distribution of semantic reasoning on the edge of internet of … reasoning iot edg… · and...

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.

Thank you!

xiang.su@oulu.fi

http://ubicomp.oulu.fi

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