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Xiang Su, Pingjiang Li, Jukka Riekki, Xiaoli LiuUniversity of Oulu, Finland
Jussi Kiljander, Juha-Pekka SoininenVTT Technical Research Centre of Finland, Finland
Christian Prehoferfortiss, An-Institut Technische Universität München, Germany
Huber Flores,University of Helsinki, Finland
Yuhong LiBeijing University of Posts and Telecommunications, China
24.04.2018
Distribution of Semantic Reasoning
on the Edge of Internet of Things
Outline
• Motivation
• Background– Edge computing
– Semantic technologies
• System Design– Design requirements
– Use case scenario and data
• Architectures
• Experiment and Analysis
• Discussion
Internet of Things• Internet of Things (IoT): the internetworking of physical
devices, vehicles, buildings, and other items—
embedded with electronics, software, sensors,
actuators, and network connectivity that enable these
objects to collect and exchange data.
Introduction
• Semantics associates meaning with IoT data and
facilitates the development of intelligent applications
and services.– Sharing and integrating IoT data, modelling, querying and
reasoning information;
– Enable computer systems to possess knowledge and support
decision making.
• Challenges:– Semantic technologies require a considerable amount of
resources;
– Big volume of IoT data and resource limitations of IoT devices.
Introduction
• Contributions:– Development of an IoT system that distributes semantic
reasoners both on Cloud and edge devices for performing
reasoning tasks;
– Three experiments to demonstrate how edge computing could
facilitate IoT systems in terms of data transferring and semantic
reasoning.
– An analysis based on real data from a smart transportation use
case.
Edge Computing for IoT
• Edge comp. shifts the
computational efforts from
centralized server to the mobile
edge, enabling analytics and
knowledge generation to occur
close to the data sources.
• A complementary technology of
Cloud computing, especially for IoT.
Edge
nodes
• Edge platforms: Radio applications Cloud Server, Cloudlet, MAUI,
LEONORE, ParaDrop, etc.
Semantic Technologie for IoT
• Semantic Web
technologies offer powerful
representations and
reasoning techniques,
and facilitate data and
knowledge modelling,
querying, reasoning,
service discovery, privacy,
and provenance.
• Building blocks: URI/IRI,
RDF, Ontology, Ruels,
Proof, Trust, UI and Apps.
Semantic Technologie for IoT
Example: Temperature sensorSyntax: (in JSON)
“temperaturemeasurment": [
{
"name": "temperature",
"temp": "24.5",
"unit": "Celsius"
}
]
Subject ObjectPredicate
RDF
Alternative RDF syntaxes:
RDF/XML, JSON for Linked Data
(JSON-LD), N-Triples, NQuads,
Turtle, RDFa, Notation 3 (N3) and
Entity Notation (EN).
Semantic reasoners: HermiT,
Owlgres, Pellet, Jena,
AndroJena.
System Design, data, and Scenario
• Design Requirements:– Scalability
– Heterogeneous data processing.
– Balance of Computation.
– Semantic data processing and knowledge extraction.
• Data: 65,000 separate taxi trajectories formed by
5,543,348 observations (72,063,524 RDF statements).
• Simple semantic rules to deduce 16 different activities
of cars.
Scenario
• Selected semantic rules:
Scenario
High level static ontology for semantic reasoning in
transportation system use case.
CRA: An architecture of deploying
semantic reasoning on the Cloud.ERA: An architecture of deploying
semantic reasoning on the Cloud
and edge nodes.
Arechitectures
Experiment and analysis
Semantic reasoning experiment test
case for CRASemantic reasoning experiment test
case for ERA
Experiment and analysis
• Experiment setup:– We want to evaluate two architectures with the same data set.
PC replays the real data collected from taxi cabs. 20-150 IoT
nodes are executed simultaneously using threads.
– Edge nodes: LG Nexus 5X Android phones.
– Cloud: Amazon EC2, physically in Frankfurt, Germany.
Experiment and analysis (CRA Scalabiltiy)
Data transfer time in CRA (Group A)
Comparison of four data formats: RDF/XML, Turtle, JSON-LD, and short EN.
Semantic reasoning time in CRA (Group A)
Data transfer time in CRA (Group B) Semantic reasoning time in CRA (Group B)
Experiment and analysis (CRA Scalabiltiy)Comparison of four data formats: RDF/XML, Turtle, JSON-LD, and short EN.
Data transfer time in CRA (Group C) Semantic reasoning time in CRA (Group C)
Data transfer time in CRA (Group D) Semantic reasoning time in CRA (Group D)
Experiment and analysis (CRA vs. ERA)
Performance
evaluation of
architectures with
RDF/XML
Performance
evaluation of
architectures with
JSON-LD
Edge nodes only perform semantic reasoning with two selected
rules, i.e. “High Acceleration” and “High De-acceleration”.
Experiment and analysis (CRA vs. ERA)
Performance
evaluation of
architectures with
Turtle
Performance
evaluation of
architectures with
short EN
Edge nodes only perform semantic reasoning with two selected
rules, i.e. “High Acceleration” and “High De-acceleration”.
Experiment and analysis (ERA with different rule sets)
Group A: all reasoning tasks on the Cloud server;
Group B: only rules related to “High Average Speed” on the edge nodes;
Group C: only rules related to “High Acceleration” and “High Deacceleration”
on edge nodes;
Group D: rules related to both “High Average Speed”, “High Acceleration”
and “High De-acceleration” on the edge nodes.
Experiment and analysis
• Main results:– The required transferring time scales linearly with the payload
size, which depends the data structures and formats.
– SizeTurtle > SizeRDF/XML > SizeJSON-LD > SizeShortEN
– Different RDF syntaxes require significantly different amount of
time in building Jena models but require the same amount of
time for reasoning after they are loaded in a model.
– Adding edge nodes accelerates data processing and reduces
need for network bandwidth.
– When only first results are required, the ERA can generate
results ten times faster than the CRA.
Discussion
• Analyzing the performance of semantic reasoning
within three experiments with a large smart
transportation data set to address the research
challenges of scalability and latency.
• Future reseach– How to handle more dynamic ontologies, rules;
– How to assign tasks on edge nodes to optimize the
performance.
– What are the minimum required resources for semantic
reasoning on edge nodes.
