Directed diffusion for wireless sensor networking


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Directed diffusion for wireless sensor networking

  1. 1. Directed Diffusion for Wireless Sensor Networking Authors: Chalermek Intanagonwiwat, Ramesh Govindan, Deborah Estrin, John Heidemann, and Fabio Silva Presented by: Md. Habibur Rahman (AIUB) Course: Sensor Networks and Wireless Computing Instructor: Md. Saidur Rahman (AIUB)
  2. 2. Wireless Networks Variety of architectures Single hop networks Multi-hop networks
  3. 3. Internet The Wireless Future …
  4. 4. Motivation Properties of Sensor Networks Data centric approach: communication based named data, not named nodes No central authority Resource constrained like limited power, computation capacities and memory Nodes are tied to physical locations Nodes may not know the topology due to rapidly changes of topology Nodes are generally stationary Q: How can we get data from the sensors?
  5. 5. Introduction(1/2) A sensor network is composed of a large number of sensor nodes, which are densely deployed either inside the phenomenon or very close to it. Random deployment Cooperative capabilities Sensor nodes scattered in a sensor field Multi-hop communication is expected Motivating factors for emergence Applications Advances in wireless technology
  6. 6. Introduction(2/2) A region requires event- monitoring Deploy sensors forming a distributed network Wireless networking Energy-limited nodes On event, sensed and/or processed information delivered to the inquiring destination
  7. 7. The Problem  Where should the data be stored?  How should queries be routed to the stored data?  How should queries for sensor networks be expressed?  Where and how should aggregation be performed? EventEvent Sources Sink Node Directed Diffusion A sensor field
  8. 8. Directed Diffusion Designed for robustness, scaling and energy efficiency Data centric Sinks place requests as interests for named data Sources satisfying the interest can be found Intermediate nodes can cache or transform data directly toward sinks Attribute-naming based Data aggregation Interest, data aggregation and data propagation are determined by localized interactions.
  9. 9. Directed Diffusion Four main features: Interests, Data, Gradients & Reinforcement Interest: a query or an interrogation which specifies what a user wants. Data: collected or processed information Gradient: direction state created in each node that receives interest. Gradient direction is toward the neighboring node which the interest is received Events start flowing from originators of interests along multiple gradient paths.
  10. 10. Directed Diffusion
  11. 11. Directed Diffusion Naming  Task descriptions are named by a list of attribute value pairs that describe a task  eg: type=wheeled vehicle // detect vehicle location interval=20ms // send events every 20 ms duration=10s // for the next 10s rect=[-100,100,200,400]// from sensors within rectangle Interests and Gradients  Interest is usually injected to the network from sink  For each active task, sink periodically broadcasts an interest message to each of its neighbors  Initial interest contains the specified rect and duration attributes but larger interval attribute  Interests tries to determine if there are any sensor nodes that detect the wheeled vehicle(exploratory).
  12. 12. Interests Interests: a query which specifies what a user wants by naming the data. Sink periodically broadcasts interest messages to each neighbor. Includes the rectangle and duration attributes from the request. Includes a larger interval attribute All nodes maintain an interest cache
  13. 13. Interest Cache
  14. 14. Sensor Node Receives interest packet Node is within the rectangle coordinates Task the sensor system to generate samples at the highest rate of all the gradients. Data is sent using unicast
  15. 15. Data Return
  16. 16. Exploratory versus Data Exploratory use lower data rates Once the sensor is able to report the data a reinforcement path is created Data gradients used to report high quality/high bandwidth data.
  17. 17. Positive Reinforcement Sink re-sends original interest message with smaller interval Neighbor nodes see the high bandwidth request and reinforce at least one neighbor using its data cache This process selects an empirically low-delay path.
  18. 18. Multiple Sources & Sinks The current rules work for multiple sources and sinks
  19. 19. Negative Reinforcement Repair can result in more than one path being reinforced Time out gradients Send negative reinforcement message
  20. 20. Repair C detects degradation Notices rate of data significantly lower Gets data from another neighbor that it hasn’t seen To avoid downstream nodes from repairing their paths C must keep sending interpolated location estimates.
  21. 21. Design Considerations
  22. 22. Simulation Environment NS2, 50 nodes in 160x160 sqm., range 40m Random 5 sources in 70x70, random 5 sinks Average node density constant in all simulations Comparison against flooding and omniscient multicast 1.6Mbps 802.11 MAC Not realistic (reliable transmission, RTS/CTS, high power, idle power ~ receive power) Set idle power to 10% of receive power, 5% of transmit power
  23. 23. Metrics Average dissipated energy per node energy dissipation / # events seen by sinks Average packet delay latency of event transmission to reception at sink Distinct event delivery # of distinct events received / # of events originally sent Both measured as functions of network size
  24. 24. Average Dissipated Energy In-network aggregation reduces DD redundancy Flooding poor because of multiple paths from source to sink flooding DiffusionMulticast Flooding DiffusionMulticast
  25. 25. Delay DD finds least delay paths, as OM – encouraging Flooding incurs latency due to high MAC contention, collision flooding Diffusion Multicast
  26. 26. Average energy and delay Average delay is misleading Directed Diffusion is better than Omniscient Multicast!? Omniscient multicast sends duplicate messages over the same paths Topology has little path diversity Why not suppress messages with Omniscient Multicast just as in Directed Diffusion?
  27. 27. Event Delivery Ratio under node failures Delivery ratio degrades with higher % node failures Graceful degradation indicates efficient negative reinforcement 0 % 10% 20%
  28. 28. Analysis Energy gains are dependent on 802.11 energy assumptions Directed Diffusion has lowest average dissipated energy Data aggregation and negative reinforcement enhance performance considerably Differences in power consumption disappear if idle– time power consumption is high Can the network always deliver at the interest’s requested rate? Can diffusion handle overloads? Does reinforcement actually work?
  29. 29. Continued…. Pros Energy – Much less traffic than flooding. Latency – Transmits data along the best path Scalability – Local interactions only Robust – Retransmissions of interests Cons The set up phase of the gradients is expensive Need of and adequate MAC layer to support an efficient implementation. The simulation analysis uses a modified 802.11 MAC protocol Design doesn’t deal with congestion or loss Periodic broadcasts of interest reduces network lifetime Nodes within range of human operator may die quickly
  30. 30. Conclusions Directed diffusion, a paradigm proposed for event monitoring sensor networks Energy efficiency achievable Diffusion mechanism resilient to fault tolerance Conservative negative reinforcements proves useful More thorough performance evaluation is required MAC for sensor networks needs to be designed carefully for further performance gains
  31. 31. Thank you 