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Efficient execution of complex context queries to enable near real-time smart IoT applications

Hassani, Alireza, Medvedev, Alexey, Zaslavsky, Arkady, Delir Haghighi, Pari, Jayaraman, Prem Prakash and Ling, Sea 2019, Efficient execution of complex context queries to enable near real-time smart IoT applications, Sensors, vol. 19, no. 24, Special Issue: Real-Time AI over IoT Data, pp. 1-34, doi: 10.3390/s19245457.

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Title Efficient execution of complex context queries to enable near real-time smart IoT applications
Author(s) Hassani, AlirezaORCID iD for Hassani, Alireza orcid.org/0000-0002-4770-8183
Medvedev, AlexeyORCID iD for Medvedev, Alexey orcid.org/0000-0003-1990-5734
Zaslavsky, ArkadyORCID iD for Zaslavsky, Arkady orcid.org/0000-0003-1990-5734
Delir Haghighi, Pari
Jayaraman, Prem Prakash
Ling, Sea
Journal name Sensors
Volume number 19
Issue number 24
Season Special Issue: Real-Time AI over IoT Data
Article ID 5457
Start page 1
End page 34
Total pages 34
Publisher MDPI
Place of publication Basel, Switzerland
Publication date 2019
ISSN 1424-8220
1424-8220
Keyword(s) CMP
IoT
complex
context
execution
query
Summary As the Internet of Things (IoT) is evolving at a fast pace, the need for contextual intelligence has become more crucial for delivering IoT intelligence, efficiency, effectiveness, performance, and sustainability. Contextual intelligence enables interactions between IoT devices such as sensors/actuators, smartphones and connected vehicles, to name but a few. Context management platforms (CMP) are emerging as a promising solution to deliver contextual intelligence for IoT. However, the development of a generic solution that allows IoT devices and services to publish, consume, monitor, and share context is still in its infancy. In this paper, we propose, validate and explain the details of a novel mechanism called Context Query Engine (CQE), which is an integral part of a pioneering CMP called Context-as-a-Service (CoaaS). CQE is responsible for efficient execution of context queries in near real-time. We present the architecture of CQE and illuminate its workflows. We also conduct extensive experimental performance and scalability evaluation of the proposed CQE. Results of experimental evaluation convincingly demonstrate that CoaaS outperforms its competitors in executing complex context queries. Moreover, the advanced functionality of the embedded query language makes CoaaS a decent candidate for real-life deployments.
Language eng
DOI 10.3390/s19245457
Indigenous content off
Field of Research 0301 Analytical Chemistry
0906 Electrical and Electronic Engineering
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30133215

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.