Loughborough University

1

TRENDY: Adaptive and Context-AwareService Discovery Protocol for 6LoWPANs

Talal Ashraf Butt, Dr. Iain Phillips, Dr. Lin Guan, Dr. George Oikomonou

2

Overview• WoT vision and Role of Service Discovery (SD)• WoT SD Requirements• Our proposed solution: TRENDY• Experiments and results• Future Work

3

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

active part of the web?

6LoWPAN Internet of Things

Service discovery

Web Services

Discoverability

Interoperability

Web of Things

4

Role of Service Discovery

RouterUser 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

5

limitedPacket

Size

limitedBandwidth

ProtocolsSleepingNodes

Limited ROM and RAM

Bulkyformats

PULL-based

Compact version for 6LoWPAN

TranslationOverhead

Architecture

6LoWPAN constraints Heavy Dependencies

6

WoT: SD Requirements

6LoWPAN General

Compact

Size

Sleep

Cycles

Heterogeneity

Scalability

Efficiency

Service

Selection

Requirements

Interoperability

Compact

packets

Service

Composition

Requirements

7

TRENDY SD Protocol

CoAP Based

Restful Web services

CoAP Based

Restful Web services

AdaptivityAdaptivity

TRENDY: An Adaptive and Context-Aware Service Discovery Protocol for 6LoWPANs

GroupingGrouping Service Composition

Service Composition

ContextAwareness

ContextAwareness

8

Service Description• Very simple at registration time (Comma

separated URLs)• Optional: Detailed IETF CoRE well-

known descriptions

9

Context awareness

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

10

Context awareness

DAUser Agent (UA)

I need temperature service in H room

of x building

H room

Service Selection

Two Options:Select the one with1. Hit count (Popularity)2. Less battery consumed3. More reliable

11

Discovery Query Options

DAUser Agent

(UA)

I need temperature service in H room

of x building

H roomUsing URI-query with:1. Keyword2. Keyword and location3. 1 or 2 with select the optimal option

URL(s) and IP address(es) of matched service(s)

12

Status maintenance

DA

GM4GM2

GM6

Interval = 412

Interval = 431

Interval = 521

Interval = 501

Basic Time window = 600GL1 = ActiveGM1 = ActiveGM2 = ActiveGM3 = Active

GM1

GM3

GM5

Interval = 511Interval = 476

o Static intervalo Multihop effect

Every node randomly selects a interval between 50 and 90% of time window for status updates

13

Adaptive timer

DA

GM4GM2

GM6

Interval = 512*2

Interval = 531*2

Interval = 521*2

Interval = 501*2

Basic Time window = 600GM1 timer counter = 2GM2 timer counter = 2GM3 timer counter = 3GM4 timer counter = 2GM5 timer counter = 3GM6 timer counter = 2

GM1

GM3

GM5

Interval = 411*3Interval = 476*3

14

Benefit: Adaptive Timer

Increasesstatus

maintenance interval

DecreasesControl

Overhead

15

Grouping

DA

GL1

GM2GM1

GM3

Interval = 412

Interval = 431 Interval = 521

Interval = 501

Basic Time window = 600GL1 = ActiveGM1 = ActiveGM2 = ActiveGM3 = Active

Implements GL CoAP resource

16

Grouping

DA

GL1

GM2GM1

GM3

Interval = 412

Interval = 431 Interval = 521

Interval = 501

Basic Time window = 600GL1 = Active

GM1 = GM of GL1

GM2 = GM of GL1

GM3 = Active

17

Multiple available GLs

DA

GL1

GM2GM1

GM3Interval = 412

Interval = 431Interval = 521

Interval = 501

Basic Time window = 600GL1 = ActiveGM1 = ActiveGM2 = ActiveGM3 = Active

GL2

Interval = 476

18

Optimal GL Selection• 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)

19

ArchitectureDA

GL

GM

GM

GM GM

GM

GL

GMGM

GMGM

GM

Area: INB01 Area: JKF01

20

Adaptive Timer: Grouping

DA

GL1

GM2GM1

GM3

Interval = 412*3

Interval = 431*3 Interval = 521*3

Interval = 501*2

Basic Time window = 600GL1 timer counter = 3

GM1 timer counter = GM GM2 timer counter = GM GM3 timer counter = 2

Group Members report to corresponding Group Leaders

21

Benefits: Grouping

Based ontheir

locations

EnableService

Composition

Group nodes

ReducesRegistry

load

22

Experiments SetupDA

• 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

23

Results

Method Control Packets Energy (J)

BASIC 810 62.08

TRENDY TIMER 163 61.27

TRENDY FULL 297 60.13

24

Future Work

Intelligent Timer (e.g. trickle) Publish/Subscribe

AdaptivityGrouping

ProxyScope

Large-scale

Load balancing More responsible GLs

Caching Interoperability

Service

composition

Loughborough University

25

Questions?Thank you