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Dirk Pesch - Networked systems research at NIMBUS (Cork Institute of Technology)
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Dirk Pesch - Networked systems research at NIMBUS (Cork Institute of Technology)



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  • 1. Networked Systems Research @ Nimbus Wireless Sensor and Vehicular Ad-hoc Networks Dirk Pesch Head of Centre NIMBUS Centre for Networked Embedded Systems Cork Institute of Technology
  • 2. Overview1. Overview of CIT and Nimbus Centre2. Selected research in wireless sensor networks for indoor applications and localisation3. Protocol design for vehicular ad-hoc networks in road safety applications
  • 3. Cork Institute of Technology• Ireland’s second largest Institute of Technology located in Cork (south of Ireland)• CIT offers Bachelor, Masters and PhD degrees in Science, Engineering, Business, Art and Music• CIT has ca. 15000 students and approx. 1000 staff
  • 4. The NIMBUS Centre• Focus on networked systems research with applications in • Energy Management • Vehicular/Traffic • Infrastructure Security • Water Management• Three research groups – Adaptive Wireless Systems • Wireless Network Design GPS • Algorithms & Protocols PDR Map Filtering • Real-time Localisation & Tracking – Smart Systems Integration • Sensor Device Integration, • Miniaturisation and Embedding of Electronics • Integral Sensing networks – TEC Centre industry R&D group
  • 5. Main Industry & Academic Partners• Industry (national/international) – Intel, UTRC, Bord Gais, Benetel, Redmere, Cylon Controls, Decawave, SocoWave, Alanya, Lincor, Eurotech, Seftec, IHG, Viva – Philips, Schneider Electric, Honeywell, ANA, BijoData, Daimler, HSG Zander, Arup, Gemalto, Ennovatis, STM• Academic(national/international) – UCC/Tyndall, UCD, TCD, NUIG – Univ. of Bremen, TU Hamburg, CEA LETI, Fraunhofer IIS, Embedded Systems Institute/TU Eindhoven, TU Dresden, Univ. College Antwerp, VTT
  • 6. Wireless Sensor Networks - WSN
  • 7. Open Issues for Protocol Design for WSN• Reliability of wireless channel is a concern in many applications• Node life-times currently one to two orders of magnitude shorter than required for many sensing applications – Requires careful duty cycle adaptation• Standards based WSN protocols are non-optimal compared to proprietary proposals• Limited understanding of deployment issues for WSN – No wireless network design for deployment – Limited understanding of WSN lifetime once deployed• No integrated network management approach• No communication protocol framework to deal with diverse range applications and QoS requirements – Results in custom designs every time which increases cost
  • 8. Indoor Wireless Network Design Methodology and Tool • First tool to support systematic design and deployment of WSAN in buildings – Integrates with IFC BIMs – Reduces equipment costs by > 20% – Order of magnitude reduction in design time for non-expert Wireless Network Design Process PHASE 1 PHASE 2 PHASE 3 PHASE 4 Requirements Automatic Design Deployment Verification Gathering & Optimisation• A. Guinard, M. S. Aslam, D. Pusceddu, S. Rea, A. McGibney, D. Pesch, “Design and Deployment Tool for In-Building Wireless Sensor Networks: a Performance Discussion”, in Proc. 7th IEEE Performance & Management of Wireless and Mobile Networks (P2MNET 2011), Bonn, Germany, Oct. 2011• A. Mc Gibney, A. Guinard, D. Pesch, “Wi-Design: A Modelling and Optimization Tool for Wireless Embedded Systems in Buildings”, in Proc. 7th IEEE Performance & Management of Wireless and Mobile Networks (P2MNET 2011), Bonn, Germany, October 2011• A. Guinard, A. McGibney, D. Pesch, “A Wireless Sensor Network Design Tool to Support Building Energy Management”, in Proc. of 1st ACM BuildSys (in conjunction with ACM SenSys), Berkeley, CA, USA, November 2009
  • 9. Design Tool Case Study Novice Designer Experienced Designer WSAN Design Tool 22% Routing 47% Routing Traffic Novice Designer 53% Sensor Traffic Traffic Experienced Designer 78% Sensor 29% Routing Traffic WSAN Design Tool 71% Sensor Traffic Traffic 3 Gateways 5 Repeaters 3 hops max 3 Gateways 1 Repeater 3 hops max 2 Gateways 2 Repeaters 2 hops max Sensing Data Data transmission Design Cost Design Comments Delivery Ratio cost (# packets) cost Savings Time No previous WSN design experience, follows EnOcean RangeNovice Designer 97.0 % 1.85 € 3300 22% Routing € 0 4h 47% Routing 53% Sensor Traffic Planning Guide 78% Sensor 29% Routing 71% Sensor Traffic Traffic Traffic TrafficExperienced Designer 97.6 % 1.21 € 2940 € 360 30 min WSN Design Expert, Sun SPOT developer TrafficWSAN Design Tool 98.2 % 1.46 € 2620 € 680 40 min WSAN Design Tool 3 Gateways 5 Repeaters 3 hops max 3 Gateways 1 Repeater 3 hops max 2 Gateways 2 Repeaters 2 hops max Sensing Data Data transmission Design Cost Design Comments Delivery Ratio cost (# packets) cost Savings Time No previous WSN design experience, follows EnOcean RangeNovice Designer 97.0 % 1.85 € 3300 €0 4h Planning GuideExperienced Designer 97.6 % 1.21 € 2940 € 360 30 min WSN Design Expert, Sun SPOT developerWSAN Design Tool 98.2 % 1.46 € 2620 € 680 40 min WSAN Design Tool
  • 10. DCLA protocol• The DCLA protocol is based START on Q-learning Any frames received? No Increase learning rate Select max inactive period max(ai)• DCLA explores and selects Yes new actions adaptively Update r(ai) according to the rewards received Preliminary Yes Select next action based on exploration phase• DCLA adapts duty cycle in round-robin Decrease No event-based scenarios exploration rate No• Implemented in OPNET and Stable state (e = 0) No Select next action based on e-greedy Greedily selected a different action? on telosB motes Yes Yes Increase exploration rate Has the reward changed? No Select next action based Increase Increase on traffic change & last END learning rate exploration rate stable R. de Paz Alberola, D. Pesch, “Duty Cycle Learning Algorithm (DCLA) for IEEE 802.15.4 Beacon-Enabled Wireless Sensor Networks”, Ad-hoc Networks, Elsevier, (
  • 11. Average Duty Cycle (DC) selection Average end-to-end delay (D) Probability of Success (PS) Energy Efficiency
  • 12. Event-based traffic• Nodes generate traffic following ON/OFF model – ON/OFF distribution follows Pareto distribution – Packet arrivals follow truncated normal distribution Event detection• A number of PIR sensors detect the event and 30m report to the sink• Other nodes generate CBR 30m
  • 13. Instantaneous DC selectionProbability of Success Energy Efficiency
  • 14. Distributed Duty Cycle Management (DDCM)• Distributed Duty Cycle Management (DDCM) for IEEE 802.15.4 Beacon-Enabled Wireless Mesh Sensor Networks. – DDCM uses DCLA to adapt node’s duty cycle to the network traffic and manages the allocation of time slots as well as the prevention and resolution of possible slot conflicts within a mesh network in a distributed fashion. T ransmit t ed T racked Superframe Ext ended Broadcast Beacon Beacons durat ion (SD) SD SD Coordinat or 1 SD ESD BSD SD ESD BSD SD ESD (BO= 3) Beacon Int erval (BI) Coordinat or 2 SD BSD BSD SD (BO= 4) Beacon Int erval (BI) Coordinat or 3 SD BSD BSD (BO= 5) Mult i-superframe durat ion (MD)R. de Paz Alberola, B. Carballido Villaverde, D. Pesch, “Distributed Duty Cycle Management (DDCM) for IEEE802.15.4 Beacon-Enabled Wireless Mesh Sensor Networks”, in Proc. of 5th IEEE International Workshop onEnabling Technologies and Standards for Wireless Mesh Networking, Valencia, Spain, October 2011
  • 15. Evaluation ResultsAverage Duty Cycle Selected Probability of Success Energy Efficiency
  • 16. IEEE802.15.4 TinyOS Implementation DCLA Duty Cycle Adaptation Clock Drift Radio Adjustment CAP SleepCC2420 Power 16 Consumption Estimation
  • 17. Localisation and TrackingMapUme is an opportunistic localisation system which fuses location relatedsensor information that is readily available to localise people and objects Clients MapUme Server MapUme – OLS Server Smart Phone GPS WiFi Tag Sensor data PDR Camera networks Map Filtering
  • 18. Platform for Safety and Security EnhancementKey components: for large critical infrastructures like airports Sensor and actuators networks – (Loc. + surveillance +environment system) Context awareness – (moving objects and unexpected events) Advanced real-time processing – for collision avoidance and navigation services. Distributed middleware –scalability, predictability, configurability and continuous commissioning, (D)GPS IIS active RFID, Symeo LPR, UWB, CIT Opportunistic localisation to cover the rest WiFi WSAN Cellular net.
  • 19. Opportunistic Localisation Ground Floor First Floor Mean Location Error of Different Technologies 8 GSM GSM GSM 7 6 GPS 5 WiFi WiFi WiFi 4 GPS 3 No PDR 2 All 1 0 Outdoor Ground Floor First FloorOutdoor
  • 20. Vehicular Communication Network Terrestrial Broadcast Satellite UMTSWiMax WiFi Hotspot Variable Message Sign V2I V2V: 802.11p, IR
  • 21. Vehicular Adhoc NETworks - VANETs• Vehicular communications has been primarily motivated by safety• Advent of Active Safety Applications
  • 22. Goal!
  • 23. Vehicular Communications - VC• Relevant Standards – WAVE: Wireless Access in Vehicular Environments • IEEE 1609 set of standards (incl. 802.11p) for VC – IEEE 802.11p: 802.11a modification for VC • V2V: Vehicle-to-Vehicle Communication • V2I: Vehicle-to-Infrastructure Communication• Our Focus: Development of a Broadcast protocol for active safety applications – Reliable Vehicular Geo-broadcast protocol (RVG)
  • 24. Challenges for Broadcasting in VANETs• Broadcasting is an extremely expensive technique – It floods the medium with a high number of redundant transmissions – Making an already unreliable medium more unreliable• Broadcasting for Safety Applications MUST satisfy: – High Packet Delivery – Low End-to-End delay – Minimal Overhead
  • 25. Reliable Vehicular Geo-broadcast protocol (RVG)• RVG can disseminate any type of application data but it has been optimised for the dissemination of safety related messages• RVG is focused on high packet delivery, low delay and low overhead• Compliant with the IEEE 1609 standards• M. Koubek, S. Rea, D. Pesch, “Reliable Broadcasting for Active Safety Applications in Vehicular Highway Networks”, in Proc. of IEEE International Symposium on Wireless Vehicular Communications (WiVeC) 2010, Taipeh, Taiwan, April 2010M. Koubeck, S. Rea, D. Pesch, “Increasing Multi-Hop Broadcasting Reliability in VANETs”, EURASIP Journal on Advances in Signal Processing, May 2010• G. Pastor Grau, D. Pusceddu, S. Rea, O. Brickley, M. Koubek, D. Pesch, “Vehicle-2-Vehicle Communication Channel Evaluation using the CVIS Platform”, In Proc. of IEEE/ IET International Symposium on Communication Systems, Networks, and Digital Signal Processing, Newcastle, UK, July 2010
  • 26. Reliable Vehicular Geo-broadcast in Comparison Advantages DisadvantagesSimple Flooding • Simplicity • Low reliability• C2C-CC, NEC, GeoNet • Low latency • RedundancyArea-based, neighbour • Medium • Algorithms fail inelimination (NE) redundancy real environ.• DRG, UMB • Low latencyMultipoint Relaying (MPR) • Low redundancy • Unreliable• TRADE • Low latencyCombination of NE & MPR • Low redundancy• RVG • Low latency • High reliability
  • 27. Environments• Urban • Highway – 600 x 600m – 60 x 2000m – 20 - 320 vehicles – 50 – 500 vehicles – Free flow & Accident – Free flow & Accident
  • 28. RVG: Delivery RatioUrban Free Flow Scenario
  • 29. RVG: Delivery Ratio Urban Free Flow Scenario Proximity Zone (125m)Veh. density 20 55 150 230 320Flood 0.17 0.49 0.92 0.92 0.89TRADE 0.11 0.24 0.43 0.21 0.21DRG 0.24 0.48 0.90 0.92 0.99RVG 0.40 0.74 0.95 0.99 1.00Achv. [%] 135 51.0 3.3 7.6 12.4
  • 30. RVG: End-to-End Delay Urban Free Flow Scenario 100ms Services
  • 31. RVG: OverheadUrban Free Flow Scenario
  • 32. PACK: Delivery RatioUrban Free Flow Scenario
  • 33. PACK: End-to-End Delay Urban Free Flow Scenario End-to-End Delay [ms] Veh. density 20 55 150 230 320SRMB 16 16 25 31 30RR-ALOHA 118 287 588 1072 2007SFR 24 27 33 46 83RVG 16 21 31 36 36Achv. [%] 5 29 24 19 19
  • 34. Summary and Outlook• Nimbus research focuses on networked systems with emphasis on wireless sensor and vehicular ad-hoc networks• The main application spaces include WSN for building energy management and VANET for traffic safety• Future plans include to combine building energy management with electric vehicle charging• Challenges here include the integration of widely heterogeneous wireless/mobile ad-hoc networks to manage these applications