The document proposes protocols for self-organization of wireless sensor networks. It discusses challenges including energy consumption and localized algorithms. It presents SMACS for link layer organization, EAR for mobility management, and SAR for multihop routing. Cooperative signal processing algorithms like SWE and MWE are introduced to reduce data communication costs through local processing. The algorithms aim to address energy efficiency while allowing scalability in wireless sensor networks.

1. PROTOCOLS FOR SELF-ORGANIZATION OF A WIRELESS SENSOR NETWORK Published in “Personal Communications, IEEE, vol 7, no 5, 2000” Presented by Saatviga S.

2. Authors KatayounSohrabi B.S & M.S degrees in Electrical Engineering, University of Missouri, Rolla. Ph.D. University of California, Los Angeles VishalAilawadhi B.S. and M.S. degrees in electrical engineering, Ph.D. in electrical engineering, University of California, Los Angeles Jay L. Gao B.S. and M.S. degrees in electrical engineering, Ph.D. in electrical engineering, University of California, Los Angeles Gregory J. Pottie B.Sc. in engineering physics, Queen’s University, Kingston, Ontario, Canada. M.Eng. And Ph.D. in electrical engineering from McMaster University, Hamilton, Ontario

3. Road Map Wireless Sensor Network – A General Scenario Design Challenges Involved Related Wireless Network Models The Research Problem Link Layer Issues Mobile MAC Issues Protocols for Wireless Sensor Networks Multihop Routing Cooperative Signal Processing Conclusion

4. Wireless Sensor Network – A General Scenario Internet Sink Node Sensor Node Wireless Sensor Network Target User WINS Sensor Node Architecture Processing Event Classification and identification Wireless network interface Signal processing for event detection Sensor Interface Control Actuator

5. Design Challenges Involved Hardware MEMS Sensor Technology Digital Circuit Design & System Integration Designing Low-power RF front-end and circuitry Wireless Networking Robust & Energy-Efficient Communication Channel Access, Routing, Mobility Management Applications Detection, Data Collection & Signal Processing

6. Related Wireless Network Models Mobile Ad hoc Network Mobile Node Wireless link Cellular Network Mobile Cluster Head Stationary Base Station Wired link Wireless link Mobile User

8. Communications in a network consumes lot of energy

9. Trade-off between data processing and wireless communications

10. More local processing done in sensors

11. Message overhead should be reduced

12. Need For Highly Localized And Distributed Algorithms For Data Processing And Networking

13. Link Layer Issues Formation of topology & Channel Access Contention/ Explicit Organization based Channel Access TDMA/FDMA/CDMA schemes Transceivers have to monitor channels at all times Expensive in the context of sensor networks Organized Channel Access Discover neighbors and then assign collision-free channels Hierarchical structure Network-wide Synchronization Centralized / Distributed Channel Assignment

14. Mobile MAC Issues Provides connectivity to mobile sensors as they interact with static networks It has to adhere to the stationary network constraints Mobility Management MANET – Through Mobile Cluster Heads Cellular Network – Hand-off Techniques by Base stations Sensor Networks Consists of mobile nodes and stationary nodes Must focus on energy consumption than anything else What is the Mechanism/Algorithm to handle mobility????

16. SMACS (Self-Organizing Medium Access Control for Sensor Networks)

17. Stationary Wireless Nodes and Mobility Management

18. EAR (Eavesdrop-And-Register) algorithm

19. Multihop Routing

20. SAR (Sequential Assignment Routing) algorithm

21. Signaling & Data Transferring

22. SWE (Single Winner Election) algorithm

24. Neighbor discovery and channel assignment phases are combined

25. TDMA slots are assigned to links and then they operate on different frequencies

26. To reduce likelihood of collisionsA D C B F

27. Link-layer self-organizing procedure Node B TYPE1 TYPE3 Initial listening time TYPE2 TYPE4 Node C TYPE2 TYPE3 TYPE1 Trans. SLOT Rec. SLOT D and A find each other T frame fx fx Node D Td fx fx Node A Ta fy Node B Tb B and C find each other fy Node C Tc

28. EAR Algorithm A Typical Wireless Sensor Network Attempts to offer continuous service to these mobile nodes under both mobile and stationary constraints. Adheres to mobile nodes’ limited power constraints within the stationary network Mobility Management Stationary sensor Wireless link Mobile sensor

29. Signaling Method Broadcast Invite (BI) Stationary node transmits invitation to surrounding neighbors –Stationary MAC protocol Mobile node extracts SNR, node ID, transmitted power etc and holds it in the registry Mobile Invite (MI) Mobile node responds to BI to request a connection Mobile Response (MR) Stationary node accepts the connection and selects the slots for communication Adds it to the registry Mobile Disconnect (MD) Disconnection of nodes are determined through predefined thresholds Timeouts for limiting errors

30. Routing Multihop Routing AODV (Ad Hoc On Demand Distance Vector) TORA (Temporally Ordered Routing Algorithm) Power –Aware Routing Algorithm Minimum energy/packet Minimum cost/packet SAR Algorithm Path Selection – Energy Resource, QoS , Priority of Packet Minimizes average weighted QoS metric Focus on High Mobility Focus on Energy Efficiency

31. Cooperative Signal Processing 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. Eliminates the communication cost for relaying the raw data to some entity outside of the sensor network for processing. Adaptive Local Routing Algorithm (SWE, MWE) Coherent and Non-Coherent event-based cooperative signal processing.

32. Noncoherent Cooperative Function Raw data is often parameterized and or highly compressed Data traffic is lower Energy minimization is best achieved by reducing the overhead in the algorithm itself. Communication cost can be significantly reduced

33. Processing Network Formation SNR (Signal to Noise Ratio) SWE , ST algorithm

34. SWE Algorithm 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 Overhead-Delay Tradeoff By the end of the SWE process, a minimum-hop spanning tree will completely cover the network.

35. ST Algorithm The routing algorithm computes a minimum-hop spanning tree connecting each participating sensor to the winner(s) of the election. No additional complexity is added to the algorithm complexity Ultimately shortens the duration of the entire network routing algorithm Also cuts overhead by compressing election and routing information into a single message.

36. Coherent Cooperative Function Raw data is only mildly filtered before combined processing takes place Data traffic is higher Communication cost associated with relaying long data streams can be prohibitively high because of energy resource limitation Focus is on finding the optimal processing node and the minimum energy routes.

38. Each node will now keep up to n of the best candidates

39. At the end of the MWE process, each sensor in the network has a set of minimum energy path to each SN

40. Total energy consumption to upload data from each SN to each node is computedFormation Process for Coherent Routing

41. Test Simulation Implementation The simulation environment models each node as a separate Parsec entity. The functionality of each layer, namely MAC, mobile MAC, and the network layer, is implemented as a function inside the node.

42. Conclusion The algorithms exploit the low mobility and abundant bandwidth, while coping with the severe energy constraint and the requirement for network scalability.

43. Thank You..

44. Related Wireless Network Models Bluetooth Network Piconet 3 Slave/Slave Bridge Master Slave Master/Slave Bridge Piconet 1 Piconet 2 Home RF