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Next Century Challenges: Scalable Coordination in sensor Networks MOBICOMM (1999)

Next Century Challenges: Scalable Coordination in sensor Networks MOBICOMM (1999). Deborah Estrin, Ramesh Govindan, John Heidemann, Satish Kumar Presented by Mohammed Alam (shahed). OUTLINE. Introduction Challenges to Sensor Networks Localized Algorithms for Coordination

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Next Century Challenges: Scalable Coordination in sensor Networks MOBICOMM (1999)

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  1. Next Century Challenges: Scalable Coordination in sensor NetworksMOBICOMM (1999) Deborah Estrin, Ramesh Govindan, John Heidemann, Satish Kumar Presented by Mohammed Alam (shahed)

  2. OUTLINE • Introduction • Challenges to Sensor Networks • Localized Algorithms for Coordination • Directed Diffusion • Related Work • Summary • Discussion

  3. NETWORKED SENSORS • Sensor devices coordinating to achieve larger sensing task. • EXAMPLE: • Tracking inventory • Tracking motion of vehicles • Temperature • Noise level

  4. EXAMPLES of Sensors TINY OS 29 Palms Fixed/Mobile Experiment Tracking vehicles with a UAV-delivered sensor network

  5. Design Challenges • Sheer number of devices • Rule out traditional network device management • Ratio of communicating nodes to users much larger (1000 :1). • Impossible to concentrate on specific sensors. • Power constraint • Device failure common • Battery supply limited

  6. Design Challenges • Frequent change in position • Sensors added • Sensors moved • Sensors removed • Out of power • Damaged • Unreachable

  7. Proposed Design Features • Data Centric • Sensors do not need identity (no IP address) • Application focus on data having attributes • Communication primitive : “request” for data • Application Specific • Intermediate nodes cache and aggregate application specific data • Forwarding requests (like routers)

  8. Proposed Solution • Localized Algorithm • Distributed algorithm • Sensors interact in restricted area • Collectively achieve global objective

  9. Localized algorithm • Achieved using clustering of sensors (Localized Clustering algorithm). • Advantages: • Scalability • Improved robustness • Efficient resource utilization (battery power)

  10. Clustering in Sensor Networks Child Sensor Parent Sensor

  11. Goal of Localized Clustering algorithm • Elect cluster-head sensor such that each sensor has a cluster-head as parent. • no asymmetric connections • Cluster adapts to network dynamics and changing energy level of nodes

  12. Localized Clustering algorithm • Assume link level procedure on sensor • Adjusts Communication range by tweaking transmission power to minimum value for full network connectivity.

  13. Localized Clustering algorithm • Assume a multi-level cluster formation • Associate sensors at a level with radius • Radius: Number of physical hops sensor advertisement will travel • Sensors at higher level = larger radii.

  14. Localized Clustering algorithm Level1 Level 0 1 2 3 4

  15. Localized Clustering algorithm Level1 Send advertisements Level 0 1 2 3 4

  16. Localized Clustering algorithm Level1 Start promotion timers Send advertisements Level 0 1 2 3 4

  17. Localized Clustering algorithm Level1 1 3 promote Level 0 2 4

  18. Localized Clustering algorithm Level1 1 3 Notify potential children Level 0 2 4

  19. Localized Clustering algorithm Level1 1 3 Select parent Level 0 2 4

  20. Localized Clustering algorithm Level1 3 Demote (no child) Level 0 1 2 4

  21. Localized Clustering algorithm Level1 3 Select parent Level 0 1 2 4

  22. Localized Clustering algorithm • All sensors start at level 0. • Sensors send periodic advertisement to sensors within radius hops. • Advertisements carry: • Hierarchy level • Parent ID (if any) • Remaining energy in sensor

  23. Localized Clustering algorithm • After sending advertisements: • Sensors wait for wait time (proportional to radius). • At end of wait time, if sensor does not have parent • Level 0 sensor starts promotion timer. • Promotion timer inversely proportional to remaining energy and number of level 0 advertisements received. • Smaller time out value for sensors in dense regions with more power.

  24. Localized Clustering algorithm • After promotion timer expires: • Sensor promotes itself to level 1. • Sends periodic advertisements at level 1 radius. • Advertisement lists potential child sensors: • Sensors whose advertisement received in level 0. • The child sensors in lower level chooses the closest parent. • All sensors keep checking (parent, child) after wait time period.

  25. Localized Clustering algorithm • If battery power of parent sensor less than certain threshold compared to children • Parent sensor drops a level down. • Election takes place so that a new parent selected with more power.

  26. Difficulty of Localized Algorithms • Should provide desired global behavior with indirect global knowledge • Converting centralized algorithm to distributed. • Difficulty in designing adaptability to different environments and converge to global behavior over range

  27. Solutions to overcome disadvantage • Adaptive Fidelity Algorithm • Quality of answer traded against battery life, network bandwidth or number of active sensors • Develop Techniques for characterizing performance of Localized Algorithms • sacrifice resource utilization, responsiveness

  28. Directed Diffusion • Set of abstractions that describe communication pattern in localized algorithms. • Sensors name data that it generates. • Data contains attributes. • Other nodes express interests based on attributes. • Network nodes propagate interests.

  29. Directed Diffusion • Interest on data creates gradients that direct diffusion of data. • Gradients are data dissemination path from source to sink (requesting information) nodes.

  30. Example of Directed Diffusion SOURCE Gradient SINK

  31. Related Work • Ad-hoc Networks • Proactive vs. reactive routing protocols • Energy-efficiency issues • Distributed Robotics • Robots cooperate to discover entire map • Internet Multicast and web caching • Lightweight session

  32. Current Developments • Smartdust project: • cubic millimeter sensors • Sensors float in air like dust • WINS (wireless integrated wireless Sensors) • WSN (Wireless Sensing Network) • Odyssey • Habitat monitoring • Great Duck Island

  33. Summary • Manage sensor networks using localized algorithm • Advantages of localized algorithm • Robustness, Energy efficient, manage sheer numbers • Cluster approach for localization • Directed Diffusion for communication among sensors

  34. QUESTIONS

  35. DISCUSSION

  36. References • http://robotics.eecs.berkeley.edu/~pister/29Palms0103/ • http://www.eecs.berkeley.edu/IPRO/Summary/01abstracts/szewczyk.1.html • http://nms.lcs.mit.edu/projects/leach/ • http://citeseer.nj.nec.com/context/1822734/0 • http://www.cens.ucla.edu/Estrin/index.shtml • http://www.greatduckisland.net/images.php • www.mdpi.net/sensors/papers/s20700286.pdf

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