TinyQP: A Query Processing System in Wireless Sensor Networks

Mo, Shangfeng; Chen, Hong; Zhang, Xiaoxin; Li, Cuiping

doi:10.1007/978-3-642-38562-9_80

Cited by 3 publications

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such edge-centric processing is important in real-time, mission-critical applications such as self-driving vehicles [7]. Significant examples of edge-centric processing are: [1], [8], [21], [23], [27], [14]. Some efforts deal with the automated separation of queries graphs into two sets [10]: queries processed at the edge devices and queries processed in the cloud.…”

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)

View full text Add to dashboard Cite

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.

show abstract

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)

View full text Add to dashboard Cite

show abstract

“…This edge-based processing is important in real-time, mission-critical applications such as self-driving cars [ 20 ]. WSN query processing systems [ 21 , 22 , 23 ] are some examples of edge-based processing where processing is performed right in the sensor node. In other types of edge-based processing, the processing is performed in the gateway-level [ 24 , 25 , 26 , 27 ].…”

Section: Related Workmentioning

confidence: 99%

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

Widya

Yustiawan

Kwon

2018

Sensors

View full text Add to dashboard Cite

The new standard oneM2M (one machine-to-machine) aims to standardize the architecture and protocols of Internet of Things (IoT) middleware for better interoperability. Although the standard seems promising, it lacks several features for efficiently searching and retrieving IoT data which satisfy users’ intentions. In this paper, we design and develop a oneM2M-based query engine, called OMQ, that provides a real-time processing over IoT data streams. For this purpose, we define a query language which enables users to retrieve IoT data from data sources using JavaScript Object Notation (JSON). We also propose efficient query processing algorithms which utilizes the oneM2M architecture consisting of two nodes: (1) the IoT node and (2) the infrastructure node. IoT nodes of OMQ are mainly sensor devices execute user queries the aggregate, transform and filter operators, whereas the infrastructure node handles the join operator of user queries. Since the query processing algorithms are implemented as the hybrid infrastructure-edge processing, user queries can be executed efficiently in each IoT node rather than only in the infrastructure node. Thus, our OMQ system reduces the query processing time and the network bandwidth. We conducted a comprehensive evaluation of OMQ using a real and a synthetic data set. Experimental results demonstrate the feasibility and efficiency of OMQ system for executing queries and transferring data from each IoT node.

show abstract