1. TRENDY: Adaptive
and Context-Aware
Service Discovery
Protocol for 6LoWPANs
Talal Ashraf Butt, Dr. Iain Phillips,
Dr. Lin Guan, Dr. George Oikomonou
Loughborough University
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2. Overview
• WoT vision and Role of Service Discovery (SD)
• WoT SD Requirements
• Our proposed solution: TRENDY
• Experiments and results
• Future Work
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3. Web of Things (WoT)
How to make application-specific WSNs to be
active part of the web?
6LoWPAN Internet of Things
Service discovery Discoverability
Web Services Interoperability
Web of Things 3

4. Role of Service Discovery
User Agent I need * service
Router
(UA)
• 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
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5. Existing Solutions: Gaps
6LoWPAN constraints Heavy Dependencies
limited Sleeping Protocols
Packet Nodes
Size
Bulky
limited Limited formats
Bandwidth ROM and RAM
Compact version for 6LoWPAN Architecture
Translation PULL-based
Overhead
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6. WoT: SD Requirements
Compact Compact
Scalability
packets Size
Sleep Efficiency
6LoWPAN General Requirements
Cycles Requirements
Service
Heterogeneity Service
Interoperability Selection
Composition
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7. TRENDY SD Protocol
CoAP
Based Service Context
Grouping Adaptivity
Restful Composition Awareness
Web services
TRENDY: An Adaptive and Context-Aware Service
Discovery Protocol for 6LoWPANs
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8. Service Description
• Very simple at registration time (Comma
separated URLs)
• Optional: Detailed IETF CoRE well-
known descriptions
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9. Context awareness
Location based tags
User Agent I need * service in * place Directory Agent
(UA) (DA)
• 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
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10. Context awareness
Service Selection
I need temperature service
in H room
User Agent of x building
DA
(UA)
Two Options:
Select the one with
1. Hit count (Popularity)
2. Less battery consumed H room
3. More reliable
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11. Discovery Query Options
I need temperature service
in H room
of x building
User Agent
(UA) DA
URL(s) and IP address(es)
of matched service(s)
Using URI-query with: H room
1. Keyword
2. Keyword and location
3. 1 or 2 with select the optimal option
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12. Status maintenance
Every node randomly selects
a interval between 50 and 90% of time window for status updates
o Static interval Basic Time window = 600
o Multihop effect DA GL1 = Active
GM1 = Active
GM2 = Active
GM3 = Active
Interval = 412
GM1
GM6 Interval = 501
Interval = 431 GM2 GM4
Interval = 521
GM5
GM3
Interval = 476
Interval = 511 12

13. Adaptive timer
Basic Time window = 600
GM1 timer counter = 2
GM2 timer counter = 2
DA
GM3 timer counter = 3
GM4 timer counter = 2
GM5 timer counter = 3
Interval = 512*2 GM6 timer counter = 2
GM1
GM6
Interval = 501*2
Interval = 531*2 GM2 GM4
Interval = 521*2
GM5
GM3
Interval = 476*3
Interval = 411*3 13

14. Benefit: Adaptive Timer
Increases
status Decreases
maintenance Control
interval Overhead
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15. Grouping
Basic Time window = 600
GL1 = Active
DA GM1 = Active
Implements GL GM2 = Active
CoAP resource GM3 = Active
GL1
GM3
Interval = 412
Interval = 501
GM1 GM2
Interval = 431 Interval = 521
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16. Grouping
Basic Time window = 600
GL1 = Active
GM1 = GM of GL1
DA
GM2 = GM of GL1
GM3 = Active
Interval = 412 GL1
GM3
Interval = 501
GM1 GM2
Interval = 431 Interval = 521
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17. Multiple available GLs
Basic Time window = 600
GL1 = Active
DA GM1 = Active
GM2 = Active
GM3 = Active
GL1
GM3
Interval = 412
GL2
Interval = 501
Interval = 476
GM2
GM1
Interval = 521
Interval = 431
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18. Optimal GL Selection
• If multiple GLs available
• Select one with high rank
Rank = st + nGM - f - (b/1000)
st b
(serving time) (battery consumption)
nGM f
(number of (number of failures)
registered GMs)
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19. Architecture
DA
GL GL
GM
GM GM
GM
GM
GM GM GM
GM
GM
Area: INB01 Area: JKF01
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20. Adaptive Timer: Grouping
Group Members report to
corresponding Group Leaders
Basic Time window = 600
GL1 timer counter = 3
DA GM1 timer counter = GM
GM2 timer counter = GM
GM3 timer counter = 2
Interval = 412*3 GL1
GM3
Interval = 501*2
GM1 GM2
Interval = 431*3 Interval = 521*3
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21. Benefits: Grouping
Group Based on Enable
nodes their Service
locations Composition
Reduces
Registry
load
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22. Experiments Setup
• Linux process
DA • Java based CoAP Implementation
COOJA: Simulator
• CONTIKI (OS)
GL
• CSMA (MAC)
• CONTIKIMAC (Duty cycling)
• ENERGEST (Energy estimation)
• C-based CoAP Implementation
GM
25 Tmote sky nodes (+1 border router)
with 10 runs for 100 minutes 22

23. Results
Control
Method Energy (J)
Packets
BASIC 810 62.08
TRENDY TIMER 163 61.27
TRENDY FULL 297 60.13
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24. Future Work
 Load balancing
More responsible GLs  Large-scale
Grouping Scope
Adaptivity Proxy
 Intelligent Timer (e.g. trickle)  Caching
 Publish/Subscribe Interoperability
Service composition
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25. T h a n k y o u
Questions?
Loughborough University
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