Pervasive Computing


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Pervasive Computing

  1. 1. MOBISYS: PERVASIVE COMPUTING ucl/birkbeck research oct. 14 2008 format: 4 slides/minutes per person speakers: • Mohamed Ahmed • Dikaios Papadogkonas • Ettore Ferranti • Panagiotis Papakos • Tom Hewer • Elisa Rondini • Eli Katsiri • Jenson Taylor • Ilias Leontiadis • Michael Zoumboulakis • Liam McNamara • Stephen Hailes • George Roussos
  2. 2. liam mcnamara [next up: panagiotis]
  3. 3. Content sharing in Urban Environments Liam McNamara <>
  4. 4. Who to download from? Short-range wireless networks can have huge churn. We need to choose someone with matching tastes that will be colocated long- enough to transfer a file. Thus, we need to predict neighbours colocation length. 'Bad' sources need to be avoided.
  5. 5. What to download from them? If files can be successfully transferred, which ones should be shared first? Pop Jazz Classical Rock Daniel Blues Country Carol Alice Rock? Pop Metal? Rock Bob Blues?
  6. 6. Future Work  File percolation through network.  Improve Symbian sharing application.  Deployment (with your help!)  Thesis :/
  7. 7. panagiotis papakos [next up: jenson]
  8. 8. [problem]: Adaptive Service Discovery in Mobile Systems Web Service discovery in mobile systems using a broker. Service Request Query Mobile Service Broker Device Repository Service Matching Binding Services Factors that may affect web service requirements in Mobile Systems •Context : Network Coverage, Other Local Devices, User Preferences etc. • Resources : Battery Level, Memory Usage, CPU load etc The broker cannot assess such variables. Panagiotis Papakos
  9. 9. [proposal] : A middleware approach Problem The web service requirements (QoS requirements, functionality etc) of a mobile device may change due to changes in the context or the resources of the mobile device. Proposal A middleware that : • Monitors the Resources of the Device • Monitors the Context of the Device • Adapts the web service Request appropriately
  10. 10. Proposed Architecture Context and Adapted Resource data Service Request Middleware Query Mobile Service Request Broker Device Matching Service Binding Services Use of States to indicate status of the device (Low Power, Low Connection, Emergency, Meeting etc) Adapt the Web Service Request according to the State of the device: • QoS Requirements • Functional Requirements • Trust
  11. 11. [future work] Work in Conjunction with the Dino Project by Arun Mukhija (SENSORIA). Future Work: • Further Development • Prototype Implementation • Case Studies Expand To Include : • Adapt requirements according to broker feedback • Interaction between devices using this middleware • Reconfiguration of the device according to state
  12. 12. jenson taylor [next up: mo]
  13. 13. [problem] : How can we make efficient use of social links for routing messages in delay tolerant networks?  How often (and how long) different individuals meet. (e.g. two friends are likely to meet at least once a week)  How often (and how long) different individuals spend within the proximity of a certain geographical location. (e.g. two individuals working in the same building)  What time are individuals likely to be in certain locations. (e.g. people working together are likely to be in the same location during working hours, and only on certain days of the week) [Jenson Taylor]
  14. 14. [Snout]  Participatory sensing  Users act as mobile nodes  In theory it seamlessly fits into everyday life - Snout platform - sensors - co,co2,sound & organic solvents - GPS - Geocentric visualisation of sensor readings
  15. 15. routing in “social delay tolerant networks”  Efficient routing in delay tolerant mobile networks  Network characteristics:  Nodes are at one with users  Nodes don’t have permanent connection to network  No logical addresses  Communication is asynchronous  Packet loss is tolerated  Efficiency definition:  Resource usage, Overhead/replication/  Message delivery speed (time)  Message delivery number of hops  Using influence maps to determine the next hop in the delivery route
  16. 16. mohamed ahmed [next up: elisa]
  17. 17. [Problems]  Tools for supporting the development of control software [With: ARAGORN, CHAT]  Control of cross-layer optimisation in wireless network [With: ARAGORN]  Information and Reputation [With: Steve] Mohamed Ahmed
  18. 18. [complications or proposals]:Plenty of both!  Components based software and policy languages for real-time and fast executing systems  ML and control architectures for management and adaptation  The limits and bounds of reputation based analysis
  19. 19. [future work]:  Can we make these systems work? How well? What are the compromises?  Development of systems  Experimentation with learning and control architectures
  20. 20. elisa rondini [next up: michael]
  21. 21. [Problem]: How do bandwidth constraints impact the distribution of intensive jobs in WSNs? 1. Computationally intensive applications. Resource-constrained devices. 8. Limited available bandwidth. Radio communication interferences. [Elisa Rondini]
  22. 22. [Proposal]: Bandwidth-Aware Task Scheduling (BATS) scheme for Distributed Wireless Ad hoc Grids (DWAG) in WSNs. • Distributed Wireless Ad hoc Grids (DWAG) to implement distributed algorithms in WSNs formed by resource-constrained devices. • Bandwidth-Aware Task Scheduling (BATS) to load share location computing tasks among sensors by assessing both node computational capabilities and local network conditions.
  23. 23. Homogeneous Tasks: application of DWAG with BATS for Collaborative Node Localisation (i.e. CCA-MAP*). * L. Li and T. Kunz, “Cooperative Node Localization for Tactical Wireless Sensor Networks”, in Proc. of the IEEE/Boeing MILCOM, October 2007. * L. Li and T. Kunz, “Cooperative Node Localization Using Non-Linear Data Projection”, in ACM Transactions on Sensor Networks, 2008.
  24. 24. [Future work]: What happens if we deal with more heterogeneous tasks? Need to combine nodes’ computational capabilities and local network conditions (external characteristics) with tasks’ CPU and Bandwidth profile characterizations.
  25. 25. michael zoumboulakis [next up: ettore]
  26. 26. Efficient Pattern Detection in WSNs [text or images here] Complex Events:  Wildfire detection,  Flash flood prediction, etc. Michael Zoumboulakis
  27. 27. Efficient Pattern Detection  Patterns that are extremely difficult or impossible to capture using thresholds.  May not be known in advance.  Symbolic Conversion is a successful technique for mining time-series.  We have adapted a SC algorithm for efficient real-time mining in WSNs.
  28. 28. Efficient Pattern Detection  Multiple-pattern detection using Suffix Arrays; Advantage: scalability.  Approximate pattern matching; Advantage: flexibility.  Unknown pattern matching; Advantage: efficiency through dynamically adjusting the sampling frequency.  Light-weight implementation optimised using integer arithmetic.
  29. 29. Current & Future Work [text or images here]  Design a framework that caters for Detection of Spatial Events.  Use a few readings to infer the nature and characteristics of the event.
  30. 30. ettore ferranti [next up: dikaios]
  31. 31. [Problem]: How To Explore Unknown Areas [Ettore Ferranti]
  32. 32. [Proposal]: Team of Mobile Agents  No prior knowledge of the area’s map. (which can change after a disaster).  Lack of exact knowledge of agents’ positions.  Long-range Agent-to-Agent communication is unreliable.
  33. 33. Give Us Algorithms! (Agent-to-Tag): Multiple D epth First S earch B rick&Mortar (Tag-to-Tag): HybridE xploration
  34. 34. Now Make It Real!
  35. 35. dikaios papadogkonas [next up: tom]
  36. 36. Pervasive Navigation  Pervasive computing systems record user interactions with physical and digital resources  Common Tasks − Usage Analysis − Prediction − Pattern Recognition  No unified methodology exists to deal with common problems
  37. 37. Proposal  A probabilistic model for the representation of user interaction with the pervasive computing space  Trail based analysis − Landmarks, significant objects in space − Trails, series of landmark interactions  Use of different metrics − Time, Space, Orientation  Use for different pervasive systems − Reality Mining, Dartmouth, Cityware
  38. 38. Trail Extraction
  39. 39. Prediction
  40. 40. tom hewer [next up: ilias]
  41. 41. [large-scale simulation of vehicular networks] Current network simulation tools available require many CPU hours to run even small simulations on a single-processor machine collision avoidance adaptive vehicle routing information dissemination charging/toll systems intelligent transport systems Given the applications required of vehicular network simulations, the results are often time-dependent [tom hewer]
  42. 42. [computational expense] complexity of the network and mobility models tight-coupling such that models interoperate the granularity of models for locale can be changed N-squared and N-log(N) problems as a function of connectivity 42
  43. 43. [challenges of HPC] how to decompose the simulation? Domain and task farming algorithms for vehicular simulations require much boundary communication Component decomposition allows us to keep this boundary communication low but still keep the simulation free of causality/synchronisation problems Technique: split the nodes into lists (per processor available) create a global simulation object on each processor and perform all processing for each node on its home processor then update the global object
  44. 44. [future work] efficiency of decomposition algorithm hierarchical binning method to split nodes adaptive binning and live movement parameter search simulations explore the parameter space and compare results node aggregation and processing efficiency visualisation and processing live view and steering validation of scenarios and applications reference to live studies accurate and sound analysis/statistics
  45. 45. ilias leontiadis [next up: eli]
  46. 46. Information Dissemination in Vehicular Networks • Growing number of vehicles with Navigation Systems • Add short-range radio (e.g. wifi) • Applications of vehicular networks – traffic information dissemination – safe navigation (warnings) – urban sensing (monitor traffic, vehicles as sensors) – parking slots (fine grained information) – gas stations fuel prices – advertising – ... • Push-based – e.g., notify me on all traffic jams on my route • Pull-based – e.g., how is the traffic on M11 ? Ilias Leontiadis, Cecilia Mascolo
  47. 47. The role of the Navigation System – Suggested routes  M o bility pa tterns predic ta ble – Route/Disseminate information in specific areas – Suggested routes  I nteres ts (e.g. receive warnings on my route) – Map information  L o c a tio n a s c o ntex t (flexible topics/matching) – Describe warnings/affected areas flexibly – Describe interests
  48. 48. Push based notifications (e.g. traffic warnings/accidents) Navigation system now provides automatic subscription to events about vehicle’s route – Use infrastructure (if available) – Vehicle-to-Vehicle communication when: • No infrastructure is available • Local information • Fine grained information (e.g. parking spots) – We use the Navigation System to: • Geographically route information in affected areas. • Keep disseminating information in affected areas • Inform only affected vehicles (given by the suggested route)
  49. 49. Pull based notifications (e.g. how is the traffic on M11 ) • Opportunistic routing to forward requests to the nearest infostations • Issue: how to deliver the reply back ? – target is moving • Solution – We use the Navigation System to Route the reply back on the suggested route of the requested vehicle
  50. 50. Future work  Currently Implementing the system − Testing with small fleet of bicycles and limited number of vehicles  Working on traffic information dissemination − Every vehicle collects traffic information and “Publish” this information to the affected vehicles. − Vehicles that receive information  estimate traffic conditions on parts of their route  select paths that will minimise TIME
  51. 51. eli katsiri [next up: kones]
  52. 52. [secure autonomy in pervasive healthcare] The Nurse SMC loads a Mission (a set of policies) onto a SMC that belongs to the patient role. E.g. LoadMission(ReadTemp); The Mission requires the Patient to: Two authorisation policies are needed: take a temperature reading • 3.A nurse SMC is the subject of an • send the data to the Nurse authorization policy that grants the nurse role permission to load missions on if their temperature is abnormal. members of the patient role. Mission ReadTemp{ 4.The patient SMC needs to be granted Temp permission to execute any remote invocations on the nurse interfaces are if Temp>40C specified as part of the mission. Nurse.Notify(Temp) } [Eli Katsiri]
  53. 53. [common representation] if (type==PKT_CMD) //remote invocation { //Access Control Module if (AC.authorise(srcid,oid,cmd)==TRUE) execute(msg); } else { //event //Policy Service call EventInterface( msg); } } Publications BSN2008, invited book chapter JCKSBE08
  54. 54. [A ECA policy interpreter] 55 Publications AMUSE, BSN2007
  55. 55. [first-order logic model] 1. Model based knowledge representation and reasoning. 3. First-order logic: expressiveness closer to user intuition 5. Other security/trust mechanisms that are applicable to BSN. Publications JKBSE08, invited book chapter JKBSE08
  56. 56. kones saranavamutu [next up: george]
  57. 57. [How trustworthy are my friends?]  Does regularity in social behavior imply trustworthiness?  How does the degree of regularity affect trustworthiness?  How can we detect/evaluate regularity? 8. Summary of the social interaction in two real world datasets. 9. Analysis of complex social-spatial-temporal context 10. Recognising social patterns in daily user activity and relationships 11. Identifying socially significant locations 12. applications which will be of significant value to e-business. [Kones Saravanamutu]
  58. 58. N. Eagle and A. Pentland (2007), quot;Eigenbehaviors: Identifying Structure in Routinequot;, Behavioral Ecology and Sociobiology [What’s the story with my MIT mates?] N. Eagle and A. Pentland (2007), quot;Eigenbehaviors: Identifying Structure in Routinequot;, Behavioral Ecology and Sociobiology
  59. 59. [What about my ENRON colleagues?] P.S. Keila and D.B. Skillicorn(2005),quot;Structure in the Enron Email Datasetquot;, Retrieved on June,2008.
  60. 60. [Next Steps] [text or images here] • Apply image processing techniques such as Eigenbehaviors and PCA to bitmaps that are derived from the visualisation. • N. H. Minsky. “Regularity-based trust in Cyberspace”. In proceedings of 1st Int, Conf. on
  61. 61. george roussos [next up: steve]
  62. 62. steve hailes [next up: discussion]
  63. 63. projects @ UCL mobisys Digital economy - privacy CARDyAL Utiforo TOUMAZ Security and Trust MARS NIRS Wireless & sensorIU-ATC CLEF SystemsCHAT RUNES SEINIT ARAGORN Divergent GRID U2010 Networking and SESAME Distributed systems SUAAVE HEN 6WINIT EIFFEL 64