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Trendy presentation-wo t-2012

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  • WoT vision6LowPAN enables IoTSD allows discoverability of available services in networkWeb Services enables InteroperabilityThe combination can enables the interoperability requires for WoT paradigm applications(1 min) Total 1 min
  • Existing solutions + Requirements (1 min)6LowPAN constraints Vsexisting solutionsHeavy protocols and formats,Adapting existing solutions, Architecture
  • Existing solutions + Requirements (1 min)6LowPAN requirementsGeneral requirementsTotal 4 mins
  • TRENDY SDP (1 min)CoAP based Restful web servicesGrouping Service compositionContext AwarenessAdaptivity1 minTotal 5 mins
  • Grouping -1 + Grouping -2 +Multiple GLs + Optimal GL selection (3 mins)All nodes send status updates to DA Creates the bottleneck problem Multihop effect also increases the problemWe propose grouping to deal with thisWhere some nodes implement the GL CoAP resourceTo let the DA know that it can play the role of GL
  • Grouping -1 + Grouping -2 +Multiple GLs + Optimal GL selection (3 mins)DA then allocates the GL other nodes in its groupThis way made group members now report to GL instead of DA
  • Grouping -1 + Grouping -2 +Multiple GLs + Optimal GL selection (3 mins)If DA find out that multiple potential GLs are available in a groupDA uses a rank equation to select the optimal one
  • Architecture + Adaptive timer: Grouping + Benefits (2 mins)
  • Architecture + Adaptive timer: Grouping + Benefits (2 mins)Group members in a group follow the counter value of their GL
  • Experiment Setup + results (2 mins)Two CoAP ImplementationUses border router between DA and 6LoWPANExperiments done with 25 nodes
  • Experiment Setup + results (2 mins)Results shows that TRENDY timer decreases the control overheadAnd grouping increases the control overhead and hints to decrease the energy consumptionTotal 19 mins
  • Future work (1 min)More intelligent timer with pub/subMore responsible GLs to enable service compositionDA as Proxy to get potential benefits of caching and HTTP interoperabilityLarge scale experiments1 min Total 20 minutes
  • Transcript

    • 1. TRENDY: Adaptiveand Context-AwareService DiscoveryProtocol for 6LoWPANs Talal Ashraf Butt, Dr. Iain Phillips, Dr. Lin Guan, Dr. George Oikomonou Loughborough University 1
    • 2. Overview• WoT vision and Role of Service Discovery (SD)• WoT SD Requirements• Our proposed solution: TRENDY• Experiments and results• Future Work 2
    • 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 4
    • 5. Existing Solutions: Gaps 6LoWPAN constraints Heavy Dependencies limited Sleeping Protocols Packet Nodes Size Bulky limited Limited formats Bandwidth ROM and RAMCompact version for 6LoWPAN Architecture Translation PULL-based Overhead 5
    • 6. WoT: SD Requirements Compact Compact Scalability packets SizeSleep Efficiency 6LoWPAN General RequirementsCycles Requirements Service Heterogeneity Service Interoperability Selection Composition 6
    • 7. TRENDY SD Protocol CoAP Based Service Context Grouping Adaptivity Restful Composition AwarenessWeb services TRENDY: An Adaptive and Context-Aware Service Discovery Protocol for 6LoWPANs 7
    • 8. Service Description• Very simple at registration time (Comma separated URLs)• Optional: Detailed IETF CoRE well- known descriptions 8
    • 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 9
    • 10. Context awareness Service Selection I need temperature service in H roomUser 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 10
    • 11. Discovery Query Options I need temperature service in H room of x buildingUser 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 11
    • 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 = ActiveInterval = 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*2Interval = 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 14
    • 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 15
    • 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 16
    • 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 17
    • 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) 18
    • 19. Architecture DA GL GL GMGM GM GM GMGM GM GM GM GM Area: INB01 Area: JKF01 19
    • 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 = 2Interval = 412*3 GL1 GM3 Interval = 501*2 GM1 GM2 Interval = 431*3 Interval = 521*3 20
    • 21. Benefits: Grouping Group Based on Enable nodes their Service locations Composition Reduces Registry load 21
    • 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 23
    • 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 24
    • 25. T h a n k y o uQuestions? Loughborough University 25

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