1 Next Century Challenges: Scalable Coordination in sensor Networks MOBICOMM (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 29 Palms Fixed/Mobile Experiment Tracking vehicles with a UAV-delivered sensor network TINY OS

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 1234 Level1 Level 0

15 Localized Clustering algorithm 1234 Level1 Level 0 Send advertisements

16 Localized Clustering algorithm 1234 Level1 Level 0 Send advertisements Start promotion timers

17 Localized Clustering algorithm Level1 Level 0 promote

18 Localized Clustering algorithm Level1 Level 0 Notify potential children

19 Localized Clustering algorithm Level1 Level 0 Select parent

20 Localized Clustering algorithm Level1 Level 0 Demote (no child)

21 Localized Clustering algorithm Level1 Level 0 Select parent

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 SINK SOURCE Gradient

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 ts/szewczyk.1.html