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

2. 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 Sensor Networks

3. 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. Current sensing methods Sensor Networks Signal analysis Sensors Object 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 Architecture

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

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

7. 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 Sensor Networks

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

10. Data naming Directed diffusion 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 Interest ( Task ) Description Type = temperature increase Threshold = 200 C Interval = 100 ms Duration = 10 hours Location = [-100, -100; 100, 100] Node data Type = temperature increase Intensity = 5 C / sec Location = [41, 73] Confidence = 0.8 Time = 10:10:35 Request Reply

11. Interest describes a task required to be done by the sensor-net sink Interest is injected at some point, sink Source Source is 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 Directed diffusion Interests

12. Diffusion & Gradient setup a b c

13. 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 Gradient setup Directed diffusion Object

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

15. Directed diffusion Reinforcement 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 Source

16. 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 Directed diffusion Performance Key metric is dissipated energy per event received  DD  Omniscient multicast o Flooding

17. 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 Performance Directed diffusion  DD  Omniscient multicast o Flooding

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

19. Broadcast characteristics SPIN Object Implosion Object Overlap Detect

20. SPIN Philosophy SPIN 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 ADV REQ DATA REQ DATA Simple Adv, Req, Data Point-to-point Extended to energy aware variant May not participate in protocol if power too low

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

23. Performance SPIN 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