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Communication Paradigm for Sensor Networks PowerPoint Presentation
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Communication Paradigm for Sensor Networks
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  1. Communication Paradigm for Sensor Networks • Sensor Networks • Directed Diffusion • SPIN Ishan Banerjee ishan@cs.umd.edu

  2. Sensor Networks Conventional Networks • Wired network • Infinite power source • Rapidly increasing bandwidth • High performance workstations • Attended nodes • Low node to user ratio • Manually configurable hosts • Hosts are reparable and replaceable • Complex global routing schemes • Fixed, named nodes

  3. Sensor Networks Application of Sensor Networks • Gathering accurate information in a distributed manner from • Inaccessible geographic area • Disaster area • Industrial location • Object tracking • Traffic data • Remote surveillance • Global objective with local interaction

  4. Sensor Networks Current sensing methods Architecture Object Signal analysis Sensors • Complex sensors far from object • Sensors generate stream of data • Sensors without computing power • Signal processing to separate signal from noise • Low signal to noise

  5. Sensor Networks Current sensing methods Architecture Object Signal analysis Sensors • Sensors close to object • Sensors generate stream of data • Sensors without computing power • Better signal to noise

  6. Sensor Networks Sensor Networks Architecture Object Event analysis Sensors Net • Sensors net close to object • Observation of each sensor is processed in-situ • Sensors coordinate to make observation • Tells host about result of observation

  7. Sensor Networks Sensor Networks • Objectives • Match-box sized devices • In network processing • Better Signal-to–noise ratio • Extend life of devices • Highly scalable • Responsive to dynamic and hostile environment • Implications • Fixed wire-less network • Low bandwidth. Avoid long distance communications • No user attendance • Deployed in large numbers • Requires self configuration • Device failure implies removal from network • Requires simple energy efficient routing

  8. Sensor Networks Paradigm • Data Centric • Sensors net is queried for specific data • Source of data is irrelevant • No sensor-specific query • Application Specific • In-sensor processing to reduce data transmitted • In-sensor caching • Localized Algorithms • Maintain minimum local connectivity – save energy • Achieve global objective through local coordination

  9. Directed diffusion Directed diffusion • PULL model for obtaining information from a sensor-net Object Sensors • Better than flooding, multicast • Energy efficient • Delay comparable to multicast • Failure tolerant

  10. Directed diffusion Data naming • Content based naming • No globally unique ID for nodes (sensors) • Name of sensors are irrelevant – ephemeral nodes • Task are named: Attribute – value pair • Selecting naming scheme is important for the sensor net Request Interest ( Task ) Description Type = temperature increase Threshold = 200 C Interval = 100 ms Duration = 10 hours Location = [-100, -100; 100, 100] Reply Node data Type = temperature increase Intensity = 5 C / sec Location = [41, 73] Confidence = 0.8 Time = 10:10:35

  11. Directed diffusion Interests • Interest describes a task required to be done by the sensor-net • Interest is injected at some point, sink • Sourceis unknown at this point • Interest diffuses through the network hop-by-hop • Interest is broadcast by a node to its neighbours • Loops are not checked for at this stage

  12. a Directed diffusion c b Diffusion & Gradient setup

  13. Directed diffusion Gradient setup Object • Interest diffuses through network • Interest does not specify node information – leads to scalability • Caching is done to reduce traffic • Specifies a data rate and a direction • No global knowledge of the topology used • Nodes aware only of neighbours • Strictly local interaction • Exhibits PULL paradigm

  14. Directed diffusion Data propagation Source Source • In-situ processing is performed to identify event • Data sent back is an event indication only – low bandwidth • Caching is used for loop detection

  15. Directed diffusion Reinforcement Source Source • Sink may receive data from multiple sources • Local rules are used to increase the data rate from a subset • Done by sending renewed interest with higher rate • Empirically determined path is reinforced • Negative reinforcement used to close multiple paths

  16. Directed diffusion Performance • DD • Omniscient multicast • Flooding Key metric is dissipated energy per event received Directed diffusion compared to flooding and omniscient multicast Directed Diffusion: A scalable and Robust Communication Paradigm for Sensor Networks http://lecs.cs.ucla.edu/~estrin/papers/diffusion.ps

  17. Directed diffusion Performance • DD • Omniscient multicast • Flooding Impact of node failure on directed diffusion. Directed Diffusion: A scalable and Robust Communication Paradigm for Sensor Networks http://lecs.cs.ucla.edu/~estrin/papers/diffusion.ps

  18. SPIN Sensor Protocol for Information via Negotiation • PUSH model for disseminating information to all nodes of a sensor-net Detect Object • Broadcast of data • Energy constrained network • Limited computation capability • Low bandwidth

  19. SPIN Broadcast characteristics Detect Object Detect Object Implosion Detect Overlap

  20. SPIN SPIN Philosophy • Application level framing • Negotiation using meta-data • Meta data describes actual data • Used for negotiations • Messages • Advertise • Request • Data transfer • Resource management • Resource aware • Protocols executed after considering energy

  21. SPIN SPIN-PP 3 – way handshake REQ • Simple • Adv, Req, Data • Point-to-point ADV DATA ADV • Extended to energy aware variant • May not participate in protocol if power too low REQ DATA

  22. SPIN SPIN-BC 3 – way handshake B C A A and C suppress their REQ ADV B C A B REQ C A DATA

  23. SPIN Performance Data acquired by network over time Corresponding Energy dissipated

  24. Comments • Demonstrate simple concepts in new domain • Primary concern is energy usage • Simulations only • Assumed congestion free network