ENSEIRB - Advanced Project

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Semantic routing in sensor network

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ENSEIRB - Advanced Project

  1. 1. SRWSN: Semantic Routing on Wireless Sensor Networks <br />HUMIDITY<br />SRWSN<br />TEMPERATURE<br />SOUND<br />PRESSURE<br />LIGHT<br />1<br />Semantic Routing on Wireless Sensor Networks<br />
  2. 2. Introduction<br /><ul><li>Conventional routing requires the destination address to be known.
  3. 3. In a scenario where the network is expected to learn its environment, the algorithm has to be flexible.
  4. 4. Wireless Sensor Networks.</li></ul>2<br />Semantic Routing on Wireless Sensor Networks<br />
  5. 5. Context<br /><ul><li>Wireless sensor networks cannot afford to have heavy routing tables.
  6. 6. The goal is to fulfill a request on a network without knowing its topology.</li></ul>3<br />SRWSN<br />TEMPERATURE?<br />I DO!<br />Semantic Routing on Wireless Sensor Networks<br />
  7. 7. Outline<br />4<br />Semantic Routing on Wireless Sensor Networks<br />
  8. 8. Outline<br />5<br />Semantic Routing on Wireless Sensor Networks<br />
  9. 9. Problem definition<br /><ul><li> Semantic Routing
  10. 10. All the metadata associated with a given message can be applied to a semantic reasoning engine in order to forward this data to the most appropriate receivers, over the most appropriate transmission media.
  11. 11. A common ontology has to be defined.</li></ul>6<br />Semantic Routing on Wireless Sensor Networks<br />
  12. 12. Problem definition<br /><ul><li> Wireless Sensor Networks
  13. 13. Strong energy constraints
  14. 14. Communications are 10 times more energy consuming than an internal process.
  15. 15. New applications
  16. 16. Surveillance
  17. 17. Nodes have to act more collaboratively.</li></ul> Using semantics to improve the process inside the network.<br />7<br />Semantic Routing on Wireless Sensor Networks<br />
  18. 18. Outline<br />8<br />Semantic Routing on Wireless Sensor Networks<br />
  19. 19. Scenario<br /><ul><li>Supervising a large property while the owners are absent:</li></ul>9<br />Semantic Routing on Wireless Sensor Networks<br />
  20. 20. Specifications<br /><ul><li> Alphabet</li></ul>10<br />Semantic Routing on Wireless Sensor Networks<br />
  21. 21. 11<br />Semantic Routing on Wireless Sensor Networks<br />Msg Type<br />Query Type<br />Query Id<br />SrcID<br />SrcMAC<br />DstMAC<br />Options<br />Specifications<br /><ul><li> Packet Type
  22. 22. MsgType : DISCOVERY, NORMAL STATE, ALERT
  23. 23. QueryType : REQUEST or ANSWER
  24. 24. QueryId: TEMP_VAL, TEMP_ERR, etc...
  25. 25. SrcID : id of the query source
  26. 26. SrcMAC : source MAC address (of the neighbor sender)
  27. 27. DstMAC : destination MAC address (of the neighbor receiver)
  28. 28. Options :
  29. 29. BLOOM FILTER DISCOVERY
  30. 30. VALUE
  31. 31. ERROR
  32. 32. ALERT_GEN
  33. 33. ALERT_TIMESTAMP
  34. 34. HOP_COUNT</li></ul>NORMAL STATE<br /> ALERT<br />
  35. 35. Specifications<br /><ul><li> Identification Number
  36. 36. Distinctly identify a node according to its direction and distance from the sink.
  37. 37. ID format:</li></ul>Direction MAC Last Byte Hops To Sink<br />12<br />A B C D<br />Semantic Routing on Wireless Sensor Networks<br />
  38. 38. Semantic routing tools adaptation<br /><ul><li>BloomTable
  39. 39. Based on « peer content »
  40. 40. Contains all the Bloom filters of the neighborhood
  41. 41. Quickly know its neighbor abilities
  42. 42. Payload reduced
  43. 43. No three way handshake to discover the abilities
  44. 44. Store information in an efficient way</li></ul>13<br />Semantic Routing on Wireless Sensor Networks<br />
  45. 45. Bloom Filters<br /><ul><li>Represent a set of n elements in a compact form
  46. 46. Detect the presence of an element</li></ul>0<br />0<br />1<br />1<br />S<br />S0<br />1<br />0<br />S1<br />1<br />g(si)<br />P1<br />1<br />P1<br />1<br />0<br />h(si)<br />P2<br />1<br />P2<br />Sn-1<br />0<br />0<br />Sn<br />0<br />14<br />Semantic Routing on Wireless Sensor Networks<br />
  47. 47. Semantic routing tools Adaptation<br /><ul><li>Learning Table
  48. 48. Based on the « query history »
  49. 49. Mimicking human actions in a social network
  50. 50. Process
  51. 51. Observe query responses sent to peers
  52. 52. Memorize the information (only by listening to traffic) according to its reliability
  53. 53. Use the information to select the most relevant peers</li></ul>15<br />Semantic Routing on Wireless Sensor Networks<br />
  54. 54. Learning Mechanism<br />Node<br />LearningPeer<br />Selection<br />RELEVANT PEER<br />QUERYID<br />QueryTypes<br />LearningTable<br />Neighborhood Table<br />ANSWER<br />16<br />Semantic Routing on Wireless Sensor Networks<br />
  55. 55. Query Types<br /><ul><li> Defines the similarities between the queries according to different scenarios:</li></ul>…<br />…<br />…<br />…<br />…<br />17<br />Semantic Routing on Wireless Sensor Networks<br />
  56. 56. Algorithm<br /> Maturity<br />Time<br />18<br />Semantic Routing on Wireless Sensor Networks<br />
  57. 57. Deployment<br />BloomTable<br />BloomGlobal<br />BloomPerso<br />19<br />Semantic Routing on Wireless Sensor Networks<br />
  58. 58. Algorithm<br /><ul><li>HandleMessage() function</li></ul>20<br />Semantic Routing on Wireless Sensor Networks<br />
  59. 59. Algorithm<br />21<br />SendResponse<br />1545<br />ForwardResponse<br />ForwardRequest<br />1256<br />SendRequest<br />1234<br />Semantic Routing on Wireless Sensor Networks<br />
  60. 60. Algorithm (intern)<br />22<br />Node<br />QUERYID<br />PEER<br />Relevant Peer<br />Bloom<br />Table<br />No<br />Reliable Peer<br />Learning<br />Table<br />No<br />Random<br />Selection<br />Random Peer<br />Semantic Routing on Wireless Sensor Networks<br />
  61. 61. Alert management<br /><ul><li>Alert message contains :
  62. 62. Value
  63. 63. Error
  64. 64. Alerts are directly (hop by hop) sent to the sink
  65. 65. If the neighborhood does not record any alert then the alert is ignored
  66. 66. Receiving a false alert message
  67. 67. Update its « reference values »</li></ul>23<br />Semantic Routing on Wireless Sensor Networks<br />
  68. 68. Outline<br />24<br />Semantic Routing on Wireless Sensor Networks<br />
  69. 69. Simulation<br /><ul><li> Simulation Software
  70. 70. Omnet++
  71. 71. Why?
  72. 72. ZigBeeunderTest
  73. 73. Abstraction of material problems (such as memory management)
  74. 74. Easy to add components
  75. 75. Other simulators
  76. 76. NS-2
  77. 77. TOSSIM
  78. 78. …</li></ul>25<br />Semantic Routing on Wireless Sensor Networks<br />
  79. 79. Work<br /><ul><li>Implementing the network layer
  80. 80. Implementing the tools
  81. 81. Wiki :
  82. 82. Algorithm
  83. 83. Platform installation
  84. 84. SVN
  85. 85. How to participate in our project</li></ul>http://rsimogaetan.online.fr/pfawiki/index.php5?title=PFAWiki<br />26<br />Semantic Routing on Wireless Sensor Networks<br />
  86. 86. Outline<br />27<br />Semantic Routing on Wireless Sensor Networks<br />
  87. 87. Performances<br /><ul><li>Storage reduction
  88. 88. Bloom filter
  89. 89. Error probability : 10%
  90. 90. Alphabet size : 20 words
  91. 91. Hash functions : 2
  92. 92. Vector length = 96 bits = 12 Bytes = Stored size
  93. 93. Without using Bloom filters
  94. 94. Stored size ≈ 1200 bits </li></ul>Compression Ratio ≈ 8%<br />28<br />Semantic Routing on Wireless Sensor Networks<br />
  95. 95. Performances<br /><ul><li>Deployment
  96. 96. Nodes : 25
  97. 97. Average number of neighbors : 3
  98. 98. Scope of a node : 100 m</li></ul> Time to deploy : 150 s <br /> Message size: 120 bits<br />29<br />Semantic Routing on Wireless Sensor Networks<br />
  99. 99. Performances<br />30<br />Semantic Routing on Wireless Sensor Networks<br />
  100. 100. Performances<br /><ul><li>Normal process
  101. 101. Random node generates a request
  102. 102. TTL = 4
  103. 103. Network learns from communications </li></ul>31<br />Semantic Routing on Wireless Sensor Networks<br />
  104. 104. Performances<br /><ul><li>Normal process
  105. 105. A learning network .</li></ul>32<br />Semantic Routing on Wireless Sensor Networks<br />Gain of 50%<br />Receivedrequests<br />Simulation time<br />
  106. 106. Outline<br />33<br />Semantic Routing on Wireless Sensor Networks<br />
  107. 107. Conclusion<br /><ul><li>Implementation of an adaptative semantic routing algorithm.
  108. 108. SRWSN meets the Wireless Sensor energy and storage constraints.
  109. 109. SRWSN is quickly deployed although nodes are randomly set.
  110. 110. SRWSN learns from its environment and can adapt to new query types.</li></ul>34<br />Semantic Routing on Wireless Sensor Networks<br />
  111. 111. Further work<br /><ul><li> Learning events
  112. 112. Recording the succession of alerts for one event
  113. 113. For instance : Fire = Light  Temperature
  114. 114. Using ID’s form to direct traffic
  115. 115. Add location-based queries
  116. 116. Especially for queries from the sink to one node
  117. 117. Use another data-oriented algorithm in our scenario to show SRWSN comparison
  118. 118. Alert management improvement
  119. 119. Using events
  120. 120. Predict a temperature alert in case of a previous light one </li></ul>35<br />
  121. 121. Thank you<br />HUMIDITY<br />Questions<br />TEMPERATURE<br />SOUND<br />PRESSURE<br />LIGHT<br />36<br />Semantic Routing on Wireless Sensor Networks<br />

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