Download pdf - Trendy service discovery protocol at WoT 2012

Loughborough University

TRENDY: Adaptive

and Context-Aware

Service Discovery

Protocol for 6LoWPANs

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Talal Ashraf Butt, Dr. Iain Phillips,

Dr. Lin Guan, Dr. George Oikomonou

Overview

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• WoT vision and Role of Service Discovery (SD)

• WoT SD Requirements

• Our proposed solution: TRENDY

• Experiments and results

• Future Work

Web of Things (WoT) How to make application-specific WSNs to be

active part of the web?

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6LoWPAN Internet of Things

Service discovery

Web Services

Discoverability

Interoperability

Web of Things

Role of Service Discovery

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Router User Agent

(UA)

I need * service

• Switch off the lights in corridor of x building

• Give me the overall temperature of x building

• Close all the windows when its windy

• Close all the windows when no one in the room

• If rooms temperature is below x and someone in the room

Then Switch on the heating and close the windows

Existing Solutions: Gaps

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limited

Packet

Size

limited

Bandwidth

Protocols Sleeping

Nodes

Limited

ROM and RAM

Bulky

formats

PULL-based

Compact version for 6LoWPAN

Translation

Overhead

Architecture

6LoWPAN constraints Heavy Dependencies

WoT: SD Requirements

6LoWPAN General

Compact

Size

Sleep

Cycles

Heterogeneity

Scalability

Efficiency

Service

Selection

Requirements

Interoperability

Compact

packets

Service

Composition

Requirements

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TRENDY SD Protocol

CoAP

Based

Restful

Web services

Adaptivity

TRENDY: An Adaptive and Context-Aware Service

Discovery Protocol for 6LoWPANs

Grouping Service

Composition

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Context

Awareness

Service Description

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• Very simple at registration time (Comma

separated URLs)

• Optional: Detailed IETF CoRE well-

known descriptions

Context awareness

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Directory Agent

(DA)

User Agent

(UA)

I need * service in * place

• Switch off the lights in corridor of x building

• Give me the overall temperature of x building

• Close all the windows when its windy

• Close all the windows when no one in the room

• If rooms temperature is below x and someone in the room

Then Switch on the heating and close the windows

Location based tags

Context awareness

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DA User Agent

(UA)

I need temperature service

in H room

of x building

H room

Service Selection

Two Options:

Select the one with

1. Hit count (Popularity)

2. Less battery consumed

3. More reliable

Discovery Query Options

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DA User Agent

(UA)

I need temperature service

in H room

of x building

H room Using URI-query with:

1. Keyword

2. Keyword and location

3. 1 or 2 with select the optimal option

URL(s) and IP address(es)

of matched service(s)

Status maintenance

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DA

GM4 GM2

GM6

Interval = 412

Interval = 431

Interval = 521

Interval = 501

Basic Time window = 600

GL1 = Active

GM1 = Active

GM2 = Active

GM3 = Active

GM1

GM3 GM5

Interval = 511 Interval = 476

o Static interval o Multihop effect

Every node randomly selects

a interval between 50 and 90% of time window for status updates

Adaptive timer

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DA

GM4 GM2

GM6

Interval = 512*2

Interval = 531*2

Interval = 521*2

Interval = 501*2


GM1 timer counter = 2






GM1

GM3 GM5

Interval = 411*3 Interval = 476*3

Benefit: Adaptive Timer

Increases

status

maintenance

interval

Decreases

Control

Overhead

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Grouping

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DA

GL1

GM2 GM1

GM3 Interval = 412


Interval = 501


GL1 = Active

GM1 = Active

GM2 = Active

GM3 = Active

Implements GL

CoAP resource

Grouping

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DA

GL1

GM2 GM1

GM3

Interval = 412


Interval = 501


GL1 = Active

GM1 = GM of GL1

GM2 = GM of GL1

GM3 = Active

Multiple available GLs

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DA

GL1

GM2 GM1

GM3 Interval = 412


Interval = 501


GL1 = Active

GM1 = Active

GM2 = Active

GM3 = Active

GL2

Interval = 476

Optimal GL Selection

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• If multiple GLs available

• Select one with high rank

Rank = st + nGM - f - (b/1000)

st

(serving time)

nGM

(number of

registered GMs)

f

(number of failures)

b

(battery consumption)

Architecture

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DA

GL

GM

GM

GM GM

GM

GL

GM GM

GM GM

GM

Area: INB01 Area: JKF01

Adaptive Timer: Grouping

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DA

GL1

GM2 GM1

GM3

Interval = 412*3

Interval = 431*3 Interval = 521*3

Interval = 501*2


GL1 timer counter = 3

GM1 timer counter = GM

GM2 timer counter = GM


Group Members report to

corresponding Group Leaders

Benefits: Grouping

Based on

their

locations

Enable

Service

Composition

Group

nodes

Reduces

Registry

load

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Experiments Setup

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DA • Linux process

• Java based CoAP Implementation

COOJA: Simulator

GL

GM

• CONTIKI (OS)

• CSMA (MAC)

• CONTIKIMAC (Duty cycling)

• ENERGEST (Energy estimation)

• C-based CoAP Implementation

25 Tmote sky nodes (+1 border router)

with 10 runs for 100 minutes

Results

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Method Control

Packets Energy (J)

BASIC 810 62.08

TRENDY TIMER 163 61.27

TRENDY FULL 297 60.13

Future Work

Intelligent Timer (e.g. trickle)

Publish/Subscribe

Adaptivity Grouping

Proxy Scope

Large-scale

Load balancing

More responsible GLs

Caching

Interoperability

Service composition

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Loughborough University

Questions? T h a n k y o u

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