Protocols For Self Organisation Of A Wireless Sensor Network

0 views
2,030 views

Published on

Published in: Technology, Business
0 Comments
2 Likes
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total views
0
On SlideShare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
159
Comments
0
Likes
2
Embeds 0
No embeds

No notes for slide
  • Each sensor will have a registry designed to hold the information regarding the best candidate(s) it knows.In the beginning, each sensor will initialize the registry with its own ID and election metric and multicast this information to all neighbors in the cooperative group.In response to an incoming Elect message, each node will comparing the proposed candidate(s) with those in its own registrywhen better candidates are found, the registry will be updated and all 1-hop neighbors belonging to the cooperative group will be notified. Each Elect message sent may spawn further exchange of Elect message as each sensor continue to compare candidates and update its own registryMessage exchange will eventually terminate when all sensors choose the same winner(s).
  • Since the energy cost of uploading long data stream to the central node is high, a Multi-Winner Election(MWE) process is used to limit the number of sensor source nodes (SN) that will provide the data.Instead of keeping record of one best candidate,each node will now keep up to n of them. Just as in the non-coherent case, for each winning SN candidate,a minimum-energy path can be computed by piggybacking link power information on the Elect messages.At the end of the MWE process, each sensor in the network has a set of minimum energy path to each SN.Then the total energy consumption to upload data from each SN to each node in the local network can becomputed.
  • Protocols For Self Organisation Of A Wireless Sensor Network

    1. 1. PROTOCOLS FOR SELF-ORGANIZATION OF A WIRELESS SENSOR NETWORK<br />Published in “Personal Communications, IEEE, vol 7, no 5, 2000”<br />Presented by <br />Saatviga S.<br />
    2. 2. Authors<br />KatayounSohrabi<br />B.S & M.S degrees in Electrical Engineering, University of Missouri, Rolla. Ph.D. University of California, Los Angeles<br />VishalAilawadhi<br />B.S. and M.S. degrees in electrical engineering, Ph.D. in electrical engineering, University of California, Los Angeles<br />Jay L. Gao<br />B.S. and M.S. degrees in electrical engineering, Ph.D. in electrical engineering, University of California, Los Angeles<br />Gregory J. Pottie<br />B.Sc. in engineering physics, Queen’s University, Kingston, Ontario, <br /> Canada. M.Eng. And Ph.D. in electrical engineering from McMaster <br /> University, Hamilton, Ontario<br />
    3. 3. Road Map<br />Wireless Sensor Network – A General Scenario<br />Design Challenges Involved<br />Related Wireless Network Models<br />The Research Problem<br />Link Layer Issues<br />Mobile MAC Issues<br />Protocols for Wireless Sensor Networks<br />Multihop Routing<br />Cooperative Signal Processing<br />Conclusion<br />
    4. 4. Wireless Sensor Network – A General Scenario<br />Internet<br />Sink Node<br />Sensor <br />Node<br />Wireless Sensor Network<br />Target<br />User<br />WINS Sensor Node Architecture<br />Processing<br />Event Classification and identification<br />Wireless network<br />interface<br />Signal processing for event detection<br />Sensor<br />Interface<br />Control<br />Actuator<br />
    5. 5. Design Challenges Involved<br />Hardware<br />MEMS Sensor Technology<br />Digital Circuit Design & System Integration<br />Designing Low-power RF front-end and circuitry<br />Wireless Networking<br />Robust & Energy-Efficient Communication<br />Channel Access, Routing, Mobility Management<br />Applications<br />Detection, Data Collection & Signal Processing<br />
    6. 6. Related Wireless Network Models<br />Mobile Ad hoc Network<br />Mobile Node<br />Wireless link<br />Cellular Network<br />Mobile <br />Cluster Head<br />Stationary Base Station<br />Wired link<br />Wireless link<br />Mobile User<br />
    7. 7. Research Problem<br /><ul><li>Energy Consumption – sensing, data processing and communications
    8. 8. Communications in a network consumes lot of energy
    9. 9. Trade-off between data processing and wireless communications
    10. 10. More local processing done in sensors
    11. 11. Message overhead should be reduced
    12. 12. Need For Highly Localized And Distributed </li></ul> Algorithms For Data Processing And Networking<br />
    13. 13. Link Layer Issues<br />Formation of topology & Channel Access<br />Contention/ Explicit Organization based Channel Access<br />TDMA/FDMA/CDMA schemes<br />Transceivers have to monitor channels at all times<br />Expensive in the context of sensor networks<br />Organized Channel Access<br />Discover neighbors and then assign collision-free channels<br />Hierarchical structure<br />Network-wide Synchronization <br />Centralized / Distributed Channel Assignment<br />
    14. 14. Mobile MAC Issues<br />Provides connectivity to mobile sensors as they interact with static networks<br />It has to adhere to the stationary network constraints<br />Mobility Management<br />MANET – Through Mobile Cluster Heads<br />Cellular Network – Hand-off Techniques by Base stations<br />Sensor Networks<br />Consists of mobile nodes and stationary nodes<br />Must focus on energy consumption than anything else<br />What is the Mechanism/Algorithm to handle mobility????<br />
    15. 15. Protocols that perform ORM<br /><ul><li>Network Start up & Link layer organization
    16. 16. SMACS (Self-Organizing Medium Access Control for Sensor Networks)
    17. 17. Stationary Wireless Nodes and Mobility Management
    18. 18. EAR (Eavesdrop-And-Register) algorithm
    19. 19. Multihop Routing
    20. 20. SAR (Sequential Assignment Routing) algorithm
    21. 21. Signaling & Data Transferring
    22. 22. SWE (Single Winner Election) algorithm
    23. 23. MWE (Multi-Winner Election) algorithm</li></li></ul><li>SMACS<br /><ul><li>It is an infrastructure building, distributed protocol that forms a flat topology
    24. 24. Neighbor discovery and channel assignment phases are combined
    25. 25. TDMA slots are assigned to links and then they operate on different frequencies
    26. 26. To reduce likelihood of collisions</li></ul>A<br />D<br />C<br />B<br />F<br />
    27. 27. Link-layer self-organizing procedure<br />Node B<br />TYPE1<br />TYPE3<br />Initial listening time<br />TYPE2<br />TYPE4<br />Node C<br />TYPE2<br />TYPE3<br />TYPE1<br />Trans. <br />SLOT<br />Rec. <br />SLOT<br />D and A find each other<br />T frame<br />fx<br />fx<br />Node D<br />Td<br />fx<br />fx<br />Node A<br />Ta<br />fy<br />Node B<br />Tb<br />B and C find each other<br />fy<br />Node C<br />Tc<br />
    28. 28. EAR Algorithm<br /> A Typical Wireless Sensor Network<br />Attempts to offer continuous service to these mobile nodes under both mobile and stationary constraints.<br />Adheres to mobile nodes’ limited power constraints within the stationary network<br />Mobility Management<br />Stationary sensor<br />Wireless link<br />Mobile sensor<br />
    29. 29. Signaling Method<br />Broadcast Invite (BI)<br />Stationary node transmits invitation to surrounding neighbors –Stationary MAC protocol<br />Mobile node extracts SNR, node ID, transmitted power etc and holds it in the registry<br />Mobile Invite (MI)<br />Mobile node responds to BI to request a connection<br />Mobile Response (MR)<br />Stationary node accepts the connection and selects the slots for communication<br />Adds it to the registry<br />Mobile Disconnect (MD)<br />Disconnection of nodes are determined through predefined thresholds<br />Timeouts for limiting errors<br />
    30. 30. Routing<br />Multihop Routing<br />AODV (Ad Hoc On Demand Distance Vector) <br />TORA (Temporally Ordered Routing Algorithm)<br />Power –Aware Routing Algorithm<br />Minimum energy/packet<br />Minimum cost/packet<br />SAR Algorithm<br />Path Selection – Energy Resource, QoS , Priority of Packet<br />Minimizes average weighted QoS metric<br />Focus on High Mobility<br />Focus on Energy Efficiency<br />
    31. 31. Cooperative Signal Processing<br />A form of hierarchical information processing where raw sensor data is first collected and processed by individual nodes to generate a parametric or filtered version of the original data, and later gathered at a single location for combined processing.<br />Eliminates the communication cost for relaying the raw data to some entity outside of the sensor network for processing.<br />Adaptive Local Routing Algorithm (SWE, MWE)<br />Coherent and<br />Non-Coherent event-based cooperative signal processing.<br />
    32. 32. Noncoherent Cooperative Function<br />Raw data is often parameterized and or highly compressed <br />Data traffic is lower<br />Energy minimization is best achieved by reducing the overhead in the algorithm itself.<br />Communication cost can be significantly reduced<br />
    33. 33. Processing Network Formation<br />SNR (Signal to Noise Ratio) <br />SWE ,<br />ST algorithm<br />
    34. 34. SWE Algorithm<br />Routing information & Election information is piggybacked on the Elect message so that a minimum-hop spanning tree can be built from each sensor node to the eventual winner(s) of the election<br />Overhead-Delay Tradeoff<br />By the end of the SWE process, a minimum-hop spanning tree will completely cover the network.<br />
    35. 35. ST Algorithm<br />The routing algorithm computes a minimum-hop spanning tree connecting each participating sensor to the winner(s) of the election.<br />No additional complexity is added to the algorithm complexity<br />Ultimately shortens the duration of the entire network routing algorithm <br />Also cuts overhead by compressing election and routing information into a single message.<br />
    36. 36. Coherent Cooperative Function<br />Raw data is only mildly filtered before combined processing takes place<br />Data traffic is higher<br />Communication cost associated with relaying long data streams can be prohibitively high because of energy resource limitation<br />Focus is on finding the optimal processing node and the minimum energy routes.<br />
    37. 37. MWE Algorithm<br /><ul><li>Limits the number of sensor nodes that provide data
    38. 38. Each node will now keep up to n of the best candidates
    39. 39. At the end of the MWE process, each sensor in the network has a set of minimum energy path to each SN
    40. 40. Total energy consumption to upload data from each SN to each node is computed</li></ul>Formation Process for Coherent Routing<br />
    41. 41. Test Simulation Implementation<br />The simulation environment models each node as a separate Parsec entity.<br />The functionality of each layer, namely MAC, mobile MAC, and the network layer, is implemented as a function inside the node.<br />
    42. 42. Conclusion<br />The algorithms exploit the low mobility and abundant bandwidth, while coping with the severe energy constraint and the requirement for network scalability.<br />
    43. 43. Thank You..<br />
    44. 44. Related Wireless Network Models<br />Bluetooth Network<br />Piconet 3<br />Slave/Slave Bridge<br />Master<br />Slave<br />Master/Slave Bridge<br />Piconet 1<br />Piconet 2<br />Home RF<br />

    ×