localization as a service in an intelligent transport system

LOCALIZATION AS A SERVICE IN INTELLIGENT TRANSPORT SYSTEM

Saber FerjaniPhD Student

Hana Lab - ENSI - Manouba University

http://www.fastcoexist.com/1681562/solar-roads-charging-roads-and-the-future-of-transportation

Traffic congestion

MOTIVATION

Problems: Waste of time Waste of energy Air pollution More accident

Solutions Construct new roads Reduce Traffic Improve transport efficiency

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MOTIVATION

ITS functional areas: ATMS: Advanced Traffic Management

Systems ATIS: Advanced Traveler Information

Systems CVO: Commercial Vehicle Operations APTS: Advanced Public Transportation

Systems AVCS: Advanced Vehicle Control Systems

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MOTIVATION

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OUTLINE

1. Localization algorithms2. Future internet trends3. Web service composition4. Conclusion

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1. LOCALIZATION ALGORITHMS

1. One hop localization2. Multi-hop localization3. Probabilistic localization

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1. LOCALIZATION ALGORITHMS1. ONE HOP LOCALIZATION

Trilateration

Determining the location of a point by measuring distances, using the geometry of circles (2D), or spheres (3D).

Triangulation

Determining the location of a point by measuring angles to it from known points at either end of a fixed baseline.

Location?Location

? 𝑅𝑎

𝑅𝑏𝑅𝑐 𝛼

𝛽

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1. PROPAGATION MODEL

The RSS (received signal strength) is provided by most radio chips.Known : The path loss model Transmission power Path lost coefficient α Receiver can determine the distance d to the transmitter :

𝑃 𝑡𝑥

𝑅𝑆𝑆

d

𝑅𝑆𝑆=𝑐𝑃 𝑡𝑥

𝑑𝛼

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2. TIME BASED1.Time of arrivalThe distance can be estimated, using the transmission time.

• The speed of propagation is known. • Receiver and sender are synchronized

2.Time Difference of arrivalUse two transmissions mediums of different propagation speeds to generate an

implicit synchronization

Tx TxRx Rx

TOATDOA

Radio signal

Radio signalUltrasonic signal

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3. ANGLE BASED1. DOA: Direction of arrival is the

direction that maximizes the RSS of directional antenna

2. AOA: Angle of arrival is the angle between DOA, and a conventional direction

Measured using:Rotatable Directional AntennasTOA, RSS Differences of Antenna arraySmart Antenna:

ESPRIT: estimation of signal parameters via rotational invariant techniques

MUSIC: MUltiple SIgnal Classification 10/40

1. LOCALIZATION ALGORITHMS2. MULTI-HOP LOCALIZATION

Centralized

Designed to run on a central machine with powerful computational capabilities. Multi-Dimensional Scaling Semi-Definite Programming

Distributed

Designed to run in network, using massive parallelism and internode communication. Beacon Based Localization

(DV-hop, DV distance, Iterative localization)

Coordinate System StitchingHigher Accuracy

Low propagation errorLow Computation costLow Communication

costHigh Computation costHigh Communication

costLower Accuracy

High propagation error 11/40

1. LOCALIZATION ALGORITHMS3. PROBABILISTIC LOCALIZATION

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In probabilistic localization, we distinguish two update steps:1) ACTION:

Use proprioceptive sensors to estimate location .

During this step, uncertainty grows.

2) PERCEPTION: Combine data from

exteroceptive sensors with the belief before the observation.

In this step, location uncertainty shrinks.

http://www.asl.ethz.ch/education/master/mobile_robotics/year2010/5b_-_Probabilistic_Map_Based_Localization_and_Markov_printable.pdf

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1. KALMAN FILTER APPROACHuses a single, well-defined

Gaussian probability density function.

Updating the parameters of the Gaussian distribution is all that is required.

For highly nonlinear systems: Extended Kalman filter Unscented Kalman filters

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2. MARKOV CHAIN APPROACH

The location state is usually represented as separate probability assignments for every possible position in its map. Complete Sampling Randomized Sampling

particle filter algorithms condensation algorithms Monte Carlo algorithms.

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1. LOCALIZATION ALGORITHMS4. CONCLUSION

2003 2010 2015 20200.5

12.5

25

50

6.3 6.8 7.2 7.6

Connected devices (Billion) World population (Billion)Cisco IBSG white paper “The Internet of Things”, April 2011

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2. FUTURE INTERNET TRENDS

1. Current challenges2. Semantic web3. Dbpedia4. SPARQL Query Language

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2. FUTURE INTERNET TRENDS2. CURRENT CHALLENGES

How can I travel to Japan?

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2. FUTURE INTERNET TRENDS2. CURRENT CHALLENGESThe web is extremely big!

And still getting bigger every minute!The meaning of web pages can be understood

only by humans!

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”The Web was designed as an information space, with the goal that it should be useful not only for human-human communication, but also that machines would be able to participate and help…”

Tim Berners-Lee, Semantic Web Roadmap, Sept 1998

2. FUTURE INTERNET TRENDS3.SEMANTIC WEB

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Ontology definition: It is a compound word, composed of

ontos- (ὄντος) meaning “being” -logia (λογία) interpreted as "science,

study, theory". Ontology, is the science or study of

beingOntology In computer science: “An ontology is an explicit, formal

specification of a shared conceptualization.”

(Thomas R. Gruber, 1993)

Top-Level Ontology

Task Ontology

Domain Ontology

Application Ontology

Dublin CoreGeneral Formal OntologyOpenCyc

Railway Domain Ontologysoccer ontologymusic ontology

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XML was created to structure, store, and transport information.

XML Separates Data from HTML: Simplifies Data Sharing Simplifies Data Transport Simplifies Platform Changes Used to Create New Internet

Languages


1.XML

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Resource can be everything must be uniquely identified and be

referencableDescription

via representing properties and relationships among resources

relationships can be represented as graphs

Framework Combination of web based protocols (URI,

HTTP, XML,...) based on formal model defines all allowed relationships among

resources


2.RDF

http://about.me/ferjani

+216 22 94 20 94

<Subject> <PREDICATE> <Object>

hasPhoneNumber

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3.RDF Schema

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3.RDF Schema

https://openhpi.de/course/semanticweb

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Controlled Vocabulary

GlossarieThesauri

Informal IS-A

Formal Is-A

Formal instance

FramesValue restrictions

General logical constraints

Disjunctness, Inversiveness, Part-of…

Informal Formal


4. Ontology engineering

heav

ywei

ght

lightweight

First Order LogicsDescription LogicsLogic

ProgrammingFormal TaxonomiesFolksonomi

esData Dictionaries

Terms

Expressivity 26/40

Acronym

Language Characteristics

XML Extensible Markup Language Extensions for arbitrary domains and specific tasks.

RDF Resource Description Framework

Syntactic conventions and simple data models to represent semantics.It supports interoperability aspects with object - attribute - value relationships.

RDFS Resource Description Framework Schema Primitives to model basic ontologies with RDF.

OWL Web Ontology Language W3C Recommandation2004-02-10; 2009-10-27; 2010-06-22;

http://www.emc-eu.de/index-Dateien/3_ONTOLOGY_UK.html



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Road classification (Motorways, dual carriageway, etc.)

Vehicle classification (Truck, car, etc.)

Location (Area, Point, Section, etc.)

Geography (Towns, Countries, etc.)

Events (Accidents, Incidents, Measures, etc.)

People (Driver, Passenger, etc.)

Routes (Urban, interurban, etc.).

http://dx.doi.org/10.1109/ITSC.2006.1707385



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2. FUTURE INTERNET TRENDS4.DBPEDIA

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Find all schools within a 5km radius around a specific location, and for each school find coffeeshops that are

closer than 1km.

2. FUTURE INTERNET TRENDS5. SPARQL QUERY LANGUAGE

1Km

1Km

5Km

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2. FUTURE INTERNET TRENDS5. SPARQL QUERY LANGUAGE

Query Result

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3. WEB SERVICE COMPOSITION

1. Web services & RESTful services2. Web process lifecycle3. Composition methods

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3. WEB SERVICE COMPOSITION1. WEB SERVICES & RESTFUL SERVICES

Client app code

Client service code

Proxy/stub

Encoding

Protocol

Transport

Skeleton

Encoding

Protocol

Transport

attachementdataheader

Jaxb, direct XML

XML, JSON

HTTP

TCP

WADL

SSL HTTP session

Client app code

Client service code

Proxy/stub

Encoding

Protocol

Transport

Skeleton

Encoding

Protocol

Transport

attachementdataheader

WSDL

Jaxb, direct XML

XML, Fast-infoset

HTTP, SIP, SMTP

TCP, UDP

WS-Trust, WS-Security, WS-

SecureConversation

WS-ReliableMessaging,

WS-AtomicTransactionsSOAP REST 33/40

3. WEB SERVICE COMPOSITION1. WEB SERVICES & RESTFUL SERVICES

Eg: Ethernet link

IP

TCP

HTTP

Payload

Constrained link

IP

UDP

CoAP

Payload

Shelby, Z. - Embedded web services - Wireless Communications, IEEE, 2010

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3. WEB SERVICE COMPOSITION2. WEB PROCESS LIFECYCLE

Annotation

Advertisement

Discovery

Selection

Composition

Execution

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3. WEB SERVICE COMPOSITION3. COMPOSITION METHODS

Web

Ser

vice

Co

mpo

sitio

n

StaticOrchestration WS-BPEL

Choreography WS-CDL, CHOReOS

Dynamic Semantic Web Service

RDF, DAML, OWL-S

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1. STATIC COMPOSITIONOrchestration

a central process takes control of the involved Web services and coordinates the execution of different operations.

Choreography

it is a collaborative effort focusing on the exchange of messages in public business processes.

coordinator

Web service

1

Web service

21

2

Web service

33

4

5Web

service 1

Web service

3

Web service

21

2

34

http://www.oracle.com/technetwork/articles/matjaz-bpel1-090575.html

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2. DYNAMIC COMPOSITION

Specification

Matchmaking

AlgorithmsGeneration

CSL language

Composabilty Model

Composition plans

Web service registri

es

Ontological organization

and description of

WS

High level descriptio

n of desired

composition

Composite Service

QoC parameter

sComposition plan cost

Orchestration

Service Composition for the Semantic Web - DOI 10.1007/978-1-4419-8465-4

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5. CONCLUSION

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5. CONCLUSION Context-aware systems uses context to provide relevant

information and services Location is one of the most important context information Proliferation of mobile devices improve real time information

sharing Semantic web allow autonomic web service composition ITS functional areas use location with different accuracy level

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Thank you for your attention!

localization as a service in an intelligent transport system

Technology