytics Queries Allocation in Edge Computing Environments

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