Semantic routing in sensor network

1. SRWSN: Semantic Routing on Wireless Sensor Networks HUMIDITY SRWSN TEMPERATURE SOUND PRESSURE LIGHT 1 Semantic Routing on Wireless Sensor Networks

3. In a scenario where the network is expected to learn its environment, the algorithm has to be flexible.

4. Wireless Sensor Networks.2 Semantic Routing on Wireless Sensor Networks

6. The goal is to fulfill a request on a network without knowing its topology.3 SRWSN TEMPERATURE? I DO! Semantic Routing on Wireless Sensor Networks

7. Outline 4 Semantic Routing on Wireless Sensor Networks

8. Outline 5 Semantic Routing on Wireless Sensor Networks

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. A common ontology has to be defined.6 Semantic Routing on Wireless Sensor Networks

13. Strong energy constraints

14. Communications are 10 times more energy consuming than an internal process.

15. New applications

16. Surveillance

17. Nodes have to act more collaboratively. Using semantics to improve the process inside the network. 7 Semantic Routing on Wireless Sensor Networks

18. Outline 8 Semantic Routing on Wireless Sensor Networks

22. MsgType : DISCOVERY, NORMAL STATE, ALERT

23. QueryType : REQUEST or ANSWER

24. QueryId: TEMP_VAL, TEMP_ERR, etc...

25. SrcID : id of the query source

26. SrcMAC : source MAC address (of the neighbor sender)

27. DstMAC : destination MAC address (of the neighbor receiver)

28. Options :

29. BLOOM FILTER DISCOVERY

30. VALUE

31. ERROR

32. ALERT_GEN

33. ALERT_TIMESTAMP

34. HOP_COUNTNORMAL STATE ALERT

36. Distinctly identify a node according to its direction and distance from the sink.

37. ID format:Direction MAC Last Byte Hops To Sink 12 A B C D Semantic Routing on Wireless Sensor Networks

39. Based on « peer content »

40. Contains all the Bloom filters of the neighborhood

41. Quickly know its neighbor abilities

42. Payload reduced

43. No three way handshake to discover the abilities

44. Store information in an efficient way13 Semantic Routing on Wireless Sensor Networks

46. Detect the presence of an element0 0 1 1 S S0 1 0 S1 1 g(si) P1 1 P1 1 0 h(si) P2 1 P2 Sn-1 0 0 Sn 0 14 Semantic Routing on Wireless Sensor Networks

48. Based on the « query history »

49. Mimicking human actions in a social network

50. Process

51. Observe query responses sent to peers

52. Memorize the information (only by listening to traffic) according to its reliability

53. Use the information to select the most relevant peers15 Semantic Routing on Wireless Sensor Networks

54. Learning Mechanism Node LearningPeer Selection RELEVANT PEER QUERYID QueryTypes LearningTable Neighborhood Table ANSWER 16 Semantic Routing on Wireless Sensor Networks

56. Algorithm Maturity Time 18 Semantic Routing on Wireless Sensor Networks

57. Deployment BloomTable BloomGlobal BloomPerso 19 Semantic Routing on Wireless Sensor Networks

59. Algorithm 21 SendResponse 1545 ForwardResponse ForwardRequest 1256 SendRequest 1234 Semantic Routing on Wireless Sensor Networks

60. Algorithm (intern) 22 Node QUERYID PEER Relevant Peer Bloom Table No Reliable Peer Learning Table No Random Selection Random Peer Semantic Routing on Wireless Sensor Networks

62. Value

63. Error

64. Alerts are directly (hop by hop) sent to the sink

65. If the neighborhood does not record any alert then the alert is ignored

66. Receiving a false alert message

67. Update its « reference values »23 Semantic Routing on Wireless Sensor Networks

68. Outline 24 Semantic Routing on Wireless Sensor Networks

70. Omnet++

71. Why?

72. ZigBeeunderTest

73. Abstraction of material problems (such as memory management)

74. Easy to add components

75. Other simulators

76. NS-2

77. TOSSIM

78. …25 Semantic Routing on Wireless Sensor Networks

80. Implementing the tools

81. Wiki :

82. Algorithm

83. Platform installation

84. SVN

85. How to participate in our projecthttp://rsimogaetan.online.fr/pfawiki/index.php5?title=PFAWiki 26 Semantic Routing on Wireless Sensor Networks

86. Outline 27 Semantic Routing on Wireless Sensor Networks

88. Bloom filter

89. Error probability : 10%

90. Alphabet size : 20 words

91. Hash functions : 2

92. Vector length = 96 bits = 12 Bytes = Stored size

93. Without using Bloom filters

94. Stored size ≈ 1200 bits Compression Ratio ≈ 8% 28 Semantic Routing on Wireless Sensor Networks

96. Nodes : 25

97. Average number of neighbors : 3

98. Scope of a node : 100 m Time to deploy : 150 s Message size: 120 bits 29 Semantic Routing on Wireless Sensor Networks

99. Performances 30 Semantic Routing on Wireless Sensor Networks

101. Random node generates a request

102. TTL = 4

103. Network learns from communications 31 Semantic Routing on Wireless Sensor Networks

105. A learning network .32 Semantic Routing on Wireless Sensor Networks Gain of 50% Receivedrequests Simulation time

106. Outline 33 Semantic Routing on Wireless Sensor Networks

108. SRWSN meets the Wireless Sensor energy and storage constraints.

109. SRWSN is quickly deployed although nodes are randomly set.

110. SRWSN learns from its environment and can adapt to new query types.34 Semantic Routing on Wireless Sensor Networks

112. Recording the succession of alerts for one event

113. For instance : Fire = Light  Temperature

114. Using ID’s form to direct traffic

115. Add location-based queries

116. Especially for queries from the sink to one node

117. Use another data-oriented algorithm in our scenario to show SRWSN comparison

118. Alert management improvement

119. Using events

120. Predict a temperature alert in case of a previous light one 35

121. Thank you HUMIDITY Questions TEMPERATURE SOUND PRESSURE LIGHT 36 Semantic Routing on Wireless Sensor Networks